Multi-dimensional financial cost control and optimization system

Abstract

The invention discloses a multi-dimensional financial cost control and optimization system, and relates to the technical field of financial management. Financial data of an enterprise is collected, a financial data set S is constructed, the financial data set S is summarized and analyzed, and a material price fluctuation index CL, an exchange rate fluctuation index HL and a cash flow index XJ are obtained; obtaining a financial risk score FX according to the material price fluctuation index CL, the exchange rate fluctuation index HL and the cash flow index XJ, obtaining a financial risk fluctuation coefficient sigma FX in combination with the financial risk score FX in a future period of time obtained by the financial risk prediction model, evaluating the financial risk state of the enterprise, and performing financial cost optimization. And the financial data after optimization are collected in real time, the financial risk score FXf after optimization is obtained, and the financial optimization effect is evaluated, so that an enterprise can quickly respond to market changes, the financial cost is controlled, and the flexibility and timeliness of financial management are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of financial management, and in particular to a multi-dimensional financial cost control and optimization system. Background Art

[0002] In modern enterprise management, financial management not only includes daily scheduling of funds and cost accounting, but also involves how to conduct forward-looking analysis and early warning of the company's financial health in a data-driven manner. Specifically, in financial cost control, companies face the challenge of effectively identifying and responding to various potential risks, such as exchange rate fluctuations, changes in market demand, and supply chain disruptions. The introduction of intelligent technical means can help companies more accurately predict risks that may affect financial performance, and then adjust financial strategies and optimize cost structures.

[0003] In the Chinese invention patent with application publication number CN118917551A, a financial data analysis system and method are disclosed. The components of the system include: basic data collection and preprocessing module, intelligent prediction model construction and optimization module, relationship map construction and analysis module, project capital investment and output analysis module, decision support and suggestion generation module. By introducing an artificial intelligence self-optimization mechanism, this system can continuously adjust the parameters and structure of the prediction model according to market changes, and deeply analyze six types of basic financial data such as investment, financing, income, cost, tax and profit distribution, providing enterprises with a comprehensive and accurate financial status assessment, thereby improving the accuracy and timeliness of financial forecasts. Through the self-optimization mechanism, the model can adapt to market changes in real time and maintain prediction accuracy. This real-time performance ensures that enterprises can respond quickly to market dynamics and make timely adjustments.

[0004] The above systems use artificial intelligence self-optimization mechanisms to adjust the forecasting models in real time according to market changes. However, they are insufficiently designed for specific financial risk management and control measures, lack a closed loop for optimized execution, and pay insufficient attention to short-term financial risks such as material cost fluctuations, exchange rate fluctuations, and tight cash flow, making it difficult to respond quickly to emergencies.

[0005] To this end, the present invention provides a multi-dimensional financial cost control and optimization system. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention provides a multi-dimensional financial cost control and optimization system, which collects the financial data of the enterprise, constructs a financial data set S, and summarizes and analyzes the financial data set S to obtain the material price fluctuation index CL, the exchange rate fluctuation index HL and the cash flow index XJ, and obtains the financial risk score FX based on the material price fluctuation index CL, the exchange rate fluctuation index HL and the cash flow index XJ, and obtains the financial risk score FX in the future period obtained by combining the financial risk prediction model to obtain the financial risk fluctuation coefficient σ FX , evaluate the financial risk status of the enterprise, optimize financial costs, collect optimized financial data in real time, and obtain the optimized financial risk score FX f , evaluate the financial optimization effect, thereby realizing the dynamic closed-loop management of risk identification, optimization execution and effect feedback, enabling enterprises to respond quickly to market changes, effectively control financial risks, improve the flexibility and timeliness of financial management, and solve the problems in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a multi-dimensional financial cost control and optimization system, including a data acquisition and preprocessing module, a data analysis module, a risk warning module, a financial cost optimization module and an intelligent feedback module;

[0008] The data collection and preprocessing module is used to collect the financial data of the enterprise based on the internal financial database of the enterprise, and preprocess the collected financial data, and construct a financial data set S based on the preprocessed financial data;

[0009] The data analysis module is used to perform summary analysis based on the financial data set S to obtain the material price fluctuation index CL, the exchange rate fluctuation index HL and the cash flow index XJ;

[0010] The risk warning module is used to perform summary calculations based on the material price volatility index CL, the exchange rate volatility index HL and the cash flow index XJ to obtain the financial risk score FX, and to build a financial risk prediction model based on the long short-term memory network LSTM to obtain the financial risk score FX for a period of time in the future, and to obtain the financial risk volatility coefficient σ based on the financial risk score FX for a period of time in the future. FX , preset the risk fluctuation threshold BD, and compare the risk fluctuation threshold BD with the financial risk fluctuation coefficient σ FX Conduct comparative analysis to assess the financial risk status of the enterprise;

[0011] The financial cost optimization module is used to optimize the financial cost according to the financial risk status result of the enterprise when the financial risk status of the enterprise is in an abnormal state;

[0012] The intelligent feedback module is used to collect the financial data of the enterprise after financial cost optimization in real time, and obtain the financial risk score FX of the enterprise after financial cost optimization f , and combined with the financial risk score FX at time point t before financial cost optimization t , obtain the financial risk change rate ΔFX, preset the risk change threshold YZ, compare and analyze the financial risk change rate ΔFX with the risk change threshold YZ, and evaluate the financial cost optimization effect.

