CN112185090A - NB-IoT-based agricultural greenhouse remote monitoring system and method - Google Patents

NB-IoT-based agricultural greenhouse remote monitoring system and method Download PDF

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CN112185090A
CN112185090A CN202010902747.1A CN202010902747A CN112185090A CN 112185090 A CN112185090 A CN 112185090A CN 202010902747 A CN202010902747 A CN 202010902747A CN 112185090 A CN112185090 A CN 112185090A
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杨涛
赵凡
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Jiangsu University
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    • G08C17/02—Arrangements for transmitting signals characterised by the use of a wireless electrical link using a radio link
    • G—PHYSICS
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Abstract

The invention relates to an agricultural greenhouse remote monitoring system and method based on NB-IoT, wherein the system consists of a main processor, a data acquisition module, a data storage module, a power supply module, a positioning module, a wireless communication module, a cloud server and a user management terminal; the data acquisition module, the power supply module, the data storage module, the positioning module and the wireless communication module are all connected with the main processor, the main processor is connected with the cloud server through the wireless communication module, and the user management terminal is connected with the cloud server and processes data of the cloud server and displays the data to a user. The invention realizes remote monitoring through NB-IoT wireless communication, has the characteristics of low cost, high efficiency, intellectualization and the like, is convenient to deploy and monitor, and also solves the redundancy of environmental data and the bandwidth pressure.

Description

NB-IoT-based agricultural greenhouse remote monitoring system and method
Technical Field
The invention relates to the field of intelligent agriculture and wireless communication, in particular to an NB-IoT-based agricultural greenhouse remote monitoring system.
Background
Agricultural production in China is developing towards intellectualization, and necessary information acquisition and monitoring are needed to be carried out on the growth environment of crops in order to improve the yield and the quality of the crops. The traditional environmental information collection operation is mainly completed manually. The method consumes manpower and material resources, has low efficiency and cannot ensure real-time performance. With the development of information technology, an environment monitoring system based on modern communication technology is considered to be used for monitoring information among farmlands, so that the purposes of deep analysis, timely management and accurate control are achieved.
In order to make agricultural management develop in a direction of being more scientific, more intelligent and more information, various agricultural monitoring systems are in the process of being carried out. Traditional green house and monitoring system adopt wired communication mode to collect sensor data to the terminal more, and not only the wiring is complicated, be not convenient for use, and the cost is higher moreover, later maintenance difficulty, and this kind of monitoring system will progressively eliminate. Some agricultural monitoring systems of relative intellectuality adopt wireless communication modes such as WIFI, Zigbee, GPRS, report to remote management platform with various environmental data that influence crop growth that the sensor was gathered, carry out analysis management, but this kind of communication mode has some drawbacks: the power consumption of the equipment is high, the wireless communication distance is short, the communication connection is unstable, and a large amount of original data occupies the bandwidth. To sum up, the current intelligent agricultural greenhouse urgently needs to solve the difficulties of high cost, difficult maintenance, high power consumption, short transmission distance, large occupied bandwidth and the like.
Disclosure of Invention
The invention aims to optimize the defects of the current intelligent agricultural greenhouse monitoring system and provides an NB-IoT-based agricultural greenhouse remote monitoring system.
The technical scheme of the invention is as follows: an agricultural greenhouse remote monitoring system based on NB-IoT is composed of a main processor, a data acquisition module, a data storage module, a power supply module, a positioning module, a wireless communication module, a cloud server and a user management terminal; the data acquisition module, the power supply module, the data storage module, the positioning module and the wireless communication module are all connected with a main processor, the main processor is connected with a cloud server through the wireless communication module, and the user management terminal is connected with and processes data of the cloud server and displays the data to a user;
the data acquisition module comprises an atmospheric temperature and humidity sensor and a soil temperature and humidity sensor and is used for monitoring environmental data in the agricultural greenhouse and sending the data to the main processor, the main processor firstly carries out filtering processing on single sensor data and then carries out data fusion processing on the same type of sensors, the processed data are sent to the cloud server through the NB-IoT wireless communication module, the data storage module is used for storing original data acquired by the data acquisition module and facilitating later-stage analysis of the environment of the agricultural greenhouse, the positioning module comprises a GPS positioning chip and is used for positioning the position of the monitoring system and sending the position information to the main processor, and the main processor sends the position information to the cloud server through the wireless communication module and displays the position information on a user management end. Further, the host processor model is STM32L 476.
