A mushroom shed intelligent monitoring method and system based on Beidou message communication

Abstract

The application discloses a mushroom shed intelligent monitoring method and system based on Beidou message communication, and comprises the following steps: acquiring a current environment control mode; collecting sensor sensing data and position information of a current time mushroom shed environment under the current environment control mode; analyzing the sensing data according to a mushroom shed environment prediction model, and generating predicted temperature and humidity data in the shed at a future preset time; controlling the temperature and humidity in the shed according to the predicted temperature and humidity data and preset comprehensive control parameters; and acquiring the sensor sensing data and position information in real time through a Beidou satellite, and sending the data to a display device for display. According to the method and system, the sensing data of the current time mushroom shed environment is accurately analyzed and predicted in real time according to the mushroom shed environment prediction model, so that the temperature and humidity in the mushroom shed are accurately controlled, the requirements of each management stage of the shiitake mushrooms on the environment are met, and the shiitake mushroom yield is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of agricultural Internet of Things, and particularly relates to a mushroom shed intelligent monitoring method and system based on Beidou message communication. BACKGROUND

[0002] With the rapid development of Beidou positioning system construction and service capability, Beidou positioning technology combined with Internet of Things technology has been widely applied in the field of agriculture, and has profoundly changed the management mode of digital agriculture.

[0003] The internal and external environmental information of the mushroom shed includes environmental factors such as temperature, humidity, carbon dioxide concentration, illumination, and nutrients. The motor control equipment includes roller blinds, roller bedding, roller sunshade nets, air coolers, fresh air fans, and micro-mist sprays. The existing mushroom shed monitoring system monitors the internal and external environmental information of the mushroom shed in real time, and manually controls the environmental factors such as temperature, humidity, CO2 concentration, and light intensity through the motor control equipment.

[0004] The mushroom emergence is divided into multiple management stages, and the relationship between environmental factors and mushroom yield and quality is very complex, so the environmental conditions are very high, among which the temperature, humidity, carbon dioxide concentration, illumination, and nutrients have a great impact on the mushroom. However, the analysis of environmental factors by the existing mushroom shed is not comprehensive and representative, which makes the existing mushroom shed monitoring system have problems such as inaccurate control method analysis, insufficient system control precision, and complex operation method. SUMMARY

[0005] In order to solve the above technical problems, the present application provides the following technical solutions:

[0006] In a first aspect, the present application provides a mushroom shed intelligent monitoring method based on Beidou message communication, which comprises:

[0007] obtaining a current environmental control mode;

[0008] collecting perception data of the mushroom shed environment at the current time under the current environmental control mode;

[0009] analyzing the perception data according to a mushroom shed environment prediction model, and generating predicted temperature and humidity data in the shed at a future preset time;

[0010] controlling the temperature and humidity in the shed according to the predicted temperature and humidity data and pre-set comprehensive control parameters.

[0011] Further, the perception data of the mushroom shed environment includes:

[0012] The mushroom shed environmental information sensing submodule collects indoor temperature data, indoor humidity data, outdoor temperature data, outdoor humidity data, mushroom shed light intensity data, indoor CO2 concentration data, outdoor wind speed data, and outdoor wind direction data.

[0013] The information sensing submodule inside the mushroom stick collects temperature and humidity data of the mushroom stick.

[0014] Furthermore, the step of analyzing the sensed data based on the mushroom shed environment prediction model and generating predicted temperature and humidity data for the shed at a future preset time includes:

[0015] Perform correlation analysis on the current time-based sensor data of the mushroom shed to obtain multiple sensor data with high correlation coefficients;

[0016] Feature analysis is performed on multiple sensing data with high correlation coefficients to determine whether multicollinearity exists in the current environmental control mode.

[0017] If multicollinearity exists, a stepwise regression method is used to construct a regression equation for the predicted temperature and humidity data in the shed at a future preset time under the environmental control mode with multicollinearity.

[0018] or,

[0019] If there is no multicollinearity, the least squares method is used to construct a regression equation for the predicted temperature and humidity data in the shed at a future preset time in an environmental control mode where multicollinearity is not present.

[0020] Furthermore, the regression equation for the predicted temperature and humidity data within the shed at a future preset time under the environmental control mode exhibiting multicollinearity includes:

[0021] Acquire the sensing data corresponding to the environmental control mode with multicollinearity, perform homogeneity of variance test, and retain the significant sensing data that have a large influence on the environmental control mode.

[0022] Based on saliency-perceived data and stepwise regression, a regression equation for the predicted temperature and humidity data inside the shed at a future preset time is constructed.