[0013] Preferably, the data acquisition and preprocessing module includes a data acquisition unit and a preprocessing unit.

[0014] The data collection unit is used to collect the financial data of the enterprise in real time based on the internal financial database of the enterprise, wherein the financial data includes raw material purchase data, sales data and cash flow data;

[0015] The raw material purchase data includes the name, purchase price and purchase batch of each raw material purchased;

[0016] The sales data includes product transaction type, transaction amount, transaction date and transaction exchange rate;

[0017] The cash flow data includes the enterprise's cash balance, the amount, date, source and purpose of cash transactions.

[0018] Preferably, the preprocessing unit is used to preprocess the collected financial data, wherein the preprocessing includes missing value processing, outlier detection and data standardization;

[0019] The missing value processing is used to fill the missing data in the financial data with the mean filling method;

[0020] The outlier detection is used to detect outliers in financial data using the Z-Score method, and to repair the detected outliers using the linear interpolation method;

[0021] The data standardization is used to perform dimensionless processing on the financial data after missing value processing and outlier detection using dimensionless processing technology to eliminate the influence of different feature dimensions;

[0022] Based on the preprocessed financial data, a financial data set S is constructed.

[0023] Preferably, the data analysis module is used to perform summary calculations based on the financial data set S to obtain a material price fluctuation index CL, an exchange rate fluctuation index HL, and a cash flow index XJ;

[0024] The material price fluctuation index CL is obtained as follows:

[0025]

[0026] In the formula, p ij represents the price of the jth material at the i-th purchase, represents the average price of the jth material purchase, represents the standard deviation of the purchase price of the jth material, p j represents the purchase price of the jth material, n j represents the total number of purchases of the jth material, i j represents the index of the j-th material purchase batch, i j =[1, 2, 3, ..., n j ], m represents the total number of material types, j represents the material type index, j = [1, 2, 3, ..., m], N represents the total number of purchases of all materials, ω j represents the weight coefficient of the jth material;

[0027] The exchange rate volatility index HL is obtained as follows:

[0028]

[0029] In the formula, T represents the time period to which the financial data belongs, R c，t represents the exchange rate at time point t, R c，avg represents the mean of the exchange rate, R c represents the exchange rate, t represents the time point to which the financial data belongs, t=[1,2,3,…,T], u represents the number of transaction types, ρ c，k represents the influence coefficient of the k-th type of transaction on exchange rate fluctuations, C transaction，k represents the amount of the kth type of transaction, k represents the index of the transaction type, k = [1, 2, 3, ..., u].

[0030] Preferably, the cash flow index XJ is obtained in the following manner:

[0031]

[0032] In the formula, C cash，t represents the cash balance of the enterprise at time point t, C cash，t-1 represents the cash balance of the enterprise at time point t-1, D represents the average daily cash demand, R represents the total operating income in the time period T, and A r It represents the balance of accounts receivable at the end of time period T, and E represents the total operating costs within time period T.

[0033] Preferably, the risk warning module includes a financial risk score calculation unit, a risk prediction unit and a risk assessment unit;

[0034] The financial risk score calculation unit is used to perform summary calculation based on the material price fluctuation index CL, the exchange rate fluctuation index HL and the cash flow index XJ to obtain the financial risk score FX. The financial risk score FX is obtained in the following manner:

[0035]

[0036] In the formula, CL represents the material price fluctuation index, HL represents the exchange rate fluctuation index, XJ represents the cash flow index, Z represents the first correction coefficient, α1, α2 and α3 represent the weight coefficients of the material price fluctuation index CL, the exchange rate fluctuation index HL and the cash flow index XJ respectively, and 0<α1<1, 0<α2<1, 0<α3<1, α1+α2+α3=1.

[0037] Preferably, the risk prediction unit is used to construct a financial risk prediction model based on the long short-term memory network LSTM, collect historical financial data based on the financial database within the enterprise, and summarize and calculate the historical financial data to obtain the financial risk score FX in the past period of time, and construct a time series set D, and the specific form of the time series set D is:

[0038] D=[FX t-d FX t-(d-1) ,…,FX t-1 FX t ];

[0039] Among them, FX t-d Indicates the financial risk score at time point td, FX t represents the financial risk score at the current time point t;

[0040] The time series set D is used as training data and input into the financial risk prediction model, and then the financial risk prediction model is trained and optimized using the cross-validation method;

[0041] Use the trained financial risk prediction model to obtain the financial risk score FX for a period of time in the future, and obtain the financial risk volatility σ based on the financial risk score FX for a period of time in the future FX , the financial risk volatility σ FX The method of obtaining is:

[0042]

[0043] In the formula, FX t+b Indicates the financial risk score at the future time point t+b, FX t represents the financial risk score at the current time point t, μ FXrepresents the average financial risk score at g time points in the future, g represents the total number of predicted time points, and b = 1, 2, 3, …, g].

[0044] Preferably, the risk assessment unit is used to preset a risk fluctuation threshold BD and compare the risk fluctuation threshold BD with the financial risk fluctuation rate σ FX Conduct comparative analysis and evaluate the financial risk status of the enterprise. The specific evaluation contents are as follows:

[0045] If the financial risk volatility σ FX Greater than or equal to the risk volatility threshold BD, that is, σ FX When ≥BD, the enterprise's financial risk fluctuation is judged to be in an abnormal state, triggering the alarm mechanism, recording the financial data and sending it to the enterprise's financial management personnel until the enterprise's financial management personnel respond to optimize and adjust the financial costs;

[0046] If the financial risk volatility σ FX Less than the risk volatility threshold BD, that is, σ FX When <BD, it is determined that the enterprise's financial risk fluctuations are in a normal state, and the changes in the enterprise's financial risks are continuously monitored.

