Rural revitalizing comprehensive service platform based on big data

Abstract

The invention belongs to the technical field of village service platforms, and provides a village revitalizing comprehensive service platform based on big data, which comprises a data collection module used for collecting data in the fields of agriculture, economy, education and medical treatment, a data preprocessing module used for cleaning, standardizing and integrating the collected data, a data storage module used for storing the collected data, and a big data processing module used for processing the collected data. The data storage module is used for safely storing the preprocessed data, the data analysis module is used for deeply analyzing the stored data, the decision support module is used for providing decision suggestions according to analysis results, and the user interaction interface module is used for interacting with users and displaying the analysis results and the decision suggestions; the data visualization module adopts an advanced algorithm formula to balance the definition of a chart and the richness of information, so that data display is more intuitive and understandable, a user is helped to quickly grasp key information, and the accuracy of decision making is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of rural service platforms, and specifically is a rural revitalization comprehensive service platform based on big data. Background Art

[0002] With the rapid development of information technology, big data has become a key force in promoting social progress and economic development. Especially in the field of rural revitalization, the application of big data has shown great potential and can provide accurate decision-making support for agricultural development, rural governance and farmers' lives.

[0003] However, existing technologies have many limitations in data collection, processing and analysis. First, existing technologies lack systematicity and comprehensiveness in data collection, which limits the quality and availability of data. Second, the cleaning, standardization and integration work in the data preprocessing process is often inefficient, and it is difficult to ensure the consistency and accuracy of the data. In addition, data analysis methods are usually simple and lack the ability to deeply explore the potential of data, making it difficult to provide accurate decision support.

[0004] To this end, technicians in this field have proposed a comprehensive rural revitalization service platform based on big data to solve the problems raised by the background technology. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a comprehensive rural revitalization service platform based on big data to solve the problems existing in the prior art.

[0006] A comprehensive rural revitalization service platform based on big data, including a data collection module for collecting data in the fields of agriculture, economy, education, and medical care;

[0007] Data preprocessing module, used to clean, standardize and integrate the collected data;

[0008] A data storage module, used for securely storing pre-processed data;

[0009] Data analysis module, used to conduct in-depth analysis of stored data;

[0010] Decision support module, used to provide decision suggestions based on analysis results;

[0011] The user interaction interface module is used to interact with users and display analysis results and decision suggestions.

[0012] Preferably, the data processing module uses the following algorithm formula to analyze and process the collected data:

[0013]

[0014] Among them, wi is the weight of the i-th data, DataValue i is the value of the i-th data item, n is the total number of data items, and the weight w i Dynamically adjust based on the importance of the data and its impact on the decision.

[0015] Preferably, the decision support module uses the output of the data processing module to provide decision suggestions through the following algorithm formula;

[0016] Decision=f(DataScore,ExternalFactors), where,

[0017] f is a decision function, ExternalFactors is the external influencing factor, and the specific implementation of f is:

[0018] f(x,y)=sigmoid(β0+β1x+β2y), β0, β1, β2 are parameters learned through training data.

[0019] Preferably, the decision function f includes a neural network model based on deep learning, which is used to predict future development trends based on historical data and current data.

[0020] Preferably, the user interaction interface module allows the user to customize the parameters of data collection and the preference settings of decision support, and provides the following algorithm formula to optimize the interface display set by the user:

[0021] UILayout=optimizeLayout(UserPreferences),

[0022] optimizeLayout(p)=minimize(layoutComplexity,layoutAesthetic),

[0023] Among them, UserPreferences is the user's preference settings, and optimizeLayout is an optimization function used to minimize the complexity of the layout and improve the aesthetics of the layout according to the user's preference settings.

[0024] Preferably, the data visualization module uses the following algorithm formula to optimize the layout and display effect of the chart:

[0025] Where, m is the number of visualization elements, VisualWeight j is the visual weight of the jth element, VisualComplexity jis the complexity of the jth element. This formula is used to balance the clarity of the chart and the richness of the information.

[0026] Preferably, the prediction module uses time series analysis to predict future data trends, specifically using the following algorithm formula for prediction:

[0027] Forecast=α×PreviousValue+(1-α)×AverageValue,

[0028] α is a smoothing constant, PreviousValue is the value at the previous time point, and AverageValue is the average value over a period of time. This formula is used to predict future data trends.

[0029] Preferably, the prediction module further includes a prediction algorithm based on the ARIMA model for processing time series data with seasonal characteristics.

