Intelligent business and financial integrated data processing system

Abstract

The invention belongs to the technical field of business and financial integration, and discloses an intelligent business and financial integration data processing system which comprises a data management module, a factor analysis module, a risk early warning module and an optimization resource configuration module. According to the system, all the modules are integrated, fusion processing and deep analysis of business data and financial data are achieved, the system can accurately recognize key factors influencing operation and fund conditions, the perspectiveness and accuracy of risk early warning are improved, meanwhile, an enterprise is assisted in dynamically adjusting resource allocation and management and control strategies, and the system has high practicability. And the cross-department cooperation efficiency and the risk response capability are enhanced, so that fine management and robust operation of an enterprise can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of business and financial integration, and in particular to an intelligent business and financial integration data processing system. Background Art

[0002] As a comprehensive and integrated management activity, financial management faces significant challenges from changes in the macroeconomic environment, industry lifecycles, and stages of enterprise development. Financial and business integration has become an inevitable trend in the development of financial management. The finance department must not only participate in the analysis of business activities but also in the decision-making of these activities. The fundamental concept of financial and business integration is to organically integrate the three primary processes of business operations—business processes, financial accounting processes, and management processes—within an IT environment encompassing networks, databases, and management software platforms, thereby improving overall operational efficiency. On this basis, through data standardization, process automation, and intelligent algorithm analysis, the data barriers between business and finance are broken down, enabling unified data collection, intelligent processing, and real-time feedback, providing multi-dimensional, high-precision data support for business decision-making.

[0003] Existing technologies mostly use static data analysis, with decentralized management of business and financial data and a lack of a unified processing mechanism. This leads to inaccurate identification of key factors, delayed risk warnings, difficulty in supporting real-time decision-making and cross-departmental collaboration, low resource allocation response efficiency, and difficulty in achieving dynamic optimization and effective risk prevention and control.

[0004] Therefore, how to provide an intelligent business and financial integrated data processing system is an urgent problem to be solved. Summary of the Invention

[0005] An embodiment of the present invention provides an intelligent business-finance integrated data processing system to address the problems in existing technologies of mostly using static data analysis, decentralized management of business and financial data, and lack of a unified processing mechanism, which leads to inaccurate identification of key factors, delayed risk warnings, difficulty in supporting real-time decision-making and cross-departmental collaboration, low resource allocation response efficiency, and difficulty in achieving dynamic optimization and effective risk prevention and control.

[0006] To provide a basic understanding of some aspects of the disclosed embodiments, the following is a brief summary. This summary is not intended to be a comprehensive review, identify key or essential elements, or delineate the scope of these embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the detailed description that follows.

[0007] According to a first aspect of an embodiment of the present invention, an intelligent business and financial integrated data processing system is provided.

[0008] In one embodiment, the intelligent business-finance integrated data processing system includes:

[0009] The data management module is used to obtain the business data and financial data of the enterprise, process them, and extract the business and financial integration feature data;

[0010] The factor analysis module is used to analyze the business-finance integration feature data using factor analysis algorithms to identify key factors that affect the company's operations and supply chain funding status;

[0011] The risk warning module is used to establish a risk warning model based on key factors and use the risk warning model to predict the potential risks of enterprises in operation and supply chain funds in the future period;

[0012] The resource allocation optimization module is used to dynamically adjust the company's internal management and control strategies and cross-departmental resource allocation based on risk warning results, thereby achieving business and financial collaborative optimization and risk prevention and control mechanisms.

[0013] In one embodiment, a factor analysis algorithm is used to analyze the business-finance integration feature data to identify key factors that affect the business operations and supply chain funding status, including:

[0014] Mark all business-finance integration feature data as unclassified, build an analysis model structure, and set central feature evaluation indicators;

[0015] Calculate the influence and dispersion ratio of each feature evaluation index, select the most representative feature evaluation index as the center, and divide similar feature evaluation indicators into the same group;

[0016] Based on the most representative feature evaluation indicators, a feature screening algorithm is executed to eliminate abnormal fluctuation indicators and improve the accuracy of feature division and classification quality;

[0017] Continue to select the least influential characteristic evaluation indicators from the unclassified business-finance integration characteristic data as new centers, and gradually complete the group coverage of all business-finance integration characteristic data;

[0018] Repeat the iteration until all business-finance integration feature data are classified or the factor analysis algorithm reaches the maximum number of iterations, output the classification results, and based on the classification results, identify the key factors affecting the company's operations and supply chain financial status.

