Rural integrated economy data analysis system

Abstract

The invention, which relates to the technical field of data analysis, discloses a rural integrated economy data analysis system comprising an acquisition module, a storage module, an analysis module, a risk assessment module and a personalized report generation module. The acquisition module is used for acquiring historical rural integrated economy data and storing the acquired data in the storage module, and the acquisition module is electrically connected with the storage module; the analysis module is electrically connected with the storage module, and the analysis module obtains historical data, analyzes historical laws based on the historical data, mines laws and information in the data, and analyzes trends of rural collective economic income in different years. According to the method, rural integrated economic organizations and related departments can make coping preparations in advance, measures are taken before risks come, loss is reduced, risk coping strategies are scientifically formulated, and powerful data support and analysis are better provided for rural economic decisions.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a rural collective economic data analysis system. Background Art

[0002] Rural collective economy is the abbreviation of rural collective ownership economy, which refers to an economic organizational form in which the main means of production are jointly owned by rural community members, who practice common labor and share the fruits of labor. It is an economic form based on collective assets such as land, with agricultural production and management as the main body, and also includes various economic activities of rural secondary and tertiary industries. With the continuous development of rural economy, the scale and complexity of rural collective economy are also increasing. The traditional rural collective economic management method mainly relies on manual statistics and simple spreadsheets, which is difficult to meet the needs of efficient and accurate analysis of large amounts of data. At present, there is a lack of a data analysis system specifically for rural collective economy, which cannot provide strong data support and analysis for rural economic decision-making. In this regard, we propose a rural collective economic data analysis system. Summary of the invention

[0003] In order to solve the above technical problems, a rural collective economic data analysis system is provided. This technical solution solves the above-mentioned problem of being unable to provide strong data support and analysis for rural economic decision-making.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is: a rural collective economic data analysis system, including: a collection module, a storage module, an analysis module, a risk assessment module and a personalized report generation module;

[0005] The collection module is used to collect historical rural collective economic data and store the collected data in the storage module. The collection module and the storage module are electrically connected;

[0006] The analysis module is electrically connected to the storage module, and the analysis module obtains historical data, analyzes historical rules based on the historical data, mines the rules and information in the data, and analyzes the trend of rural collective economic income in different years;

[0007] The risk assessment module is electrically connected to the analysis module. Based on the predicted future development trend, the risk assessment module constructs a risk assessment model for the rural collective economy. Taking into account the particularities of the rural economy, including agricultural product price fluctuations, natural disaster risks, and policy change factors, the risk assessment model predicts the impact of the rural collective economy in the future based on the machine learning algorithm and gives a risk warning.

[0008] The personalized report generation module is electrically connected to the risk assessment module. The personalized report generation module generates an analysis report based on the results obtained by the risk assessment module for people to view.

[0009] Preferably, the collection module connects with the databases of rural economic management departments at all levels to obtain statistical historical data related to rural collective economy, including detailed account information of rural collective assets, natural resource value assessment data and financial statement data of rural collective enterprises over the years. After obtaining the historical data, the collection module organizes and classifies the data, removes duplicate and invalid data, and unifies the data format, and uploads the processed data to the storage module.

[0010] Preferably, the analysis module analyzes historical patterns from the historical data in the storage module based on data mining technology, constructs a time series analysis model, observes the changing trend of income over time, fits the relationship curve between income and time, and mines out the historical development trend. If the curve shows an upward trend, it means that the rural collective economic income is expected to continue to grow; if the curve shows a downward trend, measures need to be taken to change it.

[0011] Preferably, the rural economic income variable is represented by Y and the time is represented by t, then the time series analysis model is:

[0012] Y t =a+b t +∈ t

[0013] where Y t is a time series model, a and b are the parameters to be estimated, ∈ t is the error term, and the least squares method is used to estimate the parameters a and b;

[0014] The a and b estimation steps pre-minimize the sum of squared errors:

[0015]

[0016] Take partial derivatives of a and b respectively and set them to 0, and we get the system of equations:

[0017]

[0018] Solve the system of equations to get estimates of a and b;

[0019] Where S is the sum of squared errors, is the summation symbol, indicating the summation operation from t=1 to n.