[0023] y 1 / 3 =-4.044+0.925x1+0.065x2+0.057x5,

[0024] y 全关 =1.589+0.696x1+0.029x3+0.205x4+0.009x5,

[0025] Among them, y 全关 y represents the predicted temperature and humidity data inside the shed at a preset time when the roller shutter closes. 1 / 3The predicted temperature and humidity data inside the shed are given when the curtain is opened to 1 / 3 of its maximum value at a future preset time. x1 is the temperature data inside the shed, x2 is the humidity data inside the shed, x3 is the temperature data of the mushroom logs, x4 is the temperature data outside the shed, and x5 is the light intensity data of the mushroom shed.

[0026] Furthermore, the construction of the regression equation for the predicted temperature and humidity data within the shed at a future preset time in an environmental control mode without multicollinearity includes:

[0027] By acquiring sensing data corresponding to environmental control modes where multicollinearity does not exist, and using the least squares method, a regression equation for the predicted temperature and humidity data within the shed at a future preset time is constructed.

[0028] y 2 / 3 =0.266+0.79x1+0.055x4+0.046x5,

[0029] y 全开 =-1.53+0.69x1+0.197x4+0.084x5,

[0030] Among them, y 2 / 3 The predicted temperature and humidity data for the shed at a preset time when the roller shutter is 2 / 3 open is y. 全开 This is the predicted temperature and humidity data for the shed at a preset time when the roller shutters are fully open.

[0031] Furthermore, the method also includes: acquiring sensor sensing data and location information in real time via BeiDou satellites, sending it to a database for storage, and sending the sensing data and location information to a display device for display.

[0032] Secondly, embodiments of the present invention provide an intelligent monitoring system for mushroom sheds based on BeiDou message communication. The system includes a mushroom shed information sensing module, a main control module, and a mushroom shed control module. The mushroom shed information sensing module is used to acquire the current environmental control mode and collect sensing data of the mushroom shed environment at the current time, and transmit the sensing data to the main control module. The main control module analyzes the sensing data according to the mushroom shed environment prediction model and generates predicted temperature and humidity data for the shed at a preset time in the future. The mushroom shed control module regulates the temperature and humidity inside the shed according to the predicted temperature and humidity data and preset comprehensive control parameters.

[0033] Furthermore, the mushroom shed information sensing module includes a mushroom shed environment information sensing submodule and a mushroom log internal information sensing submodule. The mushroom shed environment information sensing submodule collects indoor temperature data, indoor humidity data, outdoor temperature data, outdoor humidity data, mushroom shed light intensity data, indoor CO2 concentration data, outdoor wind speed data, and outdoor wind direction data. The mushroom log internal information sensing submodule collects mushroom log temperature data and mushroom log humidity data.

[0034] Further, the master module comprises a master chip, which receives the sensing data through a serial port and stores the sensing data into a database, analyzes the sensing data according to a mushroom shed environment prediction model, generates predicted temperature and humidity data in the shed at a future preset time, and sends an environment control command to the mushroom shed control module according to the predicted temperature and humidity data and preset comprehensive control parameters.

[0035] Further, the system further comprises a Beidou message communication module, which obtains sensor sensing data and position information in real time through the Beidou satellite, sends the sensing data and position information to the database for storage, and sends the sensing data and position information to a display device for display.

[0036] The present application has the following beneficial effects:

[0037] (1) The mushroom shed intelligent monitoring method and system based on Beidou message communication provided by the embodiment of the present application perform correlation analysis and feature analysis on multiple environment sensing data such as shed temperature data, shed humidity data, shed temperature data, shed humidity data, shed light intensity data, shed CO2 concentration data, shed wind speed data, shed wind direction data, and mushroom stick temperature data and humidity data, retain sensing data with a large influence weight on temperature data and humidity data, and then construct a predicted temperature and humidity data regression equation in the shed at a future preset time to monitor and predict the temperature and humidity data in the shed in real time; the temperature and humidity in the shed are accurately controlled according to the predicted temperature and humidity data and preset comprehensive control parameters, so as to meet the requirements of the environment in each management stage of the shiitake mushrooms and improve the yield of the shiitake mushrooms.

[0038] (2) The mushroom shed environment prediction model provided by the embodiment of the present application obtains different opening states of the roller shutter as an environment control mode, and collects sensing data of the mushroom shed environment at the current time under different opening states of the roller shutter; a predicted temperature and humidity data regression equation in the shed at a future preset time under different opening states of the roller shutter is constructed. When the mushroom shed intelligent monitoring system is working, the corresponding regression equation is selected to predict the temperature and humidity in the shed according to the sensing data under the actual opening state of the roller shutter, so as to control the temperature and humidity data in the shed. The model determination coefficient R 2 and the root mean square error are used to judge the accuracy of the mushroom shed environment prediction model, and the results show that the mushroom shed environment prediction model has a high fitting degree and small random error, has good mushroom shed temperature prediction ability, and meets the actual control requirements of the mushroom shed.

