Economic management information analysis method based on big data

Abstract

The invention provides an economic management information analysis method based on big data, belongs to the technical field of economic management, relates to the technical field of economic management, and aims at overcoming the defects of a traditional analysis method. According to the method, multi-source data is integrated, deep information is mined by using an advanced technology, a comprehensive and accurate basis is provided for decision making, and the method has the advantages of being simple in operation, convenient to use, high in efficiency and high in practicability. The economic management level and competitiveness of enterprises are improved, and the problems of one-sided data, limited analysis ability and the like of a traditional method are effectively solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of economic management, and specifically relates to an economic management information analysis method based on big data. Background Art

[0002] In today's era of economic globalization and rapid digital development, enterprises are facing an increasingly complex and changing market environment. The scientificity and timeliness of economic management decisions are crucial to the survival and development of enterprises. With the widespread application of information technology, enterprises have accumulated a large amount of economic management data in their daily operations. These data cover various aspects such as finance, sales, human resources, and markets, and contain rich information and potential value.

[0003] However, traditional economic management information analysis methods have many defects and are difficult to meet the needs of modern enterprises. On the one hand, traditional methods mostly rely on manual collection and collation of data, with limited data sources and untimely updates, resulting in a lack of comprehensiveness and timeliness in the analysis results. On the other hand, traditional data analysis methods are mainly based on simple statistical analysis and empirical judgment, which makes it difficult to handle large-scale, high-dimensional data and effectively mine the deep-level connections and rules between data. For example, in terms of sales forecasting, manual analysis is difficult to accurately capture market dynamics and changes in consumer behavior, which can easily lead to unreasonable production plans, frequent inventory backlogs or out-of-stock phenomena, and affect the company's capital turnover and market competitiveness. In terms of risk assessment, traditional methods are unable to conduct comprehensive analysis of complex financial and market data, and it is difficult to warn of potential risks in advance, which puts companies in a passive position when facing economic fluctuations and market competition.

[0004] In order to solve these problems, the present invention provides an economic management information analysis method based on big data, which aims to make full use of the advantages of big data technology to achieve efficient collection, storage, analysis and application of massive economic management data, provide enterprises with more accurate and forward-looking decision support, and enhance the economic management level and market competitiveness of enterprises. Summary of the invention

[0005] The purpose of the present invention is to provide an economic management information analysis method based on big data, aiming to solve the significant problems of traditional analysis methods in the prior art. First, they rely on manual collection and collation, and the data source is limited and the update is delayed, resulting in poor comprehensiveness and timeliness of the analysis results. Second, the analysis method based on simple statistics and empirical judgment is difficult to process large-scale high-dimensional data and cannot deeply explore the data association rules, such as inaccurate sales forecasts, untimely risk assessments, etc., which affect the company's capital turnover and competitiveness.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for analyzing economic management information based on big data, characterized by comprising the following:

[0008] S1. Data collection: Collect economic management data from multiple data sources, including the company's internal financial system, sales management system, human resource management system, and external market data platforms, industry databases, and social media; use data collection tools to preliminarily clean and organize the collected data to remove invalid data, duplicate data, and erroneous data;

[0009] S2. Data storage: storing the cleaned data in a big data storage platform, which adopts a distributed storage architecture; classifying and labeling the stored data according to at least one dimension of data type, time period, and business department;

[0010] S3. Data analysis model construction: Select a suitable data analysis model according to the analysis objectives of economic management, the data analysis model includes at least one of a time series analysis model, a cluster analysis model, a logistic regression model, and a neural network model; use historical data to train and optimize the selected model, adjust model parameters, and use cross-validation technology to prevent model overfitting and improve the generalization ability of the model;

[0011] S4. Data mining and analysis: Input the data stored in the big data platform into the constructed data analysis model, use the association rule mining algorithm to discover the hidden associations between data variables, and analyze social media and customer feedback data through sentiment analysis technology;

[0012] S5. Result visualization and report generation: Present the results of data analysis in a visual form, which includes at least one of charts, dashboards, and maps; automatically generate an economic management information analysis report based on the visualization results, which includes the background, purpose, methods, main results, conclusions, and recommended measures for the data analysis, and the report is output in PDF, Excel, or HTML format.

