Economic management data analysis system and method

Abstract

The invention provides an economic management data analysis system and method, and relates to the technical field of economic data analysis, and the system comprises the following architecture: a data collection module is responsible for collecting data from various data sources; the data visualization module is used for displaying an analysis result in a visual chart form; the data storage module is used for storing a large amount of collected data; the data cleaning module is used for preprocessing the collected data; the data analysis module is used for analyzing the cleaned data by using various data analysis methods; and the user interaction module is used for providing an operation interface for economic management personnel. Through combination of logistic regression model analysis, descriptive analysis, correlation analysis, regression analysis and clustering analysis, overall analysis can be effectively carried out for different data types, so that the data analysis efficiency is improved, the data analysis flexibility can be improved, and decision makers can comprehensively and deeply know economic operation conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of economic data analysis, and in particular to an economic management data analysis system and method. Background Art

[0002] In today's era of economic globalization and digitalization, the field of economic management is faced with massive amounts of data and information. How to efficiently process and analyze this data and provide strong support for decision-making has become a key issue facing enterprises and organizations.

[0003] Existing economic management data analysis methods have many limitations. Many traditional data analysis systems have relatively simple functions and can only perform simple statistical analysis on specific types of data. They cannot meet the needs of comprehensive analysis of multi-source and heterogeneous data in economic management. For example, financial data, market data, operational data, etc. usually come from different data sources. Traditional systems find it difficult to effectively integrate and collaboratively analyze these data, resulting in decision makers being unable to fully and deeply understand the economic operation status. Summary of the Invention

[0004] (1) Technical problems solved

[0005] In response to the shortcomings of the existing technology, the present invention provides an economic management data analysis system and method, which solves the many limitations of existing economic management data analysis methods. Many traditional data analysis systems have relatively single functions and can only perform simple statistical analysis on specific types of data. They cannot meet the needs of comprehensive analysis of multi-source and heterogeneous data in economic management. For example, financial data, market data, operational data, etc. usually come from different data sources. Traditional systems find it difficult to effectively integrate and collaboratively analyze these data, resulting in decision makers being unable to fully and deeply understand the economic operation status.

[0006] (2) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: an economic management data analysis system, including the following architecture:

[0008] The data acquisition module is responsible for collecting data from various data sources;

[0009] The data visualization module displays the analysis results in an intuitive chart format, making it easier for economic managers to understand and make decisions;

[0010] The data storage module is used to store the large amount of collected data. It needs to have high capacity and efficient data storage methods to cope with the diversity and huge amount of economic management data.

[0011] The data cleaning module pre-processes the collected data to remove noise and inconsistent data;

[0012] The data analysis module uses various data analysis methods to analyze the cleaned data;

[0013] The user interaction module is to provide an operation interface for economic managers, enabling them to input analysis requirements, parameter settings, and view and understand analysis results.

[0014] Furthermore, the data analysis method is as follows:

[0015] ① Logistic regression model analysis: It is mainly used to deal with binary classification problems. Its core is based on the linear regression model. It maps the result of linear regression to a probability value through a function called logistic function or sigmoid function, thereby realizing the probability prediction of the sample belonging to a certain category.

[0016] ② Descriptive analysis: Calculate the mean, median, and standard deviation of the data to describe the central tendency and dispersion of the data. For example, calculate the average salary of employees in each department of the company to understand the overall situation of salary levels.

[0017] ③Correlation analysis: used to study the relationship between two or more variables, such as analyzing the correlation between advertising investment and product sales to determine the effectiveness of advertising strategies. The strength and direction of this relationship can be measured by calculating the correlation coefficient;

[0018] ④ Regression analysis: Establish a mathematical model between variables, such as establishing a regression model between sales and price, promotional activities, and market demand factors to predict sales. Linear regression and multiple regression methods can be used;

[0019] ⑤ Cluster analysis: Divide data objects into different clusters, such as clustering customers according to consumption behavior and credit rating characteristics, so that companies can formulate marketing strategies for different customer groups. Commonly used clustering algorithms include K-Means clustering algorithm.

[0020] Furthermore, the expression of the logistic linear regression part is z=w0+w1x1+w2x2+…+w n x n , where x i is the characteristic variable, w i is the corresponding weight coefficient, w0 is the intercept;

[0021] Then substitute the Sigmoid function The probability that the sample belongs to the positive class, usually marked as 1, is P(y=1|x), that is, P(y=1|x)=g(z). Then the probability that the sample belongs to the negative class, usually marked as 0, is P(y=0|x)=1-g(z).

