阅读说明：本技术 基于物联网的土豆贮藏环境远程智能监控方法 (Remote intelligent monitoring method for potato storage environment based on Internet of things ) 是由 陈亮 余益军 徐时清 金尚忠 沈洋 黄帅 杨凯 徐瑞 杨家军 祝晓明 何宝元 于 2019-09-30 设计创作，主要内容包括：本发明公开了基于物联网的土豆贮藏环境远程智能监控方法,包括数据采集模块、主控器模块、通信模块；数据采集模块在土豆窖内的不同节点位置进行环境数据采集,采集的环境数据包括温度、湿度和二氧化碳浓度,数据采集模块将环境数据传输给主控器模块；主控器模块经通信模块传送至服务器数据库,以实现土豆贮藏环境的远程监控；可以通过访问服务器获得数据,了解窖中情况,这种远程智能监控方法实现了环境监测功能。并对采集数据进行了特殊的处理,使数据输出更贴合实际。且在监控基础上实现了远程调控。本发明通过物联网技术,使原有硬件与软件部分融合,形成一个有机的系统,并通过下述的环境数据处理方式,对贮藏环境做更好的监控。(The invention discloses a potato storage environment remote intelligent monitoring method based on the Internet of things, which comprises a data acquisition module, a main controller module and a communication module; the data acquisition module acquires environmental data at different node positions in the potato cellar, the acquired environmental data comprise temperature, humidity and carbon dioxide concentration, and the data acquisition module transmits the environmental data to the main controller module; the main controller module is transmitted to the server database through the communication module so as to realize remote monitoring of the potato storage environment; the data can be obtained by accessing the server to know the condition in the cellar, and the remote intelligent monitoring method realizes the environment monitoring function. And special processing is carried out on the acquired data, so that the data output is more practical. And remote regulation and control are realized on the basis of monitoring. According to the invention, the original hardware and software are partially fused to form an organic system through the technology of the Internet of things, and the storage environment is better monitored through the following environment data processing mode.)

1. The remote intelligent monitoring method for the potato storage environment based on the Internet of things is characterized by comprising the following steps: the system comprises a data acquisition module, a main controller module and a communication module; the data acquisition module acquires environmental data at different node positions in the potato cellar, the acquired environmental data comprise temperature, humidity and carbon dioxide concentration, and the data acquisition module transmits the environmental data to the main controller module; the main controller module is transmitted to the server database through the communication module so as to realize remote monitoring of the potato storage environment;

because there is the interact between the three kinds of different environmental parameters of temperature, humidity, carbon dioxide concentration, the data of the three factors of temperature, humidity and carbon dioxide concentration that the master controller module adopted the multi-sensor data fusion algorithm in to the potato cellar for storing things carries out the integrated processing, makes output data more close to after actual conditions again upload to the server database, specifically includes following step:

(1) preprocessing the data, and calculating the basic probability value, the trust degree and the similarity of each element;

setting basic probability assignment function m_i: xi as a recognition framework, so the basic probability assignment function: xi²→[0,1]And satisfies the condition that m (xi) ═ 0, Σ_a(Ξm(A)＝1；

② the trust function Y (A): xi is the recognition frame, function Y:2^Ξ→[0,1]Let the trust function Y (φ) be 1, for each proof of n

A₁,A₂,……,A_nThen Y (A)₁,A₂,A₃,A₄,A₅)≥(-1)^1+IY(U_i(IA_i) The relationship between the trust degree and the basic probability assignment function is as follows: y (A) ≧ Sigma_B(Am(B)＝1；

(iii) likelihood functions pls (a) 1-y (a);

(2) applying a D-S combination rule to obtain all values of all elements based on the comprehensive action;

combining the probability assignment functions:

(3) Because the environment standard of the proper potato is that the environment temperature is 2-4 ℃, the environment humidity is kept at 80-90% RH, and the carbon dioxide concentration is kept at 1-6%; taking the maximum trust degree and the plausibility as the results after data fusion processing, and selecting the best result;

A₁temperature, humidity and carbon dioxide concentration are appropriate;

A₂temperature is high;

A₃high temperature and low humidity;

A₄high temperature, low humidity, high carbon dioxide concentration;

A₅humidity is low;

A₆temperature is low and humidity is high;

A₇temperature, humidity, and carbon dioxide concentration are low.

