CN113869104A - Agricultural environment monitoring system based on big data - Google Patents
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Abstract
The invention provides an agricultural environment monitoring system based on big data, which comprises an environment sampling module, a big data cloud server and a data storage module, wherein the environment sampling module is used for collecting all-area agricultural environment data at T moment and storing the agricultural environment data to the big data cloud server; the big data matching module acquires all-region agricultural environment data of the big data cloud server and matches the all-region agricultural environment data with the sample data one by one to obtain the difference condition of the all-region agricultural environment data and the sample data, and sub-regions are divided according to the difference condition; the environment modeling module constructs a regional environment model according to agricultural environment data in the sub-region; and the quality monitoring analysis module inputs the sub-region agricultural environment data at the T +1 moment into the current region environment model to obtain the environment regulation and control instruction at the T +1 moment. The invention realizes the subarea monitoring of the whole-area agricultural environment, can carry out grading modeling prediction on the soil and crop growth conditions in real time, and improves the automation degree and the subarea control precision of farmland monitoring.
Description
Technical Field
The invention relates to the technical field of electric wires and cables, in particular to an agricultural environment monitoring system based on big data.
Background
In the prior art, the informatization technology is more and more emphasized in promoting the development of agriculture. Agricultural environment monitoring equipment collects important indexes in the agricultural production process, such as: meteorological information, soil nutrient conditions and the like, and monitoring the production conditions of all the farm works, the growth conditions of crops and the like. And a wireless control system is arranged according to requirements, and hardware equipment for irrigation, fertilization and the like is remotely controlled, so that information detection and standardized production monitoring of an agricultural base are realized. And continuous and reliable base agricultural environment parameters are provided for an agricultural expert consultation system and agricultural big data analysis. The system helps a user to know the land irrigation condition, the crop growth condition, the pest and disease damage condition, the agricultural environment condition and the like, and finally realizes the whole-process monitoring and disaster early warning of agricultural product production.
The current information-based agricultural ecological environment monitoring experience usually adopts large-area unified management and control, but neglects a subarea management and control strategy for ecological environments of different crops or the same crop planting area, so that the current monitoring means cannot accurately and pertinently irrigate or fertilize or improve the air environment, and the problems of low monitoring level and unscientific management exist.
Therefore, how to provide a big data-based agricultural environment monitoring system with partition management and control capability is a problem that needs to be solved urgently by those skilled in the art.
Disclosure of Invention
In view of the above, the invention provides an agricultural environment monitoring system based on big data, which implements environmental quality grade prediction on data in a region and updates the region by acquiring big data of a whole region agricultural environment and establishing a region environment model according to sample matching, thereby realizing real-time and targeted agricultural environment region monitoring.
In order to achieve the purpose, the invention adopts the following technical scheme:
an agricultural environment monitoring system based on big data comprises an environment sampling module, a big data matching module, an environment modeling module and a quality monitoring analysis module; wherein the content of the first and second substances,
the environment sampling module is used for collecting all-area agricultural environment data at the time T and storing the agricultural environment data to the big data cloud server;
the big data matching module acquires all-region agricultural environment data of the big data cloud server and matches the all-region agricultural environment data with sample data one by one to acquire the difference condition of the all-region agricultural environment data and the sample data, and sub-regions are divided according to the difference condition;
the environment modeling module constructs a regional environment model according to agricultural environment data in the sub-region;
and the quality monitoring analysis module inputs the sub-region agricultural environment data acquired by the environment sampling module in real time at the moment T +1 into the current region environment model to obtain an environment regulation and control instruction at the moment T + 1.
Preferably, the environment acquisition module classifies according to attributes of agricultural environment data and stores the agricultural environment data to the big data cloud server according to the classification; the agricultural environment data comprises soil quality data and crop remote sensing images.
Preferably, the sample data is classified according to the attributes, and N agricultural environment data of the same category are matched and compared with the sample data to obtain N difference values; and dividing the subareas according to the difference interval formed by the N difference values.