[0020] Preferably, the future development trend is predicted based on the numerical value obtained by the time series analysis model, and the future time point w is predicted in the form of:

[0021] Y tw =a+b tw

[0022] Among them, Y twis the time series model for future time points, b tw is the predicted value of the parameter at a future time point, w is the future time point, if b>0, the curve shows an upward trend, if b<0, the curve shows a downward trend. The trend in the historical data is analyzed and uploaded to the risk assessment module to predict the risk.

[0023] Preferably, the risk assessment module obtains the trend in the historical data for analysis, and the risk assessment module expression is:

[0024] D=α+f1P+f2E+f3Z+∈ t

[0025] Where D is the rural collective economic income, α is a constant term, ∈ t is the error term, P is the impact factor of agricultural product price fluctuations, E is the natural disaster risk factor, Z is the policy change impact factor, f1, f2 and f3 are weights, and the future rural collective economic income is predicted to obtain a parameter value. Based on the expert analysis method, the risk threshold H of the local rural collective economy is given, and the obtained parameter value D is compared with the risk threshold H.

[0026] Preferably, if the parameter value D obtained by comparison is greater than the risk threshold H, it is judged that the local rural collective economic data has hidden risks, so that targeted treatment can be made in a timely manner. If the parameter value D obtained by comparison is less than the risk threshold H, it is judged that the local rural collective economy has no hidden risks. The expert analysis method establishes an expert team to conduct field investigations and surveys, analyze the risk level assessment, divide it into different levels, and thus establish a risk threshold.

[0027] Preferably, if a risk is identified, a risk response mechanism is activated to conduct in-depth analysis and timely processing of the problem.

[0028] Further analyze the reasons for the rise or fall in agricultural product prices, and formulate targeted treatment measures based on the results of risk analysis.

[0029] Preferably, after the risk assessment module is completed, the personalized report generation module obtains various data output by the risk assessment module, generates an analysis report based on the data, conducts in-depth analysis and interpretation of the risk assessment results, helps to formulate a scientific development plan, and is convenient for people to view. It is presented in a variety of forms, including charts, text and web links.

[0030] Preferably, the analysis report generation step organizes the obtained prediction data, determines the structure of the report, writes and optimizes the prediction results, and finally outputs them; the forms of charts include line charts, pie charts, histograms and bar charts.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] By deeply mining the historical data in the storage module, the present invention can discover the patterns and information hidden in the data, formulate long-term strategic planning for the rural collective economy, and predict the impact of risks on the rural collective economy in advance. This forward-looking risk warning can allow rural collective economic organizations and relevant departments to prepare for response in advance, take measures before the risk comes, reduce losses, and scientifically formulate risk response strategies, thereby providing strong data support and analysis for rural economic decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a framework diagram of the analysis system of the present invention. DETAILED DESCRIPTION

[0034] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0035] Reference Figure 1 As shown, the rural collective economic data analysis system includes: a collection module, a storage module, an analysis module, a risk assessment module and a personalized report generation module;

[0036] The collection module is used to collect historical rural collective economic data and store the collected data in the storage module. The collection module and the storage module are electrically connected;

[0037] The analysis module is electrically connected to the storage module, and the analysis module obtains historical data, analyzes historical rules based on the historical data, mines the rules and information in the data, and analyzes the trend of rural collective economic income in different years;

[0038] The risk assessment module is electrically connected to the analysis module. Based on the predicted future development trend, the risk assessment module constructs a risk assessment model for the rural collective economy. Taking into account the particularities of the rural economy, including agricultural product price fluctuations, natural disaster risks, and policy change factors, the risk assessment model predicts the impact of the rural collective economy in the future based on the machine learning algorithm and gives a risk warning.

[0039] The personalized report generation module is electrically connected to the risk assessment module. The personalized report generation module generates an analysis report based on the results obtained by the risk assessment module for people to view.