[0019] In one embodiment, based on the most representative feature evaluation index, a feature screening algorithm is executed to eliminate abnormal fluctuation indicators to improve the accuracy of feature division and classification quality, including:

[0020] Select the most representative feature evaluation indicators from the business and financial integration feature set and initialize the parameters of the feature screening algorithm;

[0021] Construct a spatial projection model of characteristic evaluation indicators and calculate the fluctuation residuals of other characteristic evaluation indicators under the current central characteristic evaluation indicator;

[0022] Iteratively select the feature evaluation index with the highest projection value, dynamically update the candidate feature evaluation index combination and reconstruct the analysis space;

[0023] Based on the updated feature evaluation indicator combination in each round, a fitting verification analysis is performed to screen the feature evaluation indicator set with the smallest root mean square error, eliminate abnormal fluctuation indicators, output the optimization results, and improve the accuracy of feature segmentation and classification quality.

[0024] In one embodiment, a risk warning model is established based on key factors, and the risk warning model is used to predict the potential risks of the enterprise in terms of operation and supply chain funds in the future time period, including:

[0025] The key factors are divided into training sets and test sets, and one of the key factors is used as the main variable, and several risk warning sub-models are constructed together with other key factors;

[0026] Select the risk warning sub-model with the best training performance from several risk warning sub-models, and fuse the best risk warning sub-models to form a risk warning model;

[0027] The adjustment algorithm is used to optimize the parameters of the risk warning model, and the optimized risk warning model is used to predict the potential risks of enterprises in operations and supply chain funds in the future time period.

[0028] According to a second aspect of an embodiment of the present invention, an intelligent business-finance integrated data processing method is provided.

[0029] In one embodiment, the intelligent business-finance integrated data processing method includes:

[0030] Obtain the business data and financial data of the enterprise, process them, and extract the business and financial integration feature data;

[0031] Utilize factor analysis algorithms to analyze business-finance integration feature data and identify key factors that affect business operations and supply chain funding status;

[0032] Based on key factors, establish a risk warning model and use it to predict the potential risks of the company in terms of operations and supply chain funds in the future;

[0033] Based on the risk warning results, the company's internal management and control strategies and cross-departmental resource allocation are dynamically adjusted to achieve business and financial coordination optimization and risk prevention and control mechanisms.

[0034] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0035] In some embodiments, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0036] According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium is provided.

[0037] In one embodiment, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0038] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0039] 1. The present invention realizes the integrated processing and in-depth analysis of business data and financial data by integrating data management, factor analysis, risk warning and resource optimization allocation modules. The system can accurately identify the key factors affecting operations and financial conditions, improve the foresight and accuracy of risk warnings, and at the same time assist enterprises in dynamically adjusting resource allocation and management strategies, enhancing cross-departmental collaboration efficiency and risk response capabilities, thereby helping enterprises achieve refined management and stable operations.

[0040] 2. The present invention uses factor analysis and feature screening algorithms to accurately classify and optimize business and financial feature data, which can effectively identify key factors affecting corporate operations and supply chain financial status. The system improves the accuracy of feature division and classification quality by eliminating abnormal fluctuation indicators and optimizing feature combinations, thereby enhancing the fitting ability and stability of the model, thereby providing data support for risk warning and resource allocation, and improving the accuracy and real-time nature of decision-making.

[0041] 3. The present invention improves the prediction accuracy of the risk warning model by constructing a risk warning model, training and screening sub-models based on key factors, and introducing elite update and adaptive perturbation mechanisms to effectively optimize the model structure and core parameter configuration, thereby enhancing the risk warning model's ability to identify future operating and supply chain financial risks, avoiding falling into local optimality, and improving the stability and foresight of risk warnings, thereby providing enterprises with more effective risk prevention and control support.

