Financial data mining driven cost optimization method and system

Abstract

The invention relates to the technical field of financial cost optimization, and discloses a financial data mining driven cost optimization method and system. The method comprises the following steps: firstly, collecting a financial data set comprising a plurality of cost dimensions, and carrying out standardized preprocessing to integrate dispersed cost information to form a unified and normative data basis; a cost optimization analysis model is established, a cost mode and a potential optimization opportunity are mined from financial data, traditional analysis limitation is broken through, and an optimization space is accurately positioned; secondly, aiming at a selected business scene, applying the model to a simulation environment to execute a simulation process, dynamically displaying cost changes under different conditions, and enhancing the perspectiveness of an optimization scheme; and finally, making a cost optimization decision scheme based on a simulation result, and outputting an executable optimization instruction set, so that the optimization scheme is converted into specific operation. According to the method, the availability of cost data, the analysis depth, the scheme adaptability and the execution effectiveness are improved, and enterprises are assisted to realize more efficient cost management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of financial cost optimization, in particular to a cost optimization method and system driven by financial data mining. BACKGROUND

[0002] In the current complex market environment, enterprises are facing increasingly fierce competition, and cost control has become an important means to maintain the competitiveness of enterprises. Traditional cost management methods often rely on manual accounting and experience-based judgment, which is difficult to cope with the massive financial data brought by the expansion of enterprise scale and business diversification. These methods are usually limited to the analysis of a single cost dimension, such as focusing only on raw material procurement costs or labor costs, lacking a systematic consideration of the correlation between multiple cost dimensions, resulting in a limited view of cost optimization.

[0003] With the continuous expansion of business, financial data has shown explosive growth, and data types have become increasingly complex, covering procurement documents, production records, sales data, and human cost lists. Traditional data analysis tools are difficult to handle such multi-dimensional and large-scale data sets, often resulting in delayed data processing and lagging analysis results. At the same time, due to the lack of standardized data processing procedures, financial data formats vary across different departments and business scenarios, with different standards, resulting in poor compatibility between data, making cross-department and cross-business cost analysis difficult to effectively carry out, and easily causing fragmentation and misjudgment of cost information.

[0004] When developing cost optimization solutions, traditional methods are based on historical data for static analysis, making it difficult to simulate the dynamic changes of costs in different business scenarios, resulting in a lack of flexibility and adaptability in the developed optimization solutions. When market conditions, production processes, or business models change, existing optimization solutions often cannot be adjusted in a timely manner, which may miss the best cost optimization opportunity, and even lead to cost control failure. This passive cost management model makes it difficult for enterprises to identify potential cost risks and optimization opportunities in advance, restricting the improvement of enterprise cost management level. SUMMARY

[0005] The purpose of the present application is to provide a cost optimization method and system driven by financial data mining to solve the problems raised in the background.

[0006] To achieve the above purpose, the present application provides a cost optimization method driven by financial data mining, which comprises:

[0007] Collecting a set of financial data containing multiple cost dimensions, and standardizing the preprocessing of the set of financial data;

[0008] Establishing a cost optimization analysis model, the cost optimization analysis model is used to mine cost patterns and potential optimization opportunities from financial data;

[0009] applying the cost optimization analysis model to a simulation environment for a selected business scenario, performing a simulation process and generating simulation result data;

[0010] formulating a cost optimization decision scheme based on the simulation result data, and outputting the cost optimization decision scheme as an executable optimization instruction set.

[0011] Preferably, the collection of financial data containing multiple cost dimensions comprises extracting original cost records from an enterprise resource planning system, the original cost records involving material procurement, labor expenditure and operating expenses;

[0012] performing a data cleaning operation on the original cost records to eliminate inconsistencies and missing values;

[0013] converting the cleaned data into a structured data set, the structured data set serving as an input source for subsequent processing;

[0014] wherein the structured data set is passed to a construction process of the cost optimization analysis model.

[0015] Preferably, the establishment of the cost optimization analysis model comprises defining a cost driver identification module, the cost driver identification module analyzing cost correlation characteristics in the structured data set;

[0016] integrating an optimization rule engine, the optimization rule engine generating optimization constraints based on historical cost data;

[0017] linking the cost driver identification module and the optimization rule engine to form an interactive model framework;

[0018] outputting a cost pattern report through the interactive model framework, the cost pattern report used to guide initialization of the simulation process.

[0019] Preferably, the application of the cost optimization analysis model to a simulation environment for a selected business scenario comprises configuring business scenario parameters, the business scenario parameters reflecting specific cost optimization targets;

[0020] loading the cost optimization analysis model to a simulation platform;

[0021] performing multiple rounds of simulation operations in the simulation platform, each round of operation consuming data in the cost pattern report;

[0022] monitoring changes in cost variables during the simulation process and recording intermediate results; the intermediate results are summarized as the simulation result data.

[0023] Preferably, the formulating the cost optimization decision scheme based on the simulation result data comprises: analyzing cost benefit indicators in the simulation result data;

[0024] applying a decision tree algorithm to derive an optimization path, the optimization path comprising a plurality of alternative schemes;

[0025] evaluating a risk level of each alternative scheme;

[0026] selecting a risk-acceptable scheme as a final decision; the final decision is encoded as the executable optimization instruction set.

