Agricultural Internet Big Data Dual Engine Approach for Multi-Intelligent Greenhouse Clusters

Abstract

The present invention discloses an agricultural Internet big data dual-engine method for a multi-intelligent greenhouse cluster, and relates to the technical field of agricultural big data. The present invention comprises a terminal control module, a data aggregation module, a first execution processing module, a second execution processing module, a data storage module, a manual task module and a condition judgment module. The terminal control module performs terminal control of a big data dual-engine operation algorithm. The data aggregation module integrates the agricultural Internet big data of the multi-intelligent greenhouse cluster and sends it to the execution processing module for data processing. After the data processing is completed, the data storage module is used to store agricultural data, and the manual task module is connected to perform operation assistance or verification according to operation requirements. The agricultural Internet big data dual-engine algorithm of the present invention has a higher degree of processing completion of the agricultural Internet big data by setting two groups of execution processing modules, and realizes classification processing, and improves data processing accuracy and processing efficiency.

Description

Technical Field

[0001] The present invention relates to the field of agricultural big data technology, and in particular to an agricultural Internet big data dual-engine method for a multi-intelligent greenhouse cluster. Background Art

[0002] Agricultural intelligent greenhouse, usually referred to as multi-span greenhouse or modern greenhouse, is an advanced type of facility agriculture with a comprehensive environmental control system. The system can directly adjust indoor temperature, light, water, fertilizer, air and other factors, and can achieve high yields and steady fine vegetables and flowers throughout the year, with good economic benefits. In the cluster development of agricultural intelligent greenhouses, it is necessary to match and integrate the corresponding agricultural Internet big data;

[0003] At present, there is a lack of corresponding computing engine algorithms for agricultural Internet big data of multi-intelligent greenhouse clusters, the processing of agricultural big data is insufficient, and the efficiency and accuracy are poor. In addition, there is a lack of appropriate intervention in the calculation and processing of agricultural big data, and the operational controllability needs to be strengthened. To this end, we propose a dual-engine method for agricultural Internet big data of multi-intelligent greenhouse clusters. Summary of the invention

[0004] The main purpose of the present invention is to provide an agricultural Internet big data dual-engine method for a multi-intelligent greenhouse cluster to solve the problems raised in the above background.

[0005] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is: a dual-engine method for agricultural Internet big data of a multi-intelligent greenhouse cluster, including a terminal control module, a data aggregation module, a first execution processing module, a second execution processing module, a data storage module, a manual task module and a condition judgment module. The terminal control module performs terminal control of the big data dual-engine operation algorithm. The data aggregation module integrates the agricultural Internet big data of the multi-intelligent greenhouse cluster and sends it to the first execution processing module and the second execution processing module for data processing. After the data processing is completed, the data storage module is used to store agricultural data, and the manual task module is connected to perform operation assistance or verification according to operation requirements. The condition judgment module serves as an analysis-guiding medium for agricultural Internet big data, and guides the data flow to an adapted processing end.

[0006] When the data aggregation module is in operation, it first determines the completion of agricultural Internet data collection. When the data collection is completed, it performs the next step of target execution. The aggregated data of the data aggregation module includes environmental data and image data of the greenhouse cluster.

[0007] The environmental data collected in the data aggregation module is acquired in real time by sensors through a wired or wireless network, and users can read the data anytime and anywhere through their mobile phones. The image data is viewed in real time through the mobile phone, and the production process is remotely monitored. The cloud billiard camera is rotated, zoomed and photographed through the mobile platform, providing a channel for experts to remotely diagnose plant conditions.

[0008] The first execution processing module includes a computer and an interpreter, and the computer is electrically connected to the interpreter. The computer serves as a program operation carrier of the execution processing module. When the interpreter is executed, an identifier is read from the source code of the program each time. When the interpreter reads a PRINT, it will print the characters after the PRINT. When a GOSUB is read, the specified subroutine is executed. This process will be repeated before reaching the end of the program. The second execution processing module stores various preset test engines. After receiving the test case data and software and hardware data, the corresponding preset test engine is called to execute the test script to obtain the test results.

[0009] The data storage module includes a computer memory, which includes two groups of storage terminals, one group of storage terminals is used to store standard or conventional data, that is, data normally generated during the operation of the agricultural Internet, and the other group of storage terminals is used to store abnormal data. The storage terminal for abnormal data is communicatively connected to the manual task module. The storage capacity of the storage terminal for standard or conventional data is 1T, and the storage capacity of the storage terminal for abnormal data is 0.5T.

[0010] The manual task module includes further processing of the abnormal data of the storage terminal. The manual task module is communicated with the terminal control module and the execution processing module respectively. Relying on the terminal control module, the stored abnormal data is substituted into the execution processing module for further processing, and the whole process is monitored.

[0011] During the execution of the conditional judgment module, the terminal control module is first used to formulate judgment rules and divide the data execution processing channels, and then logical calculations are performed on whether each subsequent path meets the flow conditions, and data flow is achieved based on the calculation of the rule engine.