[0047] Preferably, the financial cost optimization module is used to optimize financial costs when the financial risk fluctuation of the enterprise is in an abnormal state, wherein the financial cost optimization includes adjusting procurement and inventory and reducing operating expenses;

[0048] The adjustment of purchases and inventory refers to renegotiation with suppliers to extend payment terms and offer discounts;

[0049] The aforementioned reduction in operating expenses refers to optimizing labor costs, streamlining redundant positions, and optimizing equipment maintenance plans.

[0050] Preferably, the intelligent feedback module is used to monitor the financial data of the enterprise after financial cost optimization in real time, and to perform summary calculation based on the financial data of the enterprise after financial cost optimization to obtain the financial risk score FX of the enterprise after financial cost optimization. f , and based on the financial cost optimization of the enterprise's financial risk score FX f and the financial risk score FX at time point t before financial cost optimization t , obtain the financial risk change rate ΔFX, the financial risk change rate ΔFX is obtained in the following manner:

[0051]

[0052] A risk change threshold YZ is preset, and the risk change threshold YZ is compared and analyzed with the financial risk change rate ΔFX to evaluate the financial cost optimization effect. The specific evaluation contents are as follows:

[0053] If the financial risk change rate ΔFX is less than the risk change threshold YZ, that is, ΔFX<YZ, it means that the financial optimization effect is qualified. At this time, the financial optimization strategy and the financial data before and after the optimization are recorded, and a financial optimization report is generated and stored in the financial database within the enterprise;

[0054] If the financial risk change rate ΔFX is greater than or equal to the risk change threshold YZ, that is, ΔFX≥YZ, it means that the financial optimization effect is unsatisfactory. At this time, a retrospective analysis of the financial optimization is conducted, and joint meetings of various departments within the enterprise are organized to review the current financial cost structure and capital operation status, re-optimize the financial cost, and continuously track the financial cost optimization process.

[0055] The present invention provides a multi-dimensional financial cost control and optimization system, which has the following beneficial effects:

[0056] (1) The present invention constructs a complete financial cost control and optimization closed loop through a risk warning module and an intelligent feedback module. The risk warning module uses three indicators, namely, the material price volatility index CL, the exchange rate volatility index HL, and the cash flow index XJ, to dynamically evaluate the financial risk status of the enterprise, and combines the long short-term memory network LSTM model to predict future risk changes. The intelligent feedback module collects the data after financial cost optimization in real time, calculates the financial risk change rate ΔFX, evaluates the financial optimization effect, and guides the next round of optimization adjustment, thereby realizing a dynamic closed-loop management of risk identification, optimization execution, and effect feedback. This mechanism enables enterprises to respond quickly to market changes, effectively control financial risks, and improve the flexibility and timeliness of financial management.

[0057] (2) The present invention uses a data analysis module to perform multi-dimensional analysis on the financial data of an enterprise, and uses quantitative indicators, such as the material price fluctuation index CL, the exchange rate fluctuation index HL and the cash flow index XJ, to summarize and calculate the complex financial data to obtain the financial risk score FX. The financial risk score FX takes into account material price fluctuations, exchange rate fluctuations and cash liquidity, and provides strong support for the financial risk assessment of the enterprise. In addition, through the financial cost optimization module, including adjusting the purchase account period, optimizing inventory management and reducing operating expenses, the implementation of optimization measures and the accuracy of cost control are ensured, thereby alleviating the operating pressure of the enterprise.

[0058] (3) The present invention monitors the financial data of the enterprise after financial cost optimization in real time through the intelligent feedback module, and quantifies the financial optimization effect based on the comparison of financial risk scores before and after optimization. At the same time, it sets the risk change threshold YZ as the judgment standard to timely evaluate and adjust the optimization measures. If the optimization effect is not ideal, the system will automatically trigger the retrospective analysis mechanism, combined with the joint review of the enterprise's financial management department, to re-optimize the cost structure and continuously track the optimization effect. This design ensures the scientific nature and continuous improvement capabilities of the optimization measures, improves the accuracy and decision-making efficiency of financial cost optimization, and reduces the waste of financial resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a block diagram of a multi-dimensional financial cost control and optimization system of the present invention. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] Example 1

[0062] See also Figure 1 , the present invention provides a multi-dimensional financial cost control and optimization system, including a data acquisition and preprocessing module, a data analysis module, a risk warning module, a financial cost optimization module and an intelligent feedback module;

[0063] The data collection and preprocessing module is used to collect the financial data of the enterprise based on the internal financial database of the enterprise, and preprocess the collected financial data, and construct a financial data set S based on the preprocessed financial data;

[0064] The data analysis module is used to perform summary analysis based on the financial data set S to obtain the material price fluctuation index CL, the exchange rate fluctuation index HL and the cash flow index XJ;