[0039] (3) The intelligent monitoring method and system for mushroom sheds based on BeiDou message communication provided in this embodiment of the invention uses a BeiDou message communication module to acquire sensor data and location information in real time via BeiDou satellites, send it to a database for storage, and then send the sensor data and location information to a display device for display. When this system is used in multiple mushroom sheds, the sensor location information of each shed can be viewed in real time and intuitively on the display device. If any abnormality is found in the display of individual location information, an early warning signal will be issued in a timely manner. This facilitates technicians in troubleshooting and repairing the intelligent monitoring system for mushroom sheds, enabling the system to resume operation as soon as possible, thereby reducing the impact of system failures on shiitake mushroom production. Attached Figure Description

[0040] Figure 1 This is a flowchart of the method described in this invention;

[0041] Figure 2 A flowchart for generating predicted temperature and humidity data in an embodiment of the present invention;

[0042] Figure 3 This is a structural block diagram of the system described in this invention;

[0043] Figure 4 This is a flowchart of the BeiDou message communication process as described in an embodiment of the present invention. Detailed Implementation

[0044] The present solution will now be described in conjunction with the accompanying drawings and specific embodiments.

[0045] like Figure 1 The flowchart shown is a method according to an embodiment of the present invention, including: obtaining the current environmental control mode; collecting sensing data of the mushroom shed environment at the current time under the current environmental control mode; analyzing the sensing data according to the mushroom shed environment prediction model and generating predicted temperature and humidity data of the shed at a future preset time; and regulating the temperature and humidity of the shed according to the predicted temperature and humidity data and preset comprehensive control parameters.

[0046] The system collects sensory data about the mushroom shed environment, including: the mushroom shed environment information sensing submodule collects indoor temperature data, indoor humidity data, outdoor temperature data, outdoor humidity data, mushroom shed light intensity data, indoor CO2 concentration data, outdoor wind speed data, and outdoor wind direction data; the mushroom log information sensing submodule collects mushroom log temperature data and mushroom log humidity data.

[0047] like Figure 2 The diagram shows a flowchart of the process for generating predicted temperature and humidity data according to an embodiment of the present invention. The main control chip (an MSP430 microcontroller is used in this embodiment) analyzes the sensed data based on the mushroom shed environment prediction model and generates predicted temperature and humidity data for the shed at a preset future time, including:

[0048] S1, correlation analysis is performed on the current time mushroom shed sensing data to obtain a plurality of sensing data with high correlation coefficients;

[0049] S2, feature analysis is performed on the plurality of sensing data with high correlation coefficients to determine whether the current environment control mode has multicollinearity;

[0050] S3, if there is multicollinearity, a stepwise regression method is used to construct a predicted temperature and humidity data regression equation in the future preset time of the environment control mode with multicollinearity;

[0051] S4, or,

[0052] If there is no multicollinearity, a least squares method is used to construct a predicted temperature and humidity data regression equation in the future preset time of the environment control mode without multicollinearity.

[0053] In this embodiment, the way to obtain the current environment control mode is not limited, which can be obtained by a sensor, or by an image or other ways. The current environment control mode is specifically the opening state of the roller shutter in the mushroom shed, which is: roller shutter closed, roller shutter opened 1 / 3, roller shutter opened 2 / 3 and roller shutter fully opened.

[0054] In this embodiment, the temperature data in the shed, the humidity data in the shed, the temperature data outside the shed, the humidity data outside the shed, the light intensity data of the mushroom shed, the CO2 concentration data in the shed, the wind speed data outside the mushroom shed, the wind direction data outside the mushroom shed, the temperature data of the mushroom stick and the humidity data of the mushroom stick are used as environmental variables. The predicted temperature data in the future preset time of the shed is taken as an example for description.

[0055] In step S1, correlation analysis is performed on the current time mushroom shed sensing data to obtain a plurality of sensing data with high correlation coefficients. As shown in Table 1, for each opening state of the roller shutter, the correlation coefficient table of the predicted temperature in the future preset time of the shed and each environmental variable.

[0056] Table 1 Correlation coefficient table of predicted temperature in future preset time of shed and each environmental variable

[0057]

[0058] Note: ** is significant (two-tailed) at p<0.01 level, and * is significant (two-tailed) at p<0.05 level. The lower the p value, the more accurate the correlation coefficient obtained.

