Agricultural storage fresh-keeping monitoring method and system based on Internet of Things

Abstract

The invention belongs to the technical field of agricultural storage, and provides an agricultural storage fresh-keeping monitoring method and system based on the Internet of Things, and the method comprises the steps: deploying an Internet of Things sensor network in a grain storage region, collecting the air flow data of the surface of a granary in real time according to a fluid mechanics algorithm, and recognizing a potential vortex region of the surface of the granary; carrying out comprehensive analysis by combining the change of the eddy current center space position of the potential eddy current region with the change of the Q value time dimension of the potential eddy current region, and identifying a steady-state eddy current region of the potential eddy current region; according to the method, the oxygen concentration of the grains in the steady-state eddy current area in the monitoring period is extracted, the potential breathing jump phenomenon degree of the grains in the steady-state eddy current area in the monitoring period is recognized, the sensitivity of the entropy theory to nonlinear breathing signals is utilized, data support is provided for accurate mildew prevention of the granary, and the method is suitable for popularization and application. And the mildew risk of grains in a steady-state vortex region due to abnormal metabolism can be effectively reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of agricultural storage, and in particular to an agricultural storage and freshness-keeping monitoring method and system based on the Internet of Things. Background Art

[0002] In the field of agricultural storage, eddy currents caused by uneven airflow distribution in grain silos can easily lead to abnormal local temperature and humidity, accelerating the respiratory metabolism and mildew of grain.

[0003] In the existing technology, traditional monitoring methods have significant defects: First, the identification of eddy areas relies on single-point anemometers or manual inspections, and cannot capture the velocity gradient changes caused by boundary layer separation in real time through multi-sensor arrays and fluid mechanics algorithms, making it difficult to accurately locate airflow stagnation areas and distinguish between steady-state and transient eddies; second, the monitoring system lacks a full-chain linkage mechanism of flow field analysis, grain condition assessment and intelligent intervention, and cannot cope with the coupling effects of complex geometric structures, variable airflow and nonlinear grain metabolism in storage environments. The accumulation of temperature and humidity caused by local eddies often leads to grain mold, resulting in economic losses and low storage management efficiency.

[0004] To this end, the present invention provides an agricultural storage and preservation monitoring method and system based on the Internet of Things. Summary of the Invention

[0005] In order to make up for the deficiencies of the prior art, at least one technical problem raised in the background technology is solved.

[0006] In a first aspect, the present invention provides an agricultural storage and preservation monitoring method based on the Internet of Things, comprising:

[0007] Deploy an IoT sensor network in the grain storage area to collect real-time air flow data on the grain silo surface using fluid dynamics algorithms to identify potential vortex areas on the grain silo surface.

[0008] By comprehensively analyzing the changes in the spatial position of the eddy center in the potential eddy area and the changes in the time dimension of the Q value in the potential eddy area, the steady-state eddy area in the potential eddy area is identified.

[0009] The oxygen concentration of grain in the steady-state vortex area during the monitoring period is extracted to determine the grain respiration rate. By analyzing the entropy change of the grain respiration rate in the steady-state vortex area, it is identified whether there is a potential respiratory jump phenomenon in the grain in the steady-state vortex area during the monitoring period, and the degree of the potential respiratory jump phenomenon is evaluated.

[0010] Preferably, the specific process of identifying the potential eddy current area on the surface of the granary is:

[0011] Velocity data is collected by a micro wind speed sensor, and the velocity gradient tensor is calculated by the central difference method. The velocity gradient tensor is decomposed into the strain rate tensor and the rotation tensor, and the square of the Frobenius norm of the strain rate tensor is calculated. and the square of the Frobenius norm of the rotation tensor , through the formula The second-order invariant Q value of the velocity gradient tensor is calculated. If it is greater than the Q threshold, the grid area where the corresponding sensor node is located is recorded as a potential eddy current area.