[0065] The risk warning module is used to perform summary calculations based on the material price volatility index CL, the exchange rate volatility index HL and the cash flow index XJ to obtain the financial risk score FX, and to build a financial risk prediction model based on the long short-term memory network LSTM to obtain the financial risk score FX for a period of time in the future, and to obtain the financial risk volatility coefficient σ based on the financial risk score FX for a period of time in the future. FX , preset the risk fluctuation threshold BD, and compare the risk fluctuation threshold BD with the financial risk fluctuation coefficient σ FXConduct comparative analysis to assess the financial risk status of the enterprise;

[0066] The financial cost optimization module is used to optimize the financial cost according to the financial risk status result of the enterprise when the financial risk status of the enterprise is in an abnormal state;

[0067] The intelligent feedback module is used to collect the financial data of the enterprise after financial cost optimization in real time, and obtain the financial risk score FX of the enterprise after financial cost optimization f , and combined with the financial risk score FX at time point t before financial cost optimization t , obtain the financial risk change rate ΔFX, preset the risk change threshold YZ, compare and analyze the financial risk change rate ΔFX with the risk change threshold YZ, and evaluate the financial cost optimization effect.

[0068] In the embodiment, a closed-loop financial cost control and optimization system is constructed through the synergy of the data acquisition and preprocessing module, the data analysis module, the risk warning module, the financial cost optimization module and the intelligent feedback module. First, the data acquisition and preprocessing module can collect multi-dimensional financial data from the internal financial database of the enterprise in real time, and improve the data quality through missing value filling, outlier detection and data standardization to ensure the accuracy of subsequent analysis. Secondly, the data analysis module quantifies the financial operation status of the enterprise from multiple angles by constructing the material price volatility index CL, the exchange rate volatility index HL and the cash flow index XJ, providing basic support for risk assessment. The risk warning module calculates the financial risk score FX using the material price volatility index CL, the exchange rate volatility index HL and the cash flow index XJ, and combines the long short-term memory network LSTM model to predict the financial risk score FX in the future period to obtain the financial risk volatility coefficient σ FX , through the financial risk volatility coefficient σ FX Through comparative analysis with the preset risk fluctuation threshold BD, the company's financial risk status is dynamically evaluated to ensure that potential risks are discovered in a timely manner. When the company's financial risk status is in an abnormal state, the financial cost optimization module proposes optimization strategies, such as adjusting procurement accounts, optimizing inventory management and reducing operating expenses, so as to reduce the company's operating costs. Finally, the intelligent feedback module monitors the optimized financial data in real time, dynamically evaluates the optimization effect based on the financial risk change rate ΔFX, and guides subsequent improvements. Through the closed-loop management mechanism, dynamic monitoring and optimization of the company's financial risks are realized, which effectively improves the accuracy and flexibility of the company's financial management.

[0069] Example 2

[0070] Please refer to Figure 1 Specifically: the data acquisition and preprocessing module includes a data acquisition unit and a preprocessing unit,

[0071] The data collection unit is used to collect the financial data of the enterprise in real time based on the internal financial database of the enterprise, wherein the financial data includes raw material purchase data, sales data and cash flow data;

[0072] The raw material purchase data includes the name, purchase price and purchase batch of each raw material purchased;

[0073] The sales data includes product transaction type, transaction amount, transaction date and transaction exchange rate;

[0074] The cash flow data includes the enterprise's cash balance, the amount, date, source and purpose of cash transactions.

[0075] The preprocessing unit is used to preprocess the collected financial data, wherein the preprocessing includes missing value processing, outlier detection and data standardization;

[0076] The missing value processing is used to fill the missing data in the financial data with the mean filling method;

[0077] The outlier detection is used to detect outliers in financial data using the Z-Score method, and to repair the detected outliers using the linear interpolation method;

[0078] The data standardization is used to perform dimensionless processing on the financial data after missing value processing and outlier detection using dimensionless processing technology to eliminate the influence of different feature dimensions;

[0079] Based on the preprocessed financial data, a financial data set S is constructed.

[0080] In this embodiment, the financial data of the enterprise is efficiently processed through the data acquisition and preprocessing module, which achieves a comprehensive improvement in data quality and a guarantee of the accuracy of financial analysis. The module is designed with a data acquisition unit and a preprocessing unit, wherein the data acquisition unit can extract multi-dimensional financial data from the internal financial database of the enterprise in real time, including raw material procurement data, sales data, and cash flow data. This design ensures the comprehensiveness and real-time nature of the financial data, and lays a solid foundation for subsequent analysis. At the same time, the preprocessing unit further improves the integrity and consistency of the data through the three steps of missing value processing, outlier detection, and data standardization. The missing value processing adopts the mean filling method to effectively solve the problem of incomplete data. The outlier detection uses the Z-Score method to accurately identify abnormal data, and combines the linear interpolation method to repair the outliers, ensuring the reliability of the data. Data standardization eliminates the dimensional differences between different features through dimensionless technology, ensuring the comparability of the data and the accuracy of the analysis results. The financial data set S constructed after preprocessing provides a reliable input for the subsequent modules, thereby effectively avoiding the influence of data noise and errors on the analysis model, improving the utilization efficiency and analysis accuracy of financial data, and providing a solid guarantee for the implementation of accurate financial risk assessment and cost optimization by enterprises.

