Optical fiber monitoring and early warning method and system for granary

Abstract

The invention relates to the technical field of grain storage monitoring, in particular to a granary optical fiber monitoring and early warning method and system. The granary optical fiber monitoring and early warning method comprises the following steps: acquiring environmental parameters in a granary and an early warning model corresponding to grain types stored in the granary; the environmental parameters comprise one or more of temperature, humidity and carbon dioxide concentration; the early warning model comprises a first-stage early warning model, a second-stage early warning model and a third-stage early warning model; the environmental parameters corresponding to the primary early warning model comprise temperature and humidity; the environmental parameters corresponding to the secondary early warning model comprise temperature; the environmental parameters corresponding to the third-stage early warning model comprise carbon dioxide concentration; determining an early warning threshold value based on the grain type and an early warning model; the early warning threshold value comprises a mildew parameter early warning threshold value and an environment parameter early warning threshold value; inputting the environmental parameters into an early warning model, and determining mildew parameters of the grains; and when the mildew parameter is greater than the mildew parameter early warning threshold value or the environmental parameter is greater than the environmental parameter early warning threshold value, giving an alarm.

Description

Technical Field

[0001] The present application relates to the technical field of grain storage monitoring, and particularly relates to a fiber optic monitoring and early warning method and system for grain bins. Background Art

[0002] As an important reserve material, mildew of grain is the main cause of storage loss. To ensure the storage quality of grain and reduce losses caused by mildew, it is of great significance to establish a perfect and effective grain condition monitoring system in the field of grain storage.

[0003] In the related art, the grain condition monitoring system mainly monitors the temperature and humidity inside the grain bin through electronic temperature and humidity sensors to determine whether the grain in the grain bin has mildewed.

[0004] In the process of implementing the related art, at least the following problems are found in the related art:

[0005] First, during the monitoring process of the electronic temperature and humidity sensors in the external (inside the grain bin) environment of the grain pile, serious drift phenomena will occur, resulting in poor reliability during long-term operation. Second, for the monitoring of the inside of the grain pile, usually the DS18B20 digital temperature sensor probe is inserted into the grain pile for temperature measurement. In the actual storage process, when the grain temperature rises, it often accompanies the occurrence of grain mildew. The traditional temperature measurement method can only monitor the mildew phenomenon and cannot achieve early warning.

[0006] Therefore, there is an urgent need to propose a new and reliable real-time monitoring system to solve the above problems. Summary of the Invention

[0007] In order to solve the above technical problems, the embodiments of the present application provide a fiber optic monitoring and early warning method and system for grain bins, which can realize the mildew early warning of grain, improve the accuracy of mildew early warning, reduce the drift phenomenon caused by the use of electronic temperature sensors, and improve the reliability of the system.

[0008] In some embodiments, a fiber optic monitoring and early warning method for a grain bin is provided, which is applied to a fiber optic monitoring and early warning system for a grain bin. The fiber optic monitoring and early warning method for a grain bin includes:

[0009] Obtain the environmental parameters in the grain bin and the early warning models corresponding to the types of grains stored in the grain bin; the environmental parameters include one or more of temperature, humidity, and carbon dioxide concentration; the early warning models include a first-level early warning model, a second-level early warning model, and a third-level early warning model; the environmental parameters corresponding to the first-level early warning model include temperature and humidity; the environmental parameters corresponding to the second-level early warning model include temperature; the environmental parameters corresponding to the third-level early warning model include carbon dioxide concentration;

[0010] Based on the grain type and the warning model, determine the warning threshold; the warning threshold includes the mildew parameter warning threshold and the environmental parameter warning threshold;

[0011] Input the environmental parameters into the warning model to determine the mildew parameters of the grain;

[0012] When the mildew parameter is greater than the mildew parameter warning threshold or the environmental parameter is greater than the environmental parameter warning threshold, issue an alarm.

[0013] Optionally, the mildew parameter includes a first mildew parameter;

[0014] The first-level warning model is shown as the following first formula:

[0015]

[0016] where β 1,n is the first temperature and humidity weight coefficient, β 2,n is the second temperature and humidity weight coefficient, β 3,n is the third temperature and humidity weight coefficient, β 1,n , β 2,n and β 3,n take values related to the grain type and state, and the state includes the adsorption state and the desorption state; M is the first mildew parameter, representing the equilibrium moisture content of the grain; t is the temperature, representing the temperature of the grain in the granary; ERH is the humidity, representing the humidity inside the grain pile; M1 is the first moisture corresponding to the desorption state of the grain; M2 is the second moisture corresponding to the adsorption state of the grain.

[0017] Optionally, the mildew parameter includes a second mildew parameter;

[0018] The fiber optic monitoring and warning system for the granary includes multiple monitoring points arranged at intervals in the granary, and the temperature includes the sub-temperatures corresponding to each monitoring point;

[0019] Input the environmental parameters into the second warning model to determine the second mildew parameter, including:

[0020] Take each monitoring point as the target monitoring point in turn;

[0021] Determine the i-th day, the first sub-temperature t i corresponding to the target monitoring point, the second sub-temperature t li corresponding to the monitoring point on the left of the target monitoring point, the third sub-temperature t ri corresponding to the monitoring point on the right of the target monitoring point, the fourth sub-temperature t ai corresponding to the monitoring point above the target monitoring point, the fifth sub-temperature t ui corresponding to the monitoring point below the target monitoring point, the sixth sub-temperature t fi, the seventh sub-temperature t corresponding to the monitoring point behind the target monitoring point bi , and on the (i + 1)-th day, the eighth sub-temperature t corresponding to the target monitoring point i+1 ;

[0022] Input t i , t li , t ri , t ai , t ui , t fi , t bi and t i+1 into the second early warning model to determine the second mildew parameter.

[0023] Optionally, input t i , t li , t ri , t ai , t ui , t fi , t bi and t i+1 into the second early warning model to determine the second mildew parameter, including:

[0024] Based on t i , t li , t ri , t ai , t ui , t fi and t bi , calculate the reference temperature T on the i-th day i ;

[0025] Calculate the difference between t i+1 and T i to determine the second mildew parameter corresponding to the target monitoring point.

[0026] Optionally, calculate the reference temperature T on the i-th day according to the following second formula i :

[0027] T i = α0t i + α1t li + α2t ri + α3t ai + α4t ui + α5t fi + α6t bi , the second formula;

[0028] Among them, α0 is the first temperature weight coefficient, α1 is the second temperature weight coefficient, α2 is the third temperature weight coefficient, α3 is the fourth temperature weight coefficient, α4 is the fifth temperature weight coefficient, α5 is the sixth temperature weight coefficient, α6 is the seventh temperature weight coefficient, and the values of α0, α1, α2, α3, α4, α5, and α6 are related to the type of grain.