2. The remote intelligent monitoring method for the potato storage environment based on the internet of things as claimed in claim 1, wherein the method comprises the following steps: the remote intelligent control system also comprises a remote intelligent control module, wherein the remote intelligent control module comprises an alarm system or an automatic adjusting system;

the alarm system detects and judges whether the received data is in a safe value or not, and if the received data exceeds a specified value, the alarm system gives an alarm through a buzzer;

the automatic regulating system comprises a main controller module and regulating equipment, wherein the main controller module realizes the automatic regulation of the regulating equipment by utilizing a fuzzy control algorithm so as to ensure the proper environment for storing potatoes, and the automatic regulating system specifically comprises the following steps:

(1) setting an input variable as the temperature deviation e (t) in the cellar, changing ec (t), and setting an output variable as the working time u (t) of the relay; e (t) ═ r (t) -x (t); wherein r (t) is a temperature standard value; x (t) the actual sampling temperature in the pit; a deviation change rate ec (t) ([ e (t)) -e (t-1) ]/t; wherein e (t) is the temperature deviation value at the current sampling, e (t-1) is the temperature deviation value at the previous sampling, and t is the sampling period;

(2)quantization factor M_e＝L/X,

(3) setting the sampling period to be 20min, and recovering the temperature from the deviation value to a preset normal value for about 30min, so that the universe of the system output u (+) is [0,40 ]; the fuzzy inference conditional statement of "if A and B then C" is chosen as follows:

Me(t)＝{NB,NM,NS,ZO,PS,PM,PB}；

Nec(t)＝{NB,NM,NS,ZO,PS,PM,PB}；

Yu(t)＝{ZO,PS1,PS2,PM1,PM2,PB1,PB2}。

3. the remote intelligent monitoring method for the potato storage environment based on the internet of things as claimed in claim 2, wherein the method comprises the following steps: the adjusting device comprises a fan, a heating device, a cooling device, a buzzer alarm and a reset device.

4. The remote intelligent monitoring method for the potato storage environment based on the internet of things as claimed in claim 1, wherein the method comprises the following steps: the data acquisition module comprises a temperature and humidity sensor and a carbon dioxide concentration sensor.

5. The remote intelligent monitoring method for the potato storage environment based on the internet of things as claimed in claim 1 or 2, wherein the method comprises the following steps: the main controller module comprises a singlechip.

6. The remote intelligent monitoring method for the potato storage environment based on the internet of things as claimed in claim 1, wherein the method comprises the following steps: the communication module comprises a Zig Bee radio frequency module.

7. The remote intelligent monitoring method for the potato storage environment based on the internet of things as claimed in claim 1, wherein the method comprises the following steps: the remote control system further comprises an upper computer, and the upper computer is in remote communication with the main controller module through GPRS.

8. The remote intelligent monitoring method for the potato storage environment based on the internet of things as claimed in claim 1, wherein the method comprises the following steps: still include the camera, the camera is installed in the potato apotheca, the camera is connected with the master controller module.

9. The remote intelligent monitoring method for the potato storage environment based on the internet of things as claimed in claim 1, wherein the method comprises the following steps: the system also comprises a liquid crystal display module which is used for displaying the environmental parameters monitored in real time and the state information of each device.

Technical Field

The invention relates to the technical field of agricultural product storage, in particular to a potato storage environment remote intelligent monitoring method based on the Internet of things.

Background

The potato is the fourth major food crop, and the position of the potato is only behind wheat, corn and rice. China has mature potato planting technology, but long-time storage is needed before sale or processing and production. As the potatoes are cool in nature, the potatoes go through a dormancy stage and a germination stage after the growth is finished, and then enter a storage stage of the potatoes, wherein the storage stage is roughly divided into an early stage, a middle stage and a later stage. It is affected by ambient temperature, humidity, carbon dioxide concentration, etc. throughout the storage. If the environmental factors are not reasonably controlled, the quality of the potatoes can be directly affected and even rotten.