Further, the data acquisition module comprises an ambient light sensor which is a photodiode, an atmospheric temperature and humidity sensor SHT20, a soil temperature sensor DS18B20 and a capacitance type soil humidity sensor SEN 0193.
Further, the data storage module adopts Micro SD; the power supply module comprises a solar panel and a lithium battery; the positioning module is a Chinese micro ATGM 336H; the model of the wireless communication module is remote BC95-B5, and the wireless communication module adopts a special SIM card for the telecom Internet of things.
The invention discloses an agricultural greenhouse remote monitoring method based on NB-IoT, which comprises the following steps:
firstly, initializing the whole system, including software initialization and hardware peripheral initialization, then respectively acquiring atmospheric temperature and humidity and soil temperature and humidity, storing original data to an SD card, sequentially performing Kalman filtering and adaptive weighting fusion on the original data, judging the current day and night through an ambient light sensor value, and setting a working mode of a wireless communication module: if the data is in the daytime, the wireless module is placed in an Active mode, processed data is formatted in JSON and sent to a cloud server; if the data is night, the wireless module is put in a PSM mode, and whether the environmental data triggers a preset threshold value is judged again: if not, the module continues to sleep; if the data is triggered, the wireless communication module is awakened, the data is formatted and sent to the cloud server, the client receives the data and displays the data on the client management panel, and a user can make corresponding decisions according to the data. The specific process of sequentially performing Kalman filtering and adaptive weighting fusion on the original data comprises the following steps:
the Kalman filtering is mainly used for fusing low-level dynamic redundant environment data; in thatIn the Kalman filtering algorithm, firstly, a system state equation and a system measurement value are introduced into a discrete linear system, and the system state equation and the system measurement value are respectively as follows: xk＝AXk-1+BUk+Wk，Zk＝HXk+VkWherein X iskIs a system state variable at the moment k; xk-1Is a system state variable at the k-1 moment; u shapekThe control quantity of the system at the moment k; A. b is a system parameter; wkIs the system process noise; vkMeasuring noise for the system; zkThe measured value of the system at the k moment is obtained; h is a measurement system parameter, and for a multi-measurement system, H is a matrix; wkAnd VkThe white Gaussian noise in the measurement process is assumed, and the covariance of the white Gaussian noise is Q and R respectively and does not change along with the change of the system state; for the agricultural condition monitoring system, the main environmental factors are temperature and humidity, Kalman filtering algorithm is respectively carried out on temperature and humidity data, the temperature of the current state is assumed to be the same as the temperature of the previous state, so A is 1, and U is not controlled by a control quantitykWhen the value is equal to 0, the operation formula is obtained as
Figure BDA0002658210650000031
Wherein
Figure BDA0002658210650000032
Is the result of prediction using the last state;
Figure BDA0002658210650000033
the result is the optimal result of the last state;
Figure BDA0002658210650000034
is that
Figure BDA0002658210650000035
A corresponding covariance;
Figure BDA0002658210650000036
is that
Figure BDA0002658210650000037
A corresponding covariance; q is the covariance of the system process, pairIn the current agricultural condition monitoring system, the measured environmental factors take a single variable atmospheric temperature as an example so that H is 1, and a single model single measurement value so that I is 1, and the obtained operation formula is as follows:
Figure BDA0002658210650000038
wherein Kg iskFor Kalman Gain (Kalman Gain), the formula is:
Figure BDA0002658210650000039
updating the estimate under the current state (k)
Figure BDA00026582106500000310
The covariance of (a) is:
Figure BDA00026582106500000311
is that
Figure BDA00026582106500000312
A corresponding covariance;
Figure BDA00026582106500000313
is that
Figure BDA00026582106500000322
And the corresponding covariance is measured continuously, recursion is carried out in sequence, and the weight is optimized continuously, so that the calculation result is closer to the real measurement result.