[0029] Optionally, determining the second sub-temperature corresponding to the monitoring point on the left side of the target monitoring point, the third sub-temperature corresponding to the monitoring point on the right side of the target monitoring point, the fourth sub-temperature corresponding to the monitoring point above the target monitoring point, the fifth sub-temperature corresponding to the monitoring point below the target monitoring point, the sixth sub-temperature corresponding to the monitoring point in front of the target monitoring point, and the seventh sub-temperature corresponding to the monitoring point behind the target monitoring point on the i-th day includes:

[0030] Determining the position information of the target monitoring point;

[0031] In the case where the position information is the leftmost, replacing the second sub-temperature with the first sub-temperature;

[0032] In the case where the position information is the rightmost, replacing the third sub-temperature with the first sub-temperature;

[0033] In the case where the position information is the uppermost, replacing the fourth sub-temperature with the first sub-temperature;

[0034] In the case where the position information is the lowermost, replacing the fifth sub-temperature with the first sub-temperature;

[0035] In the case where the position information is the foremost, replacing the sixth sub-temperature with the first sub-temperature;

[0036] In the case where the position information is the rearmost, replacing the seventh sub-temperature with the first sub-temperature.

[0037] Optionally, the mildew parameter includes the third mildew parameter;

[0038] The three-level early warning model is shown as the following third formula:

[0039] E = ||C i -C i-1 |-|C i-1 -C i-2 ||, the third formula;

[0040] Among them, E is the third mildew parameter; C i is the carbon dioxide concentration on the i-th day, C i-1 is the carbon dioxide concentration on the (i - 1)-th day, C i-2 is the carbon dioxide concentration on the (i - 2)-th day.

[0041] Optionally, the environmental parameters include carbon dioxide concentration; the mildew parameters include a first mildew parameter, a second mildew parameter, and a third mildew parameter; the mildew parameter warning thresholds include a moisture threshold, a temperature change threshold, and a carbon dioxide change threshold; the alarms include a first-level alarm, a second-level alarm, and a third-level alarm; the environmental parameter warning threshold includes a carbon dioxide threshold; when the mildew parameter is greater than the warning threshold, an alarm is issued, including:

[0042] When the first mildew parameter is greater than the moisture threshold, a first-level alarm is issued;

[0043] When the second mildew parameter is greater than the temperature change threshold, a second-level alarm is issued;

[0044] When the third mildew parameter is greater than the carbon dioxide change threshold or the carbon dioxide concentration is greater than the carbon dioxide threshold, a third-level alarm is issued.

[0045] In some embodiments, a fiber optic monitoring and warning system for a granary is provided, including:

[0046] An environmental parameter detection module and a warning module that are communicatively connected to each other;

[0047] The environmental parameter detection module is configured to obtain the environmental parameters in the granary;

[0048] The warning module is configured to obtain the environmental parameters in the granary and the warning models corresponding to the types of grains stored in the granary; the environmental parameters include one or more of temperature, humidity, and carbon dioxide concentration; the shown warning models include a first-level warning model, a second-level warning model, and a third-level warning model; the environmental parameters corresponding to the first-level warning model include temperature and humidity; the environmental parameters corresponding to the second-level warning model include temperature; the environmental parameters corresponding to the third-level warning model include carbon dioxide concentration; based on the type of grain and the warning model, the warning thresholds are determined; the warning thresholds include mildew parameter warning thresholds and environmental parameter warning thresholds; the environmental parameters are input into the warning model to determine the mildew parameters of the grains; when the mildew parameter is greater than the mildew parameter warning threshold or the environmental parameter is greater than the environmental parameter warning threshold, an alarm is issued.

[0049] Optionally, the environmental parameter detection module includes:

[0050] A fiber optic environmental parameter sensing device configured to obtain the optical signals corresponding to the environmental parameters in the granary; the fiber optic environmental parameter sensing device includes a plurality of monitoring points, and the environmental parameters of each monitoring point of the environmental parameters;

[0051] A on-site monitoring device, optically connected to the fiber optic environmental parameter sensing device, configured to demodulate the optical signals corresponding to the environmental parameters collected by the fiber optic environmental parameter sensing device to obtain the environmental parameters in the granary;

[0052] The early warning module is also configured to construct a three-dimensional environmental cloud map in the granary according to environmental parameters.

[0053] It can be understood that for the beneficial effects that can be achieved by the technical solution provided by the above-mentioned granary optical fiber monitoring and early warning system, reference can be made to the beneficial effects in the granary optical fiber monitoring and early warning method and any of its optional implementation manners, which will not be elaborated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solution of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0055] Figure 1 Schematic diagram of the interior of the granary provided by the embodiment of the present application;

[0056] Figure 2 Block diagram of the structure of the granary optical fiber monitoring and early warning system provided by the embodiment of the present application;

[0057] Figure 3 Flow chart of the granary optical fiber monitoring and early warning method provided by the embodiment of the present application;

[0058] Figure 4 Data flow diagram of the granary optical fiber monitoring and early warning method provided by the embodiment of the present application;

[0059] Figure 5 Schematic diagram of the setting of multiple monitoring points provided by the embodiment of the present application;

[0060] Figure 6 Temperature diagram of the target monitoring point and its surrounding monitoring points on the first day provided by the embodiment of the present application;

[0061] Figure 7 First example table of carbon dioxide concentration in the granary provided by the embodiment of the present application;

[0062] Figure 8 Second example table of carbon dioxide concentration in the granary provided by the embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] The technical solutions in the embodiments of the present application will be clearly described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present application.

[0064] Hereinafter, terms such as "first", "second", etc. are only for descriptive purposes and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first", "second", etc. may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0065] In addition, in the present application, orientation terms such as "upper", "lower", "inner", "outer", etc. are defined relative to the orientation in which the components in the drawings are schematically placed. It should be understood that these directional terms are relative concepts, which are used for relative description and clarification and can change accordingly with the change of the orientation in which the components in the drawings are placed.

[0066] To facilitate the understanding of the present application, the related technologies of the present application will be described first below.

[0067] Figure 1 It is a schematic diagram of the interior of a granary provided for an embodiment of the present application.

[0068] Combined with Figure 1 As shown, there is grain stacked in the granary 01, and the grain forms a grain pile 02. In the related technology, by inserting an electronic temperature and humidity sensor into the interior of the grain pile 02 to measure the temperature and humidity inside the grain pile 02, so as to judge whether the grain is mildewed according to the temperature and humidity.

[0069] During the detection process of the electronic temperature and humidity sensor, due to the coupling effect of material deformation and temperature and humidity environment, the performance of the sensor decays, resulting in a serious drift phenomenon, and the reliability of its long-term operation is poor.

[0070] Specifically, the humidity-sensitive material of the sensor (such as a ceramic microporous structure) undergoes elastic fatigue due to long-term moisture absorption-desorption cycles, triggering zero drift. Or, the significant temperature difference inside and outside the grain pile (such as the temperature difference between the surface layer and the bottom layer of the grain pile exceeding 10 °C) triggers the non-linear offset of transistor parameters, and the addition of the disturbance of humid and hot air introduced by improper ventilation exacerbates the temperature drift effect. Or, the unique dust in the granary clogs the micropores of the sensor, the organic acids released by mildew corrode the electrodes, and the intrusion of liquid water caused by condensation jointly accelerate the aging of the components. Or, the design defect of the sensor circuit (such as insufficient anti-interference ability) and long-term power supply fluctuations further amplify the drift error.

