Financial data management method and system based on big data

Abstract

The invention discloses a financial data management method and system based on big data, and relates to the technical field of financial services, the system comprises a management center, and the management center is in communication connection with a data acquisition module, a data preprocessing module, a data processing module, a data analysis module and a prediction module; financial data of an enterprise is collected through the data collection module, the collected financial data is preprocessed through the data preprocessing module to obtain standard financial data, the obtained standard financial data is processed through the data processing module to obtain a sales volume line graph and a sales volume line graph, and the sales volume line graph and the sales volume line graph are integrated. The data analysis module analyzes the obtained sales volume line graph and the sales volume line graph to obtain the changed sales volume and the standard sales volume, the prediction module obtains the predicted sales volume according to the obtained changed sales volume and the standard sales volume, the sales volume of an enterprise is accurately predicted, and the benefits of the enterprise are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of financial services, and in particular to a financial data management method and system based on big data. Background Art

[0002] With the development of information technology, financial service-related enterprises and organizations have accumulated a large amount of financial data. These data include not only traditional accounting data, but also real-time data, transaction data, market data, etc. collected from various channels. This diversification and growth makes it difficult for traditional financial management systems to process and analyze these data, so more powerful technologies and systems are needed to support management and decision-making processes. How to obtain the changed sales volume and standard sales volume, obtain the predicted sales volume based on the obtained changed sales volume and standard sales volume, and the enterprise formulates a corresponding management plan based on the obtained predicted sales volume to improve the efficiency of the enterprise is a problem we need to solve. For this purpose, a financial data management method and system based on big data is provided. Summary of the invention

[0003] In order to solve the above technical problems, the purpose of the present invention is to provide a financial data management method and system based on big data. The object of the present invention can be achieved by the following technical solutions: A financial data management system based on big data, comprising a management center, wherein the management center is communicatively connected with a data acquisition module, a data preprocessing module, a data processing module, a data analysis module and a prediction module; The data collection module is used to collect the financial data of the enterprise; The data preprocessing module is used to preprocess the collected financial data to obtain standard financial data; The data processing module is used to process the obtained standard financial data to obtain a sales line graph and a sales volume line graph; The data analysis module is used to analyze the obtained sales line graph and sales volume line graph to obtain the change sales volume and standard sales volume; The forecasting module is used to obtain forecast sales volume according to the obtained changed sales volume and standard sales volume.

[0004] Furthermore, the process of the data collection module collecting the financial data of the enterprise includes: The data collection module is composed of a number of financial data collection terminals, and the financial data collection terminals are used to collect financial data; The financial data collection terminal is configured to generate a corresponding configuration channel. The corresponding enterprise server is linked through the generated configuration channel. The financial data collection terminal obtains the financial data in the corresponding server through the configuration channel and sends the obtained financial data to the data processing module.

[0005] Furthermore, the process of preprocessing the collected financial data by the data preprocessing module includes: The data preprocessing module is provided with a data scanning unit and a data preprocessing unit, and the collected financial data is scanned by the data scanning unit to obtain the status of the financial data, and the status of the financial data includes a data missing status, a data duplication status and a data smoothness abnormality status; The data scanning unit sends the status of the financial data to the data preprocessing unit, and the data preprocessing unit processes the financial data according to the status of the financial data. When the status of the financial data is a data missing state, the missing value is filled by the method of the difference between the previous and next data. When the status of the financial data is a data duplication state, the position of the repeated value of the financial data is obtained, and the repeated value of the financial data is deleted according to the position of the obtained repeated value of the financial data. When the status of the financial data is a data smoothness abnormal state, the financial data is smoothed by polynomial fitting, and the financial data processed by the data preprocessing unit is recorded as standard financial data.

[0006] Furthermore, the process of the data processing module processing the obtained standard financial data includes: Extract features from financial data to obtain sales feature data and sales volume feature data, obtain the daily sales of the enterprise based on the obtained sales feature data, and obtain the daily sales volume of the enterprise based on the obtained sales volume feature data; Construct a two-dimensional rectangular coordinate system of time with respect to sales, map the obtained daily sales of the enterprise into the constructed two-dimensional rectangular coordinate system of time with respect to sales, generate corresponding sales data points, label the generated sales data points, record them as i, i=1, 2, 3, ..., n, n is a positive integer, and connect each sales data point in chronological order with a straight line to obtain a sales line graph; Construct a two-dimensional rectangular coordinate system of sales volume with respect to time, map the daily sales volume of the enterprise to the constructed two-dimensional rectangular coordinate system of sales volume with respect to time, generate corresponding sales data points, label the generated sales data points as i, and connect each sales data point in chronological order with a straight line to obtain a sales line graph.