[0012] The terminal control module also includes system warning and remote control. In the system warning, agricultural technicians can view warning information in real time and set warning thresholds through the mobile Internet platform. In the remote control state, the environmental conditions in the greenhouse and farmland can be accurately understood through the mobile Internet platform, and the greenhouse roller blinds, fans, humidifiers, air conditioners and water pump devices can be controlled accordingly to achieve the best growth environment for plants or a manually set environment.

[0013] The present invention has the following beneficial effects:

[0014] The agricultural Internet big data dual-engine method for a multi-intelligent greenhouse cluster provided by the present invention has a higher degree of processing completion for the agricultural Internet big data by setting two groups of execution processing modules, and realizes classification processing, thereby improving data processing accuracy and processing efficiency.

[0015] The agricultural Internet big data dual-engine method for multiple intelligent greenhouse clusters provided by the present invention utilizes the collaborative working mode of intelligent test engine processing and manual intervention to make the intelligent construction of agricultural greenhouse clusters more convenient and controllable, and has strong operational adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is an operational flow chart of the agricultural Internet big data dual-engine method for the multi-intelligent greenhouse cluster of the present invention. DETAILED DESCRIPTION

[0017] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0018] Please refer to Figure 1 As shown: The agricultural Internet big data dual-engine method of a multi-intelligent greenhouse cluster includes a terminal control module, a data aggregation module, a first execution processing module, a second execution processing module, a data storage module, a manual task module and a condition judgment module. The terminal control module performs terminal control of the big data dual-engine operation algorithm. The data aggregation module integrates the agricultural Internet big data of the multi-intelligent greenhouse cluster and sends it to the first execution processing module and the second execution processing module for data processing. After the data processing is completed, the data storage module is used to store agricultural data, and the manual task module is connected to perform operation assistance or verification according to operation requirements. The condition judgment module serves as an analysis-guiding medium for the agricultural Internet big data and guides the data flow to the adapted processing end.

[0019] When the data aggregation module is operated, the completion degree of agricultural Internet data collection is first determined. When the data collection is completed, the next target execution is carried out. The aggregated data of the data aggregation module includes environmental data and image data of the greenhouse cluster; the collection of environmental data in the data aggregation module obtains the environmental data collected by the sensor in real time through a wired or wireless network, and the user can read it anytime and anywhere through a mobile phone to realize the real-time monitoring function of environmental data. The environmental data section can also realize the viewing of historical data. The historical data is mainly based on the greenhouse and area number to query the historical data of a certain time period at the location. The time period can be set and adjusted by itself. At the same time, the data can be switched to a table mode more intuitively, and the recent trend of each environmental parameter can be viewed in the form of a trend chart; the image data can be viewed through the mobile phone for real-time image data, and the production process can be remotely monitored. The mobile platform can rotate, zoom and take pictures of the cloud table camera to provide a channel for experts to remotely diagnose plant conditions;

[0020] The first execution processing module includes a computer and an interpreter, the computer is electrically connected to the interpreter, and the computer serves as a program operation carrier of the execution processing module. When the interpreter executes, an identifier is read from the source code of the program each time. When the interpreter reads a PRINT, it will print the characters after the PRINT. When a GOSUB is read, the specified subroutine is executed. This process will be repeated before reaching the end of the program. The second execution processing module stores various preset test engines. After receiving the test case data and the software and hardware data, the corresponding preset test engine is called to execute the test script to obtain the test results; the data storage module includes a computer memory, and the computer memory includes two groups of storage terminals, one of which is used to store standard or conventional data, that is, data normally generated during the operation of the agricultural Internet, and the other is used to store abnormal data. The storage terminal for abnormal data is connected to the manual task module for communication. The storage capacity of the storage terminal for standard or conventional data is 1T, and the storage capacity of the storage terminal for abnormal data is 0.5T.

[0021] The manual task module includes further processing of abnormal data stored in the terminal. The manual task module communicates with the terminal control module and the execution processing module respectively. Relying on the terminal control module, the stored abnormal data is substituted into the execution processing module for further processing, and the whole process is monitored. During the execution of the condition judgment module, the terminal control module is first used to formulate judgment rules and divide the data execution processing channels. Then, logical calculations are performed on whether each subsequent path meets the flow conditions, and data flow is achieved according to the calculation of the rule engine. The terminal control module also includes system warning and remote control. In the system warning, agricultural technicians can view warning information in real time and set warning thresholds through the mobile Internet platform. Under remote control, the mobile Internet platform can accurately understand the environmental conditions in the greenhouse and farmland, and control the roller blinds, fans, humidifiers, air conditioners and water pump devices in the greenhouse accordingly to achieve the best growth environment for plants or artificially set environments. It also includes weather forecasts. The mobile Internet platform can connect to the local meteorological data of the park and display it on the platform homepage. Agricultural personnel can plan agricultural production arrangements based on weather forecast information to guide production more scientifically.