[0027] Preferably, the outputting the cost optimization decision scheme as the executable optimization instruction set comprises: formatting the final decision as machine-readable instructions;

[0028] adding execution context metadata, the execution context metadata comprising a timestamp and a department identifier;

[0029] verifying compatibility of the executable optimization instruction set with a target system;

[0030] deploying the executable optimization instruction set to a financial management system.

[0031] Preferably, the method further comprises: integrating a feedback mechanism in the simulation platform, the feedback mechanism capturing simulation bias data;

[0032] returning the simulation bias data to the cost optimization analysis model;

[0033] adjusting parameter settings in the optimization rule engine;

[0034] re-executing simulation operations until the simulation result data satisfies a convergence condition;

[0035] the updated simulation result data is used to revise the cost optimization decision scheme.

[0036] Preferably, the method further comprises: constructing a cost data knowledge base, the cost data knowledge base storing historical optimization cases and pattern templates;

[0037] when formulating the cost optimization decision scheme, querying the cost data knowledge base for similar cases; and fusing the matching results to the decision tree algorithm;

[0038] outputting an enhanced optimization path, the enhanced optimization path affecting generation of the executable optimization instruction set.

[0039] Preferably, the method further comprises: setting a real-time data monitoring interface, the real-time data monitoring interface being connected to an enterprise database;

[0040] Through the real-time data monitoring interface, the latest financial data is continuously acquired;

[0041] The latest financial data is input into the cost optimization analysis model;

[0042] A dynamic optimization cycle is triggered, which ensures that the cost optimization decision scheme is synchronized with the current business state.

[0043] Preferably, the application also includes a financial data mining driven cost optimization system, which comprises: a data acquisition module for performing the operation of acquiring a set of financial data containing multiple cost dimensions and outputting preprocessed data;

[0044] A model construction module for implementing the operation of establishing a cost optimization analysis model and receiving data from the data acquisition module;

[0045] An emulation application module for processing the operation of applying the cost optimization analysis model to a simulated environment for a selected business scenario and generating simulation result data;

[0046] A decision generation module for formulating a cost optimization decision scheme based on the simulation result data and outputting an executable optimization instruction set;

[0047] A system integration module for deploying the executable optimization instruction set to an external platform and receiving feedback from the real-time data monitoring interface.

[0048] Compared with the prior art, the application has the following beneficial effects:

[0049] By acquiring a set of financial data containing multiple cost dimensions and performing standardized preprocessing, cost information from different channels and formats can be integrated, differences and contradictions between data can be eliminated, and a unified and standardized cost data foundation can be formed. This standardized processing makes the originally scattered and chaotic cost data ordered and comparable, helps to break down the information barriers between different departments and different businesses, realizes the comprehensive sharing and deep integration of cost data, and provides consistent and reliable data support for subsequent cost analysis.

[0050] The cost optimization analysis model established can break through the limitations of traditional manual analysis, discover cost correlations and hidden rules that are difficult to detect with experience, and mine cost patterns and potential optimization opportunities from massive financial data with the aid of data mining technology. The model can perform comprehensive analysis on multiple cost dimensions, identify key factors and interaction relationships that affect costs, and more accurately locate cost overruns and optimization spaces, so that cost analysis no longer stays on the surface, but goes deep into the internal mechanism of cost formation, providing more insightful basis for optimization decisions.

[0051] The simulation process is performed in a simulation environment by applying the cost optimization analysis model to the selected business scenarios, which can dynamically show the cost change trend and possible results under different conditions. This simulation process allows testing and verifying various optimization schemes before actual implementation, observing the cost response under different parameter adjustments and different business strategies, and thus screening out optimization directions that are more in line with actual needs. Through simulation, possible problems and risks can be foreseen in advance, avoiding the adverse effects caused by blindly implementing optimization schemes, and enhancing the forward-looking and scientific nature of cost optimization.

[0052] Based on the simulation result data, a cost optimization decision scheme is formulated and output as an executable optimization instruction set, so that the optimization scheme can be directly converted into specific operation steps and action guidelines. This process converts abstract analysis conclusions into explicit and specific execution content, ensuring the operability and landing of cost optimization measures and avoiding the disconnection between decision-making and execution. The instruction set is easy for each execution department to understand and follow, ensuring that the optimization scheme can be efficiently and orderly implemented within the enterprise, so that the cost optimization goal can be effectively converted into actual cost saving results, improving the execution and effectiveness of cost management. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 A working principle diagram of the financial data mining driven cost optimization method described in the present application;

[0054] Figure 2 A flowchart of financial data collection and preprocessing;

[0055] Figure 3 A flowchart of cost optimization analysis model construction;

[0056] Figure 4 A flowchart of cost optimization decision scheme formulation;

[0057] Figure 5 A flowchart of simulation feedback and model adjustment. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0059] Please refer to Figure 1 The present application provides a financial data mining driven cost optimization method, which comprises:

[0060] A process of collecting a financial data set containing multiple cost dimensions and performing standardized preprocessing on the financial data set. A cost optimization analysis model is established for mining cost patterns and potential optimization opportunities from the financial data. The cost optimization analysis model is applied to a simulation environment for a selected business scenario, a simulation process is performed and simulation result data is generated. Based on the simulation result data, a cost optimization decision scheme is formulated and output as an executable optimization instruction set. The entire method realizes cost optimization through an automated process, involving the coordinated work of multiple system modules, ensuring the logical coherence of data processing, model building, simulation testing, decision generation and instruction deployment.