[0007] Furthermore, the process of the data analysis module analyzing the obtained sales line graph and sales volume line graph includes: Obtain the sales volume corresponding to the sales volume data point, obtain the slope of the straight line between the sales volume data point and the previous sales volume data point, and if the sales volume data point is the first sales volume data point, record the slope of the straight line between the sales volume data point and the previous sales volume data point as zero; Obtain the change in sales volume corresponding to the sales volume data point according to the sales volume corresponding to the acquired sales volume data point and the slope of the straight line between the sales volume data point and the previous sales volume data point; According to the survey of big data, the number of good reviews and bad reviews of the product is obtained, and the attention coefficient of the product is obtained according to the number of good reviews and bad reviews of the product; Multiply the change sales volume corresponding to the sales volume data point by the commodity's attention coefficient to obtain the standard sales volume corresponding to the sales volume data point; Get the sales amount corresponding to the sales data point, get the slope of the straight line between the sales data point and the previous sales data point, if the sales data point is the first sales data point, record the slope of the straight line between the sales data point and the previous sales data point as zero; The change in sales volume corresponding to the sales data point is obtained based on the sales volume corresponding to the acquired sales data point and the slope of the straight line between the sales data point and the previous sales data point.

[0008] Further, according to the sales volume corresponding to the acquired sales volume data point and the slope of the straight line between the sales volume data point and the previous sales volume data point, the process of obtaining the change sales volume corresponding to the sales volume data point includes: The sales volume corresponding to the sales volume data point is multiplied by the slope of the straight line between the sales volume data point and the previous sales volume data point, and the multiplication result is added to the sales volume corresponding to the sales volume data point to obtain the change in sales volume corresponding to the sales volume data point.

[0009] Furthermore, the process of obtaining the predicted sales volume according to the obtained changed sales volume and standard sales volume by the prediction module includes: A sales volume prediction model is constructed according to the relationship between the standard sales volume corresponding to the current sales volume data point and the sales volume corresponding to the next sales volume data point, the standard sales volume corresponding to the current sales volume data point is used as the input of the sales volume prediction model, the sales volume corresponding to the next sales volume data point is used as the output of the sales volume prediction model, and the sales volume prediction model is trained through the standard sales volume corresponding to the remaining sales volume data points and the sales volume corresponding to the remaining sales volume data points; The standard sales volume corresponding to the last sales volume data point is input into the sales volume prediction model, the sales volume prediction model performs output, a first predicted sales volume is obtained, the selling price of the product is obtained, and a first predicted sales amount is obtained according to the obtained first predicted sales volume and the selling price of the product; A primary sales prediction model is constructed based on the relationship between the change in sales corresponding to the current sales data point and the sales corresponding to the next sales data point, the change in sales corresponding to the current sales data point is used as the input of the primary sales prediction model, the sales corresponding to the next sales data point is used as the output of the primary sales prediction model, and the primary sales prediction model is trained through the change in sales corresponding to the remaining sales data points and the sales corresponding to the remaining sales data points; The change sales volume corresponding to the last sales volume data point is input into the primary sales volume forecasting model, and the primary sales volume forecasting model outputs the change to obtain the primary forecast sales volume; The average value of the first predicted sales volume and the primary predicted sales volume is obtained, and the average value of the first predicted sales volume and the primary predicted sales volume is the predicted sales volume; The company formulates corresponding management plans based on the forecast sales obtained.

[0010] The present invention also discloses a management method of a financial data management system based on big data, comprising the following steps: Step 1: Obtain the financial data of the enterprise, and pre-process the obtained financial data of the enterprise to obtain standard financial data; Step 2: Process the obtained standard financial data to obtain sales line chart and sales volume line chart; Step 3: Analyze the obtained sales line chart and sales volume line chart to obtain the change sales volume and standard sales volume; Step 4: Obtain forecast sales based on the obtained variable sales and standard sales volume, and the company will formulate corresponding management plans based on the obtained forecast sales.