[0013] As a preferred solution of the present invention, in the data collection step, the data collection tool includes a web crawler and an ETL tool.

[0014] As a preferred solution of the present invention, in the data analysis model building step, when a time series analysis model is adopted, the model includes an ARIMA model, and the model is optimized by adjusting the autoregressive order and the moving average order in the ARIMA model.

[0015] As a preferred solution of the present invention, in the data mining and analysis step, the association rule mining algorithm is used to analyze the association between product sales data and at least one of marketing activities, seasonal factors, and customer geographical distribution.

[0016] As a preferred solution of the present invention, in the result visualization and report generation step, the chart includes at least one of a bar chart, a line chart, and a pie chart.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] 1. In this solution, by collecting data from multiple data sources such as the company's internal finance, sales, and human resources systems as well as external market data platforms, industry databases, social media, etc., a comprehensive integration of internal and external information is achieved, and the company can obtain a broader perspective. For example, external data can provide information such as market competition status and industry trends, and internal data reflects its own operating conditions. The combination of the two lays the foundation for accurate analysis. At the same time, web crawlers and ETL tools are used for data collection and preliminary cleaning to ensure the comprehensiveness and high quality of the data. Web crawlers obtain external dynamic data, and ETL tools accurately extract and convert internal structured data. The collaborative work ensures that the data is both rich and reliable, providing solid data support for subsequent analysis.

[0019] 2. In this solution, a big data storage platform with a distributed storage architecture is used to store cleaned data, which has high availability, high scalability and fault tolerance. This means that enterprises can easily cope with the growing amount of data, ensure the safe storage of data, avoid data loss due to single point failures, and manage data by classification and labeling according to dimensions such as data type, time period, and business department. This greatly improves the efficiency of data retrieval and call, and can quickly locate data when specific analysis is required, reducing data preparation time, speeding up the analysis process, and making the entire economic management information analysis work more flexible and efficient.

[0020] 3. In this plan, appropriate data analysis models are selected according to different economic management analysis objectives, including time series analysis models, cluster analysis models, logistic regression models, neural network models, etc. For example, time series analysis models (such as ARIMA models) can be used to predict time-related data such as sales trends. By adjusting model parameters (such as autoregressive order, moving average order, etc.), the model can be optimized to accurately capture data characteristics and trends. Enterprises can plan production, sales and other strategies in advance, arrange resources reasonably, and improve operational efficiency and economic benefits. Cluster analysis models can classify customers and other objects, and enterprises can formulate targeted marketing strategies based on this to improve customer satisfaction and loyalty. Logistic regression and neural network models play their advantages in risk assessment and decision-making prediction, provide decision-making support for enterprise management, reduce decision-making risks, and ensure that enterprises make wise decisions in complex market environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0022] In the attached picture:

[0023] Figure 1 This is a flow chart of the economic management information analysis method based on big data of the present invention. DETAILED DESCRIPTION

[0024] 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.

[0025] Example 1

[0026] See also Figure 1 , the present invention provides the following technical solutions:

[0027] A method for analyzing economic management information based on big data, characterized by comprising the following:

[0028] S1. Data collection: Collect economic management data from multiple data sources, including the company's internal financial system, sales management system, human resource management system, and external market data platforms, industry databases, and social media; use data collection tools to preliminarily clean and organize the collected data to remove invalid data, duplicate data, and erroneous data;

[0029] S2. Data storage: storing the cleaned data in a big data storage platform, which adopts a distributed storage architecture; classifying and labeling the stored data according to at least one dimension of data type, time period, and business department;

[0030] S3. Data analysis model construction: Select a suitable data analysis model according to the analysis objectives of economic management, the data analysis model includes at least one of a time series analysis model, a cluster analysis model, a logistic regression model, and a neural network model; use historical data to train and optimize the selected model, adjust model parameters, and use cross-validation technology to prevent model overfitting and improve the generalization ability of the model;

[0031] S4. Data mining and analysis: Input the data stored in the big data platform into the constructed data analysis model, use the association rule mining algorithm to discover the hidden associations between data variables, and analyze social media and customer feedback data through sentiment analysis technology;

[0032] S5. Result visualization and report generation: Present the results of data analysis in a visual form, which includes at least one of charts, dashboards, and maps; automatically generate an economic management information analysis report based on the visualization results, which includes the background, purpose, methods, main results, conclusions, and recommended measures for the data analysis, and the report is output in PDF, Excel, or HTML format.