[0071] For the monitoring of the interior of the grain pile, a DS18B20 digital temperature sensor probe is usually inserted into the grain pile for temperature measurement. However, in the actual warehousing process, when the grain temperature rises, grain mildew has often occurred. The traditional temperature measurement method can only monitor the phenomenon of mildew and cannot achieve early warning. Moreover, a single parameter is difficult to accurately reflect the mildew process, and the unreasonable layout of the sensors is likely to form a monitoring blind area.

[0072] To solve the above technical problems, the embodiments of the present application provide a method and system for monitoring and warning of grain bins using optical fibers. By measuring various parameters inside the grain bin, as well as the temperature and humidity at multiple points, and combining multiple warning models, multi-angle warning of grain mildew inside the grain bin is achieved. The drift phenomenon caused by using electronic temperature and humidity sensors is reduced, the reliability of the system is improved, and early warning of grain mildew is realized, reducing the losses caused by grain mildew.

[0073] Figure 2 It is a structural block diagram of the grain bin optical fiber monitoring and warning system provided by the embodiments of the present application.

[0074] Combined with Figure 2 As shown, the embodiments of the present application provide a grain bin optical fiber monitoring and warning system 1, including: an environmental parameter detection module 11 and a warning module 12 that are communicatively connected to each other. The environmental parameter detection module 11 is configured to obtain the environmental parameters in the grain bin. The warning module 12 is configured to receive the environmental parameters collected by the environmental parameter detection module 11, obtain the warning model corresponding to the grain type in the grain bin; determine the mildew parameters of the grain in the grain bin according to the environmental parameters and the warning model; determine whether to issue a warning alarm according to the mildew parameters.

[0075] By using the grain bin optical fiber monitoring and warning system 1 provided by the embodiments of the present application, the environmental parameters in the grain bin are collected by using the environmental parameter detection module 11, and then the mildew parameters of the grain in the current grain bin are determined by combining the warning model obtained by the warning module 12. The mildew parameters are associated with the possibility of grain mildew. Specifically, the higher the possibility of grain mildew, the larger the mildew parameters.

[0076] Specifically, the grain bin optical fiber monitoring and warning system gives full play to the advantages of high sensitivity, strong anti-electromagnetic interference ability, and easy realization of large-capacity and distributed measurement of optical fiber sensors. Through real-time monitoring and data analysis of multiple parameters inside the grain bin, it can issue an early warning before the occurrence of grain mildew and take corresponding preventive measures, effectively avoiding problems such as mildew, and significantly improving the safety and management efficiency of grain storage.

[0077] Optionally, the environmental parameter detection module 11 adopts a distributed detection form. Specifically, the environmental parameter detection module 11 includes multiple monitoring points arranged at intervals, and the monitoring points are evenly distributed in the grain bin. For example, a monitoring point is set every 1 m in the grain bin, and each monitoring point is provided with an optical fiber temperature and humidity sensor for measuring the sub-temperature and sub-humidity corresponding to each monitoring point.

[0078] The main principle of the grain bin optical fiber monitoring and warning system provided by the embodiments of the present application is as follows:

[0079] In the granary environment, temperature, humidity, and moisture content affect each other. Excessive moisture content will increase the risk of grain mildew, which is also the first parameter change at the beginning of grain mildew. Therefore, the moisture content is used as the primary warning. The increase in humidity and moisture content will provide good conditions for the growth and activities of molds and other microorganisms. These microorganisms will generate respiratory heat during the decomposition process, resulting in a local temperature rise. Therefore, the temperature rise inside the grain pile is used as the secondary warning. The increase in temperature will accelerate the respiration of grains and the activities of molds and microorganisms, generating more carbon dioxide and causing its concentration to rise. Therefore, the carbon dioxide concentration is used as the tertiary warning. Humidity, moisture content, temperature, and carbon dioxide concentration are interrelated. Any abnormal change in one factor may trigger changes in other factors. Therefore, establishing a monitoring and warning system based on moisture content, temperature, and carbon dioxide concentration is crucial for ensuring the stability of the granary environment and the safety of grain quality.

[0080] Inside the wheat grain pile, fiber optic temperature and humidity sensors are arranged in a quasi-distributed series manner. A temperature and humidity sensor is connected in series every 1m, and multiple strings of temperature and humidity sensors are implanted into the grain pile to achieve accurate measurement of the temperature and humidity in the longitudinal section of the grain pile and ensure comprehensive monitoring of the temperature and humidity of the entire grain pile. On this basis, the system can display the temperature and humidity at each position in the grain pile in real time in the form of a three-dimensional cloud map in a graphical way to help managers intuitively understand the conditions at each position in the warehouse. At the same time, combined with the mapping model of temperature and humidity and grain moisture content, the system can calculate the moisture content and issue corresponding warnings according to whether the moisture content and temperature exceed the set safety range. In addition, carbon dioxide, oxygen, and phosphine sensors installed on the top of the granary can monitor the concentrations of carbon dioxide, oxygen, and phosphine in the granary in real time, so as to obtain data on the changes in the gas concentrations in the granary. If the change in carbon dioxide concentration is too fast, it may indicate that the wheat has started to mildew. The system compares with the set safety range to determine whether it exceeds the safety range and then generates corresponding warnings.

[0081] Figure 3 It is a schematic flow diagram of the granary optical fiber monitoring and warning method provided by the embodiment of the present application.

[0082] Figure 4 It is a schematic data flow diagram of the granary optical fiber monitoring and warning method provided by the embodiment of the present application.

[0083] Combined with Figure 2 the granary optical fiber monitoring and warning system shown, the embodiment of the present application also provides a granary optical fiber monitoring and warning method, as shown in Figure 3 shown, including steps S1 to S4, specifically as follows:

[0084] Step S1, obtain the environmental parameters in the granary and the warning models corresponding to the types of grains stored in the granary; the environmental parameters include one or more of temperature, humidity, and carbon dioxide concentration; the warning models include a first-level warning model, a second-level warning model, and a third-level warning model; the environmental parameters corresponding to the first-level warning model include temperature and humidity; the environmental parameters corresponding to the second-level warning model include temperature; the environmental parameters corresponding to the third-level warning model include carbon dioxide concentration.

[0085] Exemplarily, as shown in Figure 4 , input the temperature and humidity into the first-level warning model. The first-level warning model outputs a first mildew parameter. When the first mildew parameter is greater than the moisture threshold, it indicates that the current grains have a relatively high possibility of mildew, and a first-level alarm is issued. Input the temperature into the second-level warning model. The second-level warning model outputs a second mildew parameter. When the second mildew parameter is greater than the temperature change threshold, it indicates that the current grains have a relatively high possibility of mildew, and a second-level alarm is issued. Input the carbon dioxide concentration into the third-level warning model. The third-level warning model outputs a third mildew parameter. When the third mildew parameter is greater than the carbon dioxide change threshold or the carbon dioxide concentration is greater than the carbon dioxide threshold, it indicates that the current grains have a relatively high possibility of mildew, and a third-level alarm is issued.