[0030] Preferably, the prediction algorithm of the ARIMA model uses the following formula for parameter estimation:

[0031] ARIMA(p,d,q)(X t )=c+φ1X t-1 +···+φ p X t-p +θ1ε t-1 +···+θ q ε t-q +ε t ,

[0032] Among them, X t is the value of the time series at time point t, c is a constant term, φ and θ are model parameters, ε t is the error term, and p, d, q are the number of autoregressive terms, difference orders, and moving average terms, respectively.

[0033] Preferably, S1: collecting data through a data collection module;

[0034] S2: Clean and integrate data using the data preprocessing module;

[0035] S3: storing the preprocessed data into a data storage module;

[0036] S4: Analyze the stored data using a data analysis module;

[0037] S5: Based on the data analysis results, use the decision support module to provide decision suggestions;

[0038] S6: Interact with users through the user interface module to display analysis results and decision suggestions;

[0039] S7: Use the data visualization module to display the processing results in the form of graphs or charts;

[0040] S8: Use the forecasting module to predict future trends, including simple trend forecasting and seasonal time series forecasting.

[0041] Through the above technical solution,

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] 1. The present invention can quickly collect and clean data in the fields of agriculture, economy, education and medicine through integrated data collection and preprocessing modules, thereby greatly improving the efficiency of data processing and providing timely and accurate data support for decision makers. At the same time, the data analysis module can be used to conduct in-depth analysis of stored data, explore the correlations and trends behind the data, and provide more comprehensive and in-depth insights, thereby helping decision makers make more scientific and comprehensive decisions.

[0044] 2. The present invention adopts advanced algorithm formulas through the data visualization module to balance the clarity of the chart and the richness of the information, making the data display more intuitive and easy to understand, helping users to quickly grasp key information and improve the accuracy of decision-making. At the same time, the prediction module of the present invention combines time series analysis and ARIMA model to accurately predict data trends, especially for time series data with seasonal characteristics, providing more accurate prediction results.

[0045] 3. The present invention allows users to customize data collection parameters and decision support settings according to personal preferences through the user interaction interface module. By optimizing the algorithm formula, the layout aesthetics and operational convenience of the user interface are improved, thereby enhancing user satisfaction and usage experience. At the same time, by dynamically adjusting data weights and optimizing the interface display of user settings, the present invention can adapt to the needs and preferences of different users and provide more personalized services. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a system block diagram of the present invention; DETAILED DESCRIPTION

[0047] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0048] Embodiment 1: As shown in the attached Figure 1 As shown: The present invention provides a rural revitalization comprehensive service platform based on big data, including:

[0049] Data collection module: used to widely collect data related to key areas such as agriculture, economy, education, and medical care. This module can integrate data from different sources, including but not limited to government statistics, online transaction records, social media information, and remote sensing monitoring data to ensure the comprehensiveness and diversity of the data.

[0050] Data preprocessing module: responsible for cleaning, standardizing and integrating the collected data. This module uses advanced data cleaning technology to remove invalid, erroneous or incomplete data entries, and ensures the consistency of data format through the standardization process, thereby improving the quality and availability of data.

[0051] Data storage module: provides a safe and reliable data storage solution. This module uses distributed database technology to support the storage and management of large-scale data, ensuring data security and access efficiency.

[0052] Data Analysis Module: Utilizes advanced data analysis techniques to conduct in-depth analysis of stored data. This module includes data mining algorithms, statistical analysis methods, and machine learning models, which can identify patterns, associations, and trends in data and provide a scientific basis for decision making.

[0053] Decision support module: Provides decision suggestions based on data analysis results. This module uses a comprehensive decision function, considers data scores and external influencing factors, and provides accurate decision support for decision makers through parameters learned from training data.

[0054] User interface module: provides users with an intuitive and easy-to-use operation interface. This module allows users to customize the parameters of data collection and the preference settings for decision support, and minimizes the complexity of the layout by optimizing the algorithm formula, improving the aesthetics of the layout and enhancing the user experience.

[0055] As can be seen from the above, the platform includes key modules of data collection, preprocessing, storage, analysis, decision support and user interaction. It aims to provide comprehensive and accurate decision-making services for agriculture, economy, education and medical fields through efficient integration and intelligent analysis to promote the sustainable development of rural areas.