In the prior art, for example, patent documents 108052141a, 106403159a, 203941404U; for the monitoring data of the potatoes, the comprehensive influence of the environment, the humidity and the carbon dioxide concentration is not considered, the monitoring and the regulation are mostly respectively and independently carried out and/or regulated in the regulation process, and the three factors are not considered to be factors which supplement each other and mutually influence each other in the environment, so that the monitoring or the regulation is not scientific and rigorous.

Disclosure of Invention

The invention aims to provide a potato storage environment remote intelligent monitoring method based on the Internet of things, which can solve one or more of the technical problems.

In order to achieve the purpose, the technical scheme provided by the invention is as follows:

a potato storage environment remote intelligent monitoring method based on the Internet of things comprises a data acquisition module, a main controller module and a communication module; the data acquisition module acquires environmental data at different node positions in the potato cellar, the acquired environmental data comprise temperature, humidity and carbon dioxide concentration, and the data acquisition module transmits the environmental data to the main controller module; the main controller module is transmitted to the server database through the communication module so as to realize remote monitoring of the potato storage environment; the data can be obtained by accessing the server to know the condition in the cellar, and the remote intelligent monitoring method realizes the environment monitoring function.

According to the invention, the original hardware and software are partially fused to form an organic system through the technology of the Internet of things, and the storage environment is better monitored through the following environment data processing mode.

The invention adopts a scientific and efficient mode to collect and monitor the information stored by the potatoes in real time, and stores the information in a centralized scale and batch manner, so that the potatoes are always in the most suitable storage environment, the productivity is liberated, and the unnecessary loss is reduced. Through the method based on the internet of things technology, effective remote intelligent monitoring on the potato storage environment can be achieved.

Because there is the interact between the three kinds of different environmental parameters of temperature, humidity, carbon dioxide concentration, the data of the three factors of temperature, humidity and carbon dioxide concentration that the master controller module adopted the multi-sensor data fusion algorithm in to the potato cellar for storing things carries out the integrated processing, makes output data more close to after actual conditions again upload to the server database, specifically includes following step:

(1) preprocessing the data, and calculating the basic probability value, the trust degree and the similarity of each element;

setting a baseThe probability assignment function m_i: xi as a recognition framework, so the basic probability assignment function: xi²→[0,1]And satisfies the condition that m (xi) ═ 0, Σ_a(Ξm(A)＝1；

② the trust function Y (A): xi is the recognition frame, function Y:2^Ξ→[0,1]Let the trust function Y (φ) be 1, for each proof of n

A₁,A₂,......,A_nThen Y (A)₁,A₂,A₃,A₄,A₅)≥(-1)^1+IY(U_i(IA_i) The relationship between the trust degree and the basic probability assignment function is as follows: y (A) ≧ Sigma_B(Am(B)＝1；

(iii) likelihood functions pls (a) 1-y (a);

(2) applying a D-S combination rule to obtain all values of all elements based on the comprehensive action;

combining the probability assignment functions:

where K is the normalization constant of the D-S evidence reasoning algorithm: k is 1-sigma_{∩Ai＝φ1≤i≤n}m (ai); the identification framework in the potato system is as follows: xi ═ A₁,A₂,A₃,A₄,A₅,A₆,A₇}；

(3) Because the environment standard of the proper potato is that the environment temperature is 2-4 ℃, the environment humidity is kept at 80-90% RH, and the carbon dioxide concentration is kept at 1-6%; taking the maximum trust degree and the plausibility as the results after data fusion processing, and selecting the best result;

A₁temperature, humidity and carbon dioxide concentration are appropriate;

A₂temperature is high;

A₃high temperature and low humidity;

A₄high temperature, low humidity, high carbon dioxide concentration;

A₅humidity is low;

A₆temperature is low and humidity is high}；

A₇Temperature, humidity, and carbon dioxide concentration are low.

Further: on the basis of the data monitoring, the invention also provides an adjusting method, which mainly comprises a remote intelligent control module, wherein the remote intelligent control module comprises an alarm system or an automatic adjusting system. The two systems are switched between modes.