[0086] Optionally, the mildew parameter includes the first mildew parameter;

[0087] The first-level warning model is shown as the following first formula (Formula (1)):

[0088]

[0089] where β 1,n is the first temperature-humidity weight coefficient, β 2,n is the second temperature-humidity weight coefficient, β 3,n is the third temperature-humidity weight coefficient, β 1,n , β 2,n and β 3,n take values related to the type and state of the grains, and the state includes the adsorption state and the desorption state; M is the first mildew parameter, representing the equilibrium moisture content of the grains; t is the temperature, representing the temperature of the grains in the granary; ERH is the humidity, representing the humidity inside the grain pile; M1 is the first moisture corresponding to the desorption state of the grains; M2 is the second moisture corresponding to the adsorption state of the grains.

[0090] Take the above first formula as Formula (1) for subsequent description.

[0091] In this embodiment, by constructing a primary early warning model and combining temperature and humidity, the first mildew parameter of the grain in the granary is determined, and the first mildew parameter characterizes the equilibrium moisture content of the grain. Specifically, if the equilibrium moisture content of the grain is too high, the risk of grain mildew will increase, which is also the first parameter change at the beginning of grain mildew. Therefore, the equilibrium moisture content (i.e., the first mildew parameter) is used as the primary early warning model.

[0092] Specifically, in the case where there are multiple temperature and humidity sensors in the granary, the above-mentioned temperature refers to the average value of the temperatures detected by the multiple temperature and humidity sensors, and the above-mentioned humidity refers to the average value of the humidities detected by the multiple temperature and humidity sensors.

[0093] Optionally, the mildew parameter includes a second mildew parameter; the secondary early warning model is shown as the following formula (2):

[0094]

[0095] where D is the second mildew parameter, T i is the reference temperature on the i-th day, t i is the first sub-temperature corresponding to the target monitoring point on the i-th day, t li is the second sub-temperature corresponding to the monitoring point to the left of the target monitoring point on the i-th day, t ri is the third sub-temperature corresponding to the monitoring point to the right of the target monitoring point on the i-th day, t ai is the fourth sub-temperature corresponding to the monitoring point above the target monitoring point on the i-th day, t ui is the fifth sub-temperature corresponding to the monitoring point below the target monitoring point on the i-th day, t fi is the sixth sub-temperature corresponding to the monitoring point in front of the target monitoring point on the i-th day, t bi is the seventh sub-temperature corresponding to the monitoring point behind the target monitoring point on the i-th day, t i+1 is the eighth sub-temperature corresponding to the target monitoring point on the (i + 1)-th day.

[0096] Optionally, the mildew parameter includes a third mildew parameter; the tertiary early warning model is shown as the following third formula (Formula (3)):

[0097] E = ||C i - C i-1 | - |C i-1 - C i-2 ||, Formula (3);

[0098] where E is the third mildew parameter; C i is the carbon dioxide concentration on the i-th day, C i-1 is the carbon dioxide concentration on the (i - 1)-th day, C i-2 is the carbon dioxide concentration on the (i - 2)-th day.

[0099] Take the above third formula as formula (3) for subsequent description.

[0100] In this embodiment, by inputting the concentration of carbon dioxide into the three-level early warning model, the output third mildew parameter characterizes the change of the carbon dioxide concentration in the granary within the adjacent three days. If the third mildew parameter is too high, it indicates that the respiration of the grain in the current granary is excessive, indicating a high possibility of grain mildew. Specifically, the increase in temperature will accelerate the respiration of the grain and the activities of molds and microorganisms, generating more carbon dioxide and causing its concentration to rise. Therefore, the carbon dioxide concentration is used as the three-level early warning.

[0101] Step S2: Determine the early warning thresholds based on the grain type and the early warning model; the early warning thresholds include the mildew parameter early warning threshold and the environmental parameter early warning threshold.

[0102] Optionally, the mildew parameter early warning threshold includes the moisture threshold, the temperature change threshold, and the carbon dioxide change threshold.

[0103] Optionally, the environmental parameter early warning threshold includes the carbon dioxide threshold.

[0104] Step S3: Input the environmental parameters into the early warning model to determine the mildew parameters of the grain.

[0105] Optionally, the mildew parameter includes the second mildew parameter; the fiber optic monitoring and early warning system for the granary includes a plurality of monitoring points arranged at intervals in the granary, and the temperature includes the sub-temperature corresponding to each monitoring point; Step S3 includes Step S31 to Step S33, which are specifically as follows:

[0106] Step S31: Take each monitoring point as the target monitoring point in turn.

[0107] Step S32: Determine the first sub-temperature t corresponding to the target monitoring point on the i-th day i the second sub-temperature t corresponding to the monitoring point on the left of the target monitoring point li the third sub-temperature t corresponding to the monitoring point on the right of the target monitoring point ri the fourth sub-temperature t corresponding to the monitoring point above the target monitoring point ai the fifth sub-temperature t corresponding to the monitoring point below the target monitoring point ui the sixth sub-temperature t corresponding to the monitoring point in front of the target monitoring point fi the seventh sub-temperature t corresponding to the monitoring point behind the target monitoring point bi and the eighth sub-temperature t corresponding to the target monitoring point on the (i + 1)-th day i+1 .

[0108] Figure 5 It is a schematic diagram of a setting with multiple monitoring points provided by the embodiment of the present application.

[0109] Optionally, determining the second sub-temperature corresponding to the monitoring point on the left side of the target monitoring point, the third sub-temperature corresponding to the monitoring point on the right side of the target monitoring point, the fourth sub-temperature corresponding to the monitoring point above the target monitoring point, the fifth sub-temperature corresponding to the monitoring point below the target monitoring point, the sixth sub-temperature corresponding to the monitoring point in front of the target monitoring point, and the seventh sub-temperature corresponding to the monitoring point behind the target monitoring point on the i-th day includes:

[0110] Determining the position information of the target monitoring point;

[0111] When the position information is the leftmost, replacing the second sub-temperature with the first sub-temperature;

[0112] When the position information is the rightmost, replacing the third sub-temperature with the first sub-temperature;

[0113] When the position information is the uppermost, replacing the fourth sub-temperature with the first sub-temperature;

[0114] When the position information is the lowermost, replacing the fifth sub-temperature with the first sub-temperature;

[0115] When the position information is the foremost, replacing the sixth sub-temperature with the first sub-temperature;

[0116] When the position information is the rearmost, replacing the seventh sub-temperature with the first sub-temperature.

[0117] In this embodiment, considering that the target monitoring point may be located at the edge position in the monitoring point matrix, the temperature of the target monitoring point is used to replace the sub-temperature corresponding to the monitoring point in the missing azimuth, so as to facilitate the output of the second mildew parameter corresponding to each target monitoring point.