[0056] Preferably, the data processing module uses the following algorithm formula to analyze and process the collected data:

[0057]

[0058] Among them, w i is the weight of the i-th data, DataValue i is the value of the i-th data item, n is the total number of data items, and the weight w i The weight w is dynamically adjusted according to the importance of the data and its impact on the decision.i The settings are not static, but are dynamically adjusted according to the importance of the data and its impact on decision-making. This dynamic adjustment mechanism allows the platform to adapt to different decision-making scenarios and needs, ensuring that data analysis results are more accurate and practical.

[0059] Preferably, the decision support module is the core component of the rural revitalization comprehensive service platform. It is responsible for combining the comprehensive data score generated by the data processing module with external influencing factors to provide scientific and reasonable decision-making suggestions. The decision support module uses the output of the data processing module to provide decision suggestions through the following algorithm formula;

[0060] Decision=f(DataScore,ExternalFactors), where,

[0061] f is a decision function, ExternalFactors is the external influencing factor, and the specific implementation of f is:

[0062] f(x,y)=sigmoid(β0+β1x+β2y), β0, β1, and β2 are parameters learned from training data. The decision function f includes a neural network model based on deep learning, which is used to predict future development trends based on historical data and current data. The model can learn and simulate complex nonlinear relationships and predict future development trends based on historical data and current data. This model usually contains multiple hidden layers and can automatically extract features and perform pattern recognition.

[0063] Preferably, in the above algorithm formula,

[0064]

[0065] This formula is used to comprehensively consider the impact of data score DataScoreDataScore and external factors ExternalFactors on decision making, weight and Allows decision makers to adjust the importance of each factor based on actual circumstances.

[0066] As can be seen from the above, the rural revitalization comprehensive service platform of the present invention provides a comprehensive and intelligent solution for rural development through its innovative data processing and decision support mechanism. The core advantage of the platform is that its data processing module can dynamically adjust the data weight to ensure the accuracy and adaptability of the analysis results. This module not only covers data collection, cleaning, standardization and feature extraction, but also performs effective data dimension reduction and feature optimization through principal component analysis technology to extract the most critical information for decision-making.

[0067] As the brain of the platform, the decision support module integrates deep learning algorithms, enabling the platform to simulate complex nonlinear relationships, automatically extract features, perform pattern recognition, and thus predict future development trends. The decision function of this module not only integrates data scores and external factors, but also optimizes the parameters learned through training data to achieve highly personalized and precise decision recommendations. The design of the platform allows users to adjust the weights in the decision formula according to actual needs, making the decision process more flexible and user-friendly. This design not only improves the transparency of decision-making, but also enables the platform to adapt to the changing rural environment and diversified development needs.

[0068] Overall, this platform provides a powerful tool for rural revitalization through its advanced data processing technology and deep learning-driven decision support system, aiming to promote rural economic growth, social progress and environmental sustainability through data-driven insights.

[0069] Embodiment 2: As shown in the attached Figure 1 As shown: Based on the first embodiment, the user interaction interface module allows the user to customize the parameters of data collection and the preference settings of decision support, and provides the following algorithm formula to optimize the interface display set by the user:

[0070] UILayout=optimizeLayout(UserPreferences),

[0071] optimizeLayout(p)=minimize(layoutComplexity,layoutAesthetic),

[0072] Among them, UserPreferences is the user's preference settings, and optimizeLayout is an optimization function used to minimize the complexity of the layout and improve the aesthetics of the layout according to the user's preference settings.

[0073] Specifically, the design of the user interaction interface module focuses on user experience and personalized needs. The module provides a graphical operation interface that enables users to intuitively view data analysis results and decision recommendations. Users can set the way data is displayed according to personal preferences, such as selecting different chart types or adjusting the displayed data dimensions. In addition, users can also customize the parameters of the decision support module through the interface, such as adjusting the weights of different data types in the decision scoring, or specifying specific data indicators of concern. The interface module also provides a real-time feedback mechanism, allowing users to adjust settings based on feedback to obtain more accurate decision support.

[0074] Preferably, the data visualization module uses the following algorithm formula to optimize the layout and display effect of the chart:

[0075] Where, m is the number of visualization elements, VisualWeight j is the visual weight of the jth element, VisualComplexity j is the complexity of the jth element. This formula is used to balance the clarity of the chart and the richness of the information.

[0076] Preferably, the data visualization module is designed to convert complex data analysis results into intuitive graphs or charts to enhance users' understanding and insight into the data. The module supports a variety of data visualization techniques, including but not limited to line charts, bar charts, pie charts, scatter plots, and heat maps. Through these charts, users can quickly identify data trends, patterns, and anomalies. The visualization module also allows users to conduct interactive exploration, such as in-depth analysis of specific data sets or data points through zooming, panning, and filtering operations.

