Soil fertilizer science teaching training system based on intelligent big data

Abstract

The invention discloses a soil fertilizer science teaching training system based on intelligent big data, which is applied to the technical field of teaching instruments and comprises a multi-modal data acquisition module used for acquiring multi-modal data in a teaching test area in real time; the cloud server big data platform is internally provided with a data receiving and storing unit, a teaching case library and a prediction model for soil fertilizer science teaching analysis, and is used for feeding back corresponding results according to data query and model analysis requests of the teaching terminal; the plurality of teaching terminals are used for sending data query and model analysis requests to the cloud server big data platform and visually displaying feedback results; wherein the model analysis request is sent based on initial soil conditions preset by teachers and fertilization scheme parameters set by students. According to the invention, data real-time, case quantification and teaching interaction are realized, the authenticity, intuition and efficiency of soil fertilizer science teaching are effectively improved, and the system is suitable for teaching practical training of related courses in agricultural colleges and universities.

Description

Technical Field

[0001] This invention relates to the field of teaching instrument technology, and more specifically to a soil and fertilizer science teaching and training system based on intelligent big data. Background Technology

[0002] Soil and fertilizer science is a core course in agricultural colleges, and its teaching focuses on enabling students to understand the complex relationship between soil properties, fertilizer characteristics, and crop growth. Traditional teaching methods rely primarily on theoretical lectures and limited laboratory chemical analysis, which has the following significant drawbacks: (1) Data lag: The laboratory chemical analysis process is cumbersome and time-consuming, and students cannot obtain data feedback in a timely manner, making it difficult to establish dynamic data-driven thinking.

[0003] (2) Time and space limitations: It is difficult for students to access a large number of cases from different regions, soil types and planting patterns in a short period of time, thus limiting their knowledge.

[0004] (3) Poor interactivity: The teaching method is mainly one-way indoctrination, lacking an interactive platform that can stimulate students' interest, support independent exploration and virtual simulation.

[0005] (4) Theory is disconnected from reality: Traditional teaching makes it difficult to intuitively connect theoretical data with actual fertilizer effects in the field, resulting in unsatisfactory teaching results.

[0006] With the development of IoT and big data technologies, some environmental monitoring equipment has emerged on the market, but most of them focus on agricultural production site management and lack an integrated system that is specifically designed for teaching scenarios and can integrate real data, historical case data and virtual simulation functions.

[0007] Therefore, how to overcome the shortcomings of existing technologies and provide a teaching and training system for soil and fertilizer science based on intelligent big data, which can realize real-time data collection, cloud-based intelligent analysis and visual interaction on teaching terminals, and effectively improve the timeliness, intuitiveness and interactivity of teaching, is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0008] In view of this, the present invention provides a soil and fertilizer science teaching and training system based on intelligent big data. It aims to solve the problems of outdated data, limited time and space, poor interactivity, and disconnect between theory and practice in traditional soil and fertilizer science teaching. It achieves real-time data processing, a vast amount of case studies, and interactive teaching, effectively improving the authenticity, intuitiveness, and efficiency of soil and fertilizer science teaching. It is suitable for teaching and training in related courses in agricultural colleges.

[0009] To achieve the above objectives, the present invention adopts the following technical solution: A teaching and training system for soil and fertilizer science based on intelligent big data includes: a multimodal data acquisition module, a cloud server big data platform, and multiple teaching terminals; The multimodal data acquisition module is used to collect multimodal data in the teaching and experimental area in real time and transmit it to the cloud server big data platform. The cloud server big data platform has a built-in data receiving and storage unit, a teaching case library, and a predictive model for soil and fertilizer science teaching analysis, which is used to provide corresponding results based on data queries and model analysis requests from teaching terminals. The teaching terminal is used to send data query and model analysis requests to the cloud server big data platform and to visualize the feedback results; the model analysis request is issued based on the initial soil conditions preset by the teacher and the fertilization plan parameters set by the student.

[0010] Optional, the multimodal data acquisition module includes: a sensor group, an image acquisition device, and a user input terminal; The sensor array is used to collect soil physicochemical parameters and environmental parameters in the teaching and experimental area in real time. Image acquisition equipment is used to acquire images and video data of crop growth status within the teaching and experimental area; The user input terminal is used to manually enter fertilization records, crop variety information, and experimental management data for the teaching and experimental areas.

[0011] Optionally, the data receiving and storage unit is used to receive and store multimodal data as well as pre-imported historical teaching case data under different regions, soil types, crop varieties and fertilization schemes.