The adopted environment data fusion algorithm is an adaptive weighted fusion algorithm and is used for weighting and fusing a plurality of sensor data of the same type distributed in different areas into one data, and in the adaptive weighted fusion algorithm, corresponding weights w are defined for the sensors under different environmentsiPreprocessing the data x of the sensoriMultiplying with corresponding weight, and adding the equal data to obtain a fusion value
Figure BDA00026582106500000314
For the system, soil humidity data of n areas needs to be monitored in a certain agricultural environment, and the variance of soil humidity sensors in each areaIs composed of
Figure BDA00026582106500000315
The preprocessed data is xiIndependent of each other, each sensor corresponding to a weight wiFusing the values according to an adaptive weighted average algorithm
Figure BDA00026582106500000316
And the weight value wiRespectively satisfy the formula:
Figure BDA00026582106500000317
variance σ2Satisfies the formula:
Figure BDA00026582106500000318
Figure BDA00026582106500000319
because the sensor installation area is relatively far away, the data x after the sensor pretreatment can be usediConsidered independent of each other and is an unbiased estimate of x, so the formula is satisfied: e [ (x-x)i)(x-xj)]0(i ≠ j); so sigma2Can be written as:
Figure BDA00026582106500000320
and (3) obtaining an extreme value according to the multivariate function, wherein when the variance is minimum, the optimal weight value corresponding to each sensor is as follows:
Figure BDA00026582106500000321
after the data are processed according to the self-adaptive weighting fusion algorithm, a plurality of data of the same type of sensors are fused into one data, and the closer the weighted and fused data are to the real environmental data, the more the real agricultural condition can be reflected to the upper layer.
The invention has the beneficial effects that: the agricultural greenhouse remote monitoring system based on the NB-IoT uses the low-power-consumption processor, can be externally connected with a lithium battery for supplying power, and is particularly suitable for agricultural long-time monitoring because the solar panel stores electricity through the lithium battery; various sensors are adopted, so that the environment information is acquired more comprehensively; NB-IoT wireless communication is adopted, so that the power consumption is low and the transmission distance is long; the whole system is low in cost, high in efficiency, intelligent, convenient to deploy and monitor, and capable of solving the problems of redundancy of environmental data, insufficient bandwidth and the like.
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Fig. 1 is an overall hardware block diagram of a system according to an embodiment of the present invention.
Fig. 2 is a flow chart of the operation of the system of an embodiment of the present invention.
Detailed Description
As shown in fig. 1, in the embodiment of the present invention, the remote monitoring system for an agricultural greenhouse based on NB-IoT mainly includes a data acquisition terminal, a wireless communication network, and a user management terminal, where the acquisition terminal includes a main processor, an ambient light sensor, an atmospheric temperature and humidity sensor, a soil temperature sensor, a soil humidity sensor, a power module, a positioning module, a data storage module, and a wireless communication module, the wireless communication network includes a wireless communication gateway and an internet of things cloud server, and the user management terminal includes a user side server and a system control panel.
The invention provides an NB-IoT (NB-IoT) -based agricultural greenhouse remote monitoring system which is used for monitoring the growth environment of crops in an agricultural greenhouse and comprises a main processor, a data acquisition module, a data storage module, a positioning module, a wireless communication module, a cloud server and a user management terminal, wherein the data acquisition module, the data storage module, the positioning module and the wireless communication module are all connected with the main processor, the main processor is connected with the cloud server through the wireless communication module, and a user terminal server acquires environmental data from the cloud server and displays the environmental data to a user. The sensors are distributed in the area to be monitored and used for monitoring the crop growth environment and transmitting data to the main controller. The main processor carries out a preprocessing algorithm and a data fusion algorithm on data collected by soil temperature and humidity sensors distributed in the agricultural greenhouse; around the clock is judged through the ambient light sensor, and the working mode of the whole system is divided into two types: an upper layer decision mode and a lower layer decision mode.