[0012] Preferably, the specific process of identifying the steady-state eddy current region of the potential eddy current region is:

[0013] Based on any potential eddy current area, a monitoring period is preset and the monitoring period is divided into several time points with equal time intervals. The coordinates of the eddy center of the potential eddy current area are extracted at the monitoring time points, and the maximum displacement of the eddy center of the potential eddy current area within the monitoring period is calculated; if it is less than or equal to the standard value of the maximum displacement, the Q values ​​of all time points are integrated into a Q value sequence in chronological order, and the coefficient of variation formula is used to calculate the coefficient of variation of the Q value in the Q value sequence, which is recorded as the Q value fluctuation coefficient. If it is less than the Q value fluctuation coefficient threshold, the corresponding potential eddy current area is recorded as a steady-state eddy current area.

[0014] Preferably, the specific process of determining the grain respiration rate is:

[0015] The oxygen concentration in the steady-state eddy flow area is collected by an oxygen sensor, and the oxygen concentration in the steady-state eddy flow area within the monitoring period is integrated into an oxygen concentration sequence in chronological order;

[0016] Extract the oxygen concentrations of two adjacent monitoring time points from the oxygen concentration sequence as a time window, perform subtraction processing on the latter oxygen concentration and the previous oxygen concentration within the time window, take the absolute value, and obtain the oxygen concentration deviation value of the time window;

[0017] The oxygen concentration deviation value of the time window is ratioed to the duration corresponding to the time window to obtain the food respiration rate of the time window.

[0018] Preferably, the process of identifying whether there is a potential respiratory jump phenomenon in the food in the steady-state eddy current area during the monitoring period is:

[0019] The food respiration rates of all time windows are integrated into a respiration rate sequence in chronological order. For the respiration rate sequence, the embedding dimension and delay time are taken to construct subsequences. For each subsequence, the subsequences are sorted by numerical size to generate a permutation pattern. All subsequences are traversed and the probability of each permutation pattern in all subsequences is calculated by: Calculate the entropy value H of the grain respiration rate during the monitoring period, where m represents the embedding dimension, It represents the probability of occurrence of each arrangement pattern. If it is greater than the entropy threshold of the grain respiration rate during the monitoring period, it indicates that there is a potential respiratory jump phenomenon in the grain in the steady-state eddy region during the monitoring period.

[0020] Preferably, the process of evaluating the degree of potential respiratory climacteric phenomenon is:

[0021] The grain respiration rate during the monitoring period is analyzed to determine the jump amplitude value and jump time value. The jump amplitude value and the jump time value are weighted and summed to obtain the jump degree index. If it is greater than the jump index threshold, it means that the potential breathing jump phenomenon of the grain in the steady-state vortex area during the monitoring period is serious. Otherwise, it means that the potential breathing jump phenomenon of the grain in the steady-state vortex area during the monitoring period is not serious.

[0022] Preferably, the process of determining the jump amplitude value is:

[0023] The maximum value of the grain respiration rate during the monitoring period is extracted, and the absolute value is taken after subtracting it from the standard value of the grain respiration rate to obtain the jump amplitude value.

[0024] Preferably, the process of determining the transition time value is:

[0025] According to the grain respiration rate in the time window during the monitoring period, the transition window is determined, and the proportion of the transition windows in all time windows is calculated and recorded as the transition time value.

[0026] Preferably, the process of determining the transition window is:

[0027] If the grain respiration rate in the time window is greater than or equal to the standard value of the grain respiration rate, the corresponding time window is recorded as a jump window.

[0028] In a second aspect, the present invention further provides an agricultural storage and preservation monitoring system based on the Internet of Things, the system comprising:

[0029] Potential eddy current area identification module: This module deploys an IoT sensor network in the grain storage area. Using fluid dynamics algorithms, it collects real-time air flow data on the grain silo surface and identifies potential eddy current areas on the grain silo surface.

[0030] Steady-state eddy current region determination module: This module identifies the steady-state eddy current region of a potential eddy current region by comprehensively analyzing the changes in the spatial position of the eddy current center in the potential eddy current region and the changes in the Q value in the time dimension of the potential eddy current region.

[0031] Respiratory jump analysis module: extracts the oxygen concentration of grain in the steady-state vortex area during the monitoring period, determines the grain respiration rate, and identifies whether there is a potential respiratory jump phenomenon in the steady-state vortex area during the monitoring period by analyzing the entropy change of the grain respiration rate in the steady-state vortex area, and evaluates the degree of the potential respiratory jump phenomenon.