[0081] Example 3

[0082] Please refer to Figure 1 Specifically: the data analysis module is used to perform summary calculations based on the financial data set S to obtain a material price fluctuation index CL, an exchange rate fluctuation index HL, and a cash flow index XJ;

[0083] The material price fluctuation index CL is obtained as follows:

[0084]

[0085] In the formula, p ij represents the price of the jth material at the i-th purchase, represents the average price of the jth material purchase, represents the standard deviation of the purchase price of the jth material, p j represents the purchase price of the jth material, n j represents the total number of purchases of the jth material, i j represents the index of the j-th material purchase batch, i j =[1, 2, 3, ..., n j ], m represents the total number of material types, j represents the material type index, j = [1, 2, 3, ..., m], N represents the total number of purchases of all materials, ω j Indicates the weight coefficient of the jth material, where the specific value of the weight coefficient is set by the user according to the usage;

[0086] The exchange rate volatility index HL is obtained as follows:

[0087]

[0088] In the formula, T represents the time period to which the financial data belongs, R c，t represents the exchange rate at time point t, R c，avg represents the mean of the exchange rate, R c represents the exchange rate, t represents the time point to which the financial data belongs, t=[1,2,3,…,T], u represents the number of transaction types, ρ c，k represents the influence coefficient of the k-th type of transaction on exchange rate fluctuations, C transaction，k represents the amount of the k-th transaction, k represents the index of the transaction type, k = [1, 2, 3, ..., u], where the influence coefficient of the k-th transaction on the exchange rate fluctuation is ρ c，k By collecting historical financial data and building a correlation model between transaction types and exchange rate fluctuations based on linear regression, the influence coefficient ρ is optimized using mean square error (MSE). c，k The value of MSE is selected, and the influence coefficient ρ corresponding to the minimum value of MSE is selected. c，k as the final output value.

[0089] The cash flow index XJ is obtained as follows:

[0090]

[0091] In the formula, C cash，t represents the cash balance of the enterprise at time point t, C cash，t-1 represents the cash balance of the enterprise at time point t-1, D represents the average daily cash demand, R represents the total operating income in the time period T, and A r It represents the balance of accounts receivable at the end of time period T, and E represents the total operating costs within time period T.

[0092] In this embodiment, the material price fluctuation index CL, the exchange rate fluctuation index HL and the cash flow index XJ are calculated based on the financial data set S through the data analysis module, which provides efficient and accurate data support for financial risk assessment and cost optimization. First, the material price fluctuation index C is based on the purchase price and weight of various materials. By combining the calculation method of mean and standard deviation, it comprehensively reflects the overall fluctuation of material prices, which helps enterprises to reasonably plan procurement strategies and reduce raw material costs. Secondly, the exchange rate fluctuation index HL introduces the impact coefficient of transaction types on exchange rate fluctuations, constructs a correlation model between transaction types and exchange rate fluctuations through linear regression method, and optimizes the value of the impact coefficient using mean square error MSE, so as to accurately capture the contribution of different transactions to exchange rate fluctuations, and help enterprises better deal with exchange rate risks in cross-border transactions. Finally, the cash flow index XJ evaluates the cash liquidity and short-term solvency of the enterprise by dynamically calculating the ratio of the change in the enterprise's cash flow to the average daily operating capital demand, and provides a comprehensive reference for cash flow management for the enterprise. The joint analysis of these three indexes realizes the in-depth mining of multi-dimensional financial data, provides a scientific basis for the financial risk assessment and decision-making of the enterprise, and improves the ability of the enterprise to cope with market dynamic changes and the efficiency of financial management.

[0093] Example 4

[0094] Please refer to Figure 1 ,Specifically: the risk warning module includes a financial risk score calculation unit, a risk prediction unit and a risk assessment unit;

[0095] The financial risk score calculation unit is used to perform summary calculation based on the material price fluctuation index CL, the exchange rate fluctuation index HL and the cash flow index XJ to obtain the financial risk score FX. The financial risk score FX is obtained in the following manner:

[0096]

[0097] In the formula, CL represents the material price fluctuation index, HL represents the exchange rate fluctuation index, XJ represents the cash flow index, Z represents the first correction coefficient, α1, α2 and α3 represent the weight coefficients of the material price fluctuation index CL, the exchange rate fluctuation index HL and the cash flow index XJ respectively, and 0<α1<1, 0<α2<1, 0<α3<1, α1+α2+α3=1, wherein the specific value of the weight coefficient is set by the user according to the usage.

[0098] The risk prediction unit is used to build a financial risk prediction model based on the long short-term memory network LSTM, collect historical financial data based on the financial database within the enterprise, and summarize and calculate the historical financial data to obtain the financial risk score FX in the past period of time, and build a time series set D, which is specifically expressed in the form of:

[0099] D=[FX t-d FX t-(d-1) ,…,FX t-1 FX t ];

[0100] Among them, FX t-d Indicates the financial risk score at time point td, FX t represents the financial risk score at the current time point t;

[0101] The time series set D is used as training data and input into the financial risk prediction model, and then the financial risk prediction model is trained and optimized using the cross-validation method;

[0102] Use the trained financial risk prediction model to obtain the financial risk score FX for a period of time in the future, and obtain the financial risk volatility σ based on the financial risk score FX for a period of time in the future FX , the financial risk volatility σ FX The method of obtaining is:

[0103]

[0104] In the formula, FX t+b Indicates the financial risk score at the future time point t+b, FX t represents the financial risk score at the current time point t, μ FX represents the average financial risk score at g time points in the future, g represents the total number of predicted time points, and b = 1, 2, 3, …, g].