The upper layer decision mode refers to: the ambient light sensor judges that the wireless communication module is in the daytime at that time, and the wireless communication module is placed in a working state, so that all functions of the wireless communication module are normally available, and data can be sent and received. And uploading the conclusion data subjected to the preprocessing algorithm and the data fusion algorithm to a cloud server through a wireless communication module, and simultaneously storing the original data to a data storage module. The client server downloads the environment data from the cloud platform database and displays the real-time agricultural environment information to the system user on the system control panel, and the user can make corresponding decisions according to the environment information to ensure that crops are in a good growing environment.
The underlying decision mode refers to: the wireless communication module is placed in a sleep state when the ambient light sensor judges that the wireless communication module is in a night state, the power consumption of the wireless communication module is extremely low, only the RTC works and is in a network non-connection state, the paging message is not received any more, but the module can be awakened through an AT instruction or awakened through overtime of a timer. At the moment, the original data are stored in the data storage module, the processed environmental data are not directly uploaded to the cloud platform, and whether the wireless communication module is triggered to enter a connection state or not is determined by judging whether a preset threshold value is triggered or not in the main processor, so that the data receiving and sending times of the wireless communication module are reduced, and the purpose of reducing power consumption at night is achieved.
The model of the main processor is STM32L476, and the processor has the advantages of low power consumption, suitability for monitoring environmental data for a long time, processing of original data and JSON formatting of the processed data.
The model of the atmospheric temperature and humidity sensor is SHT20, the atmospheric temperature and humidity sensor is particularly suitable for a low-power-consumption small-volume system, is arranged in a greenhouse space, communicates with a main processor by using an I2C protocol, and is used for monitoring the atmospheric temperature and humidity in an agricultural greenhouse;
the soil temperature sensor model is DS18B20, adopts the stainless steel encapsulation, prevents that soil from corroding to rust, increase of service life. The system is arranged in soil in the greenhouse, communicates with the main processor by using an One Wire protocol and is used for monitoring the temperature of the soil in the agricultural greenhouse.
The capacitance type soil humidity sensor is SEN0193, is different from a common resistance type sensor, adopts capacitance to sense soil humidity, and the surface is coated with insulating paint, avoids corroding with soil direct contact, increase of service life. The system is arranged in soil in the greenhouse, communicates with the main processor by using an One Wire protocol and is used for monitoring the soil humidity in the agricultural greenhouse.
The data storage module adopts a Micro SD, communicates with the main processor through an SDIO protocol, and adopts a FATFS file system to store original environment data to a Micro SD card, so that the source tracing of the original environment data in the later period is facilitated.
The positioning module is a Chinese micro ATGM336H model, is communicated with a main processor through a UART protocol, supports multi-system combined positioning of any combination, has positioning accuracy of 2.5m, is used for positioning the position of the system, and is convenient for managing a plurality of greenhouses and a plurality of sets of systems.
The wireless communication module is in a model of remote BC95-B5, is controlled by an enhanced AT command set, is communicated with the main processor through a UART protocol, and is used for receiving JSON formatted monitoring data and uploading the monitoring data to the cloud server; the wireless communication module adopts a special SIM card for the telecommunication internet of things, communicates with a cloud server through a cellular network, and is used for the cellular network to identify the identity of the module.
The ambient light sensor is connected with the STM32L476 through the ADC and used for judging day and night of the current monitoring environment, the remote BC95-B5 wireless communication module is respectively set to be an Active mode and a PSM (Power Saving mode) mode through a judgment result, and the Active mode and the PSM (Power Saving mode) mode respectively correspond to an upper layer decision mode and a bottom layer decision mode of the system, so that the purpose of reducing power consumption is achieved.
A plurality of soil temperature sensors DS18B20 are connected to an IO port of a main processor in a parallel mode, IDs are obtained by inquiring a sensor ROM, and the soil temperatures in different areas are distinguished by matching the sensor IDs, so that distributed temperature monitoring is achieved.
A plurality of soil moisture sensors SEN0193 are connected to a plurality of IO ports of the main processor, AD conversion is carried out on the analog signals, and soil moisture in different areas is distinguished by matching different IO ports, so that distributed moisture monitoring is realized.