[0032] The beneficial effects of the present invention are as follows:

[0033] 1. The present invention achieves accurate identification of eddy areas in granaries and dynamic assessment of grain respiration status by constructing a monitoring system that integrates an Internet of Things sensor network with a fluid mechanics algorithm: utilizing a micro wind speed sensor array in a Cartesian coordinate system and a central difference method, the velocity gradient changes caused by airflow boundary layer separation are captured in real time to determine potential eddy areas, thus solving the problem that traditional methods have difficulty locating airflow stagnation areas; further, through a spatiotemporal joint analysis of eddy center displacement and Q value fluctuations, steady-state and non-steady-state eddies are distinguished, providing a basis for targeted monitoring; combining Kalman filter denoising with the dynamic center method, the eddy center positioning accuracy is improved.

[0034] 2. The present invention uses oxygen sensors to collect data in real time and converts it into a respiratory rate sequence. The permutation entropy algorithm is used to capture the disordered characteristics of the temporal pattern of the respiratory rate, solving the problem that the traditional threshold method is difficult to identify early metabolic abnormalities. The transition degree index is constructed by combining the jump amplitude and time proportion, and the weight is assigned through the hierarchical analysis method to achieve quantitative risk classification. Finally, the warehouse turnover intervention is triggered based on the jump degree, and the sensitivity of entropy theory to nonlinear respiratory signals is used to provide data support for precise mildew prevention in granaries, which can effectively reduce the risk of mildew caused by metabolic abnormalities in the steady-state eddy current area of ​​grain, and improve the intelligence and refinement of warehouse management. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The present invention will be further described below with reference to the accompanying drawings.

[0036] Figure 1 This is a flowchart of a method for monitoring agricultural storage and freshness preservation based on the Internet of Things according to an embodiment of the present invention;

[0037] Figure 2 This is a system block diagram of an agricultural storage and preservation monitoring system based on the Internet of Things in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to make the technical means, creative features, objectives and effects achieved by the present invention easier to understand, the present invention is further described below in conjunction with specific implementation methods.

[0039] Example 1

[0040] See also Figure 1As shown, an agricultural storage and freshness-keeping monitoring method based on the Internet of Things according to an embodiment of the present invention includes the following steps:

[0041] Step 1: Deploy an IoT sensor network in the grain storage area. Using fluid dynamics algorithms, the network collects real-time air flow data on the grain silo surface and identifies potential vortex areas on the grain silo surface.

[0042] Wherein, the air flow data includes speed data;

[0043] Irregular interfaces formed by bumps, corners, inlets and outlets, rough coatings, or grain accumulation on the silo surface can cause boundary layer separation when air flows through. When air flows from a smooth surface to a location with a sudden change in geometry (such as a silo eave or the edge of a vent), the velocity near the wall decreases and the pressure increases, forcing the fluid to break away from the wall and forming a vortex region downstream.

[0044] There are significant temperature and humidity differences between the normally ventilated areas outside the eddy zone and the eddy zone. For example, in the summer, hot air outside the warehouse may be trapped when flowing through the eddy zone, causing the grain temperature inside the corresponding warehouse to be 5-10°C higher than other areas, accelerating grain respiration and deteriorating its quality. Therefore, by identifying the eddy zone on the grain silo surface and monitoring the grain respiration rate in the eddy zone, the temperature can be regulated to provide reliable support for ensuring the quality of the grain inside the silo.