[0105] The risk assessment unit is used to preset a risk fluctuation threshold BD and compare the risk fluctuation threshold BD with the financial risk fluctuation rate σ FX Conduct comparative analysis and evaluate the financial risk status of the enterprise. The specific evaluation contents are as follows:

[0106] If the financial risk volatility σ FX Greater than or equal to the risk volatility threshold BD, that is, σ FX When ≥BD, the enterprise's financial risk fluctuation is judged to be in an abnormal state, triggering the alarm mechanism, recording the financial data and sending it to the enterprise's financial management personnel until the enterprise's financial management personnel respond to optimize and adjust the financial costs;

[0107] If the financial risk volatility σ FX Less than the risk volatility threshold BD, that is, σ FX When <BD, it is determined that the financial risk fluctuation of the enterprise is in a normal state, and the changes in the financial risk of the enterprise are continuously monitored.

[0108] In this embodiment, the financial risk score calculation unit dynamically summarizes and calculates the financial risk score FX based on the material price volatility index CL, the exchange rate volatility index HL and the cash flow index XJ, combined with the weight parameter and the correction coefficient, to ensure the accuracy and comprehensiveness of the financial risk score FX. Subsequently, the risk prediction unit constructs a financial risk prediction model through the long short-term memory network LSTM, and performs summary calculation based on the historical financial data of the enterprise financial database to obtain the financial risk score FX in the past period of time, trains the financial risk prediction model, and optimizes its parameter structure. The model can not only accurately predict the financial risk score FX in the future period of time, but also calculate the financial risk volatility σ FX , quantify the volatility of the forecast results, and lay the foundation for the subsequent risk status assessment. Finally, the risk assessment unit uses the financial risk volatility σ FX By comparing and analyzing with the preset risk volatility threshold BD, the financial risk status of the enterprise can be quickly determined. When the risk volatility exceeds the threshold, the system triggers the alarm mechanism, transmits the financial risk information to the enterprise managers, and guides them to take optimization measures in time. When the risk volatility is within the normal range, the system continuously monitors the financial status to avoid missing potential risks. The overall design effectively integrates risk scoring, prediction and evaluation functions, and is real-time, accurate and dynamically adaptable. It not only improves the efficiency of corporate financial risk management, but also enhances the ability of enterprises to cope with complex market changes.

[0109] Example 5

[0110] Please refer to Figure 1 Specifically: the financial cost optimization module is used to optimize financial costs when the financial risk fluctuation of the enterprise is in an abnormal state, wherein the financial cost optimization includes adjusting procurement and inventory and reducing operating expenses;

[0111] Adjusting purchases and inventories refers to renegotiating with suppliers, extending payment terms and discounts, and reducing the purchase quantity of low-demand items;

[0112] The above-mentioned reduction in operating expenses refers to optimizing labor costs, streamlining redundant positions, optimizing equipment maintenance plans, and improving equipment utilization.

[0113] In this embodiment, specific optimization measures are used to effectively alleviate the operational pressure of the enterprise under abnormal financial risk fluctuation conditions, and improve the accuracy and flexibility of cost control. In terms of procurement and inventory management, the present invention proposes measures to renegotiate with suppliers to extend the payment period and obtain discounts, thereby reducing short-term capital pressure. At the same time, by reducing the purchase quantity of low-demand items, inventory backlogs and resource waste are avoided, and inventory management efficiency is further optimized. In terms of reducing operating expenses, the present invention optimizes labor costs and streamlines redundant positions, rationally allocates human resources, and improves the labor efficiency of the enterprise. In addition, by optimizing equipment maintenance plans and improving equipment utilization, the equipment life cycle is effectively extended, and the cost expenditure caused by equipment idleness and update frequency is reduced. The above measures can not only reduce financial costs from multiple angles, but also improve the operational efficiency and risk resistance of the enterprise, so that the enterprise can maintain stable operations. This comprehensive optimization method for procurement, inventory and operating expenses ensures the scientificity and feasibility of financial cost optimization, effectively reduces the financial risks of the enterprise, and lays a solid foundation for the sustainable development of the enterprise.

[0114] Example 6

[0115] Please refer to Figure 1 Specifically: the intelligent feedback module is used to monitor the financial data of the enterprise after financial cost optimization in real time, and to summarize and calculate the financial data of the enterprise after financial cost optimization to obtain the financial risk score FX of the enterprise after financial cost optimization. f , and based on the financial cost optimization of the enterprise's financial risk score FX f and the financial risk score FX at time point t before financial cost optimization t , obtain the financial risk change rate ΔFX, the financial risk change rate ΔFX is obtained in the following manner:

[0116]

[0117] A risk change threshold YZ is preset, and the risk change threshold YZ is compared and analyzed with the financial risk change rate ΔFX to evaluate the financial cost optimization effect. The specific evaluation contents are as follows:

[0118] If the financial risk change rate ΔFX is less than the risk change threshold YZ, that is, ΔFX<YZ, it means that the financial optimization effect is qualified. At this time, the financial optimization strategy and the financial data before and after the optimization are recorded, and a financial optimization report is generated and stored in the financial database within the enterprise;

[0119] If the financial risk change rate ΔFX is greater than or equal to the risk change threshold YZ, that is, ΔFX≥YZ, it means that the financial optimization effect is unsatisfactory. At this time, a retrospective analysis of the financial optimization is conducted, and joint meetings of various departments within the enterprise are organized to review the current financial cost structure and capital operation status, re-optimize the financial cost, and continuously track the financial cost optimization process.