[0042] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0044] Figure 1This is a principle block diagram of an intelligent business-finance integrated data processing system according to an exemplary embodiment;

[0045] Figure 2 This is a flow chart showing an intelligent business-finance integrated data processing method according to an exemplary embodiment;

[0046] Figure 3 The figure is a schematic diagram showing the structure of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0047] The following description and accompanying drawings sufficiently illustrate the specific embodiments herein to enable those skilled in the art to practice them. Portions and features of some embodiments may be included in or substituted for portions and features of other embodiments. The scope of the embodiments herein includes the entire scope of the claims, including all available equivalents thereof. Herein, the terms "first," "second," and the like are used solely to distinguish one element from another and do not require or imply any actual relationship or order between these elements. In practice, the first element can also be referred to as the second element, and vice versa. Furthermore, the terms "comprise," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a structure, device, or apparatus comprising a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such structure, device, or apparatus. Without further limitation, an element defined by the phrase "comprising a..." does not preclude the presence of other identical elements in the structure, device, or apparatus comprising the element. The various embodiments herein are described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. Similar or identical parts between the various embodiments can be referenced to each other.

[0048] The terms "longitudinal", "transverse", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like used herein to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are intended only to facilitate the description of this document and simplify the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the present invention. In the description herein, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, they can be mechanical or electrical connections, or they can be internal connections between two elements, they can be directly connected, or they can be indirectly connected through an intermediate medium. For those of ordinary skill in the art, the specific meanings of the above terms can be understood according to the specific circumstances.

[0049] As used herein, unless otherwise specified, the term "plurality" means two or more.

[0050] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0051] In this article, the term "and / or" is used to describe the association relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or, A and B.

[0052] It should be understood that, although the various steps in the flowchart are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps may be performed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily performed at the same time, but may be performed at different times. The execution order of these sub-steps or stages is not necessarily to be performed in sequence, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0053] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software so that the processor can call and execute the operations corresponding to the above modules.

[0054] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0055] Figure 1 An embodiment of the intelligent business-finance integrated data processing system of the present invention is shown.

[0056] In this optional embodiment, the intelligent business-finance integrated data processing system includes:

[0057] The data management module 101 is used to obtain the business data and financial data of the enterprise, process them, and extract the business and financial integration feature data;

[0058] Specifically, business data includes sales data, procurement data, production data, inventory data, supply chain data, customer data, etc.

[0059] Specifically, financial data includes revenue data, cost data, profit data, asset and liability data, cash flow data, financial ratio data, etc.

[0060] Specifically, the characteristic data of business-finance integration include financial health indicators, business operation efficiency indicators, business and financial relationship characteristics, risk exposure characteristics, etc.

[0061] The factor analysis module 102 is used to analyze the business-finance integration feature data using a factor analysis algorithm to identify key factors that affect the business operations and supply chain funding status;

[0062] Specifically, the key factors affecting business operations include sales revenue growth rate, gross profit margin / net profit margin, customer structure and concentration, inventory turnover rate, order completion cycle / delivery timeliness rate, production and sales coordination rate, operating costs, etc.

[0063] Specifically, the key factors affecting the financial status of the supply chain include accounts receivable turnover days, accounts payable turnover days, net cash flow from operating activities, procurement payment cycle and procurement concentration, the degree of account period matching, the proportion of funds occupied by inventory, etc.

[0064] The risk warning module 103 is used to establish a risk warning model based on key factors and use the risk warning model to predict the potential risks of the enterprise in terms of operation and supply chain funds in the future time period;

[0065] Specifically, the potential risks faced by enterprises in their operations include the risk of declining revenue, the risk of cost out of control, the risk of deteriorating profitability, the risk of excessive customer concentration, the risk of declining production and operation efficiency, and the risk of delayed market response.

[0066] Specifically, the potential risks that enterprises face in supply chain finance include cash flow interruption risk, accounts receivable collection risk, payment period mismatch risk, inventory capital occupation risk, supplier default or delivery interruption risk, financing difficulty or cost increase risk, financial leverage risk, etc.

[0067] The resource allocation optimization module 104 is used to dynamically adjust the enterprise's internal management and control strategies and cross-departmental resource allocation based on risk warning results, so as to achieve business and financial collaborative optimization and risk prevention and control mechanisms.

[0068] Specifically, based on the risk warning results, the system dynamically identifies potential risk levels, matches corresponding response strategies, adjusts internal corporate budget allocation and resource investment, and optimizes capital allocation in key business links; at the same time, it links the financial and business departments, implements a collaborative adjustment mechanism, promotes cross-departmental data sharing and decision-making linkage, and realizes improved resource allocation efficiency and intelligent and real-time risk prevention and control responses.

