An agricultural science and technology talent demand prediction method and system based on big data analysis

Abstract

The present application is suitable for the field of data science, and provides an agricultural science and technology talent demand prediction method and system based on big data analysis, an agricultural talent data induction module, a demand model establishment and training module, a talent demand prediction inference module, and a result display and decision support module. Through the method and system, an agricultural science and technology talent demand prediction model related to the correlation and trend between agricultural science and technology talent demand and agricultural product yield, import volume and export volume can be established by using big data analysis, the relationship between a large number of crop types and agricultural science and technology talent demand can be considered, and the accurate prediction can help decision makers to more accurately predict the demand for agricultural science and technology talents, so as to reasonably arrange talent recruitment, training and management.

Description

Technical Field

[0001] This invention belongs to the field of data science, and in particular relates to a method and system for predicting the demand for agricultural science and technology personnel based on big data analysis. Background Technology

[0002] Big data analytics has yielded significant results in the agricultural sector. By collecting, storing, and analyzing the vast amounts of data generated in agricultural production, it is possible to provide accurate forecasts of the demand for agricultural science and technology personnel, thereby aiding in the training and allocation of such talent.

[0003] In the field of big data analytics, numerous solutions have emerged, including data acquisition, data storage and management, data cleaning and preprocessing, and data analysis and mining techniques. These advancements enable the agricultural sector to better leverage big data for decision-making and optimization.

[0004] With the development of artificial intelligence and machine learning, the agricultural sector can also utilize these technologies to predict the demand for agricultural science and technology personnel. By analyzing and modeling agricultural data, future trends in agricultural technology demand can be predicted, allowing for the targeted training and recruitment of agricultural science and technology talent.

[0005] Agricultural data comes from diverse sources, such as weather, soil, and crop growth data, making data integration and processing challenging. Choosing the right algorithms is crucial for accurately predicting talent demand, while privacy and security concerns necessitate ensuring data protection and preventing misuse. The sheer volume of agricultural data requires powerful data processing and computing capabilities to support analysis, and providing an efficient platform is also a challenge. Summary of the Invention

[0006] The purpose of this invention is to provide a method for predicting the demand for agricultural science and technology personnel based on big data analysis, aiming to solve the technical problems existing in the prior art as identified in the background section.

[0007] This invention is implemented as follows: a method for predicting the demand for agricultural science and technology personnel based on big data analysis, the method comprising:

[0008] To collect and summarize data on agricultural science and technology personnel and agricultural products;

[0009] To establish a talent prediction model that analyzes the correlation and trends between agricultural science and technology talent demand and agricultural product output, import volume, and export volume, the model is fitted and trained using selected features and talent demand data. The model parameters are determined by minimizing the error between the predicted value and the actual demand value over the years, thus obtaining the talent prediction model.

[0010] The talent forecasting model is used to predict the demand for agricultural science and technology personnel. Data on factors such as agricultural product output, import volume, and export volume are input into the model to generate corresponding predictive inferences about the demand for agricultural science and technology personnel.

[0011] Generate reports to display data and provide forecasts and inferences about the demand for agricultural science and technology personnel.

[0012] As a further aspect of the present invention, the collection and summarization of agricultural science and technology personnel and agricultural product data specifically includes:

[0013] Collect and summarize data on agricultural science and technology personnel;

[0014] Collect and summarize big data on agricultural product output, import volume, and export volume.

[0015] As a further aspect of the present invention, the establishment of a talent prediction model to analyze the correlation and trends between the demand for agricultural science and technology personnel and the output, import volume, and export volume of agricultural products specifically includes:

[0016] Based on the relationship between the demand for agricultural science and technology personnel and factors such as agricultural product output, import volume, and export volume, parameter variables are derived.