[0022] Furthermore, the goal of the logistic regression model training is to find a set of optimal weight coefficients w = w0, w1, ..., w n , so that the model can best fit the training data, the maximum likelihood estimation method is used to determine the parameters of the model. For a given training data set {(x1, y1), (x2, y2), ..., (x m ,y m )}, where x i is the eigenvector, y i is the corresponding category label (0 or 1), the likelihood function can be expressed as,

[0023]

[0024] In order to facilitate calculation, the logarithm of the likelihood function is usually taken to obtain the log-likelihood function

[0025]

[0026] Model training is to maximize the log-likelihood function through optimization algorithms (such as gradient descent, stochastic gradient descent, and Newton's method) to obtain the optimal weight coefficient w.

[0027] Furthermore, the evaluation indicators of the logistic linear regression model include

[0028] Accuracy A: refers to the proportion of samples correctly predicted by the model to the total number of samples. The calculation formula is Among them, TP is the true positive example, that is, the number of samples that are actually positive and predicted to be positive; TN is the true negative example; FP is the false positive example; FN is the false negative example;

[0029] Precision P: indicates the proportion of samples predicted to be positive that are actually positive.

[0030] Recall rate R: also called sensitivity S, refers to the proportion of samples that are actually positive that are correctly predicted to be positive.

[0031] F1 value: It is the harmonic mean of precision and recall.

[0032] AUC: The area under the ROC curve is used to measure the classification performance of the model. The value range is between 0.5 and 1. The closer the value is to 1, the better the model performance.

[0033] An economic management data analysis method comprises the following steps:

[0034] Step 1: Clearly analyze the problem and goals;

[0035] Step 2: Process and analyze the data;

[0036] Step 3: Evaluate and interpret the results;

[0037] Step 4: Continuous monitoring and feedback.

[0038] Furthermore, in step one, in-depth communication is conducted with business departments or decision makers related to economic management, followed by clarifying the scope of data that needs to be analyzed, and finally converting business problems into specific data analysis goals.

[0039] Furthermore, in the step 2, the data is first collected, then pre-processed, and finally analyzed.

[0040] Furthermore, in step three, the results first need to be evaluated and analyzed, then the analysis results need to be interpreted and analyzed, and finally recommendations and decision support need to be made.

[0041] Furthermore, after applying the analysis results to actual business decisions in step four, the implementation effects are tracked and monitored, new data is collected to observe changes in business indicators, and then based on feedback information during the implementation process, the data analysis process and results are reflected and summarized. When problems are found, the analysis methods and models are adjusted in a timely manner, and the data analysis work is continuously optimized to provide more accurate and valuable support for subsequent decision-making.

[0042] (3) Beneficial effects

[0043] The present invention provides an economic management data analysis system and method. It has the following beneficial effects:

[0044] In this plan, by combining logistic regression model analysis, descriptive analysis, correlation analysis, regression analysis and cluster analysis, we can effectively conduct a comprehensive analysis of different data types. This not only improves the efficiency of data analysis, but also improves the flexibility of data analysis, which is conducive to decision makers' comprehensive and in-depth understanding of economic operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a schematic diagram of the architecture of an economic management data analysis system proposed by the present invention;

[0046] Figure 2 This is a flow chart of an economic management data analysis method proposed in the present invention. DETAILED DESCRIPTION

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0048] Example:

[0049] like Figure 1-2 As shown, an embodiment of the present invention provides an economic management data analysis system, including the following architecture:

[0050] The data acquisition module is responsible for collecting data from various data sources. These data sources may include the company's internal financial system (such as financial statement data recorded by accounting software, such as balance sheets and income statements), sales record systems (recording product or service sales volume, price, customer information, etc.), human resources management systems (employee wages, performance data, etc.), and external data such as industry reports released by market research institutions and macroeconomic data (such as GDP growth rate and inflation rate released by the National Bureau of Statistics).

[0051] Data collection can be achieved by connecting to internal systems through data interfaces (such as application programming interfaces (APIs)) to achieve automated data extraction. For external data, web crawler technology can be used (subject to compliance with laws and regulations and data usage rights) to crawl data from relevant websites, or by purchasing data services;

[0052] The data visualization module displays the analysis results in an intuitive chart format, making it easier for economic managers to understand and make decisions;

[0053] The visualization methods include:

[0054] Histogram: used to compare the size of different categories of data, such as comparing the sales of different products. Each column represents a product, and the height of the column represents the size of the sales;

[0055] Line chart: shows the changing trend of data over time or other continuous variables, such as showing the change of quarterly profits of a company, with the horizontal axis representing quarter and the vertical axis representing profit amount;

[0056] Pie chart: used to show the proportion of each part to the whole, such as the proportion of each cost of the enterprise to the total cost;

[0057] Scatter plot: used to show the relationship between two variables, such as plotting data points of product price and sales volume on a scatter plot to observe the relationship between them;

[0058] The data storage module is used to store the large amount of collected data. It needs to have high capacity and efficient data storage methods to cope with the diversity and huge amount of economic management data.