[0061] Embodiment 1: refer to Figure 2 The operation of extracting original cost records from the enterprise resource planning system is performed by an automated data collection program. The program accesses the database server of the enterprise resource planning system through a pre-configured data connection interface, and establishes a data transmission channel using a secure encryption communication protocol. Database query statements are generated according to pre-defined cost dimension definitions, and retrieval conditions cover three major core areas of material procurement, labor expenditure and operating expenses. For the material procurement dimension, the query range includes supplier transaction records, purchase order details and inventory change logs; the labor expenditure dimension covers salary settlement data, work hour records and welfare distribution details; the operating expense dimension scans equipment maintenance costs, energy consumption streams and site rental contracts. The system automatically performs full data extraction at 1 a.m. every day, implements transaction isolation mechanisms during extraction to avoid affecting online business operations. The original cost records are output in heterogeneous formats, including a mixed data stream of CSV, XML and database binary formats.

[0062] The process of performing data cleaning operations is implemented by a layered processing engine. The first layer cleaning unit performs basic format validation: checking if date fields conform to the YYYY-MM-DD standard format; verifying if amount fields are valid numeric types; checking if classification labels exist in a pre-defined list of enumerated values. Format error records identified are transferred to an error handling queue, where missing slash delimiters are automatically supplemented or amount unit symbols are removed by repair scripts. The second layer cleaning unit focuses on logical consistency validation: converting multi-currency amounts to local currency measurement by correlating external exchange rate tables; verifying the validity of cost allocation department codes against a departmental organizational structure; identifying abnormal related transactions, such as purchase orders exceeding budget limits, using a business rules engine. The third layer cleaning unit handles missing data issues: when numeric fields are missing, forward fill techniques are used to inherit the most recent valid value; when categorical fields are missing, semantic matching is performed based on transaction description text to fill in; when key identification fields are missing, temporary placeholders are automatically generated and trigger a manual review process. After cleaning operations are completed, the system performs record deduplication actions, identifying duplicate entries based on timestamp, transaction code, and amount combination keys and retaining the latest record.

[0063] The operation of converting cleaned data into structured datasets involves a three-level mapping process. At the physical structure level, the system restructures data tables according to target model requirements: raw unstructured transaction records are reorganized into a combination of fact tables and dimension tables; fact tables contain core cost amount measurement values, and dimension tables are decomposed into time dimension, supplier dimension, and cost center dimension. In field-level mapping, original fields are converted through standardized naming: for example, "Product_ID" is mapped to the standardized field "Material Code", and "Employee_Name" is mapped to "Employee Unique Identifier". Data types are uniformly converted: text type dates are converted to DATE type, and amount strings are converted to DECIMAL(18,2) type. At the semantic integration layer, cross-system field integration is implemented: "Contract Number" from the procurement system is correlated and mapped to "Payable Voucher Number" from the financial system to generate a unified transaction index identifier.

[0064] The structured dataset is persisted in columnar storage format with partitioning strategy: main partition by fiscal year, sub-partition by month. The dataset header embeds metadata description block, recording fields including but not limited to: data version number, cleansing rule version, source system identification, extraction time range boundary value. The dataset is transmitted to model construction subsystem through message queue, containing checksum and hash value in message payload for data transmission integrity verification. After successful transmission, state update event is triggered automatically, marking the processing state of this batch of data as "pending model processing" in log system, while releasing the storage space of original data buffer area. The system generates data quality evaluation report in parallel, whose content structure contains five index partitions: completeness index calculating valid data point proportion, consistency index measuring field logical constraint compliance rate, accuracy index sampling checking data record true value, uniqueness index evaluating duplicate record proportion, and timeliness index recording data processing delay length. The report is submitted to data governance platform for storage, providing historical benchmark for long-term data quality improvement. The data set change tracking mechanism runs continuously, automatically starting the data historical version comparison program when a structure change event is captured, maintaining the data evolution track document.

[0065] Embodiment 2: refer to Figure 3 The construction of the cost driver identification module starts with the systematic processing of feature engineering. The module receives the structured dataset from the data preprocessing link as the input source. The feature processing pipeline first performs data type classification, dividing the fields into discrete classification variables and continuous numerical variables. The classification variables are converted into binary vectors through one-hot encoding, and the numerical variables undergo standardization processing to eliminate dimension differences. Then, the time series feature generation program identifies the date field and extracts periodic features such as quarter attribution and week distribution. The text description field is extracted by the natural language parsing component to extract keyword entities, which are mapped to the preset cost category directory. The feature combination program creates interaction features, such as the product of material unit price and purchase batch, forming an enhanced feature set.

[0066] The driver analysis algorithm loads the feature set to perform multi-dimensional exploration. Correlation analysis calculates the Pearson coefficient matrix to identify a subset of features that are significantly related to total cost. The regression model training process uses the elastic network algorithm, setting L1 and L2 regularization parameters to balance feature selection and overfitting control. The model output feature importance ranking list marks the core driving factors with impact coefficients exceeding the threshold. At the same time, clustering analysis divides the cost behavior patterns, applying the K-means algorithm to group the dataset according to cost structure and fluctuation characteristics. The driver analysis report is automatically generated, including the main factor list, contribution percentage, and abnormal fluctuation point annotation. The report is stored in a structured data table format, with fields covering driver identification, impact weight, confidence interval, and data source trace code.