[0040] This application analysis module can discover the rules and information hidden in the data by deeply mining the historical data in the storage module. By analyzing the historical rules, we can understand the contribution and development trend of different industries in the rural collective economy, which is helpful to optimize resource allocation. The analysis results of the historical data of the rural collective economy can provide strong support for government departments to formulate rural economic policies. The government can introduce targeted support policies based on the problems and development trends revealed by the data analysis. At the same time, when evaluating the effectiveness of rural collective economic projects, these historical data analyses can also provide objective references to judge whether the projects have achieved the expected goals and whether adjustments or improvements are needed;

[0041] The risk assessment module builds a special risk assessment model based on the future development trend forecast provided by the analysis module. Taking into account the particularities of the rural economy, such as agricultural product price fluctuations, natural disaster risks, and policy change factors, it can predict in advance the impact of these risks on the rural collective economy. This forward-looking risk warning can allow rural collective economic organizations and relevant departments to prepare for response in advance and take measures to reduce losses before risks occur. Risk assessment through machine learning algorithms can quantify risks more accurately. Rural collective economic organizations can scientifically formulate risk response strategies based on the results of risk assessment;

[0042] Rural collective economy is vulnerable to various risk factors due to its industrial characteristics and the complexity of the environment. The risk assessment module can comprehensively consider various potential risks and provide guarantees for the stable development of rural collective economy. Through timely risk warning and effective response measures, it can enhance the risk resistance of rural collective economy and ensure the sustainability of rural economic activities.

[0043] The personalized report generation module is connected to the risk assessment module, and can generate customized analysis reports based on the roles and needs of different users, providing reference for policy making; for ordinary farmers, the report can explain the risk situation and development trends of the rural collective economy in plain language, enhancing their risk awareness and enthusiasm for participating in economic activities.

[0044] The collection module connects with the databases of rural economic management departments at all levels to obtain statistical historical data related to rural collective economy. The data include detailed account information of rural collective assets, natural resource value assessment data and financial statement data of rural collective enterprises over the years. After obtaining the historical data, the collection module organizes and classifies the data, removes duplicate and invalid data, and unifies the data format, and uploads the processed data to the storage module.

[0045] This application connects with the database of the official rural economic management department, and the data source obtained is reliable. These departments usually have strict standards and processes in the process of data collection and recording, which can ensure the basic accuracy of the data. In addition, the collection module further removes duplicate and invalid data, reduces errors and interference factors in the data, and makes the final stored data more accurately reflect the actual situation of the rural collective economy;

[0046] Unifying the format of data is a critical step. Since data from different sources may have format differences, such as date format, digital precision, and measurement units, this inconsistency will cause great inconvenience to subsequent data processing and analysis. By unifying the format, the consistency of data in the storage module is ensured, making it easier for subsequent links in the analysis module to use the data more efficiently and avoiding misunderstandings or misinterpretations of data due to format issues.

[0047] The analysis module uses historical data from the storage module, based on data mining technology, to analyze historical patterns, build a time series analysis model, observe the changing trend of income over time, fit the relationship curve between income and time, and mine the historical development trend. If the curve shows an upward trend, it means that the rural collective economic income is expected to continue to grow; if the curve shows a downward trend, measures need to be taken to make changes.

[0048] Assuming that the rural economic income variable is represented by Y and time is represented by t, the time series analysis model is:

[0049] Y t =a+b t +∈ t

[0050] where Y t is a time series model, a and b are the parameters to be estimated, ∈ t is the error term, and the least squares method is used to estimate the parameters a and b;

[0051] The a and b estimation steps pre-minimize the sum of squared errors:

[0052]

[0053] Take partial derivatives of a and b respectively and set them to 0, and we get the system of equations:

[0054]

[0055] Solve the system of equations to get estimates of a and b;

[0056] Where S is the sum of squared errors, is the summation symbol, indicating the summation operation from t=1 to n.

[0057] Predicting future development trends Based on the values ​​obtained from the time series analysis model, the future time point w is predicted in the form of:

[0058] Y tw =a+b tw

[0059] Among them, Y tw is the time series model for future time points, b tw is the predicted value of the parameter at a future time point, w is the future time point, if b>0, the curve shows an upward trend, if b<0, the curve shows a downward trend. The trend in the historical data is analyzed and uploaded to the risk assessment module to predict the risk.