[0033] In a specific embodiment of the present invention, S1 data acquisition: collecting data from multiple different types of data sources can comprehensively integrate various types of information related to economic management inside and outside the enterprise, and avoid one-sided analysis caused by a single data source. The internal systems of the enterprise provide core data for enterprise operations, such as financial data reflecting the company's cash flow and profitability, sales data showing market share and product sales trends, and human resources data involving labor costs and personnel efficiency, etc.; external data sources supplement information such as the overall dynamics of the industry, market competition situation, and consumer opinion, providing a broader perspective for comprehensive analysis. Preliminary data cleaning and organization through data collection tools is the basis for ensuring the accuracy and reliability of subsequent analysis. Invalid data, duplicate data, and erroneous data may interfere with the analysis results and even lead to wrong decisions. Removing these bad data can improve data quality and make subsequent data-based model training and analysis more accurate and efficient.

[0034] S2 Data Storage: A big data storage platform that uses a distributed storage architecture, which has the advantages of high availability, high scalability, and fault tolerance. With the continuous growth of data volume, especially the massive data accumulated in the field of economic management over a long period of time, this architecture can easily cope with the increase in storage demand, ensure the safe storage of data and prevent data loss due to single point failures. Classifying and labeling data according to multiple dimensions greatly improves the efficiency of data retrieval and call. When analysis is required for a specific business department, a specific time period, or a specific data type, relevant data can be quickly located and obtained, reducing the time cost of data preparation, speeding up the entire analysis process, and enabling analysis work to be carried out more flexibly and efficiently.

[0035] S3 data analysis model construction: Selecting the appropriate data analysis model according to different economic management analysis objectives is the key to achieving accurate analysis. Time series analysis models are suitable for processing data with time sequence, such as predicting sales trends, market demand fluctuations, etc., which can help enterprises plan production, procurement and sales strategies in advance, arrange resources reasonably, and improve operational efficiency and economic benefits. Cluster analysis models can classify objects such as customers, markets or products according to the characteristics of the data. Enterprises can identify the differences in needs of different customer groups based on this, so as to formulate targeted marketing strategies, optimize market positioning, and improve customer satisfaction and loyalty. Logistic regression models and neural network models have advantages in risk assessment and decision prediction, such as assessing the financial risk and credit risk of an enterprise, or predicting the possible results of an economic decision, providing decision support for enterprise management and reducing decision risks. The model is trained and optimized through historical data, and cross-validation technology is used to ensure that the model has good generalization ability when processing new data, that is, the model can accurately predict and analyze unseen data, rather than just overfitting the training data, thereby improving the practicality and reliability of the model in actual economic management scenarios.

[0036] S4 Data Mining and Analysis: Input the data in the big data platform into the constructed data analysis model for mining and analysis. The association rule mining algorithm can be used to discover the hidden associations between data variables. For example, companies can understand the intrinsic connection between product sales and promotional activities, seasonal changes, regional differences and other factors, thereby optimizing marketing mix strategies and improving marketing effectiveness. By analyzing social media and customer feedback data through sentiment analysis technology, customers' satisfaction, opinions and emotional tendencies towards the company's products or services can be timely grasped. Companies can quickly adjust product features, improve service quality, enhance market competitiveness, maintain the company's brand image and customer relationships based on these feedbacks, and provide strong support for the company's sustainable development.