[0059] Relational databases (such as MySQL and Oracle) can be used to store structured data, such as tabular data in financial statements. For unstructured data, such as documents and images in market research reports, non-relational databases (such as MongoDB) or data warehouses (such as Hadoop's Hive) can be used. To ensure data security and recoverability, data backup and disaster recovery strategies need to be deployed, such as regular full and incremental backups.

[0060] The data cleaning module pre-processes the collected data to remove noise data (such as erroneous records and duplicate data) and inconsistent data (such as inconsistent data formats, some date formats are "YYYY-MM-DD" and some are "MM / DD / YYYY");

[0061] The data cleaning process involves identifying and removing duplicate data by comparing key identifiers of data records (such as sales order numbers, employee numbers, etc.). For inconsistent data formats, data conversion rules can be written to unify all date formats into a standard format. Missing values ​​can also be handled by using filling strategies such as the mean, median, or based on the logical relationship of the data.

[0062] The data analysis module uses various data analysis methods to analyze the cleaned data;

[0063] The data were analyzed using the following analytical methods:

[0064] ① Logistic regression model analysis: It is mainly used to deal with binary classification problems. Its core is based on the linear regression model. It maps the result of linear regression to a probability value through a function called logistic function or sigmoid function, thereby realizing the probability prediction of the sample belonging to a certain category.

[0065] The linear regression part is expressed as z = w0 + w1x1 + w2x3 + ... + w n x n , where x i is the characteristic variable, w i is the corresponding weight coefficient, w0 is the intercept;

[0066] Then substitute the Sigmoid function The probability that the sample belongs to the positive class (usually marked as 1) is P(y=1|x), that is, P(y=1|x)=g(z), then the probability that it belongs to the negative class (usually marked as 0) is P(y=0|x)=1-g(z);

[0067] The goal of logistic regression model training is to find a set of optimal weight coefficients w = w0, w1, ..., w n , so that the model can best fit the training data, the maximum likelihood estimation method is used to determine the parameters of the model. For a given training data set {(x1, y1), (x2, y2), ..., (x m ,y m )}, where x i is the eigenvector, y i is the corresponding category label (0 or 1), the likelihood function can be expressed as,

[0068]

[0069] In order to facilitate calculation, the logarithm of the likelihood function is usually taken to obtain the log-likelihood function

[0070]

[0071] Model training is to maximize the log-likelihood function through optimization algorithms (such as gradient descent, stochastic gradient descent, Newton's method, etc.) to obtain the optimal weight coefficient w;

[0072] The evaluation indicators include

[0073] Accuracy A: refers to the ratio of the number of samples correctly predicted by the model to the total number of samples. The calculation formula is Among them, TP (True Positive) is the true positive example, that is, the number of samples that are actually positive and predicted to be positive; TN (True Negative) is the true negative example; FP (False Positive) is the false positive example; FN (False Negative) is the false negative example;

[0074] Precision P (Precision): indicates the proportion of samples predicted to be positive that are actually positive.

[0075]

[0076] Recall rate R (Recall): also called sensitivity S (Sensitivity), refers to the proportion of samples that are actually positive that are correctly predicted to be positive.

[0077] F1 value: is the harmonic mean of precision and recall.

[0078] AUC (Area Under Curve): The area under the ROC curve is used to measure the classification performance of the model. The value range is between 0.5 and 1. The closer the value is to 1, the better the model performance.