While in the alarm system; the alarm system detects and judges whether the received data is at a safe value, and if the received data exceeds a specified value, the alarm system gives an alarm through a buzzer.

When the potato storage device is in an automatic adjusting system, the automatic adjusting system is input into a main controller module (a PLC controller), the main controller module utilizes a fuzzy control algorithm to realize automatic adjustment of each adjusting device (such as a fan, a heating device, a humidifying device, a cooling device and the like, wherein heating, humidifying and cooling can be realized through an air conditioning system) so as to ensure a proper environment for potato storage, and the fuzzy control algorithm specifically comprises the following steps:

setting an input variable as the temperature deviation e (t) in the cellar, changing ec (t), and setting an output variable as the working time u (t) of the relay; e (t) ═ r (t) -x (t); wherein r (t) is a temperature standard value; x (t) the actual sampling temperature in the pit; a deviation change rate ec (t) ([ e (t)) -e (t-1) ]/t; wherein e (t) is the temperature deviation value at the current sampling, e (t-1) is the temperature deviation value at the previous sampling, and t is the sampling period;

quantization factor M_e＝L/X,M_ecY/N; wherein L is the maximum value of the input variable quantization discourse domain; x is the input variable domain maximum; y is the maximum value of the output variable quantization discourse domain; n is the maximum value of the output variable domain;

setting the sampling period to be 20min, and recovering the temperature from the deviation value to a preset normal value for about 30min, so that the universe of the system output u (+) is [0,40 ]; the fuzzy inference conditional statement of "if A and B then C" is chosen as follows:

Me(t)＝{NB,NM,NS,ZO,PS,PM,PB}；

Nec(t)＝{NB,NM,NS,ZO,PS,PM,PB}；

Yu(t)＝{ZO,PS1,PS2,PM1,PM2,PB1,PB2}。

further: the adjusting device comprises a fan, a heating device, a cooling device, a buzzer alarm and a reset device. These devices are all common devices in the art, and their automatic control switches are also common knowledge of those skilled in the art and will not be described in detail here.

Further: the data acquisition module comprises a temperature and humidity sensor and a carbon dioxide concentration sensor. And carrying out data detection on different nodes in the potato storage chamber.

Further: the main controller module comprises a singlechip.

Further: the communication module comprises a Zig Bee radio frequency module.

Further: the remote control system further comprises an upper computer, and the upper computer is in remote communication with the main controller module through GPRS. The GPRS module is connected with an upper computer (PC or mobile phone) by means of an Internet network, and a long-distance communication function is realized by introducing the GPRS module. The TCP protocol is selected for communication, and the singlechip can realize a series of control operations on GPRS by applying AT commands through the serial port.

Further: still include the camera, the camera is installed in the potato apotheca, the camera is connected with the master controller module. And carrying out real-time video monitoring by the camera.

Further: the system also comprises a liquid crystal display module which is used for displaying the environmental parameters monitored in real time and the state information of each device.

The invention has the technical effects that:

the invention utilizes the prior device, can more accurately monitor the storage environment of the potatoes by processing the environmental data of each node, realizes autonomous regulation by depending on the monitoring system, and has stable, simple and reliable whole system.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention.

In the drawings:

FIG. 1 is a block diagram of the overall principle of the hardware system design of the present invention.

Fig. 2 is a schematic diagram of the hardware structure of the data acquisition module of the present invention.

FIG. 3 is a flow chart of a sensor acquisition design of the present invention.

FIG. 4 is a schematic diagram of a multi-sensor data fusion algorithm process according to the present invention.

Fig. 5 is a schematic diagram of the structure of the intelligent adjustment method of the present invention.

FIG. 6 is a schematic diagram of a fuzzy control system according to the present invention.

Fig. 7 is an illustration of a frame structure of a temperature and humidity data communication protocol according to the present invention.

Fig. 8 is an illustration of the CO2 concentration data communication protocol frame structure of the present invention.

Fig. 9 is a GPRS driver flow diagram of the present invention.