[0067] The optimization rule engine is initialized and built synchronously. The rule base imports historical decision archives from the business system, including the procurement approval rule set, budget control regulations, and expense reimbursement specifications. The rule parser converts the text rules into machine-executable logic statements. The historical cost data is loaded into the rule trainer, and the statistical analysis module calculates the distribution parameters of various cost indicators to automatically generate dynamic constraint conditions.

[0068] The module interaction framework establishes a connection through an interface adapter. The output port of the cost driving factor identification module is connected to the input port of the optimization rule engine through a Socket connection, and the data exchange frequency is set to seconds. The driving factor weight vector is injected into the evaluator of the rule engine in real time, and the rule activation threshold is dynamically adjusted. The rule execution result is returned to the driving factor module, triggering the feature importance recalculation cycle. The interaction system uses state machine design, defining three operating modes: idle state, data transmission state, and model update state. The state transition is driven by event signals. The interface protocol defines the data transmission format as a ProtocolBuffer serialized structure, and the fields include timestamp, session ID, data payload, and check code.

[0069] The simulation environment configuration process starts with the scene parameter loader. Users define the key variables of the business scenario through a graphical interface: the target cost reduction rate is a continuous input value, the time window is a discrete option, and the resource constraint conditions are set in the form of key-value pairs. The parameter verifier checks the logical validity and prevents invalid configurations such as setting a "100% reduction rate." The configuration file is stored in YAML format and consists of two parts: core parameters and extended parameters. The cost optimization analysis model is loaded into the simulation platform through a dynamic link library, and the model instantiation process injects the initial values of the scene parameters. A duplex communication channel is established between the model and the platform to support dynamic adjustment of runtime parameters.

[0070] Multiple rounds of simulation execution use a distributed task scheduling mechanism. The main controller generates a simulation job queue based on the preset number of rounds, and each round is executed in an independent container resource. In the initialization phase, the cost mode report data is loaded and decomposed into a fixed parameter set and a variable parameter set. The simulation core engine implements the discrete event simulation algorithm, and the time advancement mechanism steps in units of accounting periods. The material procurement module simulates the supplier price fluctuation model, the labor cost component integrates the labor market change curve, and the operating expense unit loads the equipment depreciation calculation formula. During each round of operation, the real-time monitor tracks 100+ cost indicator variables, including in-transit inventory cost present value, overtime pay cumulative value, and other dynamic data. The sampling frequency of the indicators is set according to the importance of the variables, with key indicators collected once every virtual day and secondary indicators collected every cycle.

[0071] The data collection system works continuously during the simulation process. The monitoring agent is deployed inside the virtual machine to capture variable change events and generate data snapshots. The snapshot data unit contains variable name, timestamp, current value, change amount, and associated transaction ID. The intermediate result buffer uses a ring buffer structure to store the data stream in isolation by round. When a single round simulation reaches the termination condition, the result integrator starts the summary process: the original data set is filtered for outliers, and the time dimension alignment operation is performed; the derived indicators such as cost saving efficiency value are calculated; and the standardized data blocks are generated. Finally, all round results are collected into the central repository to build a three-dimensional data structure (round x time point x indicator item) and encoded into a columnar storage format. The simulation result data package is accompanied by metadata tags that record the configuration parameter version and the computing environment fingerprint.

[0072] Example 3: see Figure 4 The simulation result data is processed by a distributed data analysis cluster. The cluster deploys a stream processing engine, and the data input channel is connected to the simulation result repository. The analysis program first identifies the data structure dimensions: the round index number, the time series stamp, and the cost indicator set form a three-dimensional data space. The indicator parser deconstructs the core field, which includes five types of basic elements: cumulative cost saving amount (absolute numerical type), return on investment rate (percentage type), execution cycle length (time type), resource consumption (integer type), and fluctuation coefficient (floating point type). Each element independently establishes an extraction pipeline. The saving amount field is converted to a benchmark value in the base currency by a unit converter, the return on investment rate is stripped of the percentage symbol and converted to decimal format, and the time period value is uniformly converted to a working day base. The analysis process performs exception boundary detection, and when the return on value exceeds the preset range [0, 1], the data verification process is triggered, and the original simulation log is reloaded for trace correction.

[0073] The decision tree algorithm performs environment initialization before execution. The pre-trained model library loads the decision tree template matching the current business scenario, and the template structure includes the maximum depth constraint and the minimum leaf node size parameter. The feature vector V in is constructed as the four-dimensional data parsed from the output:

[0074] V in = [C s ,R o ,T p ,S v ]

[0075] where C s represents the normalized cost saving amount, R o represents the return on investment rate coefficient, T p represents the execution cycle length, and S vThe coefficient of variation is denoted. The recursive partitioning algorithm adopts the information gain ratio criterion, and the split point selection implements the greedy search strategy. In the continuous feature space, the split threshold is dynamically calculated as the mean point of adjacent sample values. The extended path generation process creates a multi-branch decision tree, and each leaf node is associated with an optimized strategy combination. The strategy scheme coding rule is defined as follows: the procurement strategy adjustment is marked as a P-class operation sequence, the manual configuration optimization is marked as an L-class operation group, and the operation mode change is marked as an O-class instruction package. The scheme feasibility verification module intercepts branches that exceed the physical resource limit, such as automatically pruning the path when the scheme requires a reduction period that exceeds the contract validity period.