The main processor processes data collected by the sensors, and respectively performs Kalman filtering algorithm on atmospheric temperature and atmospheric humidity data sent by atmospheric temperature and humidity sensors to eliminate Gaussian noise in the sensor collection process, and respectively performs adaptive weighting fusion algorithm on soil temperature data collected by a plurality of soil temperature sensors and soil humidity data collected by soil humidity sensors to correspond to environmental differences of different monitoring areas in the greenhouse.
As shown in fig. 1, in the embodiment of the present invention, the atmospheric temperature and humidity sensor SHT20 communicates with the main processor through an I2C protocol, the multiple soil temperature sensors DS18B20 are connected in parallel, the data line potentials are pulled up by using pull-up resistors, and communicate with the main processor through an One Wire protocol, and the multiple soil humidity sensors SEN0193 communicate with multiple IO ports of the main processor through the One Wire protocol; the positioning module CEMICRO ATGM336H communicates with the main processor through UART protocol; the data storage module Micro SD is communicated with the main processor through an SDIO protocol; the NB-IoT wireless communication module remote BC95-B5 communicates with a main processor through a UART protocol; the devices are powered by a power module.
As shown in fig. 2, in the embodiment of the present invention, the overall operation flow of the system is as follows: firstly, initializing the whole system, including software initialization and hardware peripheral initialization, then respectively acquiring atmospheric temperature and humidity and soil temperature and humidity, storing original data to an SD card, sequentially performing Kalman filtering and adaptive weighting fusion on the original data, judging the current day and night through an ambient light sensor value, and setting a working mode of a wireless communication module: if the data is in the daytime, the wireless module is placed in an Active mode, processed data is formatted in JSON and sent to a cloud server; if the data is night, the wireless module is put in a PSM mode, and whether the environmental data triggers a preset threshold value is judged again: if not, the module continues to sleep; if the trigger is triggered, the wireless communication module is awakened, and then data is formatted and sent to the cloud server. The client receives the data and displays the data on the user management panel, and the user can make corresponding decisions according to the data.
In the embodiment of the invention, the adopted environment data preprocessing algorithm is a Kalman filtering algorithm, is mainly used for fusing low-level dynamic redundant environment data, can effectively reduce the general deviation of agricultural environment data, and particularly has a better filtering effect on white Gaussian noise.
In the Kalman filtering algorithm, firstly, a system state equation and a system measurement value are introduced into a discrete linear system, and the system state equation and the system measurement value are respectively as follows: xk＝AXk-1+BUk+Wk，Zk＝HXk+VkWherein X iskIs a system state variable at the moment k; xk-1Is a system state variable at the k-1 moment; u shapekThe control quantity of the system at the moment k; A. b is a system parameter; wkIs the system process noise; vkMeasuring noise for the system; zkThe measured value of the system at the k moment is obtained; h is a measurement system parameter, and for a multi-measurement system, H is a matrix; wkAnd VkThe white Gaussian noise in the measurement process is assumed, and the covariance of the white Gaussian noise is Q and R respectively and does not change along with the change of the system state; for the agricultural condition monitoring system, the main environmental factors are temperature and humidity, and a Kalman filtering algorithm is respectively carried out on temperature and humidity data, wherein the atmospheric temperature is taken as an example: assume that the current state temperature is the same as the previous state temperature so that a equals 1, there is no control quantity so UkWhen the value is equal to 0, the operation formula is obtained as
Figure BDA0002658210650000061
Wherein
Figure BDA0002658210650000062
Is the result of prediction using the last state;
Figure BDA0002658210650000063
the result is the optimal result of the last state;
Figure BDA0002658210650000064
is that
Figure BDA0002658210650000065
A corresponding covariance;
Figure BDA0002658210650000066
is that
Figure BDA0002658210650000067
A corresponding covariance; q is the covariance of the system process. For the current agricultural condition monitoring system, the measured environmental factors take a single variable atmospheric temperature as an example so that H is 1, and a single model single measurement value so that I is 1, and the obtained operation formula is as follows:
Figure BDA0002658210650000071
wherein Kg iskFor Kalman Gain (Kalman Gain), the formula is:
Figure BDA0002658210650000072
updating the estimate under the current state (k)
Figure BDA0002658210650000073
The covariance of (a) is:
Figure BDA0002658210650000074
is that
Figure BDA0002658210650000075
A corresponding covariance;
Figure BDA0002658210650000076
is that
Figure BDA0002658210650000077
The corresponding covariance. Through continuous measurement, sequential recursion and continuous weight optimization, the calculation result is closer to the real measurement result.