[0037] S5 Result Visualization and Report Generation: Data analysis results are presented in a visual form, such as charts, dashboards, and maps, which can present complex data information to users in an intuitive and easy-to-understand way. Charts can clearly present data trends, proportions, distribution and other characteristics, allowing users to quickly grasp key information; dashboards can centrally display multiple key indicators, reflecting the status of various indicators of corporate economic management in real time, facilitating real-time monitoring and decision-making by management; maps can intuitively display analysis results related to geographic information such as market distribution and customer regional distribution, providing a reference for the geographical dimension for the company's market expansion and resource allocation. Detailed economic management information analysis reports are automatically generated based on the visualization results, and output in a variety of common electronic document formats for users to view, share and archive. The complete analysis content in the report provides a comprehensive decision-making basis for corporate management, enabling them to have an in-depth understanding of the entire process and results of data analysis, so as to make wise economic management decisions based on scientific analysis and promote the stable development and continuous progress of the company.

[0038] For details, please refer to Figure 1 In the data collection step, the data collection tools include web crawlers and ETL tools.

[0039] In this embodiment: the data collection tools are web crawlers and ETL tools. These two tools are complementary and can efficiently obtain economic management data from multiple data sources. Web crawlers are mainly responsible for capturing relevant data from external market data platforms, industry databases, social media and other open data sources. For example, for market data platforms, web crawlers can regularly collect data such as market price trends and competitor product release information in a specific industry according to preset rules and keywords; for social media, they can capture text data such as users' discussions and evaluations on the company's products or hot topics in related industries. These data reflect the market dynamics and consumers' voices, and provide important references for the company's market analysis and brand building.

[0040] ETL tools focus on extracting, converting and loading data from structured data sources such as the company's internal financial system, sales management system and human resource management system. Taking the financial system as an example, ETL tools can accurately extract various indicator data in financial statements, such as income, cost, profit, etc., and convert and clean these data to meet the requirements of subsequent analysis. The collaborative work of these two tools ensures that the collected data is both comprehensive and of high quality, laying a solid data foundation for the entire economic management information analysis process.

[0041] For details, please refer to Figure 1In the data analysis model construction step, when a time series analysis model is used, the model includes an ARIMA model, and the model is optimized by adjusting the autoregressive order and the moving average order in the ARIMA model.

[0042] In this embodiment: when the time series analysis model is used, the ARIMA model is selected, and the model is optimized by adjusting its autoregressive order (p), moving average order (q) and difference order (d). Taking the product sales data of the enterprise as an example, the sales data is first tested for stability. If the data is not stable, it is stabilized by differential operation to determine the appropriate difference order d. Then, the range of values ​​of the autoregressive order p and the moving average order q is preliminarily determined by observing the truncation and tailing of the autocorrelation function (ACF) and the partial autocorrelation function (PACF). Then, the ARIMA model with different parameter combinations is trained and evaluated using historical sales data, such as using indicators such as mean square error (MSE) and mean absolute error (MAE) to measure the prediction accuracy of the model. After multiple tests and comparisons, the parameter combination that optimizes the model performance is finally determined, so that the ARIMA model can accurately capture the time series characteristics and trends of product sales data, and provide a reliable basis for economic management decisions such as sales forecasting and production planning for the enterprise.

[0043] For details, please refer to Figure 1 In the data mining and analysis step, the association rule mining algorithm is used to analyze the association relationship between product sales data and at least one of marketing activities, seasonal factors, and customer geographical distribution.

[0044] In this embodiment: the association rule mining algorithm is used to deeply analyze the association between product sales data and marketing activities, seasonal factors, customer geographical distribution and other factors. Taking a clothing company as an example, through the association analysis of sales data and marketing activity data, it is found that when online promotion activities are carried out on a specific social media platform and combined with experiential marketing activities in offline stores, the sales volume of products will increase significantly, which shows that this marketing combination method can effectively promote sales, and the company can optimize future marketing strategies accordingly.

[0045] When analyzing the relationship between seasonal factors and sales data, it was found that the sales volume of certain warm clothing in winter is significantly higher than that in other seasons, and is closely related to seasonal factors such as temperature and holidays. Based on this, companies can arrange production plans in advance, increase inventory preparation for winter products, and formulate targeted promotion plans before the peak sales season arrives to improve market response speed and sales efficiency.