[0077] Furthermore, the prediction module uses time series analysis to predict future data trends. Specifically, the following algorithm formula is used for prediction:

[0078] Forecast=α×PreviousValue+(1-α)×AverageValue,

[0079] α is a smoothing constant, PreviousValue is the value at the previous time point, and AverageValue is the average value over a period of time. This formula is used to predict future data trends.

[0080] Specifically, the prediction module is an important part of the present invention, which uses time series analysis technology to predict future trends of rural revitalization-related data. The module first processes the original time series data through difference and smoothing methods to eliminate non-stationarity, and then uses the autoregressive integrated moving average (ARIMA) model for prediction. The ARIMA model can capture the autocorrelation and seasonal characteristics of the data and provide more accurate prediction results. The module also includes model diagnostic tools to evaluate the goodness of fit and prediction error of the prediction model.

[0081] Preferably, the ARIMA model in the prediction module is a widely used statistical model in the field of time series prediction. The model combines three parts: autoregression (AR), difference (I) and moving average (MA) to adapt to the seasonal changes and nonlinear characteristics of the data. The AR part captures the relationship between the historical value and the current value of the data; the difference part is used to make the non-stationary series stable; the moving average part uses the weighted average of past prediction errors to improve the prediction. By combining these methods, the ARIMA model can provide reliable predictions of the future trends of time series data.

[0082] The forecasting module further includes a forecasting algorithm based on the ARIMA model to process time series data with seasonal characteristics. The forecasting algorithm of the ARIMA model uses the following formula for parameter estimation,

[0083] ARIMA(p,d,q)(X t )=c+φ1X t-1 +···+φ p X t-p +θ1ε t-1 +···+θ q ε t-q +ε t ,

[0084] Among them, X t is the value of the time series at time point t, c is a constant term, φ and θ are model parameters, ε t is the error term, and p, d, q are the number of autoregressive terms, difference orders, and moving average terms, respectively.

[0085] Embodiment 2: As shown in the attached Figure 1 As shown: Different from the previous embodiment, this embodiment provides a method for using a rural revitalization comprehensive service platform based on big data, which is applied to the above platform, and specifically includes the following: S1: collecting data through a data collection module. The method first starts the data collection module, which collects data related to agricultural production, market transactions, climate change, educational resources, and medical conditions through multiple channels such as an integrated sensor network, an online data interface, satellite remote sensing technology, a public database, and user input;

[0086] S2: Clean and integrate data using the data preprocessing module. The collected raw data will be transferred to the data preprocessing module. At this stage, the data will be cleaned to remove invalid or erroneous records, standardized to unify the data format, and missing values ​​processed to ensure the completeness and accuracy of the data set;

[0087] S3: storing the preprocessed data into a data storage module;

[0088] S4: Analyze the stored data using the data analysis module, and further analyze the pre-processed data using the data processing module, identify key data features using feature extraction technology, and integrate features from different sources and types of data using data fusion technology to form a comprehensive data view that can be used for decision support;

[0089] S5: Based on the data analysis results, the decision support module is used to provide decision suggestions. The decision support module uses deep learning algorithms to model the feature data, trains the model through the training set, and evaluates and optimizes the model through the validation set to ensure the accuracy and generalization ability of the model;

[0090] S6: Interact with users through the user interface module to display analysis results and decision suggestions. Users interact with the system through the interface module and adjust decision parameters according to personal needs, such as setting weights for different data indicators or specifying to pay attention to specific agricultural market trends;

[0091] S7: Use the data visualization module to display the processing results in the form of graphs or charts. The data analysis module conducts in-depth analysis of the fused data to identify patterns and trends in the data. The data visualization module converts the analysis results into intuitive charts and graphs, allowing users to quickly understand the meaning behind the data.

[0092] S8: Use the forecasting module to forecast future trends, including simple trend forecasting and seasonal time series forecasting. Based on the forecast results of the deep learning model and the parameters adjusted by the user, the decision support module generates decision suggestions that take into account the combined impact of data scores and external factors, providing a scientific basis for user decision-making. The forecasting module uses time series analysis and ARIMA models to analyze historical data, predict future market trends and climate change, and help users make long-term plans.

[0093] Finally, the system can generate detailed analysis reports and decision-making recommendation documents based on user needs, which can be used for internal decision support or shared with stakeholders.