[0069] In this optional embodiment, the data management module 101 includes a data acquisition module (not shown in the figure) and a data processing module (not shown in the figure), wherein the data acquisition module is used to acquire the business data and financial data of the enterprise, and pre-process the business data and financial data to obtain high-quality business data and financial data; the data processing module is used to use a feature extraction algorithm to extract features from the obtained high-quality business data and financial data to obtain business and financial integrated feature data.

[0070] Specifically, feature extraction algorithms include but are not limited to principal component analysis (PCA), maximum information coefficient (MIC), mutual information method and LASSO regression. Based on these algorithms, the system evaluates the correlation, redundancy and representativeness of high-quality business data and financial data, extracts key variables that can reflect the business-finance linkage relationship, and forms business-finance integrated feature data for subsequent factor analysis and risk modeling.

[0071] In this optional embodiment, when using the factor analysis algorithm to analyze the business-finance integration feature data and identify the key factors affecting the business operations and supply chain financial status, all business-finance integration feature data can be marked as unclassified, an analysis model structure can be constructed, and a central feature evaluation index can be set; the influence and dispersion ratio of each feature evaluation index is calculated, the most representative feature evaluation index is selected as the center, and similar feature evaluation indicators are divided into the same group; based on the selected most representative feature evaluation index, a feature screening algorithm is executed to eliminate abnormal fluctuation indicators and improve the accuracy of feature division and classification quality; the feature evaluation index with the smallest influence continues to be selected as the new center from the unclassified business-finance integration feature data, and the group coverage of all business-finance integration feature data is gradually completed; the iteration is repeated until all business-finance integration feature data are classified or the factor analysis algorithm reaches the maximum number of iterations, the classification results are output, and based on the classification results, the key factors affecting the business operations and supply chain financial status are identified.

[0072] Specifically, the factor analysis algorithm is an overlapping box covering algorithm, a cluster analysis method based on the similarity of feature distributions. It achieves feature classification by constructing "feature boxes" that cover similar data. In this paper, this algorithm is used to iteratively select central features, group similar features, and filter out anomalies to identify key factors.

[0073] Specifically, the collected business-finance integration feature data is first marked as unclassified, an analysis model is constructed, and the initial central feature is set. Next, the influence and dispersion ratio of each feature is calculated, and the most representative feature is selected as the central classification category. Feature screening is then performed to eliminate abnormal fluctuation indicators. The system continuously iteratively selects new central features until all data is classified and the key influencing factors are finally identified. This improves the accuracy of feature classification and enhances the stability and practicality of factor identification.

[0074] In this optional embodiment, when executing a feature screening algorithm based on the most representative feature evaluation indicator selected, eliminating abnormal fluctuation indicators, and improving the accuracy of feature division and classification quality, the most representative feature evaluation indicator can be selected from the business and financial integration feature set to initialize the parameters of the feature screening algorithm; a feature evaluation indicator space projection model is constructed to calculate the fluctuation residuals of other feature evaluation indicators under the current central feature evaluation indicator; the feature evaluation indicator with the highest projection value is iteratively selected, the candidate feature evaluation indicator combination is dynamically updated and the analysis space is reconstructed; based on the feature evaluation indicator combination after each round of update, a fitting verification analysis is performed to screen the feature evaluation indicator set with the smallest root mean square error, eliminate abnormal fluctuation indicators, output the optimization results, and improve the accuracy of feature division and classification quality.

[0075] Specifically, the feature screening algorithm is a continuous projection algorithm, a feature selection method that iteratively calculates orthogonal projection relationships between features to select the optimal feature subset. In this paper, the algorithm is used to calculate the fluctuation residuals between feature indicators. Through iterative projection and reconstruction of the analysis space, the most representative feature combinations are ultimately selected and abnormal indicators are eliminated.