In the embodiment of the invention, the adopted environment data fusion algorithm is a self-adaptive weighted fusion algorithm and is used for weighting and fusing data of a plurality of sensors of the same type distributed in different areas into one data, so that the wireless communication bandwidth pressure is reduced, the macroscopic data of the whole dynamic monitoring environment is acquired, and a decision maker can make optimal judgment conveniently. The algorithm multiplies the same kind of data in different areas by respective weights, and adds the data to obtain a fusion value. The self-adaptation is embodied in the selection of the weight, and the optimal weight corresponding to each sensor when the variance is minimum is determined through the variance of data of a plurality of sensors.
In the self-adaptive weighting fusion algorithm, corresponding weights w are defined for the sensors under different environmentsiPreprocessing the data x of the sensoriMultiplying with corresponding weight, and adding the equal data to obtain a fusion value
Figure BDA0002658210650000078
For the agricultural condition system, taking soil temperature as an example, soil humidity data of n areas need to be monitored in a certain agricultural environment, and the variance of a soil humidity sensor in each area is
Figure BDA0002658210650000079
The preprocessed data is xiIndependent of each other, each sensor corresponding to a weight wiFusing the values according to an adaptive weighted average algorithm
Figure BDA00026582106500000710
And the weight value wiRespectively satisfy the formula:
Figure BDA00026582106500000711
Figure BDA00026582106500000716
variance σ2Satisfies the formula:
Figure BDA00026582106500000712
Figure BDA00026582106500000713
because the sensor installation area is relatively far away, the data x after the sensor pretreatment can be usediConsidered independent of each other and is an unbiased estimate of x, so the formula is satisfied: e [ (x-x)i)(x-xj)]0(i ≠ j); so sigma2Can be written as:
Figure BDA00026582106500000714
and (3) obtaining an extreme value according to the multivariate function, wherein when the variance is minimum, the optimal weight value corresponding to each sensor is as follows:
Figure BDA00026582106500000715
after the data are processed according to the self-adaptive weighting fusion algorithm, a plurality of data of the same type of sensors are fused into one data. The closer the weighted and fused data is to the real environmental data, the more the real agricultural condition can be reflected to the upper layer, the data support is provided for the subsequent judgment and decision making, and the convenience is provided for agricultural production.
In the embodiment of the invention, the NB-IoT wireless communication module is moved to a remote BC95-B5, and the SIM card is detected, the network is registered, the signal strength is acquired, the data is sent, the switching between the Active mode and the PSM mode is carried out and the like through the initialization of the enhanced AT command control module. The module has compact size, high reliability and low operation power consumption, meets the environmental monitoring requirement in a greenhouse under a complex environment, and provides perfect data transmission service.
In the embodiment of the invention, the remote monitoring of the environmental data of the agricultural greenhouse is realized, the hardware cost is low, the operation efficiency is high, the data processing is intelligent, the deployment is convenient, the monitoring is convenient, and the redundancy and the bandwidth pressure of the environmental data are solved.