[0118] Exemplarily, as shown in combination with Figure 5 the monitoring point at the outermost edge such as Figure 5 monitoring point A shown in. When this monitoring point is used as the target monitoring point, the position information of this monitoring point is the lowermost, the leftmost, and the foremost, that is, there are no monitoring points below, to the left, and in front of it, and the second sub-temperature, the fifth sub-temperature, and the sixth sub-temperature cannot be obtained. Then, the first sub-temperature of this point is used to replace the second sub-temperature, the fifth sub-temperature, and the sixth sub-temperature.

[0119] Exemplarily, Figure 5 as shown in, there are multiple monitoring points 03 (exemplarily marked as cylinders) arranged in the granary, and each monitoring point 03 is provided with an optical fiber temperature and humidity sensor to obtain the temperature and / or humidity of each monitoring point.

[0120] Step S33, taking t i 、t li 、tri , t ai , t ui , t fi , t bi and t i+1 , input the second warning model to determine the second mildew parameter.

[0121] Optionally, step S33 includes step S331 and step S332, which are specifically as follows:

[0122] Step S331, based on t i , t li , t ri , t ai , t ui , t fi and t bi , calculate the reference temperature T i of the i-th day.

[0123] Optionally, use the formula for calculating the reference temperature in Equation (2) as Equation (4), that is, calculate the reference temperature T i of the i-th day according to the following second formula:

[0124] T i = α0t i + α1t li + α2t ri + α3t ai + α4t ui + α5t fi + α6t bi , Equation (4);

[0125] where, α0 is the first temperature weight coefficient, α1 is the second temperature weight coefficient, α2 is the third temperature weight coefficient, α3 is the fourth temperature weight coefficient, α4 is the fifth temperature weight coefficient, α5 is the sixth temperature weight coefficient, α6 is the seventh temperature weight coefficient, and the values of α0, α1, α2, α3, α4, α5 and α6 are related to the type of grain.

[0126] Step S332, calculate the difference between t i+1 and T i to determine the second mildew parameter corresponding to the target monitoring point.

[0127] In this embodiment, by sequentially taking each monitoring point as the target monitoring point, the second mildew parameter corresponding to each monitoring point is determined respectively, so as to determine the possibility of grain mildew around each monitoring point, and realize the mildew warning of the grain in the granary. Specifically, the increase in humidity and moisture content will provide good conditions for the growth and activities of molds and other microorganisms. These microorganisms will generate respiratory heat during the decomposition process, resulting in a local temperature rise. Therefore, the temperature rise inside the grain pile is used as the secondary warning.

[0128] Moreover, by arranging distributed monitoring points, multi-point mildew warning in the granary is realized, so as to accurately predict the mildew location.

[0129] Exemplarily, the current monitoring point has the greatest influence on the reference temperature, so the set weight coefficient (α0) is the largest; the front, back, left and right are on the same layer as the current monitoring point, with similar environments and relatively large influences, so the set weight coefficients (α1, α2, α5 and α6) for the front, back, left and right are a bit larger and the same; the upper and lower are not on the same layer as the current monitoring point, and there are differences between the environments, so the set weight coefficients (α3 and α4) for the upper and lower are a bit smaller and the same.

[0130] Step S4, when the mildew parameter is greater than the mildew parameter warning threshold or the environmental parameter is greater than the environmental parameter warning threshold, an alarm is issued.

[0131] Specifically, when the mildew parameter is greater than the warning threshold or the environmental parameter is greater than the environmental parameter warning threshold, the possibility of mildew in the grain is relatively high.

[0132] Since the mildew of grain is predicted by using a multi-warning model, multiple mildew parameters will be generated. When any mildew parameter is greater than the warning threshold, it may indicate a relatively high possibility of mildew in the grain, and timely adjustment is required.

[0133] Optionally, the environmental parameter includes carbon dioxide concentration; the alarm includes a first-level alarm, a second-level alarm and a third-level alarm; the environmental parameter warning threshold includes a carbon dioxide threshold; step S4 includes steps S41 to S43, which are specifically as follows:

[0134] Step S41, when the first mildew parameter is greater than the moisture threshold, a first-level alarm is issued.

[0135] Step S42, when the second mildew parameter is greater than the temperature change threshold, a second-level alarm is issued.

[0136] Step S43, when the third mildew parameter is greater than the carbon dioxide change threshold or the carbon dioxide concentration is greater than the carbon dioxide threshold, a third-level alarm is issued.

[0137] In this embodiment, by issuing a graded alarm, the possibility of mildew in the current grain in the granary is more accurately indicated, so as to improve the accuracy of grain mildew warning.

[0138] Exemplarily, in combination with the principles of the present application described above, it can be known that when the moisture content of grains increases, the risk of grain mildew will increase. At this time, issuing a first-level warning can prompt the administrator to perform operations such as grain moisture removal. Further, when the humidity in the granary begins to generally increase, molds and microorganisms have a good breeding environment. At this time, issuing a second-level warning can prompt the administrator to perform operations such as grain moisture removal and temperature reduction to reduce the reproduction of molds and microorganisms, thereby inhibiting the mildew of grains. Finally, when molds and microorganisms reproduce in large numbers, a large amount of carbon dioxide will be produced. At this time, issuing a third-level warning can prompt the administrator to check the mildew situation in the granary, etc.

[0139] By using the granary optical fiber monitoring and warning method provided in the embodiments of the present application, by measuring various parameters in the granary, to determine the mildew possibility of grains according to multi-dimensional parameters, and issue an alarm when the mildew possibility is relatively high to remind the administrator to intervene. Realize the mildew warning of grains and improve the accuracy of mildew warning. Reduce the drift phenomenon generated by using electronic temperature sensors and improve the reliability of the system.

[0140] The following respectively give application examples of each prediction model.

[0141] Exemplarily, assume that the environmental parameters of the wheat inside the monitored granary in the current state are as shown in Table 1 below:

[0142] Table 1

[0143] Temperature Humidity Carbon dioxide concentration 25℃ 46％ 0.2％ ;

[0144] Specifically, in the case of distributed temperature and humidity measurement (i.e., setting multiple monitoring points in the granary to obtain the temperature and humidity at multiple positions), the above temperature is the average temperature in the granary, and the humidity is the average humidity in the granary.

[0145] Exemplarily, when the grain stored in the granary is wheat, the weight coefficients of the first-level warning model corresponding to wheat are as shown in Table 2 below:

[0146] Table 2

[0147] <![CDATA[β 1,n > <![CDATA[β 2,n > <![CDATA[β 3,n > Adsorption (n = 2) 617.668 127.828 0.151 Desorption (n = 1) 441.219 51.581 0.163 ;

[0148] Substitute the temperature and humidity in the environmental parameters of wheat into this model, and the original moisture content of wheat can be calculated. Desorption: M1 = 12.9%, adsorption: M2 = 10.92%, average: 11.91%. That is, the first mildew parameter M = 11.91%.