[0017] The selected parameter variables and historical talent demand data are fitted and trained. The model parameters are determined by minimizing the error between the predicted value and the actual demand value, thereby establishing a talent demand prediction model related to factors such as agricultural product output, import volume, and export volume. The analytical formula of the talent prediction model is as follows:

[0018] Y=β0+β1χ1+β1χ1+β2χ2+...+β n χ n Where Y represents the demand for agricultural science and technology personnel, and χ1, χ2, χ n The selected features are β1, β2, β n , are the parameters of the model;

[0019] The established talent prediction model is evaluated and optimized. The accuracy and reliability of the model are assessed by comparing the deviation between the predicted inferences and the actual needs.

[0020] As a further aspect of the present invention, the generation of corresponding predictions and inferences regarding the demand for agricultural science and technology personnel specifically includes:

[0021] By inputting data on agricultural product output, import volume, and export volume into the talent prediction model, corresponding predictions and inferences about the demand for agricultural science and technology personnel are generated.

[0022] The generated demand forecasts are evaluated and verified by comparing the differences between the forecasts and the actual demand values.

[0023] As a further aspect of the present invention, the generation of report display data reports, providing forecasts and inferences on the demand for agricultural science and technology personnel, specifically includes:

[0024] The forecasts and inferences of agricultural science and technology talent demand generated by the forecasting module are collected and integrated.

[0025] The predicted demand for agricultural science and technology personnel will be presented in the form of reports.

[0026] Another objective of this invention is to provide an agricultural technology talent demand forecasting system based on big data analysis, the system comprising:

[0027] The agricultural talent data aggregation module is used to collect and aggregate data on agricultural science and technology personnel and agricultural products.

[0028] The demand model establishment and training module is used to build a talent prediction model that analyzes the relationship and trend between the demand for agricultural science and technology talents and the output, import volume, and export volume of agricultural products. It fits and trains the selected features and talent demand data, and determines the parameters of the model by minimizing the error between the predicted value and the actual demand value over the years, thereby obtaining the talent prediction model.

[0029] The talent demand forecasting and inference module is used to predict the demand for agricultural science and technology talents using a talent forecasting model. It inputs data on factors such as agricultural product output, import volume, and export volume into the model to generate corresponding forecasts and inferences on the demand for agricultural science and technology talents.

[0030] The results display and decision support module is used to generate reports to display data and provide forecasts and inferences about the demand for agricultural science and technology personnel.

[0031] As a further embodiment of the present invention, the agricultural talent data summarization module specifically includes:

[0032] The agricultural science and technology personnel data collection unit is used to collect and summarize agricultural science and technology personnel data.

[0033] The agricultural product big data collection unit is used to collect and summarize big data on agricultural product output, import volume, and export volume.

[0034] As a further embodiment of the present invention, the requirement model establishment and training module specifically includes:

[0035] The parameter variable selection unit is used to derive parameter variables based on the relationship between the demand for agricultural science and technology personnel and factors such as agricultural product output, import volume, and export volume.

[0036] The data fitting and training unit is used to fit and train the selected parameter variables and historical talent demand data. It determines the model parameters by minimizing the error between the predicted and actual demand values, thereby establishing a talent demand prediction model related to factors such as agricultural output, import volume, and export volume. The analytical formula for the talent prediction model is as follows:

[0037] Y=β0+β1χ1+β1χ1+β2χ2+...+β n χ n Where Y represents the demand for agricultural science and technology personnel, and χ1, χ2, χ n The selected features are β1, β2, β n , are the parameters of the model;

[0038] The model evaluation and optimization unit is used to evaluate and optimize the established talent prediction model. By comparing the deviation between the predicted inference and the actual needs, the accuracy and reliability of the model are assessed.

[0039] As a further embodiment of the present invention, the talent demand prediction and inference module specifically includes:

[0040] The prediction and inference generation unit is used to input agricultural product output, import volume, and export volume data into the talent prediction model to generate corresponding predictions and inferences about the demand for agricultural science and technology talents.

[0041] The forecasting and inference evaluation unit is used to evaluate and verify the generated demand forecasts and inferences, and to compare the differences between the forecast values ​​and the actual demand values.

[0042] As a further embodiment of the present invention, the result display and decision support module specifically includes:

[0043] The integrated forecasting and inference unit is used to aggregate and integrate the forecasts and inferences on agricultural science and technology talent demand generated by the forecasting module.