In summary, the invention relates to an agricultural greenhouse remote monitoring system based on NB-IoT, which comprises a data acquisition terminal, a wireless communication network and a user management terminal; the data acquisition terminal comprises a main processor, a data acquisition module, a data storage module, a power supply module, a positioning module and a wireless communication network; the data acquisition module also comprises an ambient light sensor, an atmospheric temperature and humidity sensor, a soil temperature sensor and a soil humidity sensor; the wireless communication network comprises a telecommunication operator base station and an Internet of things cloud platform; the user management end comprises a user server and a system control center. The data acquisition module, the data storage module, the positioning module and the wireless communication module are directly connected with the main controller and are powered by the power supply module; the main controller is connected with the cloud platform server through the wireless communication module, environment data are uploaded, and the user side server acquires the environment data from the cloud platform server and displays the environment data on the system control panel. The sensors are distributed in the area to be monitored and used for monitoring real-time environment data and transmitting the data to the main controller. The main controller processes data collected by sensors distributed in the agricultural greenhouse, the processed data are connected with the cloud server through the wireless communication module and send the original data to the storage module, the client server downloads environmental data from the cloud platform database and displays real-time environmental information of a monitoring area to a system user, and the user can take corresponding measures according to the environmental information to guarantee that crops are in a good growing environment. The invention realizes remote monitoring through NB-IoT wireless communication, has the characteristics of low cost, high efficiency, intellectualization and the like, is convenient to deploy and monitor, and also solves the redundancy of environmental data and the bandwidth pressure.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples" or the like mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims (6)

1. An agricultural greenhouse remote monitoring system based on NB-IoT is characterized by comprising a main processor, a data acquisition module, a data storage module, a power supply module, a positioning module, a wireless communication module, a cloud server and a user management terminal; the data acquisition module, the power supply module, the data storage module, the positioning module and the wireless communication module are all connected with a main processor, the main processor is connected with a cloud server through the wireless communication module, and the user management terminal is connected with and processes data of the cloud server and displays the data to a user;
the data acquisition module comprises an atmospheric temperature and humidity sensor and a soil temperature and humidity sensor and is used for monitoring environmental data in the agricultural greenhouse and sending the data to the main processor, the main processor firstly carries out filtering processing on single sensor data and then carries out data fusion processing on the same type of sensors, the processed data are sent to the cloud server through the NB-IoT wireless communication module, the data storage module is used for storing original data acquired by the data acquisition module and facilitating later-stage analysis of the environment of the agricultural greenhouse, the positioning module comprises a GPS positioning chip and is used for positioning the position of the monitoring system and sending the position information to the main processor, and the main processor sends the position information to the cloud server through the wireless communication module and displays the position information on a user management end.
2. The NB-IoT based agricultural greenhouse remote monitoring system as recited in claim 1, wherein: the host processor model is STM32L 476.
3. The NB-IoT based agricultural greenhouse remote monitoring system as recited in claim 1, wherein: the data acquisition module comprises an ambient light sensor which is a photosensitive diode, an atmospheric temperature and humidity sensor SHT20, a soil temperature sensor DS18B20 and a capacitance type soil humidity sensor SEN 0193.
4. The NB-IoT based agricultural greenhouse remote monitoring system as recited in claim 1, wherein: the data storage module adopts Micro SD; the power supply module comprises a solar panel and a lithium battery; the positioning module is a Chinese micro ATGM 336H; the model of the wireless communication module is remote BC95-B5, and the wireless communication module adopts a special SIM card for the telecom Internet of things.
5. An NB-IoT-based agricultural greenhouse remote monitoring method is characterized by comprising the following steps:
firstly, initializing the whole system, including software initialization and hardware peripheral initialization, then respectively acquiring atmospheric temperature and humidity and soil temperature and humidity, storing original data to an SD card, sequentially performing Kalman filtering and adaptive weighting fusion on the original data, judging the current day and night through an ambient light sensor value, and setting a working mode of a wireless communication module: if the data is in the daytime, the wireless module is placed in an Active mode, processed data is formatted in JSON and sent to a cloud server; if the data is night, the wireless module is put in a PSM mode, and whether the environmental data triggers a preset threshold value is judged again: if not, the module continues to sleep; if the data is triggered, the wireless communication module is awakened, the data is formatted and sent to the cloud server, the client receives the data and displays the data on the client management panel, and a user can make corresponding decisions according to the data.