[0059] The correlation analysis shows that the temperature in the shed, the humidity in the shed, the temperature of the mushroom stick, the temperature outside the shed and the light intensity of the mushroom shed have high correlation with the temperature in the future preset time of the shed, so the three environmental variables with low correlation, i.e. humidity of the mushroom stick, wind speed and wind direction, are removed.

[0060] In step S2, the characteristic analysis is performed on the plurality of environmental variables with high correlation coefficients to determine whether the current environmental control mode has multicollinearity. The calculation formula of the multicollinearity test is:

[0061]

[0062] wherein k represents the number of environmental variables, and the environmental variables are the indoor temperature, the indoor humidity, the stick body temperature, the outdoor temperature, and the light intensity of the mushroom shed, which are represented by x1-x5 in sequence. represents the fitting degree obtained by regressing the remaining k-1 environmental variables with the mth environmental variable as the dependent variable; and VIF represents the variance inflation factor of the mth environmental variable. As shown in Table 2, a VIF value is formed into a roller blind opening degree multicollinearity table.

[0063] If VIF>10, it indicates that the environmental variable has a large multicollinearity, and the stepwise regression method needs to be used to reduce the multicollinearity.

[0064] Table 2: Roller blind opening degree multicollinearity table

[0065]

[0066] The characteristic analysis and the multicollinearity test show that the roller blind closing and the roller blind opening 1 / 3 have multicollinearity problems.

[0067] In step S3, the regression equation of the predicted temperature and humidity data in the shed at a future preset time is constructed for the environmental control mode with multicollinearity, including:

[0068] The perception data corresponding to the environmental control mode with multicollinearity is obtained, and the homogeneity of variance is tested to retain the significant perception data that greatly affect the weight of the environmental control mode.

[0069] According to the significant perception data and the stepwise regression method, the regression equation of the predicted temperature and humidity data in the shed at a future preset time is constructed,

[0070] y 1 / 3 =-4.044+0.925x1+0.065x2+0.057x 5，

[0071] y 全关 =1.589+0.696x1+0.029x3+0.205x4+0.009x5,

[0072] wherein y 全关 is the predicted temperature and humidity data in the shed at a future preset time when the roller blind is closed, and y 1 / 3 is the predicted temperature and humidity data in the shed at a future preset time when the roller blind is opened 1 / 3.

[0073] To solve the problem of multiple collinearity, variance homogeneity test is performed on the two opening degree states of the roller shutter closed and the roller shutter opened 1 / 3, and according to the size of the test value, the significant environmental variables are reserved and the insignificant environmental variables are eliminated, so as to reduce the multiple collinearity. As shown in Table 3, the stepwise regression table formed after eliminating the insignificant environmental variables.

[0074] Table 3 Stepwise regression table

[0075]

[0076] The test results in Table 3 show that the significant level Sig of the constant and environmental variables under the two opening degree states of the roller shutter closed and the roller shutter opened 1 / 3 is less than 0.05, the environmental variables that significantly affect the predicted temperature and humidity in the shed at the future preset time when the roller shutter is closed are the shed temperature data, the mushroom stick body temperature data, the shed outside temperature data and the mushroom shed light intensity data, and the perception data that significantly affect the predicted temperature and humidity in the shed at the future preset time when the roller shutter is opened 1 / 3 are the shed temperature data, the shed humidity data and the mushroom shed light intensity data. Therefore, according to the reserved significant environmental variables, the predicted temperature and humidity data regression equations in the shed at the future preset time under the two opening degree states of the roller shutter closed and the roller shutter opened 1 / 3 are constructed respectively.

[0077] In step S4, the predicted temperature and humidity data regression equation of the environmental control mode without multiple collinearity in the future preset time in the shed is constructed, including:

[0078] The perception data corresponding to the environmental control mode without multiple collinearity is obtained, and the least square method is used to construct the predicted temperature and humidity data regression equation in the future preset time in the shed,

[0079] y 2 / 3 = 0.266 + 0.79x1 + 0.055x4 + 0.046x5,

[0080] y 全开 = 1.53 + 0.69x1 + 0.197x4 + 0.084x5,

[0081] Wherein, y 2 / 3 is the predicted temperature and humidity data in the shed at the future preset time when the roller shutter is opened 2 / 3, and y 全开 is the predicted temperature and humidity data in the shed at the future preset time when the roller shutter is fully opened.

[0082] The variance homogeneity test is performed on the environmental variables corresponding to the two opening degree states of the roller shutter opened 2 / 3 and the roller shutter fully opened, and the least square regression table formed is shown in Table 4.