[0060] This application provides a forward-looking decision-making basis for rural collective economic organizations by exploring historical development trends. Managers can predict future economic conditions based on income trends and plan industrial layout, resource allocation and capital investment strategies in advance. If it is predicted that income will continue to grow, the organization can consider expanding production scale, introducing new technologies or exploring new markets; if a downward trend is predicted, the business direction can be adjusted in a timely manner, such as optimizing the industrial structure, cutting unnecessary costs, and avoiding further expansion of economic losses. Based on historical laws and trend analysis, the optimal allocation of rural collective economic resources can be achieved, the changing trends of income in different periods can be understood, and it can be determined which industries or projects contribute more to income growth and which resources have relatively low input-output ratios.

[0061] The risk assessment module obtains the trend in the historical data for analysis. The risk assessment module expression is:

[0062] D=α+f1P+f2E+f3Z+∈ t

[0063] Where D is the rural collective economic income, α is a constant term, ∈ t is the error term, P is the impact factor of agricultural product price fluctuations, E is the natural disaster risk factor, Z is the policy change impact factor, f1, f2 and f3 are weights, and the future rural collective economic income is predicted to obtain a parameter value. Based on the expert analysis method, the risk threshold H of the local rural collective economy is given, and the obtained parameter value D is compared with the risk threshold H.

[0064] If the parameter value D obtained by comparison is greater than the risk threshold H, it is judged that the local rural collective economic data has hidden risks, and timely targeted treatment is taken; if the parameter value D obtained by comparison is less than the risk threshold H, it is judged that the local rural collective economy has no hidden risks; the expert analysis method establishes an expert team to conduct field investigations and surveys, analyze the risk level assessment, divide it into different levels, and thus establish a risk threshold.

[0065] If risks are identified, the risk response mechanism will be activated to conduct in-depth analysis and timely handling of the problems.

[0066] Further analyze the reasons for the rise or fall in agricultural product prices, and formulate targeted treatment measures based on the results of risk analysis.

[0067] The targeted treatment measures formulated in this application based on the results of risk analysis can more efficiently deal with the risks of agricultural product price fluctuations. If the price drop is due to oversupply in the market, measures can be taken to adjust the planting structure and reduce the planting area of ​​surplus agricultural products; if it is due to poor sales channels, cooperation with e-commerce platforms can be strengthened and new sales markets can be expanded to increase product sales. This targeted strategy can directly act on the key links of the problem, improve the efficiency of problem solving, and reduce unnecessary waste of resources.

[0068] After the risk assessment module is completed, the personalized report generation module obtains the various data output by the risk assessment module, generates an analysis report based on the data, conducts in-depth analysis and interpretation of the risk assessment results, helps to formulate a scientific development plan, and is convenient for people to view. It is presented in a variety of forms, including charts, text and web links.

[0069] This application presents the analysis report in the form of charts to make complex data and information more intuitive and easy to understand. It uses bar graphs to compare the impact of different risk factors and line graphs to show the changing trend of rural collective economic income over time, which allows readers to quickly grasp key information and improve the readability of the report. The text form can provide detailed explanations and descriptions of the charts to further deepen the readers' understanding of the report content.

[0070] The analysis report generation step includes organizing the obtained forecast data, determining the structure of the report, writing and optimizing the forecast results, and finally outputting them; the charts include line charts, pie charts, histograms and bar charts.

[0071] This application organizes the acquired forecast data and can check the completeness and accuracy of the data. During the organization process, errors, missing values ​​or outliers in the data can be discovered and corrected to ensure that the forecast data used for analysis reports are reliable. By comparing data from different data sources, inconsistencies in the data can be discovered and corrected, the quality of the data can be improved, providing a solid foundation for subsequent analysis and report generation.

[0072] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention, and these changes and improvements fall within the scope of the present invention claimed.

Claims

1. The rural collective economic data analysis system is characterized by: include: Acquisition module, storage module, analysis module, risk assessment module and personalized report generation module; The collection module is used to collect historical rural collective economic data and store the collected data in the storage module. The collection module and the storage module are electrically connected; The analysis module is electrically connected to the storage module, and the analysis module obtains historical data, analyzes historical rules based on the historical data, mines the rules and information in the data, and analyzes the trend of rural collective economic income in different years; The risk assessment module is electrically connected to the analysis module. Based on the predicted future development trend, the risk assessment module constructs a risk assessment model for the rural collective economy. Taking into account the particularities of the rural economy, including agricultural product price fluctuations, natural disaster risks, and policy change factors, the risk assessment model predicts the impact of the rural collective economy in the future based on the machine learning algorithm and gives a risk warning. The personalized report generation module is electrically connected to the risk assessment module. The personalized report generation module generates an analysis report based on the results obtained by the risk assessment module for people to view.