[0076] The risk assessment model constructs a probability calculation framework. The input of this model is the feature vector V in of the alternative scheme f . The risk factor is decomposed into market risk r m , execution risk r e , and integration risk r i , and the risk level calculation formula is:

[0077] P s =w m ·I m +w e ·I e +w i ·I i

[0078] where P s represents the scheme failure probability, w m is the market risk weight coefficient (from the industry historical database), I m is the market influence index (calculated according to the supply and demand fluctuation model), w e is the execution risk weight (based on enterprise resource allocation data), I e is the execution capability gap, w i is the system integration risk weight (from the technology architecture evaluation), and I i is the interface compatibility index. The weight coefficient is calibrated through Monte Carlo simulation, and a random disturbance factor ε is injected each time, where εN(0, 0.1). The risk score outputs a three-dimensional vector:

[0079] R s =[P s ,S m ,L c ]

[0080] where P s represents the comprehensive risk value, S m represents the maximum risk component, and L cIndicates the confidence level. The score result is stored as a risk matrix table, with row index corresponding to the project ID, and column field containing seven risk indicators.

[0081] The project screening mechanism implements multi-level filtering. The first-level filter implements risk threshold control: set P s <0.35 as the acceptable boundary, triggering automatic elimination of high-risk projects. The second-level filter applies Pareto optimization: select projects that meet R o >0.25 and T p <90. The third-level filter implements a manual decision-making interface: the remaining project list is output to the review board, supporting multi-point touch selection operations. The final decision record contains the project unique identification code, risk summary field, and expected benefit summary. After the decision is locked, a 64-bit digital signature is generated.

[0082] The instruction conversion engine starts the code generation process. The final decision object is first decomposed into an atomic operation set: each procurement strategy is converted into 1-5 procurement instructions, each human resource configuration variable generates 3-8 HR process commands, and each operation adjustment is compiled into a device scheduling sequence. Transaction integrity is guaranteed by a sequence number chain: a global transaction ID is inserted into the head of the instruction set, and each instruction block is assigned a continuous local sequence number. The execution time limit policy is coded as a time expression language, such as "after 15:00 & before 22:00" to represent the execution time window constraint.

[0083] Metadata binding operations enhance instruction semantics. The context builder creates a metadata structure, including timestamp (Greenwich millisecond count), department identification (enterprise organization code 32-bit hash value), and business scenario label (scenario encoded in UTF-8 format). After metadata encryption, a 256-bit verification code is generated as an extended suffix of the instruction data packet. The compatibility verifier implements four-stage testing: syntax parsing test loads XML schema definition document for format verification; business rule test calls rule engine for logical conflict detection; system interface test simulates target system input / output protocol; stress test injects high-concurrency instruction stream to monitor response delay. The verification report records the pass status and performance indicators of each test, and the failed cases are redirected to the diagnosis module.

[0084] The instruction deployment system adopts a phased pushing strategy. The target system classifier identifies the financial management system version characteristics and generates an adapted communication protocol package. The first stage deployment selects a test environment: a copy of the instruction set is transmitted to the sandbox system through a secure channel and performs automated smoke testing. The second stage implements a gray release: 10% of the production nodes are selected for instruction pilot, and the system return status code and abnormal log are monitored. The final stage pushes the full amount and enables the rollback mechanism: the deployment controller creates a transaction snapshot, and a 30-minute observation window is preset to automatically restore the original state in the event of a failure signal. After successful deployment, the operation trajectory is recorded in the audit system, including deployment timestamp, execution node fingerprint, and three-in-one log of instruction distribution status code.

[0085] The instruction lifecycle monitoring module runs continuously. The heartbeat detector polls the target system every 5 minutes to check the instruction execution status code. The exception catcher listens to the system alarm event stream and matches the instruction transaction ID to start the diagnosis process. The final execution result is summarized into a closed-loop report, with key fields including actual savings amount, execution cycle deviation value, and resource consumption difference degree. These data are fed back to the simulation model library to form a system-level knowledge closed loop.

[0086] Embodiment 4: The specific implementation of the simulation platform integrated feedback mechanism involves the construction of the bias monitoring system. This mechanism is activated simultaneously when the simulation operation starts, and the monitoring agent is deployed at three key nodes of the virtual environment: the cost calculation engine output, the rule executor log stream, and the resource allocation simulator state interface. The bias data collection frequency is synchronized with the simulation clock, and a complete monitoring cycle is triggered every time a virtual accounting period (usually set to 1 month) is advanced. The collected raw bias data includes two types: numerical differences and logical conflicts. Numerical differences are recorded as the absolute difference between actual output values and expected values, and logical conflicts are marked as abnormal event codes that violate business rules.