Detailed Description

The present invention will now be described in detail with reference to the drawings and specific embodiments, wherein the exemplary embodiments and descriptions are provided only for the purpose of illustrating the present invention and are not to be construed as unduly limiting the invention.

It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.

As shown in fig. 1, in the present invention, a sensor and related components (e.g., ADC) form a data acquisition unit, a master controller module (PLC, data processing method and protocol, operating system, microprocessor) is a processing and monitoring unit, a network, a protocol MAC and a transceiver form a communication module, and by using the internet of things technology, the original hardware and software are technically integrated to form an organic system, which coordinates to monitor the storage environment of potatoes and adjust the storage environment, so that the monitoring is accurate and the adjustment is stable and reliable.

Fig. 2 is a schematic diagram of a hardware structure of a data acquisition module according to the present invention, in which the data acquisition module plays a role of sensing in the whole system, and mainly includes a sensor for acquiring temperature, humidity and carbon dioxide data, a main controller (single chip), a Zig Bee radio frequency module, a power module, and the like. The method comprises the steps that firstly, data in a potato cellar are collected by a sensor which is in charge of temperature, humidity and carbon dioxide concentration, meanwhile, the data are transmitted to a wireless radio frequency module after being processed by a main controller (a single chip microcomputer), and the data are continuously transmitted to the upper level (a server and an upper computer) through the wireless radio frequency module.

FIG. 3 is a flow chart of a sensor acquisition design of the present invention. The data acquisition comprises the acquisition of temperature, humidity and carbon dioxide, the single chip microcomputer enters an automatic acquisition mode, namely, the single chip microcomputer acquires one remaining time sleep within preset time, the time setting is that organic scheduling of different tasks is completed by using an extremely accurate timing interruption mechanism, the acquired data are finally stored in a flash, and can be read and clarified by inputting a specific command, for example, a temperature sensor is used, and the temperature and humidity data are extracted firstly. According to the sensor data, corresponding codes are written out, and the key of reading is the timing problem.

FIG. 4 is a schematic diagram of a multi-sensor data fusion algorithm process according to the present invention. In view of the fact that environmental factors in the potato cellar are very complex, specific conditions in each place in the cellar are different, attributes of each sensor are different, and corresponding weights of the sensors are different, data collected by different sensor nodes of the same type are different, and therefore single-factor data fusion needs to be further optimized. And the data collected by the design relate to temperature, humidity and CO₂The three environmental parameters of concentration can influence each other, and one of the parameters can be adjusted and controlled arbitrarily to cause the change of other parameters, so that the multi-factor data fusion algorithm can be further optimized for the interaction among different factors, and the reliability and the accuracy of monitoring data and automatic regulation control are enhanced.

The main controller module adopts a multi-sensor data fusion algorithm to comprehensively process data of three factors, namely temperature, humidity and carbon dioxide concentration in the potato cellar, so that output data are closer to actual conditions and then uploaded to a server database, and the method specifically comprises the following steps:

(1) preprocessing the data, and calculating the basic probability value, the trust degree and the similarity of each element;

setting basic summaryRate assignment function m_i: xi as a recognition framework, so the basic probability assignment function: xi²→[0,1]And satisfies the condition that m (xi) ═ 0, Σ_a(Ξm(A)＝1；

② the trust function Y (A): xi is the recognition frame, function Y:2^Ξ→[0,1]Let the trust function Y (φ) be 1, for each proof of n

A₁,A₂,......,A_nThen Y (A)₁,A₂,A₃,A₄,A₅)≥(-1)^1+IY(U_i(IA_i) The relationship between the trust degree and the basic probability assignment function is as follows: y (A) ≧ Sigma_B(Am(B)＝1；

(iii) likelihood functions pls (a) 1-y (a);

(2) applying a D-S combination rule to obtain all values of all elements based on the comprehensive action;

combining the probability assignment functions:

where K is the normalization constant of the D-S evidence reasoning algorithm: k is 1-sigma_{∩Ai＝φ1≤i≤n}m (ai); the identification framework in the potato system is as follows: xi ═ A₁,A₂,A₃,A₄,A₅,A₆,A₇}；

(3) Because the environment standard of the proper potato is that the environment temperature is 2-4 ℃, the environment humidity is kept at 80-90% RH, and the carbon dioxide concentration is kept at 1-6%; taking the maximum trust degree and the plausibility as the results after data fusion processing, and selecting the best result;

A₁temperature, humidity and carbon dioxide concentration are appropriate;

A₂temperature is high;

A₃high temperature and low humidity;

A₄high temperature, low humidity, high carbon dioxide concentration;

A₅humidity is low;

A₆temperature is low and humidity is high;

A₇temperature, humidity, and carbon dioxide concentration are low.