[0079] ② Descriptive analysis: Calculate statistical indicators such as the mean, median, and standard deviation of the data to describe the central tendency and dispersion of the data. For example, calculate the average salary of employees in each department of a company to understand the overall situation of salary levels;

[0080] ③ Correlation analysis: used to study the relationship between two or more variables, such as analyzing the correlation between advertising investment and product sales to determine the effectiveness of advertising strategies. The strength and direction of this relationship can be measured by calculating the correlation coefficient (such as the Pearson correlation coefficient);

[0081] ④ Regression analysis: Establish a mathematical model between variables, such as establishing a regression model between sales and factors such as price, promotional activities, and market demand, to predict sales. Methods such as linear regression and multiple regression can be used;

[0082] ⑤ Cluster analysis: Divide data objects into different clusters, such as clustering customers according to consumption behavior, credit rating and other characteristics, so that companies can formulate marketing strategies for different customer groups. Commonly used clustering algorithms include K-Means clustering algorithm;

[0083] The user interaction module is to provide an operation interface for economic managers, enabling them to input analysis requirements, parameter settings, and view and understand analysis results;

[0084] Through a web-based interface, users can log in to the system through a browser, select the data range to be analyzed (such as sales data for a specific time period), analysis method (such as correlation analysis), and view the generated charts and reports. At the same time, the system can provide explanatory text descriptions to help users better understand the analysis results.

[0085] An economic management data analysis method comprises the following steps:

[0086] Step 1: Clarify the analysis problem and objectives: Through in-depth communication with business departments or decision makers related to economic management, understand the problems they face in their actual work, such as declining market sales, cost control difficulties, and confusion in investment decisions. Then, clarify the data scope to be analyzed, including the time span, business field, product or service scope, etc. Finally, translate the business problem into specific data analysis objectives, such as analyzing the reasons for declining sales, identifying excessively high costs, and evaluating the feasibility of investment projects.

[0087] Step 2: Process and analyze the data: first collect the data, then pre-process the data, and finally analyze the data;

[0088] Data collection requires multiple approaches. First, relevant data, such as financial data, sales data, production data, and customer data, is obtained from the company's internal databases and information systems. Macroeconomic data, industry data, and competitor data are then collected. Sources include government statistics, industry research reports, data from market research institutions, and social media data. Finally, data from various channels and formats is integrated and stored in a data warehouse or data lake for subsequent processing and analysis.

[0089] Data preprocessing first requires identifying missing values ​​in the data. Appropriate processing methods are selected based on the data characteristics and analysis requirements, such as deleting records with missing values, using statistics such as the mean, median, and mode to fill missing values, or using machine learning algorithms to predict and fill missing values. Outliers are then identified through methods such as box plots and standard deviations. It is determined whether the outliers are due to data entry errors or real special circumstances, and the erroneous data is corrected or deleted. For real special values, whether to retain them is determined based on the specific circumstances.

[0090] To analyze data, we first need to select appropriate data analysis methods based on the analysis objectives and data characteristics, such as descriptive statistical analysis, correlation analysis, regression analysis, cluster analysis, factor analysis, time series analysis, etc. Then, for analysis methods that require model building, such as regression models and time series models, we select appropriate model forms and estimate model parameters based on data characteristics and theoretical foundations. Finally, we diagnose the established model to check the model's goodness of fit, parameter significance, residual normality and independence, etc. Based on the diagnostic results, we optimize the model, such as adjusting the model structure, adding or deleting variables, etc.

[0091] Step 3: Evaluate and interpret the results: First, evaluate and analyze the results, then interpret the results of the analysis, and finally provide recommendations and decision support;

[0092] The analysis results are evaluated by using appropriate evaluation indicators, such as R in regression analysis. 2, mean square error, silhouette coefficient in cluster analysis, etc., and verify the stability and reliability of the results through cross-validation, holdout method, etc., and then present the data analysis results to business personnel and decision makers in an intuitive and easy-to-understand manner, such as using charts and reports to show data trends, relationships and conclusions, and combining economic management theory with business practice to deeply explain the significance of the results and their impact on the business. Finally, based on the analysis results, put forward targeted suggestions and solutions to provide strong support for the company's economic management decision-making;

[0093] Step 4: Continuous monitoring and feedback: After applying the analysis results to actual business decisions, track and monitor the implementation effects, collect new data to observe changes in business indicators, and then reflect on and summarize the data analysis process and results based on feedback information during the implementation process. If problems are found, adjust the analysis methods and models in a timely manner, continuously optimize the data analysis work, and provide more accurate and valuable support for subsequent decision-making.

[0094] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An economic management data analysis system, characterized by: The following architectures are included: The data acquisition module is responsible for collecting data from various data sources; The data visualization module displays the analysis results in an intuitive chart format, making it easier for economic managers to understand and make decisions; The data storage module is used to store the large amount of collected data. It needs to have high capacity and efficient data storage methods to cope with the diversity and huge amount of economic management data. The data cleaning module pre-processes the collected data to remove noise and inconsistent data; The data analysis module uses various data analysis methods to analyze the cleaned data; The user interaction module is to provide an operation interface for economic managers, enabling them to input analysis requirements, parameter settings, and view and understand analysis results.