[0087] The bias data processing pipeline consists of four stages. Raw data first enters the classifier, which divides it into three data sets: procurement, labor, and operations, according to the source of the bias. The classified data flows through the standardization converter, which converts numerical values in different units to percentage bias rates. Subsequently, the aggregation analyzer calculates the moving average bias rate according to the preset time window (such as quarterly) to identify long-term trend changes. Finally, the root cause labeling is performed, and the rule matching algorithm is used to associate the bias pattern with the known problem feature library. The processed bias data packet structure is shown in the following table:

[0088] Table 1: Processed bias data packet structure.

[0089]

[0090] The feedback data is transmitted back to the cost optimization analysis model using a two-way verification mechanism. The transmission channel is protected by an encrypted tunnel, and the data packet encapsulation format includes a header verification section (CRC32 check code), a payload section (deviation data entity), and a tail control section (retransmission request flag). The model receiving end sets up a data buffer pool, and when it detects that the same type of deviation in three consecutive periods exceeds the threshold, it triggers the model parameter emergency update process. For regular deviation data, the model uses incremental learning to absorb it, and each update retains a version snapshot of the previous parameters.

[0091] The adjustment process of the optimization rule engine implements a hierarchical control strategy. The basic parameter layer adjustment is completed through automated scripts, such as modifying the price fluctuation tolerance threshold in RULE-P-0042 rule from ±5% to ±8%. Structural level adjustment requires manual review intervention, such as adding specific constraint clauses RULE-P-0042-A for new suppliers. The rule version control system maintains a change history map, supporting quick rollback to any historical version. Parameter adjustment verification runs in shadow mode, with the new and old rules processing the same input data in parallel, and only when the difference rate of the output results is less than 2% is the new parameter confirmed to take effect.

[0092] The re-execution process of the dynamic optimization cycle is designed as a fault-tolerant architecture. When the system detects major changes in the rule engine, it automatically clears the current simulation result cache and restarts the operation from the latest valid checkpoint. The restart process prioritizes loading the historical optimal parameter combination as the initial value to speed up the convergence process. The cycle control module sets a maximum iteration limit (default 50 rounds), and when the limit is reached and the convergence condition is not met, it triggers the expert intervention process. The convergence judgment standard is based on a comprehensive evaluation of multiple indicators: the moving average of the deviation rate of the main cost indicators needs to be kept within ±1.5% for 5 consecutive periods, and the number of rule conflict events per period should not exceed 3.

[0093] The construction of the cost data knowledge base adopts a star topology structure. The central fact table stores the core indicators of historical optimization cases, including case ID, effective time period, savings amount, execution period, and other basic fields. The dimension table radiates a variety of auxiliary information: the strategy combination adopted is recorded in the scheme feature dimension table, the market index at that time is saved in the environment condition dimension table, and the actual effectiveness of the subsequent three quarters is tracked in the execution effect dimension table. The knowledge base index system realizes multi-level caching, and hot data (such as the last 12 months of cases) resides in the memory retrieval area. Table association is achieved through hash connection, for example, through the case ID, all its corresponding dimension information can be instantly associated.

[0094] Case matching query implements a hybrid retrieval strategy. The input query features are first vectorized, converting the 40 key features of the current decision-making scenario into a 512-dimensional feature vector. The similarity calculation uses an improved cosine similarity algorithm, which introduces a feature weight coefficient based on the original calculation. The retrieval results are ranked in descending order of similarity, and the top K most relevant cases are returned (K value is dynamically adjusted, default is 5). The matching process pays special attention to the time decay effect, and applies a similarity penalty factor to historical cases over 36 months.

[0095] The knowledge fusion module realizes two-way information integration. The strategy elements of the matching cases are decomposed into atomic operation units, and implanted into the existing solution space through the branch expansion mechanism of the decision tree algorithm. The reverse fusion channel also exists, and the new strategy combination generated in the current decision-making process is stored in the case library as incremental knowledge after effectiveness verification. The fusion process produces an enhanced optimization path document, which uses a hierarchical structure to display: the basic layer retains the original decision tree derivation path, the enhanced layer marks optimization suggestions from historical cases, and the risk prompt layer integrates the actual execution deviation records of matching cases.

[0096] Real-time monitoring and dynamic updating system form a closed-loop control. The knowledge base update listener captures two types of trigger events: timer events (perform routine checks at 2 am every day) and threshold events (when the matching degree of new business scenarios is less than 65%). The update processor performs knowledge distillation operations, eliminating outdated cases (more than 5 years without reference), merging similar cases (feature overlap > 85%), and verifying new cases (simulation test pass rate > 92%). The version release uses a blue-green deployment mode, with the new and old knowledge bases running in parallel for a week before switching completely.

[0097] The entire feedback optimization system realizes communication between components through an event bus. Event type definitions include 12 standard messages such as deviation alert events, rule update events, and knowledge base synchronization events. The event processor implements priority queue management to ensure that high-severity deviations (such as deviation rate > 15%) can trigger emergency response processes within 500 milliseconds. The system running state dashboard displays four key indicators in real time: the current number of active deviations, the version distribution of the rule engine, the knowledge base retrieval hit rate, and the optimization cycle iteration progress, providing a panoramic monitoring view for operations personnel.