[0046] The correlation analysis between customer geographic distribution and sales data shows that customers in a certain region have a higher preference and purchase rate for clothing of specific styles and colors. Based on this finding, companies can optimize product distribution and inventory management in the region, and at the same time, design and promote products in a way that is closer to the needs and preferences of local customers, further expand market share, and improve customer satisfaction and loyalty.

[0047] For details, please refer to Figure 1 In the result visualization and report generation step, the chart includes at least one of a bar chart, a line chart, and a pie chart.

[0048] In this embodiment: the charts used for result visualization include bar charts, line charts and pie charts. Taking the financial analysis of an enterprise as an example, a bar chart is used to compare various cost expenditures in different quarters or years, such as raw material procurement costs, labor costs, marketing costs, etc., so that the management can intuitively see the differences in various costs, thereby quickly identifying the key areas of cost control.

[0049] Line charts are used to show the trend of key economic indicators such as sales and profits over time, so that management can clearly understand whether the company's operating conditions are rising, falling or stable, which helps to formulate reasonable development strategies and goals. For example, if the line chart shows that sales have been steadily rising in the past few quarters, but profit growth has been relatively slow, management can further analyze the cost structure and market pricing strategy to find ways to increase profit margins.

[0050] Pie charts are mainly used to present the proportion of each business department or product category in total revenue, total cost, total profit, etc. For example, showing the contribution of different product lines to the total profit of the company can help management identify core businesses and potential business growth points, allocate resources reasonably, optimize business layout, and make more informed investment and development decisions. At the same time, the economic management information analysis report automatically generated based on these visualization results is output in PDF format, which is convenient for management to display, discuss and archive in meetings, ensuring that the analysis results can be effectively transformed into actual economic management decisions and promote the sustainable development of the company.

[0051] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for analyzing economic management information based on big data, characterized in that: Includes the following: S1. Data collection: Collect economic management data from multiple data sources, including the company's internal financial system, sales management system, human resource management system, and external market data platforms, industry databases, and social media; use data collection tools to preliminarily clean and organize the collected data to remove invalid data, duplicate data, and erroneous data; S2. Data storage: storing the cleaned data in a big data storage platform, where the big data storage platform adopts a distributed storage architecture; Classify and label the stored data according to at least one of the following dimensions: data type, time period, and business department; S3. Data analysis model construction: Select a suitable data analysis model according to the analysis objectives of economic management, the data analysis model includes at least one of a time series analysis model, a cluster analysis model, a logistic regression model, and a neural network model; use historical data to train and optimize the selected model, adjust model parameters, and use cross-validation technology to prevent model overfitting and improve the generalization ability of the model; S4. Data mining and analysis: Input the data stored in the big data platform into the constructed data analysis model, use the association rule mining algorithm to discover the hidden associations between data variables, and analyze social media and customer feedback data through sentiment analysis technology; S5. Result visualization and report generation: Present the results of data analysis in a visual form, which includes at least one of charts, dashboards, and maps; automatically generate an economic management information analysis report based on the visualization results, which includes the background, purpose, methods, main results, conclusions, and recommended measures for the data analysis, and the report is output in PDF, Excel, or HTML format.

2. The economic management information analysis method based on big data according to claim 1 is characterized by: In the data collection step, the data collection tools include web crawlers and ETL tools.

3. The economic management information analysis method based on big data according to claim 1 is characterized by: In the data analysis model construction step, when a time series analysis model is used, the model includes an ARIMA model, and the model is optimized by adjusting the autoregressive order and the moving average order in the ARIMA model.

4. The economic management information analysis method based on big data according to claim 1 is characterized by: In the data mining and analysis step, the association rule mining algorithm is used to analyze the association relationship between product sales data and at least one of marketing activities, seasonal factors, and customer geographical distribution.

5. The economic management information analysis method based on big data according to claim 1 is characterized by: In the result visualization and report generation step, the chart includes at least one of a bar chart, a line chart, and a pie chart.

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