[0044] Generate visual report units to present the predicted agricultural technology talent demand results in report form.

[0045] The beneficial effects of this invention are: through this method and system, a talent prediction model can be established by using big data analysis to establish the correlation and trend between the demand for agricultural science and technology talents and the output, import volume, and export volume of agricultural products. It can take into account the relationship between the vast variety of crops and the demand for agricultural science and technology talents. This accurate prediction can help decision-makers more accurately predict the demand for agricultural science and technology talents, thereby rationally arranging talent recruitment, training and management. Attached Figure Description

[0046] Figure 1A flowchart illustrating a method for predicting the demand for agricultural science and technology personnel based on big data analysis, provided as an embodiment of the present invention;

[0047] Figure 2 A flowchart for collecting and summarizing data on agricultural science and technology personnel and agricultural products is provided for embodiments of the present invention;

[0048] Figure 3 A flowchart for establishing a talent prediction model to analyze the correlation and trends between the demand for agricultural science and technology personnel and the output, import volume, and export volume of agricultural products, provided in an embodiment of the present invention;

[0049] Figure 4 A flowchart for predicting and inferring the corresponding demand for agricultural science and technology personnel, provided for embodiments of the present invention;

[0050] Figure 5 The present invention provides a flowchart for generating and displaying data reports and for predicting and inferring the demand for agricultural science and technology personnel, as provided in the embodiments of the present invention.

[0051] Figure 6 A structural block diagram of an agricultural science and technology talent demand prediction system based on big data analysis provided in an embodiment of the present invention;

[0052] Figure 7 This is a structural block diagram of the agricultural talent data summarization module provided in an embodiment of the present invention;

[0053] Figure 8 A structural block diagram of the training module for the requirement model provided in this embodiment of the invention;

[0054] Figure 9 This is a structural block diagram of the talent demand prediction and inference module provided in an embodiment of the present invention;

[0055] Figure 10 This is a structural block diagram of the result display and decision support module provided in the embodiments of the present invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0057] It is understood that the terms "first," "second," etc., used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are used only to distinguish one element from another. For example, without departing from the scope of this application, a first script may be referred to as a second script, and similarly, a second script may be referred to as a first script.

[0058] Figure 1 A flowchart illustrating a method for predicting the demand for agricultural science and technology personnel based on big data analysis, as provided in this embodiment of the invention, is shown below. Figure 1 As shown, a method for predicting the demand for agricultural science and technology personnel based on big data analysis is described, the method comprising:

[0059] S100 gathers and summarizes agricultural science and technology personnel and agricultural product data;

[0060] This step is a key component of the agricultural science and technology talent demand forecasting system based on big data analysis. The agricultural science and technology talent data collection unit gathers and summarizes information such as the educational background, professional fields, research directions, and work experience of agricultural science and technology talents through methods such as questionnaires, talent recruitment platforms, academic paper databases, and talent databases of research institutions and agricultural-related organizations. The agricultural product big data collection unit utilizes agricultural survey data, publicly available government data, agricultural product trading platform data, and international trade data to obtain data on agricultural product output, import volume, and export volume.

[0061] Significant advancements and advantages of this step include: broad data coverage, enabling the acquisition of comprehensive data on agricultural science and technology personnel and agricultural products; real-time data coverage, allowing for real-time monitoring and updating of agricultural science and technology personnel demand and agricultural product data; data correlation, through the establishment of talent prediction models, linking agricultural science and technology personnel demand with factors such as agricultural product output, import volume, and export volume for trend analysis; and data accuracy, through multi-channel data collection, obtaining more accurate and authentic data on agricultural science and technology personnel and agricultural products.

[0062] S200: Establish a talent prediction model to analyze the relationship and trend between agricultural science and technology talent demand and agricultural product output, import volume, and export volume. Fit and train the selected features and talent demand data, determine the model parameters by minimizing the error between the predicted value and the actual demand value over the years, and then obtain the talent prediction model.