[0149] The average moisture content of 11.91% was used as the original moisture content of wheat for analysis. Compared with the set threshold, the moisture content: 11.91% < 12.5%, that is, less than the set threshold, indicating that the wheat is in a normal state at this time.

[0150] Exemplarily, when the grain stored in the granary is corn, the weight coefficients of the first-level early warning model corresponding to the corn are shown in Table 3 below:

[0151] Table 3

[0152] <![CDATA[β 1,n > <![CDATA[β 2,n > <![CDATA[β 3,n > Adsorption (n = 2) 863.159 108.443 0.216 Desorption (n = 1) 581.393 35.840 0.235 。

[0153] Exemplarily, when the grain stored in the granary is rice, the weight coefficients of the first-level early warning model corresponding to the rice are shown in Table 4 below:

[0154] Table 4

[0155] <![CDATA[β 1,n > <![CDATA[β 2,n > <![CDATA[β 3,n > Adsorption (n = 2) 784.894 143.337 0.174 Desorption (n = 1) 588.376 59.026 0.180 。

[0156] Figure 6 This is the temperature schematic diagram of the target monitoring point and its surrounding monitoring points on the first day provided by the embodiment of the present application.

[0157] Exemplarily, as shown in Figure 6 , on the first day, the first sub-temperature t1 = 25°C, the second sub-temperature t l1 = 25.5°C, the third sub-temperature t r1 = 25.5°C, the fourth sub-temperature t a1 = 24°C, the fifth sub-temperature t u1 = 26°C, the sixth sub-temperature t f1 = 25°C, the seventh sub-temperature t b1 = 25°C. On the second day, the eighth sub-temperature t2 = 25.5°C.

[0158] Exemplarily, when the grain in the granary is wheat, let α0 = 0.2, α1 = 0.15, α2 = 0.15, α3 = 0.1, α4 = 0.1, α5 = 0.15, and α6 = 0.15. That is, the reference temperature T i = 0.2t1 + 0.15t l1 + 0.15t r1 + 0.1t a1 + 0.1t u1 + 0.15t f1 + 0.15t b1 。

[0159] Inputting the above-collected temperature data into the secondary early warning model can obtain the reference temperature T1 on the first day as:

[0160] T1 = 0.2×25 + 0.15×25.5 + 0.15×25.5 + 0.1×24 + 0.1×26 + 0.15×25 + 0.15×25 = 25.15 °C.

[0161] Further calculate the second mildew parameter D as: D = t2 - T1 = 25.5 - 25.15 = 0.35 °C.

[0162] Considering the law of temperature rise and change during grain mildew, set the temperature change threshold k = 0.5 °C. If the second mildew parameter corresponding to the target monitoring point on the second day exceeds the temperature change threshold of 0.5 °C, that is, D > 0.5 °C, it indicates that the internal respiration of the grain pile is strong and the risk of grain mildew is increasing. The system will issue a secondary alarm, indicating a higher risk, and some preventive measures should be taken; on the contrary, if the second mildew parameter corresponding to the target monitoring point on the second day does not exceed the temperature change threshold of 0.5 °C, that is, D ≤ 0.5 °C, it indicates that the respiration state of the grain at this monitoring point inside the granary is normal. The system will use the temperature t2 of the target monitoring point on the second day and the surrounding temperatures t l2 、t r2 、t a2 、t u2 、t f2 and t b2 to recalculate the new reference temperature T2 and compare it with the temperature t3 of this target monitoring point on the next day. The system will compare the actual temperature t i of the target monitoring point every day with the reference temperature T i-1 calculated on the previous day. This process will be iterated every day and compared with the temperature change threshold of 0.5 °C.

[0163] Exemplarily, the reference temperature of a certain monitoring point of wheat on the first day is T1 = 25.15 °C, and the temperature of this monitoring point on the second day is t2 = 25.5 °C, that is, D = t2 - T1 = 25.5 °C - 25.15 °C = 0.35 °C < 0.5 °C, indicating that the temperature of the monitoring point on the second day is normal. Then the system will use the temperature of this monitoring point on the second day as t2 and calculate T2 in combination with the surrounding temperatures on the second day for comparison on the next day; if the temperature of this monitoring point on the second day is t2 = 26 °C, that is, D = t2 - T1 = 26 °C - 25.15 °C = 0.85 °C > 0.5 °C, the system will issue a secondary alarm.

[0164] Exemplarily, set the carbon dioxide change threshold m = 0.05% and the carbon dioxide threshold n = 5%. If the third mildew parameter is less than or equal to 0.05% and the current carbon dioxide concentration is lower than 5%, then the current change is considered normal. If the third mildew parameter is greater than 0.05%, or the current carbon dioxide concentration is higher than 5%, it indicates a high risk and the grain may have mildewed. The system issues a tertiary alarm and immediate measures need to be taken for treatment.

[0165] Figure 7 This is the first example table of carbon dioxide concentration in a granary provided in the embodiment of the present application. Specifically, Figure 7 This is a schematic diagram of the changes in carbon dioxide concentration in the granary when the wheat in the granary is not moldy.

[0166] For example, in combination Figure 7 As shown, the carbon dioxide concentration produced by wheat itself (unit: %) is: C0=0.2, C1=0.265, C2=0.3274, C3=0.395, C4=0.4626, C5=0.5198, C6=0.5874, C7=0.655, C8=0.7148.

[0167] The above-mentioned carbon dioxide concentration is input into the three-level early warning model, and the third mildew parameter E on the seventh day is obtained: |(|C7-C6|-|C6-C5|)|=|(|0.7148-0.655|-|0.655-0.5874|)|=|0.0598-0.0676|=0.0078≤0.05, and C7=0.655%≤5%, indicating that the current changes in wheat are normal.

[0168] Figure 8 This is a second example table of carbon dioxide concentration in a granary provided in the embodiment of the present application. Specifically, Figure 8 This is a schematic diagram of the changes in carbon dioxide concentration in a granary when wheat in the granary becomes moldy.

[0169] For example, in combination Figure 8 As shown, the carbon dioxide concentration produced by wheat itself (unit: %) is: C0=0.2, C1=0.2625, C2=0.45, C3=0.65, C4=0.9, C5=1.075, C6=1.125, C7=1.175, C8=1.2.

[0170] The above carbon dioxide concentration is input into the three-level warning model, and the third mildew parameter E on the second day is obtained = |(|C2-C1|-|C1-C0|)| = |(|0.45-0.2625|-|0.2625-0.2|)| = |0.1875-0.0625| = 0.125>0.05, indicating that the wheat may have become mildewed, and the system issues a three-level alarm.

[0171] In the absence of mildew, the CO2 concentration changes relatively smoothly, the amplitude of changes between data points is small, and the difference between data points satisfies the formula, indicating that the wheat is safe. However, in the case of mildew, the CO2 concentration changes more dramatically, especially in the early stage of mildew, the amplitude of changes increases significantly, and does not satisfy the formula, indicating a high risk, and the system will issue a level 3 alarm.