2. An economic management data analysis system according to claim 1, characterized in that: The method of data analysis is as follows: ① Logistic regression model analysis: It is mainly used to deal with binary classification problems. Its core is based on the linear regression model. It maps the result of linear regression to a probability value through a function called logistic function or sigmoid function, thereby realizing the probability prediction of the sample belonging to a certain category. ② Descriptive analysis: Calculate the mean, median, and standard deviation of the data to describe the central tendency and dispersion of the data. For example, calculate the average salary of employees in each department of the company to understand the overall situation of salary levels. ③Correlation analysis: used to study the relationship between two or more variables, such as analyzing the correlation between advertising investment and product sales to determine the effectiveness of advertising strategies. The strength and direction of this relationship can be measured by calculating the correlation coefficient; ④ Regression analysis: Establish a mathematical model between variables, such as establishing a regression model between sales and price, promotional activities, and market demand factors to predict sales. Linear regression and multiple regression methods can be used; ⑤ Cluster analysis: Divide data objects into different clusters, such as clustering customers according to consumption behavior and credit rating characteristics, so that companies can formulate marketing strategies for different customer groups. Commonly used clustering algorithms include K-Means clustering algorithm.

3. The economic management data analysis system according to claim 1, characterized in that: The expression of the logistic linear regression part is z=w0+w1x1+w2x2+…+w n x n , where x i is the characteristic variable, w i is the corresponding weight coefficient, w0 is the intercept; Then substitute the Sigmoid function The probability that the sample belongs to the positive class, usually marked as 1, is P(y=1|x), that is, P(y=1|x)=g(z). Then the probability that the sample belongs to the negative class, usually marked as 0, is P(y=0|x)=1-g(z).

4. The economic management data analysis system according to claim 1, characterized in that: The goal of the logistic regression model training is to find a set of optimal weight coefficients w = w0, w1, ..., w n , so that the model can best fit the training data, the maximum likelihood estimation method is used to determine the parameters of the model. For a given training data set {(x1, y1), (x2, y2), ..., (x m ,y m )}, where x i is the eigenvector, y i is the corresponding category label (0 or 1), the likelihood function can be expressed as, In order to facilitate calculation, the logarithm of the likelihood function is usually taken to obtain the log-likelihood function Model training is to maximize the log-likelihood function through optimization algorithms (such as gradient descent, stochastic gradient descent, and Newton's method) to obtain the optimal weight coefficient w.

5. The economic management data analysis system according to claim 4, characterized in that: The evaluation indicators of the logistic linear regression model include Accuracy A: refers to the proportion of samples correctly predicted by the model to the total number of samples. The calculation formula is Among them, TP is the true positive example, that is, the number of samples that are actually positive and predicted to be positive; TN is the true negative example; FP is the false positive example; FN is the false negative example; Precision P: indicates the proportion of samples predicted to be positive that are actually positive. Recall rate R: also called sensitivity S, refers to the proportion of samples that are actually positive that are correctly predicted to be positive. F1 value: is the harmonic mean of precision and recall. AUC: The area under the ROC curve is used to measure the classification performance of the model. The value range is between 0.5 and 1. The closer the value is to 1, the better the model performance.

6. A method for analyzing economic management data, characterized by: The following steps are involved: Step 1: Clearly analyze the problem and goals; Step 2: Process and analyze the data; Step 3: Evaluate and interpret the results; Step 4: Continuous monitoring and feedback.

7. The economic management data analysis method according to claim 6, characterized in that: The first step is to communicate in depth with business departments or decision makers related to economic management, then clarify the scope of data that needs to be analyzed, and finally convert business problems into specific data analysis goals.

8. The economic management data analysis method according to claim 6, characterized in that: In the step 2, the data are first collected, then pre-processed, and finally analyzed.

9. The economic management data analysis method according to claim 6, characterized in that: In step three, the results must first be evaluated and analyzed, followed by interpretation and analysis of the analysis results, and finally recommendations and decision support must be provided.

10. The economic management data analysis method according to claim 6, characterized in that: After applying the analysis results to actual business decisions in step 4, track and monitor the implementation effects, collect new data to observe changes in business indicators, and then reflect on and summarize the data analysis process and results based on feedback information during the implementation process. If problems are found, adjust the analysis methods and models in a timely manner, continuously optimize the data analysis work, and provide more accurate and valuable support for subsequent decision-making.

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