[0063] This step is a core module in the big data analytics-based agricultural science and technology talent demand forecasting system. The parameter variable selection unit determines parameters closely related to talent demand based on the correlation between agricultural science and technology talent demand and factors such as agricultural product output, import volume, and export volume. In this step, statistical methods and machine learning algorithms can be used to screen and select the most influential and predictive features. For example, correlation analysis, regression analysis, and principal component analysis can be used to select the most relevant parameters from candidate features. The data fitting and training unit fits and trains the selected parameters and historical talent demand data to establish a talent forecasting model related to agricultural product output, import volume, and export volume. During the evaluation process, various evaluation indicators and methods can be used to measure the model's accuracy and reliability, such as root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R-squared). By comparing with actual demand, the model's predictive performance can be understood, and necessary optimizations and adjustments can be made to improve the model's predictive accuracy and stability.

[0064] The significant advancements and advantages of this step are reflected in the following: By establishing a talent forecasting model, the correlation analysis between the demand for agricultural science and technology personnel and factors such as agricultural product output, import volume, and export volume can reveal the intrinsic link between talent demand and agricultural industry development, providing more comprehensive and accurate forecasts. Based on big data analysis, multiple parameter variables are used, and the forecasting model is built by fitting and training historical data, making the forecasts more credible and accurate. Flexible adjustments can be made to feature selection, data fitting, and model evaluation based on changes in agricultural science and technology personnel demand and the agricultural product market to adapt to demand forecasting in different periods and scenarios.

[0065] S300 uses a talent prediction model to predict the demand for agricultural science and technology personnel. It inputs data on factors such as agricultural product output, import volume, and export volume into the model to generate corresponding predictions and inferences about the demand for agricultural science and technology personnel.

[0066] This step involves inputting data on factors such as agricultural product output, import volume, and export volume into the established talent forecasting model to generate corresponding forecasts of agricultural science and technology talent demand. The generated demand forecasts are then evaluated and verified. By comparing them with actual demand, the differences between the predicted values ​​and actual demand are quantified, assessing the accuracy and reliability of the forecasting model.

[0067] The significant advancements and advantages of this step lie in data-driven forecasting, predictive inference evaluation, flexibility and refined forecasting, as well as real-time performance and scalability. By leveraging big data analytics and information from multiple factors, this module improves the accuracy and reliability of forecasts. Simultaneously, through predictive inference evaluation and validation, biases and shortcomings in the forecasting model can be identified and corrected in a timely manner, providing decision-makers with reliable predictive inferences and a solid foundation. The talent demand forecasting module also possesses the capability for flexible and refined forecasting, generating accurate predictive inferences based on different agricultural products and related factors. Furthermore, the module's design allows for the addition and adjustment of data inputs and forecasting models as needed to adapt to demand forecasts for different regions, different agricultural products, and different time periods.

[0068] S400 generates reports to display data and provides forecasts and inferences about the demand for agricultural technology personnel.

[0069] This step combines forecasts from different time periods, agricultural products, and regions to create a comprehensive agricultural science and technology talent demand forecast report. The generated visualization report unit presents the integrated forecasts in an intuitive and easy-to-understand report format, providing decision-makers with insights into agricultural science and technology talent demand forecasts.

[0070] This step, by collecting and integrating forecasts from different time periods, agricultural products, and regions, generates a comprehensive report on the demand for agricultural science and technology personnel. This integrated approach allows decision-makers to clearly understand the overall trends and changes in personnel demand. Through data visualization technology, these forecasts are transformed into charts, images, and other visual formats, enabling decision-makers to more intuitively understand and analyze the changing trends in agricultural science and technology personnel demand. This method of generating visual reports allows decision-makers to quickly obtain key information and make appropriate decisions.

[0071] Figure 2 A flowchart for collecting and summarizing agricultural science and technology personnel and agricultural product data provided in this embodiment of the invention, such as... Figure 2 As shown, the collection and summarization of agricultural science and technology personnel and agricultural product data specifically includes:

[0072] S110, which collects and summarizes data on agricultural science and technology personnel;

[0073] S120 collects and summarizes big data on agricultural product output, import volume, and export volume.