[0076] Specifically, the system first selects the most representative feature evaluation indicators from the integrated business and financial feature set and initializes the feature screening algorithm parameters. A spatial projection model is then constructed to calculate the fluctuation residuals of the remaining features under this central feature. The system iteratively selects the feature with the highest projection value, dynamically updates the feature combination, and reconstructs the analysis space. Finally, a fitting verification analysis is performed to select the feature set with the smallest root mean square error, eliminate abnormal fluctuation indicators, and output the optimized results. This enhances the robustness of feature screening, improves classification accuracy, and improves data modeling quality.

[0077] In this optional embodiment, the formula for calculating the fluctuation residuals of other characteristic evaluation indicators under the current central characteristic evaluation indicator is:

[0078]

[0079] Where R j F represents the fluctuation residual of the characteristic evaluation index under the current central characteristic evaluation index; jRepresents the numerical vector of the jth feature evaluation index in the feature space; C (n-1) represents the feature evaluation index vector selected as the center in the n-1th iteration; T represents the transpose operation, which is used to convert the column vector into a row vector so that the inner product calculation can be performed between the vectors.

[0080] In this optional embodiment, based on the updated feature evaluation indicator combination after each round, a fitting verification analysis is performed, the feature evaluation indicator set with the smallest root mean square error is screened, abnormal fluctuation indicators are eliminated, and the optimization results are output to improve the accuracy of feature division and the quality of classification. A multivariate linear regression model can be constructed based on the updated feature evaluation indicator combination to perform a fitting verification analysis; the root mean square error corresponding to each set of feature evaluation indicator combinations is calculated to evaluate its goodness of fit to the business and financial relationship; the feature evaluation indicator set with the smallest error is screened to identify the most representative central feature evaluation indicator; abnormal fluctuation indicators or redundant features are eliminated, and the optimized feature evaluation indicator combination is output to improve the accuracy of feature division and the quality of classification.

[0081] In this optional embodiment, when establishing a risk warning model based on key factors and using the risk warning model to predict the potential risks of the enterprise in terms of operations and supply chain funds in the future time period, the key factors can be divided into a training set and a test set, and one of the key factors can be used as the main variable, and together with other key factors, several risk warning sub-models can be constructed; the risk warning sub-model with the best training performance is selected from several risk warning sub-models, and the best risk warning sub-models are integrated to form a risk warning model; the parameters of the risk warning model are optimized using an adjustment algorithm, and the optimized risk warning model is used to predict the potential risks of the enterprise in terms of operations and supply chain funds in the future time period.

[0082] Specifically, the identified key factors are first divided into training and test sets. One key factor is selected as the primary variable and combined with the remaining factors to construct multiple risk warning sub-models. The training performance of each sub-model is evaluated, and the best performing ones are selected and integrated into a comprehensive risk warning model. Subsequently, an adjustment algorithm is applied to optimize the model parameter configuration, and finally, the optimized model is used to predict the company's future operating and supply chain financial risks. This improves the accuracy of risk prediction and the adaptability of the model, strengthening the warning response capability.

[0083] In this optional embodiment, when using the adjustment algorithm to optimize the parameters of the risk warning model and predicting the potential risks of the enterprise in terms of operation and supply chain funds in the future time period through the optimized risk warning model, the parameters of the adjustment algorithm can be initialized, a perturbation sequence can be constructed, and the optimization goals and initial parameter configurations of the risk warning model can be set; the goodness of fit of each parameter configuration combination can be evaluated, and the risk warning model structure and core parameter configuration can be dynamically optimized in combination with the elite update strategy; an adaptive perturbation mechanism is introduced in the search process to expand the global exploration scope, and an elite competition selection mechanism is used to avoid the risk warning model from falling into a local optimal configuration; if the maximum number of iterations is reached, the optimal parameter configuration is output as the optimized parameter configuration of the risk warning model, and the optimized risk warning model is used to predict the potential risks of the enterprise in terms of operation and supply chain funds in the future time period.

[0084] Specifically, the adjustment algorithm is the Marine Predator Optimization Algorithm, an intelligent optimization algorithm that simulates marine predator behavior and possesses strong global search and local optimization capabilities. In this paper, this algorithm is used to dynamically optimize the parameters of the risk warning model. Through a perturbation mechanism and iterative search using an elite strategy, the model's predictive performance and stability are improved.