[0098] Example 5: Real-time data monitoring interface deployment adopts a dual-channel redundant architecture. The physical layer deploys a dedicated data collection server, configured with dual 10 GbE optical network cards directly connected to the enterprise core database cluster. The logical layer implements a protocol conversion gateway, supporting simultaneous parsing of Oracle database Redo logs and SQL Server CDC change streams. Connection authentication uses a triple verification mechanism: device certificate verification (X.509 certificate check), application identity authentication (OAuth2.0 token exchange), and data access authorization (RBAC permission matrix). The interface running mode defines two states: listening state continuously captures database transaction logs, and transmission state starts batch pushing when data changes accumulate to a threshold. The data freshness control parameter is set to a maximum delay tolerance of 5 minutes, and timeout triggers forced transmission.

[0099] The data acquisition engine implements an intelligent polling strategy. In the initial state, full table scanning is performed to establish a baseline data snapshot. In incremental mode, the change tracker is started: row version tracking is enabled for the material procurement table, with a monotonically increasing version stamp added to each row record; a trigger mechanism is used for the manual expenditure table, generating JSON format change packages upon transaction submission; and a memory mirror is implemented for the operating expenses table, with differences compared every 60 seconds. The transmission protocol is designed in adaptive mode: Avro binary format is used when network bandwidth is sufficient, and switches to Snappy compressed JSON array when bandwidth is limited. The data packet structure includes a header control block (data field identification, record start and end timestamps), a payload block (change data set), and an integrity verification block (SHA-256 digest value).

[0100] The data preprocessing pipeline is designed as a stream-batch integrated architecture. The raw data stream first enters the temporal consistency processor, which solves the timestamp alignment problem of cross-table changes. The processor maintains a distributed clock sequence and applies a global logical timestamp to each transaction. The data cleaning module dynamically loads configuration rules, keeping synchronization with the batch processing rule library of Example 1. The key improvement lies in real-time processing of specific logic: for procurement price mutation events (price difference between adjacent records > 10%), trigger a real-time verification process of supplier information; for salary settlement outliers (outside the historical mean ± 3 standard deviations), link to the human resources system to verify the employee's active status. The processed records are marked with a processing timestamp and written to a low-latency message queue.

[0101] The model incremental update system realizes hot switching capability. When the trigger monitor message queue depth reaches a certain threshold (default is 1000), the model update sequence is started. The update mode is divided into two levels of operation: parameter fine-tuning loads online learning algorithm, and adjusts the model weight through small batch gradient descent; structure reconstruction triggers full model retraining when the data distribution deviation detector alarms (KL divergence > 0.2). The model version management implements the blue-green deployment strategy: the online service continues to use the stable version model (vN), while the updated version model (vN+1) is generated in the background. The version switching decision is based on the A / B test results: the two versions process real-time data streams in parallel, and when the prediction accuracy of the new version is continuously 1.5% higher than that of the old version for more than 30 minutes, the traffic is gradually migrated to the new version.

[0102] The trigger logic design event-driven mechanism of the dynamic optimization cycle. The core scheduler monitors three event sources: data freshness event (new data ready notification), model change event (version upgrade completion), and business plan change event (signal from ERP system). In priority setting, the business plan change event has the highest interrupt authority, and immediately terminates the current task chain and restarts the new optimization period. The standardized optimization cycle contains six stages: data quality verification stage to scan the integrity and consistency indicators of new data; feature extraction stage to perform streaming feature engineering; model inference stage to generate initial optimization strategy; simulation warm-up stage to load the state of the latest successful case; cost simulation stage to perform incremental simulation calculation; and result verification stage to perform logical conflict detection.

[0103] The cycle interruption processing realizes state persistence. At the end of each stage, the intermediate state is automatically saved to the distributed storage system, forming a continuous checkpoint. The unexpected interruption recovery mechanism can locate the last valid checkpoint and load the memory snapshot at that time to continue the operation. The resource isolation strategy ensures the stability of the production environment: the dynamic optimization process runs in a dedicated resource pool of the container cluster, with CPU quota controlled below 40% of the total resources, and memory allocation with hard upper limit control.

[0104] The continuous monitoring system realizes full-link tracking. The tracking probe is implanted in 15 key nodes of the data processing pipeline, generating tracking logs containing process instance ID, node identifier, and timestamp. The real-time monitoring dashboard integrates three-dimensional visualization: time dimension shows the processing delay of each stage, data dimension shows the record processing throughput, and quality dimension shows the proportion of abnormal records. When the single-cycle optimization delay exceeds the preset threshold (default is 2 hours), the optimization process analysis report is automatically generated. The report locates the bottleneck link and provides a basis for system parameter tuning.

[0105] The historical state archiving system builds a long-term knowledge base. At the end of each optimization cycle, the system automatically packages the complete context: input data samples (10% sampling), model configuration snapshots, simulation process records, final decision instruction sets. The archive files are in self-describing format, and cross-cycle retrieval is achieved through a metadata indexing system. The data retention policy is set to rolling storage: the last 12 cycles are retained in hot storage, 13-52 cycles are transferred to warm storage, and historical data is compressed and stored in cold storage. Archival data is used for long-term pattern analysis, and through the similarity comparison of historical cycle parameters and current features, the optimization strategy preloading capability is achieved.