[0074] Figure 3 A flowchart illustrating the process of establishing and analyzing the correlation and trends between agricultural science and technology talent demand and agricultural product output, import volume, and export volume, as provided in this embodiment of the invention, is shown below. Figure 3As shown, the talent forecasting model established to analyze the correlation and trends between the demand for agricultural science and technology personnel and the output, import volume, and export volume of agricultural products specifically includes:

[0075] S210, based on the relationship between the demand for agricultural science and technology personnel and factors such as agricultural product output, import volume, and export volume, the parameter variables are derived;

[0076] S220, the selected parameter variables and historical talent demand data are fitted and trained, and the model parameters are determined by minimizing the error between the predicted value and the actual demand value, thereby establishing a talent demand prediction model related to factors such as agricultural product output, import volume, and export volume. The analytical formula of the talent prediction model is as follows:

[0077] Y=β0+β1χ1+β1χ1+β2χ2+...+β n χ n Where Y represents the demand for agricultural science and technology personnel, and χ1, χ2, χ n The selected features are β1, β2, β n , are the parameters of the model;

[0078] S230 evaluates and optimizes the established talent prediction model by comparing the deviation between the predicted inferences and actual needs to assess the accuracy and reliability of the model.

[0079] Figure 4 A flowchart for predicting and inferring the corresponding demand for agricultural science and technology personnel is provided for embodiments of the present invention, such as... Figure 4 As shown, the generation of corresponding predictions and inferences regarding the demand for agricultural science and technology personnel specifically includes:

[0080] S310: Input agricultural product output, import volume, and export volume data into the talent prediction model to generate corresponding predictions and inferences about the demand for agricultural science and technology talents.

[0081] S320 evaluates and verifies the generated demand forecast inferences, comparing the differences between the forecast values ​​and the actual demand values.

[0082] Figure 5 The present invention provides a flowchart for generating and displaying data reports, including a flowchart for predicting and inferring the demand for agricultural science and technology personnel, as shown in the embodiments of the present invention. Figure 5 As shown, the generated report displays data and provides forecasts and inferences about the demand for agricultural science and technology personnel, specifically including:

[0083] S410 aggregates and integrates the agricultural science and technology talent demand forecasts generated by the forecasting module.

[0084] S420 presents the predicted demand for agricultural science and technology personnel in the form of a report.

[0085] Figure 6 A structural block diagram of an agricultural science and technology talent demand prediction system based on big data analysis provided in an embodiment of the present invention is shown below. Figure 6 As shown, an agricultural technology talent demand forecasting system based on big data analysis is provided, the system comprising:

[0086] The Agricultural Talent Data Summary Module 100 is used to collect and summarize data on agricultural science and technology talents and agricultural products.

[0087] This module is a key component of the agricultural science and technology talent demand forecasting system based on big data analysis. The agricultural science and technology talent data collection unit gathers and summarizes information on agricultural science and technology talents' education, professional fields, research directions, and work experience through methods such as questionnaires, talent recruitment platforms, academic paper databases, and talent databases of research institutions and agricultural-related organizations. The agricultural product big data collection unit utilizes agricultural survey data, publicly available government data, agricultural product trading platform data, and international trade data to obtain data on agricultural product output, import volume, and export volume.

[0088] Significant advancements and advantages of this module include: broad data coverage, enabling the acquisition of comprehensive data on agricultural science and technology personnel and agricultural products; real-time data monitoring and updates of agricultural science and technology personnel demand and agricultural product data; data correlation, through the establishment of talent prediction models, linking and trend-analyzing the demand for agricultural science and technology personnel with factors such as agricultural product output, import volume, and export volume; and data accuracy, through multi-channel data collection, obtaining more accurate and authentic data on agricultural science and technology personnel and agricultural products.

[0089] The demand model establishment and training module 200 is used to establish a talent prediction model that analyzes the relationship and trend between the demand for agricultural science and technology talents and the output, import volume, and export volume of agricultural products. It fits and trains the selected features and talent demand data, and determines the parameters of the model by minimizing the error between the predicted value and the actual demand value over the years, thereby obtaining the talent prediction model.