[0085] In this optional embodiment, the formula for the elite update strategy is:

[0086]

[0087] Where, Indicates the updated value of the ath parameter configuration combination in the t+1th iteration; Indicates the current value of the ath parameter configuration combination in the tth iteration; It represents the bth elite parameter configuration combination ranked in the top k in terms of goodness of fit in the tth iteration (ranked in the top k by error); α represents the elite guidance coefficient, which is used to control the intensity of the influence of the elite parameter combination on the optimization direction; β represents the disturbance adjustment coefficient, which is used to control the degree of diversity in the parameter search process; ε represents the random disturbance factor (such as Gaussian noise), which is used to introduce moderate exploration to avoid falling into local optimality; k represents the number of elite parameter configuration combinations (retaining the top k optimal solutions in terms of goodness of fit).

[0088] Figure 2 An embodiment of the intelligent business-finance integrated data processing method of the present invention is shown.

[0089] In this optional embodiment, the intelligent business-finance integrated data processing method includes:

[0090] Step S201: Acquire the business data and financial data of the enterprise, process them, and extract business and financial integration feature data;

[0091] Step S202: Analyze the business-finance integration feature data using a factor analysis algorithm to identify key factors that affect the business operations and supply chain funding status;

[0092] Step S203: Based on the key factors, a risk warning model is established, and the risk warning model is used to predict the potential risks of the enterprise in terms of operation and supply chain funds in the future time period;

[0093] Step S204: Based on the risk warning results, dynamically adjust the company's internal management and control strategies and cross-departmental resource allocation to achieve business and financial collaboration optimization and risk prevention and control mechanisms.

[0094] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the steps of the above-mentioned method embodiment are implemented.

[0095] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0096] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0097] In addition, the present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0098] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0099] The present invention is not limited to the structures described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. An intelligent business and financial integrated data processing system, characterized in that: include: The data management module is used to obtain the business data and financial data of the enterprise, process them, and extract the business and financial integration feature data; The factor analysis module is used to analyze the business-finance integration feature data using factor analysis algorithms to identify key factors that affect the company's operations and supply chain funding status; The risk warning module is used to establish a risk warning model based on key factors and use the risk warning model to predict the potential risks of enterprises in operation and supply chain funds in the future period; The resource allocation optimization module is used to dynamically adjust the company's internal management and control strategies and cross-departmental resource allocation based on risk warning results, thereby achieving business and financial collaborative optimization and risk prevention and control mechanisms.

2. The intelligent business-finance integrated data processing system according to claim 1, characterized in that: The data management module includes: The data acquisition module is used to obtain the business data and financial data of the enterprise, and pre-process the business data and financial data to obtain high-quality business data and financial data; The data processing module is used to extract features from the obtained high-quality business data and financial data using feature extraction algorithms to obtain integrated business and financial feature data.

3. The intelligent business-finance integrated data processing system according to claim 1, characterized in that: The above-mentioned factor analysis algorithm is used to analyze the business-finance integration characteristic data and identify the key factors affecting the business operation and supply chain funding status, including: Mark all business-finance integration feature data as unclassified, build an analysis model structure, and set central feature evaluation indicators; Calculate the influence and dispersion ratio of each feature evaluation index, select the most representative feature evaluation index as the center, and divide similar feature evaluation indicators into the same group; Based on the most representative feature evaluation indicators, a feature screening algorithm is executed to eliminate abnormal fluctuation indicators and improve the accuracy of feature division and classification quality; Continue to select the least influential characteristic evaluation indicators from the unclassified business-finance integration characteristic data as new centers, and gradually complete the group coverage of all business-finance integration characteristic data; Repeat the iteration until all business-finance integration feature data are classified or the factor analysis algorithm reaches the maximum number of iterations, output the classification results, and based on the classification results, identify the key factors affecting the company's operations and supply chain financial status.

4. The intelligent business-finance integrated data processing system according to claim 3, characterized in that: The method of executing a feature screening algorithm based on the most representative feature evaluation index selected to eliminate abnormal fluctuation indicators and improve the accuracy of feature division and classification quality includes: Select the most representative feature evaluation indicators from the business and financial integration feature set and initialize the parameters of the feature screening algorithm; Construct a spatial projection model of characteristic evaluation indicators and calculate the fluctuation residuals of other characteristic evaluation indicators under the current central characteristic evaluation indicator; Iteratively select the feature evaluation index with the highest projection value, dynamically update the candidate feature evaluation index combination and reconstruct the analysis space; Based on the updated feature evaluation indicator combination in each round, a fitting verification analysis is performed to screen the feature evaluation indicator set with the smallest root mean square error, eliminate abnormal fluctuation indicators, output the optimization results, and improve the accuracy of feature segmentation and classification quality.