[0011] Compared with the prior art, the beneficial effects of the present invention are: collecting the financial data of the enterprise, preprocessing the collected financial data, obtaining standard financial data, processing the obtained standard financial data, obtaining a sales line graph and a sales volume line graph, analyzing the obtained sales line graph and sales volume line graph, obtaining changed sales and standard sales, obtaining predicted sales based on the obtained changed sales and standard sales, accurately predicting the sales of the enterprise, and improving the efficiency and management quality of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a schematic diagram of the present invention. DETAILED DESCRIPTION

[0013] like Figure 1As shown, a financial data management system based on big data includes a management center, and the management center is communicatively connected with a data acquisition module, a data preprocessing module, a data processing module, a data analysis module and a prediction module; The data collection module is used to collect the financial data of the enterprise; It should be further explained that, in the specific implementation process, the process of the data collection module collecting the financial data of the enterprise includes: The data collection module is composed of a number of financial data collection terminals, and the financial data collection terminals are used to collect financial data; The financial data collection terminal is configured to generate a corresponding configuration channel. The corresponding enterprise server is linked through the generated configuration channel. The financial data collection terminal obtains the financial data in the corresponding server through the configuration channel and sends the obtained financial data to the data processing module.

[0014] The data preprocessing module is used to preprocess the collected financial data to obtain standard financial data; It should be further explained that, in the specific implementation process, the process of preprocessing the collected financial data by the data preprocessing module includes: The data preprocessing module is provided with a data scanning unit and a data preprocessing unit, and the collected financial data is scanned by the data scanning unit to obtain the status of the financial data, and the status of the financial data includes a data missing status, a data duplication status and a data smoothness abnormality status; The data scanning unit sends the status of the financial data to the data preprocessing unit, and the data preprocessing unit processes the financial data according to the status of the financial data. When the status of the financial data is a data missing state, the missing value is filled by the method of the difference between the previous and next data. When the status of the financial data is a data duplication state, the position of the repeated value of the financial data is obtained, and the repeated value of the financial data is deleted according to the position of the obtained repeated value of the financial data. When the status of the financial data is a data smoothness abnormal state, the financial data is smoothed by polynomial fitting, and the financial data processed by the data preprocessing unit is recorded as standard financial data.

[0015] The data processing module is used to process the obtained standard financial data to obtain a sales line graph and a sales volume line graph; It should be further explained that, in the specific implementation process, the process of the data processing module processing the obtained standard financial data includes: Extract features from financial data to obtain sales feature data and sales volume feature data, obtain the daily sales of the enterprise based on the obtained sales feature data, and obtain the daily sales volume of the enterprise based on the obtained sales volume feature data; Construct a two-dimensional rectangular coordinate system of time with respect to sales, map the obtained daily sales of the enterprise into the constructed two-dimensional rectangular coordinate system of time with respect to sales, generate corresponding sales data points, label the generated sales data points, record them as i, i=1, 2, 3, ..., n, n is a positive integer, and connect each sales data point in chronological order with a straight line to obtain a sales line graph; Construct a two-dimensional rectangular coordinate system of sales volume with respect to time, map the daily sales volume of the enterprise to the constructed two-dimensional rectangular coordinate system of sales volume with respect to time, generate corresponding sales data points, label the generated sales data points as i, and connect each sales data point in chronological order with a straight line to obtain a sales line graph.

[0016] The data analysis module is used to analyze the obtained sales line graph and sales volume line graph to obtain the change sales volume and standard sales volume; It should be further explained that, in the specific implementation process, the process of the data analysis module analyzing the obtained sales line chart and sales volume line chart includes: Obtain the sales volume corresponding to the sales volume data point, and record the sales volume corresponding to the obtained sales volume data point as L i , obtain the slope of the straight line between the sales data point and the previous sales data point, and record the slope of the straight line between the obtained sales data point and the previous sales data point as k 量i , if the sales data point is the first sales data point, the slope of the straight line between the sales data point and the previous sales data point is recorded as zero; The change sales volume corresponding to the sales volume data point is obtained according to the sales volume corresponding to the obtained sales volume data point and the slope of the straight line between the sales volume data point and the previous sales volume data point, the sales volume corresponding to the sales volume data point is multiplied by the slope of the straight line between the sales volume data point and the previous sales volume data point, the multiplication result is added to the sales volume corresponding to the sales volume data point, and the change sales volume corresponding to the sales volume data point is obtained, and the change sales volume corresponding to the obtained sales volume data point is recorded as L 变i ; in, ; According to the big data survey, the number of positive reviews and negative reviews of the product is obtained, and the number of positive reviews of the product is recorded as H i , the number of negative reviews of the product is recorded as Ci ; The product attention coefficient is obtained based on the number of positive and negative reviews of the product, and the obtained product attention coefficient is recorded as HC i ; in, ; C0=0, H0=0, kp_q is the adjustment coefficient; Obtain the standard sales volume corresponding to the sales volume data point according to the change sales volume corresponding to the obtained sales volume data point and the commodity attention coefficient, and multiply the change sales volume corresponding to the sales volume data point by the commodity attention coefficient to obtain the standard sales volume corresponding to the sales volume data point; Get the sales amount corresponding to the sales data point, and record the sales amount corresponding to the obtained sales data point as E i , obtain the slope of the straight line between the sales data point and the previous sales data point, and record the slope of the straight line between the obtained sales data point and the previous sales data point as k 额i , if the sales data point is the first sales data point, the slope of the straight line between the sales data point and the previous sales data point is recorded as zero; The change in sales corresponding to the sales data point is obtained based on the sales corresponding to the obtained sales data point and the slope of the straight line between the sales data point and the previous sales data point. The sales corresponding to the sales data point is multiplied by the slope of the straight line between the sales data point and the previous sales data point. The multiplication result is added to the sales corresponding to the sales data point to obtain the change in sales corresponding to the sales data point. The change in sales corresponding to the obtained sales data point is recorded as E. 变i ; in, .