[0094] Through this series of steps, the method of the present invention realizes a complete process from data collection to decision support, aiming to provide comprehensive, dynamic and personalized services for rural revitalization. This method not only improves the efficiency and quality of decision-making, but also provides strong technical support for the sustainable development of rural areas.

[0095] The embodiments of the present invention are provided for the purpose of illustration and description. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present invention. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present invention.

[0096] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

Claims

1. A rural revitalization comprehensive service platform based on big data, characterized in that: include: Data collection module, used to collect data in the fields of agriculture, economy, education, and medicine; Data preprocessing module, used to clean, standardize and integrate the collected data; A data storage module, used for securely storing pre-processed data; Data analysis module, used to conduct in-depth analysis of stored data; Decision support module, used to provide decision suggestions based on analysis results; The user interaction interface module is used to interact with users and display analysis results and decision suggestions.

2. A rural revitalization comprehensive service platform based on big data as claimed in claim 1, characterized in that: The data processing module uses the following algorithm formula to analyze and process the collected data: Among them, w i is the weight of the i-th data, DataValue i is the value of the i-th data item, n is the total number of data items, and the weight w i Dynamically adjust based on the importance of the data and its impact on the decision.

3. A rural revitalization comprehensive service platform based on big data as claimed in claim 1, characterized in that: The decision support module uses the output of the data processing module to provide decision suggestions through the following algorithm formula; Decision=f(DataScore,ExternalFactors), where, f is a decision function, ExternalFactors is the external influencing factor, and the specific implementation of f is: f(x,y)=sigmoid(β0+β1x+β2y) β0, β1, β2 are parameters learned through training data.

4. A rural revitalization comprehensive service platform based on big data as claimed in claim 3, characterized in that: The decision function f includes a neural network model based on deep learning, which is used to predict future development trends based on historical data and current data.

5. A rural revitalization comprehensive service platform based on big data as claimed in claim 4, characterized in that: The user interaction interface module allows users to customize the parameters of data collection and the preference settings of decision support, and provides the following algorithm formula to optimize the interface display set by the user: UILayout=optimizeLayout(UserPreferences), optimizeLayout(p)=minimize(layoutComplexity,layoutAesthetic), Among them, UserPreferences is the user's preference settings, and optimizeLayout is an optimization function used to minimize the complexity of the layout and improve the aesthetics of the layout according to the user's preference settings.

6. A rural revitalization comprehensive service platform based on big data as claimed in claim 5, characterized in that: The data visualization module uses the following algorithm formula to optimize the layout and display effect of the chart: Where, m is the number of visualization elements, VisualWeight j is the visual weight of the jth element, VisualComplexity j is the complexity of the jth element, and the formula is used to balance the clarity of the chart and the richness of the information.

7. A rural revitalization comprehensive service platform based on big data as claimed in claim 6, characterized in that: The prediction module uses time series analysis to predict future data trends, specifically using the following algorithm formula for prediction: Forecast=α×PreviousValue+(1-α)×AverageValue, α is a smoothing constant, PreviousValue is the value at the previous time point, and AverageValue is the average value over a period of time. This formula is used to predict future data trends.

8. A rural revitalization comprehensive service platform based on big data as claimed in claim 7, characterized in that: The forecasting module further includes a forecasting algorithm based on the ARIMA model for processing time series data with seasonal characteristics.

9. A rural revitalization comprehensive service platform based on big data as claimed in claim 8, characterized in that: The forecasting algorithm of the ARIMA model uses the following formula for parameter estimation, ARIMA(p,d,q)(X t )=c+φ1X t-1 +···+φ p X t-p +θ1ε t-1 +···+θ q e t-q +e t Among them, X t is the value of the time series at time point t, c is a constant term, φ and θ are model parameters, ε t is the error term, and p, d, q are the number of autoregressive terms, difference orders, and moving average terms, respectively.

10. A method for using a rural revitalization comprehensive service platform based on big data, comprising the platform as claimed in any one of claims 1 to 9, specifically comprising the following: S1: Collect data through the data collection module; S2: Clean and integrate data using the data preprocessing module; S3: storing the preprocessed data into a data storage module; S4: Analyze the stored data using a data analysis module; S5: Based on the data analysis results, use the decision support module to provide decision suggestions; S6: Interact with users through the user interface module to display analysis results and decision suggestions; S7: Use the data visualization module to display the processing results in the form of graphs or charts; S8: Use the forecasting module to predict future trends, including simple trend forecasting and seasonal time series forecasting.

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