[0172] Corresponding to the embodiments of the foregoing method for monitoring and warning of grain bin optical fiber, the present application also provides embodiments of a grain bin optical fiber monitoring and warning system. The grain bin optical fiber monitoring and warning system includes an environmental parameter detection module and a warning module that are communicatively connected to each other; the environmental parameter detection module is configured to obtain environmental parameters in the grain bin; the warning module is configured to obtain environmental parameters in the grain bin and a warning model corresponding to the types of grains stored in the grain bin; the environmental parameters include one or more of temperature, humidity, and carbon dioxide concentration; the warning models include a primary warning model, a secondary warning model, and a tertiary warning model; the environmental parameters corresponding to the primary warning model include temperature and humidity; the environmental parameters corresponding to the secondary warning model include temperature; the environmental parameters corresponding to the tertiary warning model include carbon dioxide concentration; based on the types of grains and the warning models, warning thresholds are determined; the warning thresholds include a mildew parameter warning threshold and an environmental parameter warning threshold; the environmental parameters are input into the warning model to determine the mildew parameters of the grains; and an alarm is issued when the mildew parameters are greater than the mildew parameter warning threshold or the environmental parameters are greater than the environmental parameter warning threshold.

[0173] By using the grain bin optical fiber monitoring and warning system provided by the embodiments of the present application, by measuring various parameters in the grain bin, the mildew possibility of the grains is determined based on multi-dimensional parameters, and an alarm is issued when the mildew possibility is relatively high to remind the manager to intervene. The mildew warning of the grains is realized, and the accuracy of the mildew warning is improved. The drift phenomenon caused by the use of electronic temperature sensors is reduced, and the reliability of the system is improved.

[0174] Optionally, the environmental parameter detection module includes an optical fiber environmental parameter sensing device and a field monitoring device. The optical fiber environmental parameter sensing device is configured to obtain optical signals corresponding to environmental parameters in the grain bin; the optical fiber environmental parameter sensing device includes a plurality of monitoring points, and the environmental parameters of each monitoring point of the environmental parameters; the field monitoring device is optically connected to the optical fiber environmental parameter sensing device and is configured to demodulate the optical signals corresponding to the environmental parameters collected by the optical fiber environmental parameter sensing device to obtain the environmental parameters in the grain bin; the warning module is further configured to construct a three-dimensional environmental cloud map of the grain bin according to the environmental parameters.

[0175] Specifically, by constructing a three-dimensional environmental cloud map according to the environmental parameters, the environmental parameters in the grain bin are displayed in a three-dimensional and intuitive manner, so as to more specifically reflect the environmental changes in the grain bin.

[0176] Combined with Figure 5As shown in the figure, the fiber optic environmental parameter sensing device includes: a plurality of fiber optic temperature and humidity sensors, a carbon dioxide sensor 04, an oxygen sensor 05, and a phosphine sensor 06. Each monitoring point 03 corresponds to a fiber optic temperature and humidity sensor, and there is a preset distance between each fiber optic temperature and humidity sensor. The carbon dioxide sensor 04, the oxygen sensor 05, and the phosphine sensor 06 are all arranged on the top of the granary 01. The fiber optic temperature and humidity sensor is used to detect temperature and humidity, the carbon dioxide sensor 04 is used to detect the carbon dioxide concentration in the granary, the oxygen sensor 05 is used to detect the oxygen concentration in the granary, and the phosphine sensor 06 is used to detect the phosphine concentration in the granary.

[0177] Combined with Figure 5 For the fiber optic environmental parameter sensing device shown in the figure, the environmental three-dimensional cloud map includes the temperature and humidity corresponding to each monitoring point, so as to intuitively display the temperature and humidity of each spatial area in the granary, so as to reflect where the risk of grain mildew is relatively high. Exemplarily, when a secondary alarm is determined to exist, the monitoring point corresponding to the secondary alarm can be colored to highlight that the risk of mildew of the grain at this monitoring point and its accessories is relatively high. For example, the color of the monitoring point without a secondary alarm is blue, and the shade of blue varies according to the temperature and humidity, while the color of the monitoring point with a secondary alarm is red.

[0178] Specifically, a low-oxygen environment (usually with an oxygen concentration below 5%) can inhibit the respiration of molds, pests, and the grain itself through controlled atmosphere storage (CA), and delay spoilage. An excessively high oxygen concentration will accelerate the growth of microorganisms and oxidation reactions. If the oxygen concentration rises abnormally, it may indicate poor airtightness of the silo or improper ventilation, resulting in an increased risk of spoilage. Moreover, when the grain deteriorates, the activities of microorganisms and pests will consume oxygen and release carbon dioxide, resulting in a decrease in the oxygen concentration. Continuously monitoring the change of oxygen can indirectly reflect whether the grain has mildewed or been infested by pests. When the oxygen concentration is too high or decreases abnormally, an alarm needs to be issued to indicate possible mildew.

[0179] Phosphine is a commonly used fumigant. An effective concentration (usually ≥ 300 ppm, maintained for 5 - 7 days) can kill storage pests. Insufficient concentration will lead to fumigation failure and increased grain loss due to pest activities. A rapid decrease in concentration may indicate silo leakage or strong grain adsorption, and top-up is required; a continuously low concentration may indicate the emergence of drug-resistant pests. When the phosphine concentration is too low, an alarm needs to be issued to indicate the need to supplement the drug to eliminate pests, and when the phosphine concentration remains low, an alarm needs to be issued to indicate the possible emergence of drug-resistant pests.

[0180] Optionally, a level 4 alarm is issued when the oxygen concentration decreases, the carbon dioxide concentration increases, and the phosphine concentration decreases. In this way, it is used to indicate active microorganisms or fumigation failure.

[0181] Optionally, when the oxygen concentration is normal and the phosphine concentration fluctuates abnormally, a level-five alarm is issued. In this way, it is prompted to check the fumigation system or the airtightness.

[0182] It should be noted that those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the application disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include well-known common general knowledge or conventional technical means in the technical field not disclosed in the present application. The specification and examples are only regarded as exemplary, and the true scope of the present application is pointed out by the claims.

Claims

1. A granary optical fiber monitoring and early warning method, characterized in that: Applied to the granary optical fiber monitoring and early warning system, the granary optical fiber monitoring and early warning method includes: Acquire environmental parameters in a granary and early warning models corresponding to the types of grain stored in the granary; the environmental parameters include one or more of temperature, humidity and carbon dioxide concentration; the early warning models include a primary early warning model, a secondary early warning model and a tertiary early warning model; the environmental parameters corresponding to the primary early warning model include the temperature and the humidity; the environmental parameters corresponding to the secondary early warning model include the temperature; the environmental parameters corresponding to the tertiary early warning model include the carbon dioxide concentration; Based on the food type and the warning model, determining a warning threshold; the warning threshold includes a mold parameter warning threshold and an environmental parameter warning threshold; Inputting the environmental parameters into the early warning model to determine the mildew parameters of the grain; When the mildew parameter is greater than the mildew parameter warning threshold or the environmental parameter is greater than the environmental parameter warning threshold, an alarm is issued.