6. The NB-IoT based agricultural greenhouse remote monitoring method as claimed in claim 5, wherein the specific processes of Kalman filtering and adaptive weighting fusion to the original data in turn are as follows:
the Kalman filtering is mainly used for fusing low-level dynamic redundant environment data; in the Kalman filtering algorithm, firstly, a system state equation and a system measurement value are introduced into a discrete linear system, and the system state equation and the system measurement value are respectively as follows: xk＝AXk-1+BUk+Wk，Zk＝HXk+VkWherein X iskIs a system state variable at the moment k; xk-1Is a system state variable at the k-1 moment; u shapekThe control quantity of the system at the moment k; A. b is a system parameter; wkIs the system process noise; vkMeasuring noise for the system; zkThe measured value of the system at the k moment is obtained; h is a measurement system parameter, and for a multi-measurement system, H is a matrix; wkAnd VkThe white Gaussian noise in the measurement process is assumed, and the covariance of the white Gaussian noise is Q and R respectively and does not change along with the change of the system state(ii) a For the agricultural condition monitoring system, the main environmental factors are temperature and humidity, Kalman filtering algorithm is respectively carried out on temperature and humidity data, the temperature of the current state is assumed to be the same as the temperature of the previous state, so A is 1, and U is not controlled by a control quantitykWhen the value is equal to 0, the operation formula is obtained as
Figure FDA0002658210640000021
Wherein
Figure FDA0002658210640000022
Is the result of prediction using the last state;
Figure FDA0002658210640000023
the result is the optimal result of the last state;
Figure FDA0002658210640000024
is that
Figure FDA0002658210640000025
A corresponding covariance;
Figure FDA0002658210640000026
is that
Figure FDA0002658210640000027
A corresponding covariance; q is the covariance of the system process, for the current agricultural condition monitoring system, the measured environmental factors take the single variable atmospheric temperature as an example so that H is 1, the single model single measurement value so that I is 1, and the obtained operation formula is:
Figure FDA0002658210640000028
wherein Kg iskFor Kalman Gain (Kalman Gain), the formula is:
Figure FDA0002658210640000029
updating the estimate under the current state (k)
Figure FDA00026582106400000210
The covariance of (a) is:
Figure FDA00026582106400000211
Figure FDA00026582106400000212
is that
Figure FDA00026582106400000213
A corresponding covariance;
Figure FDA00026582106400000214
is that
Figure FDA00026582106400000215
And the corresponding covariance is measured continuously, recursion is carried out in sequence, and the weight is optimized continuously, so that the calculation result is closer to the real measurement result.
The adopted environment data fusion algorithm is an adaptive weighted fusion algorithm and is used for weighting and fusing a plurality of sensor data of the same type distributed in different areas into one data, and in the adaptive weighted fusion algorithm, corresponding weights w are defined for the sensors under different environmentsiPreprocessing the data x of the sensoriMultiplying with corresponding weight, and adding the equal data to obtain a fusion value
Figure FDA00026582106400000216
For the system, soil humidity data of n areas needs to be monitored in a certain agricultural environment, and the variance of a soil humidity sensor in each area is
Figure FDA00026582106400000217
The preprocessed data is xiIndependent of each other, each sensor corresponding to a weight wiFusing the values according to an adaptive weighted average algorithm
Figure FDA00026582106400000218
And the weight value wiRespectively satisfy the formula:
Figure FDA00026582106400000219
variance σ2Satisfies the formula:
Figure FDA00026582106400000220
Figure FDA0002658210640000031
because the sensor installation area is relatively far away, the data x after the sensor pretreatment can be usediConsidered independent of each other and is an unbiased estimate of x, so the formula is satisfied: e [ (x-x)i)(x-xj)]0(i ≠ j); so sigma2Can be written as:
Figure FDA0002658210640000032
and (3) obtaining an extreme value according to the multivariate function, wherein when the variance is minimum, the optimal weight value corresponding to each sensor is as follows:
Figure FDA0002658210640000033
after the data are processed according to the self-adaptive weighting fusion algorithm, a plurality of data of the same type of sensors are fused into one data, and the closer the weighted and fused data are to the real environmental data, the more the real agricultural condition can be reflected to the upper layer.
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