[0083] Table 4 Least square regression table

[0084]

[0085] The test results in Table 4 show that, under the two opening degree states of 2 / 3 of the roller shutter and full opening of the roller shutter, the constant and the significant level Sig of the environmental variable are less than 0.05, the environmental variables that have a significant influence on the predicted temperature and humidity in the mushroom shed at a future preset time are the temperature data in the shed, the temperature data outside the shed and the light intensity data in the mushroom shed, and there is a significant linear relationship. Therefore, according to the reserved significant environmental variables, the regression equations of the predicted temperature and humidity data in the shed at a future preset time under the two opening degree states of 2 / 3 of the roller shutter and full opening of the roller shutter are respectively constructed.

[0086] The model determination coefficient R 2 , the root mean square error and other regression evaluation indexes are used to determine the accuracy of the mushroom shed environment prediction model, and a mushroom shed environment prediction model evaluation parameter summary table is formed, as shown in Table 5.

[0087] Table 5 Mushroom shed environment prediction model evaluation parameter summary table

[0088]

[0089]

[0090] The results in Table 5 show that, when the roller shutter is closed, the model determination coefficient R 2 = 0.930, the random error estimate When the roller shutter opening degree is 1 / 3, the model determination coefficient R 2 = 0.945, the random error estimate When the roller shutter is opened 2 / 3, the model determination coefficient R 2 = 0.887, the random error estimate When the roller shutter is fully opened, the model determination coefficient R 2 = 0.902, the random error estimate Therefore, under different opening states of the roller shutter, the regression evaluation indexes R 2 respectively can reach more than 0.900, indicating that the mushroom shed environment prediction model has a high fitting degree, a small random error and a good mushroom shed temperature prediction capability, and meets the actual control requirements of the mushroom shed.

[0091] The mushroom shed environment prediction model provided in the embodiment of the application acquires different opening degree states of the roller shutter as an environmental control mode, and collects perception data of the current time mushroom shed environment under different opening degree states of the roller shutter; regression equations of predicted temperature and humidity data in the shed at a future preset time under different opening degree states of the roller shutter are respectively constructed. When the mushroom shed intelligent monitoring system is working, according to the perception data under the actual opening degree state of the roller shutter, a corresponding regression equation is selected to predict the temperature and humidity in the shed, so as to regulate and control the temperature and humidity data in the shed. The model determination coefficient R 2Root mean square error judges the precision of the mushroom shed environment prediction model, and the results show that the mushroom shed environment prediction model has high fitting degree and small random error, has good mushroom shed temperature prediction ability, and meets the actual control demand of the mushroom shed.

[0092] Because the predicted temperature and humidity in the mushroom shed change with other environmental variables in the same way, the mushroom shed environment prediction model provided by the embodiment of the application is suitable for predicting the temperature and humidity in the shed at a future preset time.

[0093] The main control chip sends an environment control command to the mushroom shed control module, and the mushroom shed control module regulates the temperature and humidity in the shed.

[0094] The method also includes obtaining sensor sensing data and position information in real time through a Beidou satellite, sending the sensing data and position information to a database for storage, and sending the sensing data and position information to a display device for display.

[0095] As shown in Figure 3 The system structure diagram of the embodiment of the application includes a mushroom shed information sensing module, a main control module and a mushroom shed control module, the mushroom shed information sensing module is used for obtaining a current environment control mode and collecting sensing data of a current time mushroom shed environment, and the sensing data is transmitted to the main control module, the main control module analyzes the sensing data according to a mushroom shed environment prediction model, and generates predicted temperature and humidity data in the shed at a future preset time, and the mushroom shed control module regulates the temperature and humidity in the shed according to the predicted temperature and humidity data and preset comprehensive regulation parameters.

[0096] The mushroom shed information sensing module includes a mushroom shed environment information sensing sub-module and a mushroom stick body information sensing sub-module, the mushroom shed environment information sensing sub-module collects shed temperature data, shed humidity data, shed outside temperature data, shed outside humidity data, mushroom shed light intensity data, shed CO2 concentration data, mushroom shed outside wind speed data and mushroom shed outside wind direction data, and the mushroom stick body information sensing sub-module collects mushroom stick body temperature data and mushroom stick body humidity data.

[0097] The mushroom shed environment information sensing sub-module includes:

[0098] The temperature and humidity sensor (model DB-171) is connected to the I2C bus, and the collected shed temperature data and shed humidity data are output in the form of the I2C bus;

[0099] The CO2 concentration sensor (model E+E820) collects shed CO2 concentration data;

[0100] The light intensity sensor (model DBQ-6) collects mushroom shed light intensity data;

[0101] The wind speed sensor (model ZK-FS) collects mushroom shed outside wind speed data;

[0102] and wind direction sensor (model YGC-FX) to collect the wind direction data outside the mushroom shed;

[0103] The in-vitro information sensing sub-module of the mushroom stick includes a plurality of in-vitro temperature and humidity sensors (model 5TE) for collecting the temperature data and humidity data of the mushroom stick.