[0012] Optional predictive models for teaching analysis in soil and fertilizer science include: soil fertility evaluation models, fertilizer recommendation models, and crop growth prediction models.

[0013] Optional data query includes: data query and management of teaching experimental areas, and historical teaching case query of the teaching case library.

[0014] Optional fertilization parameters include: fertilizer type, fertilizer dosage, and fertilization cycle.

[0015] Optional visualizations include: data dashboards, soil nutrient heat maps, crop growth simulation animations, and fertilization program comparison charts.

[0016] Optionally, the cloud server big data platform is also connected to an expert knowledge base to provide theoretical explanations for the analysis results of the prediction model and to respond to Q&A requests initiated by the teaching terminal.

[0017] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a soil fertility teaching and training system based on intelligent big data, which achieves the following beneficial effects: (1) Real-time and authenticity: Data from the teaching experimental field is collected in real time through IoT sensors, transforming abstract theories into intuitive and dynamic data streams, enabling students to observe changes in soil fertility in real time, which greatly enhances the authenticity and timeliness of teaching.

[0018] (2) Massive Case Studies and Inquiry-Based Learning: The cloud-based big data platform integrates real-time data and historical case data, providing students with a huge "data sandbox" to support them in conducting independent data queries, comparative analysis and inquiry-based learning, breaking through the time and space limitations of traditional teaching.

[0019] (3) High level of interactivity and virtual simulation: The interactive software of the teaching terminal provides powerful data visualization and virtual simulation functions. Students can simulate "if-then" scenarios and intuitively see the long-term effects that different fertilization decisions may bring, thereby gaining a deep understanding of the core principles of soil fertility science.

[0020] (4) High teaching efficiency: The system integrates data collection, processing, analysis and display. Teachers can easily give lectures based on the visualization reports generated by the system, and students can access learning resources anytime and anywhere through the terminal, which significantly improves the efficiency of teaching and learning. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of the system structure provided by the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Example 1: Embodiment 1 of this invention discloses a teaching and training system for soil and fertilizer science based on intelligent big data, such as... Figure 1 As shown, it includes: a multimodal data acquisition module, a cloud server big data platform, and multiple teaching terminals; The multimodal data acquisition module is used to collect multimodal data in the teaching and experimental area in real time and transmit it to the cloud server big data platform.

[0025] The multimodal data acquisition module is deployed in the teaching and experimental area and includes: sensor group, image acquisition equipment and user input terminal; The sensor group is used to collect soil physicochemical parameters and environmental parameters in the teaching and experimental area in real time. The sensor group includes: soil moisture sensor, soil pH sensor, soil EC value sensor, as well as environmental temperature and humidity sensor and light intensity sensor. Image acquisition equipment is used to acquire image and video data of crop growth status within the teaching and experimental area. The image acquisition equipment includes: a high-definition camera and a drone. When the image acquisition equipment is a high-definition camera, it is fixedly installed on a bracket in the teaching and experimental area, with the lens facing the crop growth area. When the image acquisition equipment is a drone, it can cruise and photograph the teaching and experimental area according to a preset flight path. The user input terminal is used to manually enter fertilization records, crop variety information, and experimental management data for the teaching and experimental areas.

[0026] The cloud server big data platform has built-in data receiving and storage units, a teaching case library, and a predictive model for soil and fertilizer science teaching analysis, which is used to provide corresponding results based on data queries and model analysis requests from teaching terminals.

[0027] The teaching case database stores historical teaching case data from different regions, soil types, crop varieties, and fertilization schemes.

[0028] The data receiving and storage unit is used to receive and store multimodal data as well as pre-imported historical teaching case data under different regions, soil types, crop varieties and fertilization schemes.

[0029] Predictive models used for teaching and analysis in soil and fertilizer science include: soil fertility evaluation models, fertilizer recommendation models, and crop growth prediction models.

[0030] Data query includes: data query and management of teaching experimental areas, and historical teaching case query of the teaching case database.

[0031] The teaching terminal is a computer or mobile smart device, used to send data query and model analysis requests to the cloud server big data platform and to visualize the feedback results; among them, the model analysis request is issued based on the initial soil conditions preset by the teacher and the fertilization plan parameters set by the student.

[0032] Fertilization program parameters include: fertilizer type, fertilizer dosage, and fertilization cycle.