Preferably, the classifying the crop remote sensing image comprises: and judging the optimal classification time phase combination of the remote sensing images by using J-M distance separability according to the sample image information, and classifying crops in the remote sensing images by calculating the difference between each type of sample image information and the remote sensing images.
Preferably, the regional environment model adopts a neural network learning method, the neural network inputs agricultural environment data in the sub-region and outputs the agricultural environment data in the current region as the environment quality grade, and different environment quality grades correspond to different environment regulation and control instructions.
Preferably, the environment sampling module includes a plurality of sampling points distributed in the whole area, and the quality monitoring analysis module redistributes sub-areas to the sampling points according to the environment quality level of the current sampling point at the T +1 th moment.
Preferably, the classified soil quality data collected by the sampling points are sent to a sink node, the sink node sinks the received soil quality data and sends the soil quality data to the quality monitoring and analyzing module, and the quality monitoring and analyzing module performs deletion detection on the soil quality data and performs data filling on the detected deletion sequence; inputting the filled agricultural environment data into a current regional environment model; the filling mode comprises averaging value processing.
Preferably, the classified crop remote sensing images collected by the sampling points are sent to a sink node, the sink node sinks the received crop remote sensing images and sends the crop remote sensing images to the quality monitoring analysis module, and the quality monitoring analysis module carries out noise point detection on soil quality data and carries out elimination processing on the detected noise points; and inputting the agricultural environment data with the noise points removed into the current region environment model.
Through the technical scheme, compared with the prior art, the invention has the beneficial effects that:
based on big data technology, the invention realizes the regional monitoring of the whole-region agricultural environment, can carry out graded modeling prediction on the growth condition of soil and crops in real time, and improves the automation degree and the regional control precision of farmland monitoring. The invention can also divide the sampling points into real-time areas according to the real-time change condition of agricultural data, namely, the real-time adjustment of the environment regulation and control instruction is carried out according to the environmental condition of the sampling points at the current moment, thereby reducing the burden of artificial regulation and control. The invention realizes the subarea management and control strategy for the ecological environment of different crops or the same crop planting area, is convenient for accurately and pertinently irrigating or fertilizing the agricultural environment or improving the air environment, effectively improves the monitoring level and has more scientific management on the urban agricultural ecological environment.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts;
FIG. 1 is a block diagram of a big data based agricultural environmental monitoring system according to an embodiment of the present invention;
fig. 2 is a work flow chart of an agricultural environment monitoring system based on big data according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The agricultural environment monitoring system based on big data disclosed by the embodiment needs to be supported by a big data storage technology provided by a cloud server, and the technical requirements for collecting and processing regional agricultural environment data are met. The system comprises an environment sampling module, a big data matching module, an environment modeling module and a quality monitoring and analyzing module; the environment sampling module collects all-area agricultural environment data at the time T and stores the agricultural environment data to the big data cloud server; the big data matching module acquires all-region agricultural environment data of the big data cloud server and matches the all-region agricultural environment data with the sample data one by one to obtain the difference condition of the all-region agricultural environment data and the sample data, and sub-regions are divided according to the difference condition; the environment modeling module constructs a regional environment model according to agricultural environment data in the sub-region; and the quality monitoring analysis module inputs the sub-region agricultural environment data acquired in real time by the T +1 moment environment sampling module into the current region environment model to obtain the environment regulation and control instruction at the T +1 moment.
The execution process of the embodiment is as follows:
the environment sampling module firstly plays a modeling data acquisition function, and performs all-around acquisition aiming at agricultural environment data in the whole area, wherein the whole area is determined according to the requirement of a technical implementation range. The collected data is current agricultural environment data.
The sample data is pre-stored standard agricultural environment data, the current agricultural environment data and the standard agricultural environment data are matched and compared, the difference interval is divided by the compared difference value, and the difference interval in which the difference value of the current agricultural environment data is located is judged, so that the sub-area to which the difference value belongs is determined. All sub-areas constitute a full area.