[0106] The cross-system coordination module realizes two-way synchronization. After the financial management system instruction execution is completed, the execution result is returned through a standardized API. The result processor analyzes the core indicators such as actual savings amount and execution deviation rate, and performs difference analysis with the predicted value. When the difference value exceeds the allowed range for three consecutive cycles, a model calibration work order is generated and pushed to the data analysis queue. At the same time, the cost data knowledge base (established in embodiment 4) receives actual execution cases, which are included in the historical case library after desensitization processing. The knowledge base version number and the optimization cycle number are linked to ensure the timeliness alignment of decision reference information.

[0107] It should be noted that, in this text, the relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the term "includes", "contains" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment.

[0108] Although the embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A cost optimization method driven by financial data mining, characterized in that, The method includes: collecting a set of financial data containing multiple cost dimensions, and performing standardized preprocessing on the set of financial data; A cost optimization analysis model is established, which is used to mine cost patterns and potential optimization opportunities from financial data; For the selected business scenario, the cost optimization analysis model is applied to the simulation environment, the simulation process is executed, and simulation result data is generated; Based on the simulation results data, a cost optimization decision scheme is formulated; the cost optimization decision scheme is output as an executable set of optimization instructions.

2. The cost optimization method driven by financial data mining according to claim 1, characterized in that, The collection includes a set of financial data across multiple cost dimensions, including: extracting original cost records from the enterprise resource planning system, which involve material procurement, labor costs, and operating expenses; Perform data cleaning operations on the original cost records to eliminate inconsistencies and missing values; The cleaned data is converted into a structured dataset, which serves as the input source for subsequent processing. The structured dataset is passed to the construction process of the cost optimization analysis model.

3. The cost optimization method driven by financial data mining according to claim 2, characterized in that, The establishment of the cost optimization analysis model includes: defining a cost driver identification module, which analyzes the cost correlation characteristics in the structured dataset; An integrated optimization rule engine is used to generate optimization constraints based on historical cost data. The cost driver identification module and the optimization rule engine are linked to form an interactive model framework; The interactive model framework outputs a cost model report, which guides the initialization of the simulation process.

4. The cost optimization method driven by financial data mining according to claim 3, characterized in that, Applying the cost optimization analysis model to the simulation environment for a selected business scenario includes: configuring business scenario parameters, wherein the business scenario parameters reflect specific cost optimization objectives; Load the cost optimization analysis model into the simulation platform; Multiple rounds of simulation calculations are performed in the simulation platform, with each round consuming data from the cost model report; Monitor changes in cost variables during the simulation process and record intermediate results; the intermediate results are then summarized into the simulation result data.

5. The cost optimization method driven by financial data mining according to claim 4, characterized in that, The step of formulating a cost optimization decision-making scheme based on the simulation result data includes: analyzing the cost-benefit indicators in the simulation result data; An optimal path is derived using a decision tree algorithm, and the optimal path includes multiple alternative solutions. Assess the risk level of each alternative; The option with acceptable risk is selected as the final decision; the final decision is encoded into the executable set of optimized instructions.

6. The cost optimization method driven by financial data mining according to claim 5, characterized in that, The step of outputting the cost optimization decision scheme as an executable set of optimization instructions includes: formatting the final decision into machine-readable instructions; Add execution context metadata, which includes a timestamp and a department identifier; Verify the compatibility of the executable optimized instruction set with the target system; Deploy the executable optimized instruction set to the financial management system.

7. The cost optimization method driven by financial data mining according to claim 6, characterized in that, The method further includes: integrating a feedback mechanism into the simulation platform, the feedback mechanism capturing simulation deviation data; The simulation deviation data is returned to the cost optimization analysis model; Adjust the parameter settings in the optimization rule engine; Re-execute the simulation calculation until the simulation result data meets the convergence condition; The updated simulation results data are used to revise the cost optimization decision scheme.

8. The cost optimization method driven by financial data mining according to claim 7, characterized in that, The method further includes: constructing a cost data knowledge base, which stores historical optimization cases and pattern templates; When formulating a cost optimization decision-making scheme, the cost data knowledge base is queried to match similar cases; the matching results are then integrated into the decision tree algorithm. Output an enhanced optimization path, which affects the generation of the executable optimized instruction set.

9. The cost optimization method driven by financial data mining according to claim 8, characterized in that, The method further includes: setting a real-time data monitoring interface, wherein the real-time data monitoring interface is connected to the enterprise database; The latest financial data is continuously acquired through the real-time data monitoring interface. Input the latest financial data into the cost optimization analysis model; A dynamic optimization loop is triggered, which ensures that the cost optimization decision scheme is synchronized with the current business status.

10. A cost optimization system driven by financial data mining, characterized in that, The system includes: a data acquisition module, used to perform the operation of acquiring a set of financial data containing multiple cost dimensions, and outputting preprocessed data; The model building module is used to perform the operation of establishing the cost optimization analysis model and to receive data from the data acquisition module; The simulation application module is used to process the operation of applying the cost optimization analysis model to the simulation environment for the selected business scenario and generate simulation result data. The decision generation module is used to formulate a cost optimization decision scheme based on the simulation result data and output an executable set of optimization instructions; The system integration module is used to deploy the executable optimized instruction set to an external platform and receive feedback from the real-time data monitoring interface.

Images