[0090] This module is the core module of the agricultural science and technology talent demand forecasting system based on big data analysis. The parameter variable selection unit determines parameter variables closely related to talent demand based on the correlation between agricultural science and technology talent demand and factors such as agricultural product output, import volume, and export volume. In this step, statistical methods and machine learning algorithms can be used to screen and select the most influential and predictive features. For example, correlation analysis, regression analysis, and principal component analysis can be used to select the most relevant parameter variables from candidate features. The data fitting and training unit performs data fitting and training on the selected parameter variables and historical talent demand data to establish a talent forecasting model related to agricultural product output, import volume, and export volume. During the evaluation process, various evaluation indicators and methods can be used to measure the accuracy and reliability of the model, such as root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R-squared). By comparing with actual demand, the predictive effect of the model can be understood, and necessary optimization adjustments can be made to improve the model's predictive accuracy and stability.

[0091] The significant advancements and advantages of this module are reflected in the following aspects: By establishing a talent forecasting model, it correlates the demand for agricultural science and technology personnel with factors such as agricultural product output, import volume, and export volume, revealing the intrinsic link between talent demand and agricultural industry development, and providing more comprehensive and accurate forecasts. Based on big data analysis, it uses multiple parameter variables and builds a forecasting model by fitting and training historical data, making the forecasts more credible and accurate. It allows for flexible adjustments in feature selection, data fitting, and model evaluation based on changes in agricultural science and technology personnel demand and the agricultural product market, adapting to demand forecasting in different periods and scenarios.

[0092] The talent demand forecasting and inference module 300 is used to predict the demand for agricultural science and technology talents using a talent forecasting model. It inputs data on factors such as agricultural product output, import volume, and export volume into the model to generate corresponding forecasts and inferences on the demand for agricultural science and technology talents.

[0093] This module is responsible for inputting data on factors such as agricultural product output, import volume, and export volume into a pre-established talent forecasting model to generate corresponding forecasts of agricultural science and technology talent demand. The generated demand forecasts are then evaluated and verified. By comparing them with actual demand, the differences between the predicted values ​​and actual demand are quantified, assessing the accuracy and reliability of the forecasting model.

[0094] The significant advancements and advantages of the talent demand forecasting and inference module are reflected in data-driven forecasting, forecast inference evaluation, flexibility and refined forecasting, as well as real-time performance and scalability. By leveraging big data analytics and information from multiple factors, this module improves the accuracy and reliability of forecasts. Simultaneously, through forecast inference evaluation and validation, biases and deficiencies in the forecasting model can be identified and corrected in a timely manner, providing decision-makers with reliable forecast inferences and a solid foundation. The talent demand forecasting and inference module also possesses the capability for flexible and refined forecasting, generating accurate forecast inferences based on different agricultural products and related factors. Furthermore, the module's design allows for the addition and adjustment of data inputs and forecasting models as needed to adapt to demand forecasts for different regions, different agricultural products, and different time periods.

[0095] The Results Display and Decision Support Module 400 is used to generate reports to display data and provide forecasts and inferences about the demand for agricultural science and technology personnel.

[0096] This module can merge forecasts and inferences from different time periods, different agricultural products, and different regions to form a comprehensive agricultural science and technology talent demand forecast report. The visualization report generation unit presents the integrated forecasts and inferences in an intuitive and easy-to-understand report format, providing decision-makers with forecasts and insights into agricultural science and technology talent demand.

[0097] This module aggregates and integrates forecasts from different time periods, agricultural products, and regions to generate a comprehensive report on the demand for agricultural science and technology personnel. This integrated approach allows decision-makers to clearly understand the overall trends and changes in talent demand. Through data visualization technology, these forecasts are transformed into charts, images, and other visual formats, enabling decision-makers to more intuitively understand and analyze the changing trends in agricultural science and technology personnel demand. This method of generating visual reports allows decision-makers to quickly obtain key information and make appropriate decisions.