[0120] In this embodiment, the optimized financial data is summarized and analyzed to calculate the financial risk score FX of the enterprise after financial cost optimization. f , and combined with the financial risk score FX at time point t before financial cost optimization t , obtain the financial risk change rate ΔFX, and measure the effectiveness of the optimization measures through quantitative evaluation of the financial risk change rate ΔFX. At the same time, the preset risk change threshold YZ is used as the judgment standard to clearly classify the optimization effect. If the financial risk change rate ΔFX is less than the risk change threshold YZ, the system automatically archives the optimization results and related financial data records, and generates a financial optimization report to provide a scientific basis for the company's subsequent financial decision-making; if the financial risk change rate ΔFX is greater than or equal to the risk change threshold YZ, the retrospective analysis mechanism is triggered, combined with the joint review of multiple departments within the company, the optimization strategy is readjusted, and the optimization effect is continuously tracked to ensure the effective resolution of the problem. This design improves the accuracy and real-time performance of financial optimization, avoids blind decision-making or repeated resource investment, and at the same time, through a quantitative and dynamic feedback mechanism, ensures that the company can respond quickly to financial changes and continuously improve the financial structure in multiple rounds of optimization, effectively reduce costs, control risks, and improve the company's overall financial management level and market competitiveness.

[0121] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-dimensional financial cost control and optimization system, characterized by: It includes data collection and preprocessing module, data analysis module, risk warning module, financial cost optimization module and intelligent feedback module; The data collection and preprocessing module is used to collect the financial data of the enterprise based on the internal financial database of the enterprise, and preprocess the collected financial data, and construct a financial data set S based on the preprocessed financial data; The data analysis module is used to perform summary analysis based on the financial data set S to obtain the material price fluctuation index CL, the exchange rate fluctuation index HL and the cash flow index XJ; The risk warning module is used to perform summary calculations based on the material price volatility index CL, the exchange rate volatility index HL and the cash flow index XJ to obtain the financial risk score FX, and to build a financial risk prediction model based on the long short-term memory network LSTM to obtain the financial risk score FX for a period of time in the future, and to obtain the financial risk volatility coefficient σ based on the financial risk score FX for a period of time in the future. FX , preset the risk fluctuation threshold BD, and compare the risk fluctuation threshold BD with the financial risk fluctuation coefficient σ FX Conduct comparative analysis to assess the financial risk status of the enterprise; The financial cost optimization module is used to optimize the financial cost according to the financial risk status result of the enterprise when the financial risk status of the enterprise is in an abnormal state; The intelligent feedback module is used to collect the financial data of the enterprise after financial cost optimization in real time, and obtain the financial risk score FX of the enterprise after financial cost optimization f , and combined with the financial risk score FX at time point t before financial cost optimization t , obtain the financial risk change rate ΔFX, preset the risk change threshold YZ, compare and analyze the financial risk change rate ΔFX with the risk change threshold YZ, and evaluate the financial cost optimization effect.

2. A multi-dimensional financial cost control and optimization system according to claim 1, characterized in that: The data acquisition and preprocessing module includes a data acquisition unit and a preprocessing unit. The data collection unit is used to collect the financial data of the enterprise in real time based on the internal financial database of the enterprise, wherein the financial data includes raw material purchase data, sales data and cash flow data; The raw material purchase data includes the name, purchase price and purchase batch of each raw material purchased; The sales data includes product transaction type, transaction amount, transaction date and transaction exchange rate; The cash flow data includes the enterprise's cash balance, the amount, date, source and purpose of cash transactions.

3. A multi-dimensional financial cost control and optimization system according to claim 2, characterized in that: The preprocessing unit is used to preprocess the collected financial data, wherein the preprocessing includes missing value processing, outlier detection and data standardization; The missing value processing is used to fill the missing data in the financial data with the mean filling method; The outlier detection is used to detect outliers in financial data using the Z-Score method, and to repair the detected outliers using the linear interpolation method; The data standardization is used to perform dimensionless processing on the financial data after missing value processing and outlier detection using dimensionless processing technology to eliminate the influence of different feature dimensions; Based on the preprocessed financial data, a financial data set S is constructed.

4. A multi-dimensional financial cost control and optimization system according to claim 3, characterized in that: The data analysis module is used to perform summary calculations based on the financial data set S to obtain a material price fluctuation index CL, an exchange rate fluctuation index HL, and a cash flow index XJ; The material price fluctuation index CL is obtained as follows: In the formula, p ij represents the price of the jth material at the i-th purchase, represents the average price of the jth material purchase, represents the standard deviation of the purchase price of the jth material, p j represents the purchase price of the jth material, n j represents the total number of purchases of the jth material, i j represents the index of the j-th material purchase batch, i j =[1, 2, 3, ..., n j ], m represents the total number of material types, j represents the material type index, j = [1, 2, 3, ..., m], N represents the total number of purchases of all materials, ω j represents the weight coefficient of the jth material; The exchange rate volatility index HL is obtained as follows: In the formula, T represents the time period to which the financial data belongs, R c，t represents the exchange rate at time point t, R c，avg represents the mean of the exchange rate, R c represents the exchange rate, t represents the time point to which the financial data belongs, t=[1,2,3,…,T], u represents the number of transaction types, ρ c，k represents the influence coefficient of the k-th type of transaction on exchange rate fluctuations, C transaction，k represents the amount of the kth type of transaction, k represents the index of the transaction type, k = [1, 2, 3, ..., u].