2. The method for monitoring and early warning of grain silos using optical fiber according to claim 1, characterized in that: The mildew parameters include a first mildew parameter; The first-level warning model is shown in the following first formula: , the first formula; in, is the first temperature and humidity weight coefficient, is the second temperature and humidity weight coefficient, is the third temperature and humidity weight coefficient, , and stated The value of is related to the type and state of the food, and the state includes an adsorption state and a desorption state; The first mildew parameter characterizes the equilibrium moisture content of grain; is the temperature, representing the temperature of the grain in the granary; The humidity represents the humidity inside the grain pile; The first moisture content of the food corresponding to the desorption state; The second moisture content of the food corresponds to the adsorption state.

3. The method for monitoring and early warning of grain silo using optical fiber according to claim 1, characterized in that: The mildew parameter includes a second mildew parameter; The granary optical fiber monitoring and early warning system comprises a plurality of monitoring points arranged at intervals in the granary, and the temperature comprises a sub-temperature corresponding to each of the monitoring points; Inputting the environmental parameter into the second early warning model to determine the second mildew parameter includes: Taking each of the monitoring points as a target monitoring point in turn; Determine day, the first sub-temperature corresponding to the target monitoring point , the second sub-temperature corresponding to the monitoring point located on the left side of the target monitoring point , the third sub-temperature corresponding to the monitoring point located on the right side of the target monitoring point , the fourth sub-temperature corresponding to the monitoring point located above the target monitoring point , the fifth sub-temperature corresponding to the monitoring point located below the target monitoring point , the sixth sub-temperature corresponding to the monitoring point located in front of the target monitoring point , the seventh sub-temperature corresponding to the monitoring point located behind the target monitoring point , and day, the eighth sub-temperature corresponding to the target monitoring point ; The , , , , , , and stated , input the second early warning model to determine the second mildew parameter.

4. The method for monitoring and early warning of grain silo using optical fiber according to claim 3 is characterized in that: The said , , , , , , and stated , inputting the second early warning model to determine the second mildew parameter, including: Based on the , , , , , and stated , calculate the Base temperature of the day ; Calculate the and stated The difference between the two is used to determine the second mildew parameter corresponding to the target monitoring point.

5. The method for monitoring and early warning of grain silos using optical fiber according to claim 4, characterized in that: Calculate the second formula as follows Base temperature of the day : , the second formula; in, is the first temperature weight coefficient, is the second temperature weight coefficient, is the third temperature weight coefficient, is the fourth temperature weight coefficient, is the fifth temperature weight coefficient, is the sixth temperature weight coefficient, is the seventh temperature weight coefficient, , , , , , and The value of is related to the type of food.

6. The method for monitoring and early warning of grain silo using optical fiber according to claim 3, characterized in that: Determine day, a second sub-temperature corresponding to the monitoring point located on the left side of the target monitoring point, a third sub-temperature corresponding to the monitoring point located on the right side of the target monitoring point, a fourth sub-temperature corresponding to the monitoring point located above the target monitoring point, a fifth sub-temperature corresponding to the monitoring point located below the target monitoring point, a sixth sub-temperature corresponding to the monitoring point located in front of the target monitoring point, and a seventh sub-temperature corresponding to the monitoring point located behind the target monitoring point, including: Determining the location information of the target monitoring point; When the position information is at the leftmost position, replacing the second sub-temperature with the first sub-temperature; When the position information is the rightmost one, replacing the third sub-temperature with the first sub-temperature; When the position information is at the top, replacing the fourth sub-temperature with the first sub-temperature; When the position information is at the bottom, the fifth sub-temperature is replaced by the first sub-temperature; When the position information is the frontmost, replacing the sixth sub-temperature with the first sub-temperature; When the position information is the last one, the seventh sub-temperature is replaced by the first sub-temperature.

7. The method for monitoring and early warning of grain silos using optical fiber according to claim 1, characterized in that: The mildew parameter includes a third mildew parameter; The three-level warning model is shown in the third formula below: , the third formula; in, is the third mildew parameter; For the The daily carbon dioxide concentration, For the The daily carbon dioxide concentration, For the The daily carbon dioxide concentration.

8. The method for monitoring and early warning of grain silo using optical fiber according to claim 1, characterized in that: The environmental parameters include carbon dioxide concentration; the mildew parameters include a first mildew parameter, a second mildew parameter and a third mildew parameter; the mildew parameter warning thresholds include a moisture threshold, a temperature change threshold and a carbon dioxide change threshold; the alarms include a first level alarm, a second level alarm and a third level alarm; The environmental parameter warning threshold includes a carbon dioxide threshold; when the mildew parameter is greater than the warning threshold, issuing an alarm includes: When the first mildew parameter is greater than the moisture threshold, issuing the first level alarm; When the second mildew parameter is greater than the temperature change threshold, issuing the second level alarm; When the third mildew parameter is greater than the carbon dioxide change threshold, or the carbon dioxide concentration is greater than the carbon dioxide threshold, the third level alarm is issued.

9. A granary optical fiber monitoring and early warning system, characterized in that: include: Environmental parameter detection module and early warning module that are connected to each other in communication; The environmental parameter detection module is configured to obtain environmental parameters in the granary; The early warning module is configured to obtain the environmental parameters in the granary and the early warning model corresponding to the type of grain stored in the granary; the environmental parameters include one or more of temperature, humidity and carbon dioxide concentration; the early warning model includes a first-level early warning model, a second-level early warning model and a third-level early warning model; the environmental parameters corresponding to the first-level early warning model include the temperature and the humidity; the environmental parameters corresponding to the second-level early warning model include the temperature; the environmental parameters corresponding to the third-level early warning model include the carbon dioxide concentration; based on the type of grain and the early warning model, determine the early warning threshold; the warning threshold includes a mold parameter early warning threshold and an environmental parameter early warning threshold; input the environmental parameters into the early warning model to determine the mold parameters of the grain; and issue an alarm when the mold parameter is greater than the mold parameter early warning threshold or the environmental parameter is greater than the environmental parameter early warning threshold.

10. The grain silo optical fiber monitoring and early warning system according to claim 9, characterized in that: The environmental parameter detection module comprises: The optical fiber environmental parameter sensing device is configured to obtain an optical signal corresponding to the environmental parameter in the granary; the optical fiber environmental parameter sensing device includes a plurality of monitoring points, and the environmental parameter is an environmental parameter of each of the monitoring points; A field monitoring device, optically connected to the optical fiber environmental parameter sensing device, configured to demodulate an optical signal corresponding to the environmental parameter collected by the optical fiber environmental parameter sensing device to obtain the environmental parameter in the granary; The early warning module is also configured to construct a three-dimensional cloud map of the environment in the granary according to the environmental parameters.

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