[0033] Visual displays include: data dashboards, soil nutrient heat maps, crop growth simulation animations, and comparison charts of fertilization programs.

[0034] The cloud server big data platform is also connected to an expert knowledge base, which stores theoretical knowledge of soil and fertilizer science, answers to frequently asked questions, and experimental guidance plans. This is used to provide theoretical explanations for the analysis results of the prediction model and to respond to Q&A requests initiated by the teaching terminal.

[0035] Example 2: Embodiment 2 of the present invention discloses a specific application of a soil fertility teaching and training system based on intelligent big data, as follows: The multimodal data acquisition module of this invention is deployed in the school's smart agriculture teaching greenhouse. Sensor arrays (including soil three-parameter sensors and environmental sensors) are buried in experimental fields in different zones, collecting data in real time and uploading it to a cloud server via a Wi-Fi network. Students manage different experimental fields in groups, recording each fertilization and management operation through user input terminals (such as tablets).

[0036] The cloud server big data platform cleans, stores, and analyzes the received data. When students log in to the interactive teaching software on their teaching terminals (such as computers in the computer lab or personal tablets), they can select the experimental field they manage and view the real-time soil data dashboard; they can also query the teaching case library to compare the nutrient differences between the brown soil of the North China Plain and the red soil of the South.

[0037] In theoretical lessons, teachers use the virtual fertilization simulation module on the teaching terminal. The teacher sets initial soil conditions (such as a low-nitrogen state), and then students work in groups to design fertilization plans and input the parameters into the system. The big data analysis and processing unit, based on a built-in crop growth model, quickly simulates changes in crop growth and leaf color over the next 30 days, displaying the results in animated charts. Through this gamified interaction, students gain a deeper understanding of the role of nitrogen fertilizer in crop growth and the risks of over-fertilization.

[0038] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0039] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A smart big data-based soil fertilization teaching and training system, characterized in that, The utility model relates to a soil and fertilizer science teaching system based on multi-modal data acquisition and cloud server big data platform, which comprises a multi-modal data acquisition module, a cloud server big data platform and a plurality of teaching terminals. The multi-modal data acquisition module is used for collecting multi-modal data in a teaching experiment area in real time and transmitting the data to the cloud server big data platform. The cloud server big data platform is internally provided with a data receiving and storing unit, a teaching case library and a prediction model for soil and fertilizer science teaching analysis, and is used for feeding back corresponding results according to data query and model analysis requests of the teaching terminals. The teaching terminal is used for sending data query and model analysis requests to the cloud server big data platform and visually displaying feedback results, wherein the model analysis request is issued based on a teacher's preset soil initial condition and a student's set fertilization scheme parameter. The multi-modal data acquisition module comprises a sensor group, an image acquisition device and a user input end. 2.The soil fertility teaching and training system based on intelligent big data according to claim 1, wherein The sensor group is used for collecting soil physicochemical parameters and environmental parameters of the teaching experiment area in real time. The image acquisition device is used for acquiring image and video data of the crop growth state in the teaching experiment area. The user input end is used for manually inputting fertilization records, crop variety information and experiment management data of the teaching experiment area. The data receiving and storing unit is used for receiving and storing the multi-modal data and pre-imported historical teaching case data under different regions, different soil types, different crop varieties and different fertilization schemes. 3.The soil fertility teaching and training system based on intelligent big data according to claim 1, characterized in that, The prediction model for soil and fertilizer science teaching analysis comprises a soil fertility evaluation model, a fertilizer recommendation model and a crop growth prediction model.

4. The intelligent big data-based soil fertilization teaching and training system according to claim 1, characterized in that, The data query comprises data query and management of the teaching experiment area and historical teaching case query of the teaching case library.

5. The intelligent big data-based soil fertilization teaching and training system according to claim 1, characterized in that, The fertilization scheme parameter comprises fertilization type, fertilization dose and fertilization cycle. 6.The soil fertility teaching and training system based on intelligent big data according to claim 1, characterized in that, The visual display comprises a data dashboard, a soil nutrient heat map, a crop growth simulation animation and a fertilization scheme comparison chart. 7.The soil fertility teaching and training system based on intelligent big data according to claim 1, wherein, The cloud server big data platform is further connected with an expert knowledge base, which is used for providing theoretical explanation for analysis results of the prediction model and responding to question-answering requests initiated by the teaching terminal. 8.The soil fertility teaching and training system based on intelligent big data according to claim 1, characterized in that, ​

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