[0022] In this solution, agricultural big data refers to the practice of big data concepts, technologies and methods in agriculture. Agricultural big data involves various links such as arable land, sowing, fertilization, pest control, harvesting, storage, breeding, etc. It is cross-industry, cross-professional and cross-business data analysis and mining, as well as data visualization, including agricultural environment and resource big data, agricultural production big data, agricultural market and agricultural management big data;

[0023] The application of agricultural Internet big data in multi-intelligent greenhouse clusters includes predicting market demand. Farmers can use the consumer demand reports collected by the big data platform to conduct market analysis in advance, plan production in advance, and reduce production risks; agricultural environmental monitoring, through agricultural big data, can detect the production environment of crops through sensors to perceive the production of crops, collect various index data in the crop growth environment, and then put these collected data into local or cloud data centers, so as to analyze the historical data and real-time monitoring data of agricultural production, and improve the correlation monitoring capabilities of crop planting area, production progress, agricultural product output, weather conditions, temperature conditions, disaster intensity, and soil moisture;

[0024] Agricultural products are traceable. Agricultural product producers, suppliers and transporters use IoT sensor technology, scanning equipment and analysis tools to monitor and collect relevant data from the supply chain. For example, the quality of agricultural products during the production and transportation process can be monitored in real time through sensors with GPS functions, which helps prevent foodborne diseases and reduce supply chain waste, ensuring food safety from the source; driving the agricultural industry chain, using ground observation, sensors and GPRS information technology, etc., to strengthen the collection, aggregation and correlation analysis of agricultural production environment, production facilities and animal and plant body perception data, improve the intelligent monitoring system for agricultural production progress, strengthen real-time monitoring and analysis of agricultural data, and improve data support capabilities for agricultural production management, command and dispatch.

[0025] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. The agricultural Internet big data dual-engine method of multiple intelligent greenhouse clusters is characterized by: It includes a terminal control module, a data aggregation module, a first execution processing module, a second execution processing module, a data storage module, a manual task module and a condition judgment module. The terminal control module performs terminal control of the big data dual-engine operation algorithm. The data aggregation module integrates the agricultural Internet big data of multiple intelligent greenhouse clusters and sends them to the first execution processing module and the second execution processing module for data processing. After the data processing is completed, the data storage module is used to store agricultural data, and the manual task module is connected to the manual task module for operation assistance or verification according to operation requirements. The condition judgment module serves as an analysis and guidance medium for agricultural Internet big data, and guides the data flow to the adapted processing end. The first execution processing module includes a computer and an interpreter. The computer is electrically connected to the interpreter. The computer serves as the program operation carrier of the execution processing module. When the interpreter is executed, an identifier is read from the source code of the program each time. When the interpreter reads a PRINT, it will print the characters after the PRINT. When a GOSUB is read, the specified subroutine is executed. This process is repeated before reaching the end of the program. The second execution processing module stores various preset test engines. After receiving the test case data and software and hardware data, the corresponding preset test engine is called to execute the test script to obtain the test results. During the execution of the condition judgment module, the terminal control module is first used to formulate judgment rules and divide the data execution processing channels. Then, logical calculations are performed on whether each subsequent path meets the flow conditions, and data flow is achieved according to the calculation of the rule engine.

2. The agricultural Internet big data dual-engine method of multiple intelligent greenhouse clusters according to claim 1 is characterized by: When the data aggregation module is in operation, it first determines the completion of agricultural Internet data collection. When the data collection is completed, it performs the next step of target execution. The aggregated data of the data aggregation module includes environmental data and image data of the greenhouse cluster.

3. The agricultural Internet big data dual-engine method of multiple intelligent greenhouse clusters according to claim 2 is characterized by: The environmental data collected in the data aggregation module is acquired in real time by sensors through a wired or wireless network, and users can read the data anytime and anywhere through their mobile phones. The image data is viewed in real time through the mobile phone, and the production process is remotely monitored. The cloud billiard camera is rotated, zoomed and photographed through the mobile platform, providing a channel for experts to remotely diagnose plant conditions.

4. The agricultural Internet big data dual-engine method of multiple intelligent greenhouse clusters according to claim 1 is characterized by: The data storage module includes a computer memory, which includes two groups of storage terminals, one group of storage terminals is used to store standard or conventional data, that is, data normally generated during the operation of the agricultural Internet, and the other group of storage terminals is used to store abnormal data. The storage terminal for abnormal data is communicatively connected to the manual task module. The storage capacity of the storage terminal for standard or conventional data is 1T, and the storage capacity of the storage terminal for abnormal data is 0.5T.

5. The agricultural Internet big data dual-engine method of multiple intelligent greenhouse clusters according to claim 4 is characterized in that: The manual task module includes further processing of the abnormal data of the storage terminal. The manual task module is communicated with the terminal control module and the execution processing module respectively. Relying on the terminal control module, the stored abnormal data is substituted into the execution processing module for further processing, and the whole process is monitored.

6. The agricultural Internet big data dual-engine method of multiple intelligent greenhouse clusters according to claim 5 is characterized by: The terminal control module also includes system warning and remote control. In the system warning, agricultural technicians can view warning information in real time and set warning thresholds through the mobile Internet platform. In the remote control state, the environmental conditions in the greenhouse and farmland can be accurately understood through the mobile Internet platform, and the roller shutters, fans, humidifiers, air conditioners and water pump devices in the greenhouse can be controlled accordingly to achieve the best growth environment for plants or a manually set environment.

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