[0098] Figure 7 The structural block diagram of the agricultural talent data summarization module provided in the embodiments of the present invention is as follows: Figure 7 As shown, the agricultural talent data summarization module specifically includes:

[0099] Agricultural science and technology personnel data collection unit 110 is used to collect and summarize agricultural science and technology personnel data;

[0100] The agricultural product big data collection unit 120 is used to collect and summarize big data on agricultural product output, import volume, and export volume.

[0101] Figure 8 The structural block diagram of the training module for the demand model provided in the embodiments of the present invention is as follows: Figure 8 As shown, the requirement model establishment and training module specifically includes:

[0102] The parameter variable selection unit 210 is used to derive parameter variables based on the relationship between the demand for agricultural science and technology personnel and factors such as agricultural product output, import volume, and export volume.

[0103] The data fitting and training unit 220 is used to perform data fitting and training on selected parameter variables and historical talent demand data. It determines the model parameters by minimizing the error between the predicted and actual demand values, thereby establishing a talent demand prediction model related to factors such as agricultural product output, import volume, and export volume. The analytical formula of the talent prediction model is as follows:

[0104] Y=β0+β1χ1+β1χ1+β2χ2+...+β n χ n Where Y represents the demand for agricultural science and technology personnel, and χ1, χ2, χ n The selected features are β1, β2, β n , are the parameters of the model;

[0105] The model evaluation and optimization unit 230 is used to evaluate and optimize the established talent prediction model. By comparing the deviation between the predicted inference and the actual needs, the accuracy and reliability of the model are evaluated.

[0106] Figure 9 The structural block diagram of the talent demand prediction and inference module provided in the embodiments of the present invention is as follows: Figure 9 As shown, the talent demand prediction and inference module specifically includes:

[0107] The prediction and inference generation unit 310 is used to input agricultural product output, import volume, and export volume data into the talent prediction model to generate corresponding predictions and inferences about the demand for agricultural science and technology talents.

[0108] The forecasting and inference evaluation unit 320 is used to evaluate and verify the generated demand forecasting inferences and compare the differences between the forecast values ​​and the actual demand values.

[0109] Figure 10 The structural block diagram of the result display and decision support module provided in the embodiments of the present invention is as follows: Figure 10 As shown, the results display and decision support module specifically includes:

[0110] The integrated prediction and inference unit 410 is used to collect and integrate the agricultural science and technology talent demand predictions and inferences generated by the prediction module.

[0111] Generate visualization report unit 420 to present the predicted agricultural technology talent demand results in the form of reports.

[0112] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0113] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0114] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0115] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0116] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for predicting the demand for agricultural science and technology personnel based on big data analysis, characterized in that, The method includes: To collect and summarize data on agricultural science and technology personnel and agricultural products; To establish a talent prediction model that analyzes the correlation and trends between agricultural science and technology talent demand and agricultural product output, import volume, and export volume, the model is fitted and trained using selected features and talent demand data. The model parameters are determined by minimizing the error between the predicted value and the actual demand value over the years, thus obtaining the talent prediction model. The talent forecasting model is used to predict the demand for agricultural science and technology personnel. Data on factors such as agricultural product output, import volume, and export volume are input into the model to generate corresponding predictive inferences about the demand for agricultural science and technology personnel. Generate reports to display data and provide forecasts and inferences about the demand for agricultural science and technology personnel.

2. The method according to claim 1, characterized in that, The collection and summarization of agricultural science and technology personnel and agricultural product data specifically includes: Collect and summarize data on agricultural science and technology personnel; Collect and summarize big data on agricultural product output, import volume, and export volume.

3. The method according to claim 2, characterized in that, The establishment of a talent forecasting model to analyze the correlation and trends between the demand for agricultural science and technology personnel and agricultural product output, import volume, and export volume specifically includes: Based on the relationship between the demand for agricultural science and technology personnel and factors such as agricultural product output, import volume, and export volume, parameter variables are derived. The selected parameter variables and historical talent demand data are fitted and trained. The model parameters are determined by minimizing the error between the predicted value and the actual demand value, thereby establishing a talent demand prediction model related to factors such as agricultural product output, import volume, and export volume. The analytical formula of the talent prediction model is as follows: Y=β0+β1χ1+β1χ1+β2χ2+...+β n χ n Where Y represents the demand for agricultural science and technology personnel, and χ1, χ2, χ n The selected features are β1, β2, β n , are the parameters of the model; The established talent prediction model is evaluated and optimized. The accuracy and reliability of the model are assessed by comparing the deviation between the predicted inferences and the actual needs.