[0104] In this embodiment, the wind direction sensor and the wind speed sensor are current type sensors, which output data in the form of traditional analog signals 4-20mA and configure parameters through the RS485 interface. The light intensity sensor DBQ-6 is a current type sensor, which outputs data in the form of traditional analog signals 4-20mA and the power voltage is 24V. The CO2 concentration sensor outputs current 4-20mA and outputs data in the form of Modbus, and measures the CO2 concentration by using temperature compensation.

[0105] The main control module includes a main control chip, which receives sensing data through the RS485 serial port and stores it in the database, analyzes the sensing data according to the mushroom shed environment prediction model, generates predicted temperature and humidity data in the shed at a future preset time, and sends environment control commands to the mushroom shed control module according to the predicted temperature and humidity data and the preset comprehensive control parameters.

[0106] The mushroom shed information sensing module is arranged with one main sensing node and a plurality of slave sensing nodes. One of the slave sensing nodes includes a plurality of sensors and corresponding LoRa modules, and the sensors transmit sensing data to the main sensing node at regular intervals. The main sensing node includes a main control chip and corresponding LoRa modules, and the upper computer platform receives the sensing data collected by the main sensing node through the serial port and stores it in the database.

[0107] The main control chip sends environment control commands to the mushroom shed control module, and the mushroom shed control module controls the temperature and humidity in the shed.

[0108] The Beidou message communication module includes a Beidou message communication module sending end and a Beidou message communication module receiving end. The Beidou message communication module obtains sensor sensing data and position information in real time through the Beidou satellite, sends them to the database for storage, and sends the sensing data and position information to the display device for display.

[0109] As shown in Figure 4 , it is the Beidou message communication flowchart of the embodiment of the present application, and the steps are as follows:

[0110] (1) The addresses of the Beidou message communication module sending end and the Beidou message communication module receiving end are set in the serial port transmission software to realize point-to-point communication of the Beidou message communication module sending end and the Beidou message communication module receiving end;

[0111] (2) The sending end of the Beidou message communication module sends an information collection instruction to the master sensing node, and the master sensing node controls a plurality of slave sensing nodes through a sensor wake-up instruction. After receiving the wake-up instruction, the slave sensing nodes collect sensing data and position information of the sensors;

[0112] (3) The sending end of the Beidou message communication module converts the collected sensor sensing data and position information into message information conforming to the Beidou message communication protocol, and forwards the message information to the receiving end of the Beidou message communication module through the Beidou satellite;

[0113] (4) After receiving the message information, the receiving end of the Beidou message communication module triggers an event of receiving the message information, and feeds back a successful information message to the sending end of the Beidou message communication module, indicating that the message information is successfully sent;

[0114] (5) Configure the port number and baud rate in the serial port transmission software, and use the host computer platform as a display device to receive and display the sensor sensing data and position information received by the receiving end of the Beidou message communication module.

[0115] The system further comprises an image and video acquisition module, which comprises a monitoring camera, a computer, a VTU video router, a 48V power supply, a 12V power supply, a network cable, an Internet of Things SIM card, a VTU management platform, and a video management platform. The VTU video router uploads the collected image and video data of the growth of shiitake mushrooms and the mushroom shed to the video management platform through a wireless method (4G, 5G, or Wifi).

[0116] The 48V power supply is connected to the VTU video router, the 12V power supply is connected to the monitoring camera, and the monitoring camera is connected to the computer network cable. The host computer platform receives the image and video data sent by the video management platform through the port.

[0117] The sensing data of the mushroom shed environment, the predicted temperature and humidity data, the environment control command, the mushroom shed position information, and the image and video data are sent to a database for storage, accessed through the host computer platform, or transmitted to a mobile software client through the master control chip in a wireless transmission manner and accessed through the mobile software client app, facilitating remote control by the staff.

[0118] The monitoring camera inserted with the Internet of Things SIM card uploads the collected image and video data of the growth of shiitake mushrooms and the mushroom shed to the video management platform through the VTU video router. The host computer platform and the mobile software client display the image and video data collected by the video management platform in real time and at regular intervals through the port, ensuring sufficient picture data in each stage of shiitake mushroom growth.