5. The intelligent business-finance integrated data processing system according to claim 4, characterized in that: The formula for calculating the volatility residuals of other characteristic evaluation indicators under the current central characteristic evaluation indicator is: Where R j F represents the fluctuation residual of the characteristic evaluation index under the current central characteristic evaluation index; j Represents the numerical vector of the jth feature evaluation index in the feature space; C (n-1) represents the feature evaluation index vector selected as the center in the n-1th iteration; T represents the transpose operation.

6. The intelligent business-finance integrated data processing system according to claim 4, characterized in that: The above mentioned method is to perform fitting verification analysis based on the updated feature evaluation index combination in each round, select the feature evaluation index set with the smallest root mean square error, eliminate abnormal fluctuation indicators, output the optimization results, and improve the accuracy of feature segmentation and classification quality, including: Based on the updated feature evaluation indicator combination, a multivariate linear regression model is constructed and fit verification analysis is performed; Calculate the root mean square error corresponding to each set of feature evaluation indicators to evaluate its goodness of fit to the business and financial relationship; Screen the feature evaluation index set with the smallest error and identify the most representative central feature evaluation index; Eliminate abnormal fluctuation indicators or redundant features, output the optimized feature evaluation indicator combination, and improve the accuracy of feature division and classification quality.

7. The intelligent business-finance integrated data processing system according to claim 1, characterized in that: The risk warning model is established based on key factors, and the risk warning model is used to predict the potential risks of enterprises in operation and supply chain funds in the future period, including: The key factors are divided into training sets and test sets, and one of the key factors is used as the main variable, and several risk warning sub-models are constructed together with other key factors; Select the risk warning sub-model with the best training performance from several risk warning sub-models, and fuse the best risk warning sub-models to form a risk warning model; The adjustment algorithm is used to optimize the parameters of the risk warning model, and the optimized risk warning model is used to predict the potential risks of enterprises in operations and supply chain funds in the future time period.

8. The intelligent business-finance integrated data processing system according to claim 7, characterized in that: The adjustment algorithm is used to optimize the parameters of the risk warning model, and the optimized risk warning model is used to predict the potential risks of the enterprise in terms of operation and supply chain funds in the future time period, including: Initialize the parameters of the adjustment algorithm, build the disturbance sequence, set the initial parameter configuration of the optimization target and risk warning model; Evaluate the goodness of fit of each parameter configuration combination, combine it with the elite update strategy, and dynamically optimize the risk warning model structure and core parameter configuration; Introducing an adaptive perturbation mechanism during the search process to expand the global exploration scope, and using an elite competition selection mechanism to prevent the risk warning model from falling into a local optimal configuration; If the maximum number of iterations is reached, the optimal parameter configuration is output as the optimized parameter configuration of the risk warning model, and the optimized risk warning model is used to predict the potential risks of the enterprise in operation and supply chain funds in the future time period.

9. The intelligent business-finance integrated data processing system according to claim 8, characterized in that: The formula of the elite update strategy is: Where, Indicates the updated value of the ath parameter configuration combination in the t+1th iteration; Indicates the current value of the ath parameter configuration combination in the tth iteration; It represents the bth elite parameter configuration combination with the top k goodness of fit in the tth iteration; α represents the elite guidance coefficient; β represents the disturbance adjustment coefficient; ε represents the random disturbance factor; k represents the number of elite parameter configuration combinations.

10. An intelligent business-finance integrated data processing method, characterized in that: include: Obtain the business data and financial data of the enterprise, process them, and extract the business and financial integration feature data; Utilize factor analysis algorithms to analyze business-finance integration feature data and identify key factors that affect business operations and supply chain funding status; Based on key factors, establish a risk warning model and use it to predict the potential risks of the company in terms of operations and supply chain funds in the future; Based on the risk warning results, the company's internal management and control strategies and cross-departmental resource allocation are dynamically adjusted to achieve business and financial coordination optimization and risk prevention and control mechanisms.

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