And constructing a region environment model for each sub-region, wherein the region environment model is used for predicting the environment quality represented by the agricultural environment data in the current sub-region.
And the environment sampling module continuously collects new all-area agricultural environment data in real time, judges which sub-area belongs to according to the geographical position of the collection point, inputs the new agricultural environment data collected by the corresponding collection point into the corresponding area environment model, acquires the real-time environment quality of the collection point, and calls an environment regulation and control instruction according to the quality grade.
In one embodiment, the environment acquisition module classifies the agricultural environment data according to the attributes of the agricultural environment data and stores the agricultural environment data to the big data cloud server according to the classification; the agricultural environment data comprises soil quality data and crop remote sensing images.
In this embodiment, the attributes include the content of different elements in the soil, the sudden humidity, the soil temperature, the pixel values of different crops, the pixel values of different growth states of the same crop, and the like.
In one embodiment, sample data is classified according to attributes, and N agricultural environment data of the same category are matched and compared with the sample data to obtain N difference values; and dividing the subareas according to the difference interval formed by the N difference values.
As will be understood by those skilled in the art, before classification according to attributes, quantization processing is performed on each attribute value to further calculate a difference value.
In one embodiment, the classifying the remote sensing images of the crops comprises: and judging the optimal classification time phase combination of the remote sensing images by using J-M distance separability according to the sample image information, and classifying crops in the remote sensing images by calculating the difference between each type of sample image information and the remote sensing images.
In one embodiment, the regional environment model adopts a neural network learning method, the neural network inputs agricultural environment data in a subregion and outputs the agricultural environment data in the current region to the environmental quality grade, and different environmental quality grades correspond to different environmental regulation and control instructions.
In one embodiment, the environment sampling module comprises a plurality of sampling points distributed in the whole area, and the quality monitoring analysis module redistributes the sub-areas for the sampling points according to the environment quality level of the current sampling point at the T +1 th moment.
In the embodiment, the area environment model outputs the environment quality level of the current sampling point in the current sub-area, and if the sampling point is in the first environment quality level, the sampling point is divided into the sub-areas in the previous-level difference interval; and if the sampling point is in the last environmental quality level, dividing the sampling point into sub-areas of the next-level difference interval. Real-time sub-area division enables the system to adjust the regulation and control instruction for different time states of different sampling points in real time.
The regulation instructions comprise irrigation or fertilization or air environment improvement strategy instructions.
In one embodiment, classified soil quality data collected by sampling points are sent to a sink node, the sink node sinks the received soil quality data and sends the soil quality data to a quality monitoring and analyzing module, and the quality monitoring and analyzing module performs deletion detection on the soil quality data and performs data filling on a detected deletion sequence; inputting the filled agricultural environment data into a current regional environment model; the filling mode comprises averaging value processing.
In one embodiment, classified crop remote sensing images collected by sampling points are sent to a sink node, the sink node collects the received crop remote sensing images and sends the crop remote sensing images to a quality monitoring analysis module, and the quality monitoring analysis module carries out noise point detection on soil quality data and carries out elimination processing on the detected noise points; and inputting the agricultural environment data with the noise points removed into the current region environment model.
In this embodiment, the noise influence of the remote sensing image is eliminated by the thermal noise removal method, and the thermal noise-removed image is generated.
According to the method, the large data of the whole regional agricultural environment are collected, a regional environment model is established according to sample matching, environmental quality grade prediction is carried out on the data in the region, the region is updated, and real-time and targeted monitoring of the agricultural environment region is achieved.