5. A multi-dimensional financial cost control and optimization system according to claim 4, characterized in that: The cash flow index XJ is obtained as follows: In the formula, C cash，t represents the cash balance of the enterprise at time point t, C cash，t-1 It represents the cash balance of the enterprise at time point t-1, D represents the average daily cash demand, R represents the total operating income within the time period T, Ar represents the accounts receivable balance at the end of the time period T, and E represents the total operating costs within the time period T.

6. A multi-dimensional financial cost control and optimization system according to claim 5, characterized in that: The risk warning module includes a financial risk score calculation unit, a risk prediction unit and a risk assessment unit; The financial risk score calculation unit is used to perform summary calculation based on the material price fluctuation index CL, the exchange rate fluctuation index HL and the cash flow index XJ to obtain the financial risk score FX. The financial risk score FX is obtained in the following manner: In the formula, CL represents the material price fluctuation index, HL represents the exchange rate fluctuation index, XJ represents the cash flow index, Z represents the first correction coefficient, α1, α2 and α3 represent the weight coefficients of the material price fluctuation index CL, the exchange rate fluctuation index HL and the cash flow index XJ respectively, and 0<α1<1, 0<α2<1, 0<α3<1, α1+α2+α3=1.

7. A multi-dimensional financial cost control and optimization system according to claim 6, characterized in that: The risk prediction unit is used to build a financial risk prediction model based on the long short-term memory network LSTM, collect historical financial data based on the financial database within the enterprise, and summarize and calculate the historical financial data to obtain the financial risk score FX in the past period of time, and build a time series set D, which is specifically expressed in the form of: D=[FX t-d ,FX t-(d-1) ,…,FX t-1 ,FX t ]; Among them, FX t-d Indicates the financial risk score at time point td, FX t represents the financial risk score at the current time point t; The time series set D is used as training data and input into the financial risk prediction model, and then the financial risk prediction model is trained and optimized using the cross-validation method; Use the trained financial risk prediction model to obtain the financial risk score FX for a period of time in the future, and obtain the financial risk volatility σ based on the financial risk score FX for a period of time in the future FX , the financial risk volatility σ FX The method of obtaining is: In the formula, FX t+b Indicates the financial risk score at the future time point t+b, FX t represents the financial risk score at the current time point t, μ FX represents the average financial risk score at g time points in the future, g represents the total number of predicted time points, and b = 1, 2, 3, …, g].

8. A multi-dimensional financial cost control and optimization system according to claim 7, characterized in that: The risk assessment unit is used to preset a risk fluctuation threshold BD and compare the risk fluctuation threshold BD with the financial risk fluctuation rate σ FX Conduct comparative analysis and evaluate the financial risk status of the enterprise. The specific evaluation contents are as follows: If the financial risk volatility σ FX Greater than or equal to the risk volatility threshold BD, that is, σ FX When ≥BD, the enterprise's financial risk fluctuation is judged to be in an abnormal state, triggering the alarm mechanism, recording the financial data and sending it to the enterprise's financial management personnel until the enterprise's financial management personnel respond to optimize and adjust the financial costs; If the financial risk volatility σ FX Less than the risk volatility threshold BD, that is, σ FX When <BD, it is determined that the financial risk fluctuation of the enterprise is in a normal state, and the changes in the financial risk of the enterprise are continuously monitored.

9. A multi-dimensional financial cost control and optimization system according to claim 8, characterized in that: The financial cost optimization module is used to optimize financial costs when the financial risk fluctuation of the enterprise is in an abnormal state, wherein the financial cost optimization includes adjusting procurement and inventory and reducing operating expenses; The adjustment of purchases and inventory refers to renegotiation with suppliers to extend payment terms and offer discounts; The aforementioned reduction in operating expenses refers to optimizing labor costs, streamlining redundant positions, and optimizing equipment maintenance plans.

10. A multi-dimensional financial cost control and optimization system according to claim 9, characterized in that: The intelligent feedback module is used to monitor the financial data of the enterprise after financial cost optimization in real time, and to summarize and calculate the financial data of the enterprise after financial cost optimization to obtain the financial risk score FX of the enterprise after financial cost optimization. f , and based on the financial cost optimization of the enterprise's financial risk score FX f and the financial risk score FX at time point t before financial cost optimization t , obtain the financial risk change rate ΔFX, the financial risk change rate ΔFX is obtained in the following manner: A risk change threshold YZ is preset, and the risk change threshold YZ is compared and analyzed with the financial risk change rate ΔFX to evaluate the financial cost optimization effect. The specific evaluation contents are as follows: If the financial risk change rate ΔFX is less than the risk change threshold YZ, that is, ΔFX<YZ, it means that the financial optimization effect is qualified. At this time, the financial optimization strategy and the financial data before and after the optimization are recorded, and a financial optimization report is generated and stored in the financial database within the enterprise; If the financial risk change rate ΔFX is greater than or equal to the risk change threshold YZ, that is, ΔFX≥YZ, it means that the financial optimization effect is unsatisfactory. At this time, a retrospective analysis of the financial optimization is conducted, and joint meetings of various departments within the enterprise are organized to review the current financial cost structure and capital operation status, re-optimize the financial cost, and continuously track the financial cost optimization process.

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