[0119] The operation of the image and video acquisition module comprises the following steps:

[0120] (1) The VTU video router is configured as a local area network with a computer, and the monitoring camera is connected to the computer via a network cable to configure a local area network;

[0121] (2) An Internet of Things SIM card is configured and inserted into the VTU video router, the VTU management platform is entered, the unique device ID number of the VTU video router is configured, and the information transmission channel is selected;

[0122] (3) The video management platform is entered, the device ID number of the VTU video router is configured, the device ID number is consistent with the VTU management platform number, the server address, the TCP port and the monitoring camera IP address are configured, and the VTU video router connection mode is configured as a wireless access mode;

[0123] (4) The host computer platform receives the image video data sent by the video management platform through the TCP port, and stores it in the database;

[0124] (5) After the VTU video router is online, the VTU video router is connected to the monitoring camera via a network cable, and the monitoring camera is connected to a 12V monitoring power supply;

[0125] (6) The mobile software client visualizes and displays the image video data stored in the database through the TCP port.

[0126] The host computer platform used in this embodiment is based on an Ali cloud server, and is built with a server having a 4-core CPU and 8G memory.

[0127] When the system is used in multiple mushroom sheds, the position information of the sensors in each mushroom shed can be viewed on the display device in real time and intuitively. Once the position information of an individual sensor is found to be displayed abnormally, a warning signal is sent in time to inform the technical personnel to troubleshoot and maintain the mushroom shed intelligent monitoring system, so that the system can be restarted as soon as possible, and the influence of system failure on the yield of shiitake mushrooms is reduced.

[0128] The system and method described in this embodiment can realize the comprehensiveness, real-time nature and accuracy of mushroom shed data acquisition, solve the problems of low efficiency, poor real-time performance and data errors caused by manual data entry, realize efficient data transmission, centralized storage and accurate analysis by the main control module, realize the positioning display of the position information of the mushroom shed sensors by the Beidou message communication module, the Beidou satellite and the display device, and effectively solve the problem of poor control precision of the mushroom shed by the mushroom shed control module, thereby realizing intelligent control of the mushroom shed.

[0129] It is to be noted that, in the present text, relational terms such as "first" and "second", and the like, can only be used to distinguish one entity or action from another entity or action, without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises a... " does not, without more constraints, exclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element.

Claims

1. A method for intelligent monitoring of mushroom sheds based on BeiDou message communication, characterized in that, The method includes: Obtain the current environmental control mode; Collect sensor data on the mushroom shed environment at the current time under the current environmental control mode; The sensor data is analyzed based on the mushroom shed environment prediction model, and the predicted temperature and humidity data in the shed at a future preset time are generated, including: performing correlation analysis on the current time of the mushroom shed sensor data to obtain multiple sensor data with high correlation coefficients; Feature analysis is performed on multiple sensing data with high correlation coefficients to determine whether multicollinearity exists in the current environmental control mode. If multicollinearity exists, a stepwise regression method is used to construct a regression equation for the predicted temperature and humidity data within the shed at a future preset time under the environmental control model with multicollinearity, including: Acquire the sensing data corresponding to the environmental control mode with multicollinearity, perform homogeneity of variance test, and retain the significant sensing data that have a large influence on the environmental control mode. Based on saliency-perceived data and stepwise regression, a regression equation for the predicted temperature and humidity data inside the shed at a future preset time is constructed. y 1 / 3 =-4.044+0.925x1+0.065x2+0.057x5, y 全关 =1.589+0.696x1+0.029x3+0.205x4+0.009x5, Among them, y 全关 y represents the predicted temperature and humidity data inside the shed at a preset time when the roller shutter closes. 1 / 3 The predicted temperature and humidity data inside the shed are given when the curtain is opened to 1 / 3 of its length. x1 is the temperature data inside the shed, x2 is the humidity data inside the shed, x3 is the temperature data of the mushroom logs, x4 is the temperature data outside the shed, and x5 is the light intensity data of the mushroom shed. or, If no multicollinearity exists, the least squares method is used to construct a regression equation for the predicted temperature and humidity data within the shed at a future preset time under an environmental control model without multicollinearity, including: By acquiring sensing data corresponding to environmental control modes where multicollinearity does not exist, and using the least squares method, a regression equation for the predicted temperature and humidity data within the shed at a future preset time is constructed. y 2 / 3 =0.266+0.79x1+0.055x4+0.046x5, y 全开 =-1.53+0.69x1+0.197x4+0.084x5, Among them, y 2 / 3 The predicted temperature and humidity data for the shed at a preset time when the roller shutter is 2 / 3 open is y. 全开 The predicted temperature and humidity data inside the shed at a preset time when the roller blinds are fully open; The temperature and humidity inside the greenhouse are regulated based on predicted temperature and humidity data and preset comprehensive control parameters.