4. The method according to claim 3, characterized in that, The generation of corresponding predictions and inferences regarding the demand for agricultural science and technology personnel specifically includes: By inputting data on agricultural product output, import volume, and export volume into the talent prediction model, corresponding predictions and inferences about the demand for agricultural science and technology personnel are generated. The generated demand forecasts are evaluated and verified by comparing the differences between the forecasts and the actual demand values.

5. The method according to claim 4, characterized in that, The generated reports display data and provide forecasts and inferences about the demand for agricultural science and technology personnel, specifically including: The generated forecasts and inferences regarding the demand for agricultural science and technology personnel are collected and integrated. The predicted demand for agricultural science and technology personnel will be presented in the form of reports.

6. A system for predicting the demand for agricultural science and technology personnel based on big data analysis, characterized in that: The system includes: The agricultural talent data aggregation module is used to collect and aggregate data on agricultural science and technology personnel and agricultural products. The demand model establishment and training module is used to build a talent prediction model that analyzes the relationship and trend between the demand for agricultural science and technology talents and the output, import volume, and export volume of agricultural products. It fits and trains the selected features and talent demand data, and determines the parameters of the model by minimizing the error between the predicted value and the actual demand value over the years, thereby obtaining the talent prediction model. The talent demand forecasting and inference module is used to predict the demand for agricultural science and technology talents using a talent forecasting model. It inputs data on factors such as agricultural product output, import volume, and export volume into the model to generate corresponding forecasts and inferences on the demand for agricultural science and technology talents. The results display and decision support module is used to generate reports to display data and provide forecasts and inferences about the demand for agricultural science and technology personnel.

7. The system according to claim 6, characterized in that, The agricultural talent data summarization module specifically includes: The agricultural science and technology personnel data collection unit is used to collect and summarize agricultural science and technology personnel data. The agricultural product big data collection unit is used to collect and summarize big data on agricultural product output, import volume, and export volume.

8. The system according to claim 7, characterized in that, The demand model establishment and training module specifically includes: The parameter variable selection unit is used to derive parameter variables based on the relationship between the demand for agricultural science and technology personnel and factors such as agricultural product output, import volume, and export volume. The data fitting and training unit is used to fit and train the selected parameter variables and historical talent demand data. It determines the model parameters by minimizing the error between the predicted and actual demand values, thereby establishing a talent demand prediction model related to factors such as agricultural output, import volume, and export volume. The analytical formula for the talent prediction model is as follows: Y=β0+β1χ1+β1χ1+β2χ2+...+β n χ n Where Y represents the demand for agricultural science and technology personnel, and χ1, χ2, χ n The selected features are β1, β2, β n , are the parameters of the model; The model evaluation and optimization unit is used to evaluate and optimize the established talent prediction model. By comparing the deviation between the predicted inference and the actual needs, the accuracy and reliability of the model are assessed.

9. The system according to claim 8, characterized in that, The talent demand forecasting and inference module specifically includes: The prediction and inference generation unit is used to input agricultural product output, import volume, and export volume data into the talent prediction model to generate corresponding predictions and inferences about the demand for agricultural science and technology talents. The forecasting and inference evaluation unit is used to evaluate and verify the generated demand forecasts and inferences, and to compare the differences between the forecast values ​​and the actual demand values.

10. The system according to claim 9, characterized in that, The results display and decision support module specifically includes: The integrated forecasting and inference unit is used to aggregate and integrate the forecasts and inferences on agricultural science and technology talent demand generated by the forecasting module. Generate visual report units to present the predicted agricultural technology talent demand results in report form.

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