[0017] The forecasting module is used to obtain forecast sales volume according to the obtained changed sales volume and standard sales volume; It should be further explained that, in a specific implementation process, the process of obtaining the predicted sales volume according to the obtained changed sales volume and standard sales volume by the prediction module includes: A sales volume prediction model is constructed according to the relationship between the standard sales volume corresponding to the current sales volume data point and the sales volume corresponding to the next sales volume data point, the standard sales volume corresponding to the current sales volume data point is used as the input of the sales volume prediction model, the sales volume corresponding to the next sales volume data point is used as the output of the sales volume prediction model, and the sales volume prediction model is trained through the standard sales volume corresponding to the remaining sales volume data points and the sales volume corresponding to the remaining sales volume data points, so as to realize continuous optimization of the sales volume prediction model; The standard sales volume corresponding to the last sales volume data point is input into the sales volume prediction model, the sales volume prediction model performs output, a first predicted sales volume is obtained, the selling price of the product is obtained, and a first predicted sales amount is obtained according to the obtained first predicted sales volume and the selling price of the product; A primary sales prediction model is constructed according to the relationship between the change in sales corresponding to the current sales data point and the sales corresponding to the next sales data point, the change in sales corresponding to the current sales data point is used as the input of the primary sales prediction model, the sales corresponding to the next sales data point is used as the output of the primary sales prediction model, and the primary sales prediction model is trained through the change in sales corresponding to the remaining sales data points and the sales corresponding to the remaining sales data points, so as to realize continuous optimization of the primary sales prediction model; The change sales volume corresponding to the last sales volume data point is input into the primary sales volume forecasting model, and the primary sales volume forecasting model outputs the change to obtain the primary forecast sales volume; The average value of the first predicted sales volume and the primary predicted sales volume is obtained, and the average value of the first predicted sales volume and the primary predicted sales volume is the predicted sales volume; The company formulates corresponding management plans based on the forecast sales obtained.

[0018] The present invention also discloses a management method of a financial data management system based on big data, comprising the following steps: Step 1: Obtain the financial data of the enterprise, and pre-process the obtained financial data of the enterprise to obtain standard financial data; Step 2: Process the obtained standard financial data to obtain sales line chart and sales volume line chart; Step 3: Analyze the obtained sales line chart and sales volume line chart to obtain the change sales volume and standard sales volume; Step 4: Obtain forecast sales based on the obtained variable sales and standard sales volume, and the company will formulate corresponding management plans based on the obtained forecast sales.

[0019] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. The present invention discloses a financial data management method and system based on big data, which relates to the field of financial service technology, including a management center, wherein the management center is communicatively connected with a data acquisition module, a data preprocessing module, a data processing module, a data analysis module and a prediction module; the financial data of an enterprise is collected by the data acquisition module, the collected financial data is preprocessed by the data preprocessing module to obtain standard financial data, the obtained standard financial data is processed by the data processing module to obtain a sales line graph and a sales volume line graph, the obtained sales line graph and the sales volume line graph are analyzed by the data analysis module to obtain a changed sales volume and a standard sales volume, the prediction module obtains a predicted sales volume according to the obtained changed sales volume and the standard sales volume, the sales volume of the enterprise is accurately predicted, and the efficiency of the enterprise is improved.

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