The agricultural environment monitoring system based on big data provided by the invention is described in detail, a specific example is applied in the system to explain the principle and the implementation mode of the invention, and the description of the embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended 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 (8)
1. An agricultural environment monitoring system based on big data is characterized by comprising an environment sampling module, a big data matching module, an environment modeling module and a quality monitoring analysis module; wherein the content of the first and second substances,
the environment sampling module is used for collecting all-area agricultural environment data at the time T and storing the agricultural environment data to the big data cloud server;
the big data matching module acquires all-region agricultural environment data of the big data cloud server and matches the all-region agricultural environment data with sample data one by one to acquire the difference condition of the all-region agricultural environment data and the sample data, and sub-regions are divided according to the difference condition;
the environment modeling module constructs a regional environment model according to agricultural environment data in the sub-region;
and the quality monitoring analysis module inputs the sub-region agricultural environment data acquired by the environment sampling module in real time at the moment T +1 into the current region environment model to obtain an environment regulation and control instruction at the moment T + 1.
2. The big data based agricultural environment monitoring system according to claim 1, wherein the environment collection module classifies according to attributes of agricultural environment data and stores the classified attributes to the big data cloud server; the agricultural environment data comprises soil quality data and crop remote sensing images.
3. The big data based agricultural environment monitoring system according to claim 2, wherein the sample data is classified according to the attributes, and N agricultural environment data of the same category and the sample data are matched and compared to obtain N difference values; and dividing the subareas according to the difference interval formed by the N difference values.
4. The big data based agricultural environmental monitoring system of claim 2, wherein the classifying the crop remote sensing images comprises: and judging the optimal classification time phase combination of the remote sensing images by using J-M distance separability according to the sample image information, and classifying crops in the remote sensing images by calculating the difference between each type of sample image information and the remote sensing images.
5. The agricultural environment monitoring system based on big data as claimed in claim 1, wherein the regional environment model adopts a neural network learning method, the neural network inputs agricultural environment data in a sub-region, and outputs environmental quality grades to which the current regional agricultural environment data belongs, and different environmental quality grades correspond to different environmental regulation and control instructions.
6. The big data based agricultural environment monitoring system according to claim 5, wherein the environment sampling module comprises a plurality of sampling points distributed in the whole area, and the quality monitoring analysis module redistributes sub-areas for the sampling points according to the environment quality level of the current sampling points at the T +1 th moment.
7. The big data based agricultural environment monitoring system according to claim 2, wherein classified soil quality data collected by sampling points are sent to a sink node, the sink node sinks the received soil quality data and sends the soil quality data to the quality monitoring and analyzing module, and the quality monitoring and analyzing module performs missing detection on the soil quality data and performs data filling on a detected missing sequence; inputting the filled agricultural environment data into a current regional environment model; the filling mode comprises averaging value processing.
8. The big data based agricultural environment monitoring system according to claim 2, wherein the classified crop remote sensing images collected by the sampling points are sent to the sink node, the sink node sinks the received crop remote sensing images and sends the received crop remote sensing images to the quality monitoring analysis module, and the quality monitoring analysis module detects noise points of soil quality data and eliminates the detected noise points; and inputting the agricultural environment data with the noise points removed into the current region environment model.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114486764A (en) * | 2022-01-26 | 2022-05-13 | 安徽新宇环保科技股份有限公司 | Agricultural non-point source pollution monitoring system based on full-spectrum water quality analyzer |
| CN115389738A (en) * | 2022-05-25 | 2022-11-25 | 赣州数源科技有限公司 | Intelligent agricultural integrated information system environment control device |
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| CN114486764A (en) * | 2022-01-26 | 2022-05-13 | 安徽新宇环保科技股份有限公司 | Agricultural non-point source pollution monitoring system based on full-spectrum water quality analyzer |
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| CN115389738A (en) * | 2022-05-25 | 2022-11-25 | 赣州数源科技有限公司 | Intelligent agricultural integrated information system environment control device |
| CN115456479A (en) * | 2022-10-21 | 2022-12-09 | 河南经贸职业学院 | Wisdom green house environmental monitoring system based on thing networking |
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