2. The intelligent monitoring method for mushroom sheds based on BeiDou message communication according to claim 1, characterized in that, Collect sensor data on the mushroom shed environment, including: The mushroom shed environmental information sensing submodule collects indoor temperature data, indoor humidity data, outdoor temperature data, outdoor humidity data, mushroom shed light intensity data, indoor CO2 concentration data, outdoor wind speed data, and outdoor wind direction data. The information sensing submodule inside the mushroom stick collects temperature and humidity data of the mushroom stick.

3. The intelligent monitoring method for mushroom sheds based on BeiDou message communication according to claim 1, characterized in that, The method further includes: acquiring sensor sensing data and location information in real time via BeiDou satellites, sending it to a database for storage, and sending the sensing data and location information to a display device for display.

4. A smart monitoring system for mushroom sheds based on BeiDou message communication, characterized in that, Based on the method described in any one of claims 1-3, the system includes a mushroom shed information sensing module, a main control module, and a mushroom shed control module. The mushroom shed information sensing module is used to acquire the current environmental control mode and collect sensing data of the mushroom shed environment at the current time, and transmit the sensing data to the main control module. The main control module analyzes the sensing data according to the mushroom shed environment prediction model and generates predicted temperature and humidity data of the shed at a future preset time, including: performing correlation analysis on the mushroom shed sensing data at the current time and acquiring multiple sensing data with high correlation coefficients. Feature analysis is performed on multiple sensing data with high correlation coefficients to determine whether multicollinearity exists in the current environmental control mode. If multicollinearity exists, a stepwise regression method is used to construct a regression equation for the predicted temperature and humidity data within the shed at a future preset time under the environmental control model with multicollinearity, including: Acquire the sensing data corresponding to the environmental control mode with multicollinearity, perform homogeneity of variance test, and retain the significant sensing data that have a large influence on the environmental control mode. Based on saliency-perceived data and stepwise regression, a regression equation for the predicted temperature and humidity data inside the shed at a future preset time is constructed. y 1 / 3 =-4.044+0.925x1+0.065x2+0.057x5, y 全关 =1.589+0.696x1+0.029x3+0.205x4+0.009x5, Among them, y 全关 y represents the predicted temperature and humidity data inside the shed at a preset time when the roller shutter closes. 1 / 3 The predicted temperature and humidity data inside the shed are given when the curtain is opened to 1 / 3 of its length. x1 is the temperature data inside the shed, x2 is the humidity data inside the shed, x3 is the temperature data of the mushroom logs, x4 is the temperature data outside the shed, and x5 is the light intensity data of the mushroom shed. Alternatively, if multicollinearity is not present, the least squares method is used to construct a regression equation for the predicted temperature and humidity data within the shed at a future preset time in an environmental control mode without multicollinearity, including: By acquiring sensing data corresponding to environmental control modes where multicollinearity does not exist, and using the least squares method, a regression equation for the predicted temperature and humidity data within the shed at a future preset time is constructed. y 2 / 3 =0.266+0.79x1+0.055x4+0.046x5, y 全开 =-1.53+0.69x1+0.197x4+0.084x5, Among them, y 2 / 3 The predicted temperature and humidity data for the shed at a preset time when the roller shutter is 2 / 3 open is y. 全开 The predicted temperature and humidity data inside the shed at a preset time when the roller blinds are fully open; The mushroom shed control module regulates the temperature and humidity inside the shed based on predicted temperature and humidity data and preset comprehensive control parameters.

5. The intelligent monitoring system for mushroom sheds based on BeiDou message communication according to claim 4, characterized in that, The mushroom shed information sensing module includes a mushroom shed environment information sensing submodule and a mushroom log internal information sensing submodule. The mushroom shed environment information sensing submodule collects indoor temperature data, indoor humidity data, outdoor temperature data, outdoor humidity data, mushroom shed light intensity data, indoor CO2 concentration data, outdoor wind speed data, and outdoor wind direction data. The mushroom log internal information sensing submodule collects mushroom log temperature data and mushroom log humidity data.

6. The intelligent monitoring system for mushroom sheds based on BeiDou message communication according to claim 4, characterized in that, The main control module includes a main control chip. The main control chip receives sensing data through a serial port and stores it in a database. It analyzes the sensing data according to the mushroom shed environment prediction model, generates predicted temperature and humidity data for the shed at a preset time, and sends environmental control commands to the mushroom shed control module based on the predicted temperature and humidity data and preset comprehensive control parameters.

7. The intelligent monitoring system for mushroom sheds based on BeiDou message communication according to claim 4, characterized in that, The system also includes a BeiDou message communication module, which acquires sensor data and location information in real time through BeiDou satellites, sends it to a database for storage, and sends the sensor data and location information to a display device for display.

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