Fig. 5 is a schematic diagram of the structure of the intelligent regulation method (system) based on the monitoring method. The system mainly comprises a microprocessor (singlechip) for processing acquired data and adjusting equipment (a fan, a heating device, a cooling device, a buzzer alarm, reset and the like); the liquid crystal display module displays information such as real-time monitored environmental parameters and equipment states; real-time adjustment is performed by a fuzzy control system. Or directly switching an alarm system to alarm.

FIG. 6 is a schematic diagram of a fuzzy control system according to the present invention. The fuzzy control algorithm has an automatic information adjusting function, and the formula is as follows:

setting an input variable as the temperature deviation e (t) in the cellar, changing ec (t), and setting an output variable as the working time u (t) of the relay; e (t) ═ r (t) -x (t); wherein r (t) is a temperature standard value; x (t) the actual sampling temperature in the pit; a deviation change rate ec (t) ([ e (t)) -e (t-1) ]/t; wherein e (t) is the temperature deviation value at the current sampling, e (t-1) is the temperature deviation value at the previous sampling, and t is the sampling period;

quantization factor M_e＝L/X,M_ecY/N; wherein L is the maximum value of the input variable quantization discourse domain; x is the input variable domain maximum; y is the maximum value of the output variable quantization discourse domain; n is the maximum value of the output variable domain;

setting the sampling period to be 20min, and recovering the temperature from the deviation value to a preset normal value for about 30min, so that the universe of the system output u (+) is [0,40 ]; the fuzzy inference conditional statement of "if A and B then C" is chosen as follows:

Me(t)＝{NB,NM,NS,ZO,PS,PM,PB}；

Nec(t)＝{NB,NM,NS,ZO,PS,PM,PB}；

Yu(t)＝{ZO,PS1,PS2,PM1,PM2,PB1,PB2}。

fig. 7 is an illustration of a frame structure of a temperature and humidity data communication protocol according to the present invention. Fig. 8 is an illustration of the CO2 concentration data communication protocol frame structure of the present invention. After the ZigBee network is successfully built, the temperature, humidity and carbon dioxide sensor nodes finish data acquisition and conversion, the data are uniformly transmitted to the coordinator, and then the data are transmitted to the control center for processing through serial port communication. It is now desirable that the coordinator can distinguish from which node the data originates and determine the corresponding physical information type. The working principle of frame counting is as follows: and adding 1 to the frame count when the terminal node sends one frame of data, counting the node frames by the upper computer, and if the frame is discontinuous, losing the frames of the data, thereby calculating the frame loss rate of the corresponding equipment node.

Fig. 9 is a GPRS driver flow diagram of the present invention. The GPRS module is connected with the PC through the Internet, and the remote communication function is realized by introducing the GPRS module. The TCP protocol is selected for communication, the singlechip can realize a series of control operations on GPRS by applying AT commands through the serial port, and the GPRS module drives the following steps:

"AT + IPR 115200"// set the Baud rate to 115200 bps;

the method comprises the following steps that (1) AT + CGDCONT, IP, CMNET and/or access gateway are set as a mobile dream network;

"AT + CGCLASS ═ B"// set the class of the mobile terminal to class B, allowing only one service to proceed AT the same time, GPRS or GSM;

"AT + CGACT ═ 1"// GPRS functions are activated. If the OK is returned, the GPRS connection is successful; if ERROR is returned, the GPRS connection is failed;

"AT + CIPSTART ═ TCP", "121.41.46.166", "10000", "// establishing a TCP connection, access port number" 10000 ".

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.