2. The rural collective economic data analysis system according to claim 1 is characterized in that: The collection module connects with the databases of rural economic management departments at all levels to obtain statistical historical data related to rural collective economy. The data include detailed account information of rural collective assets, natural resource value assessment data and financial statement data of rural collective enterprises over the years. After obtaining the historical data, the collection module organizes and classifies the data, removes duplicate and invalid data, and unifies the data format, and uploads the processed data to the storage module.

3. The rural collective economic data analysis system according to claim 1 is characterized in that: The analysis module uses historical data from the storage module, based on data mining technology, to analyze historical patterns, build a time series analysis model, observe the changing trend of income over time, fit the relationship curve between income and time, and mine the historical development trend. If the curve shows an upward trend, it means that the rural collective economic income is expected to continue to grow; if the curve shows a downward trend, measures need to be taken to make changes.

4. The rural collective economic data analysis system according to claim 3 is characterized in that: Assuming that the rural economic income variable is represented by Y and time is represented by t, the time series analysis model is: AND t =a+b t +∈ t where Y t is a time series model, a and b are the parameters to be estimated, ∈ t is the error term, and the least squares method is used to estimate the parameters a and b; The a and b estimation steps pre-minimize the sum of squared errors: Take partial derivatives of a and b respectively and set them to 0, and we get the system of equations: Solve the system of equations to get estimates of a and b; Where S is the sum of squared errors, is the summation symbol, indicating the summation operation from t=1 to n.

5. The rural collective economic data analysis system according to claim 4 is characterized in that: Predicting future development trends Based on the values ​​obtained from the time series analysis model, the future time point w is predicted in the form of: AND tw =a+b tw Among them, Y tw is the time series model for future time points, b tw is the predicted value of the parameter at a future time point, w is the future time point, if b>0, the curve shows an upward trend, if b<0, the curve shows a downward trend. The trend in the historical data is analyzed and uploaded to the risk assessment module to predict the risk.

6. The rural collective economic data analysis system according to claim 1 is characterized in that: The risk assessment module obtains the trend in historical data for analysis. The risk assessment module expression is: D=α+f1P+f2E+f3Z+∈ t Where D is the rural collective economic income, α is a constant term, ∈ t is the error term, P is the impact factor of agricultural product price fluctuations, E is the natural disaster risk factor, Z is the policy change impact factor, f1, f2 and f3 are weights, and the future rural collective economic income is predicted to obtain a parameter value. Based on the expert analysis method, the risk threshold H of the local rural collective economy is given, and the obtained parameter value D is compared with the risk threshold H.

7. The rural collective economic data analysis system according to claim 6 is characterized in that: If the parameter value D obtained by comparison is greater than the risk threshold H, it is judged that the local rural collective economic data has hidden risks, and timely targeted treatment is taken; if the parameter value D obtained by comparison is less than the risk threshold H, it is judged that the local rural collective economy has no hidden risks; the expert analysis method establishes an expert team to conduct field investigations and surveys, analyze the risk level assessment, divide it into different levels, and thus establish a risk threshold.

8. The rural collective economic data analysis system according to claim 7 is characterized in that: If risks are identified, the risk response mechanism will be activated to conduct in-depth analysis and timely handling of problems that arise, further analyze the reasons for the rise or fall in agricultural product prices, and formulate targeted handling measures based on the results of the risk analysis.

9. The rural collective economic data analysis system according to claim 1, characterized in that: After the risk assessment module is completed, the personalized report generation module obtains the various data output by the risk assessment module, generates an analysis report based on the data, conducts in-depth analysis and interpretation of the risk assessment results, helps to formulate a scientific development plan, and is convenient for people to view. It is presented in a variety of forms, including charts, text and web links.

10. The rural collective economic data analysis system according to claim 9, characterized in that: The analysis report generation step includes organizing the obtained forecast data, determining the structure of the report, writing and optimizing the forecast results, and finally outputting them; the charts include line charts, pie charts, histograms and bar charts.

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