[0045] Micro wind speed sensors are densely arranged in the Cartesian coordinate system (x, y, z) on the rough surface of the granary to form a grid monitoring network, covering the geometric mutation locations that may cause flow separation;

[0046] A distributed synchronous acquisition system is used to eliminate multi-sensor delay errors; Kalman filtering is used to eliminate high-frequency noise and retain the low-frequency pulsation characteristics of the flow field;

[0047] The speed data is collected by micro wind speed sensors. The position of the arranged micro wind speed sensor is used as a sensor node. The speed data of the adjacent 3*3 grid sensor nodes are used to calculate the velocity gradient tensor by the central difference method. ,in, Represents the velocity component in the i-th direction, i=1,2,3 corresponds to x, y, z, Represents the spatial coordinate of the jth direction, j=1,2,3 corresponds to x, y, z;

[0048] For example, It represents the rate of change of the x-direction velocity on the x-coordinate. Indicates the rate of change of the y-direction velocity in the z-coordinate;

[0049] The velocity gradient tensor describes the rate of change of velocity in the flow field in the spatial coordinates and reflects the motion state of the fluid micro-groups;

[0050] According to linear algebra, any second-order tensor can be uniquely decomposed into a symmetric part and an antisymmetric part, and the velocity gradient tensor can be decomposed into a strain rate tensor and a rotation tensor;

[0051] It should be noted that the strain rate tensor represents the deformation characteristics of the fluid, and the rotation tensor represents the rotation characteristics of the fluid;

[0052] According to the strain rate tensor, the strain rate tensor matrix is ​​constructed. The elements of the strain rate tensor matrix are: , according to the rotation tensor, construct the rotation tensor matrix, the rotation tensor matrix elements are: ;

[0053] Calculate the square of the Frobenius norm of the strain rate tensor based on the strain rate tensor matrix elements , the specific calculation formula is: ;

[0054] Computes the square of the Frobenius norm of the rotation tensor based on the rotation tensor matrix elements. , the specific calculation formula is: ;

[0055] By formula Calculate the second-order invariant Q value of the velocity gradient tensor;

[0056] Compare the Q value to the Q threshold:

[0057] If the Q value is greater than the Q threshold, the grid area where the corresponding sensor node is located is recorded as a potential eddy current area;

[0058] If the Q value is less than or equal to the Q threshold, the grid area where the corresponding sensor node is located is recorded as a normal area;

[0059] Step 2: Through comprehensive analysis of the changes in the spatial position of the eddy center in the potential eddy area and the changes in the Q value in the time dimension of the potential eddy area, the steady-state eddy area in the potential eddy area is identified;

[0060] Based on any potential eddy current area, a monitoring period is preset and divided into several time points with equal time intervals. The coordinates of the eddy current center in the potential eddy current area are extracted at the monitoring time points. ,in, The horizontal one-dimensional coordinate representing the vortex center coordinate, Another dimension in the horizontal direction representing the coordinates of the vortex center, Indicates the vertical coordinate of the eddy center and records the Q value simultaneously , it is necessary to first use Kalman filtering to eliminate random errors in the center coordinate calculation (such as coordinate jitter caused by noise);

[0061] It should be noted that the vortex center coordinates are calculated using the dynamic center method with the rotation intensity of the potential vortex area as the weight. The rotation intensity of the potential vortex area is the modulus of the vorticity, and the finite difference method is used to calculate the vorticity of the potential vortex area.

[0062] Calculate the maximum displacement of the eddy center in the potential eddy current area during the monitoring period. The specific calculation formula is:

[0063] The maximum displacement of the eddy center in the potential eddy current region is compared with the maximum displacement standard value, which is set by those skilled in the art based on historical experience:

[0064] If the maximum displacement of the eddy center in the potential eddy region is less than or equal to the standard value of the maximum displacement, it means that the spatial position of the eddy center in the potential eddy region is stable;

[0065] If the maximum displacement of the eddy center in the potential eddy region is greater than the standard value of the maximum displacement, it means that the spatial position of the eddy center in the potential eddy region is unstable;

[0066] Based on the stability of the spatial position of the eddy center in the potential eddy area, the Q value at all time points Integrate into a Q value sequence in chronological order, calculate the standard deviation of the Q value in the Q value sequence using the standard deviation formula, calculate the mean of the Q value in the Q value sequence using the mean formula, and use the coefficient of variation formula to compare the standard deviation of the Q value in the Q value sequence with the mean of the Q value to obtain the coefficient of variation of the Q value in the Q value sequence, which is recorded as the Q value fluctuation coefficient;

[0067] Compare the Q-value fluctuation coefficient with the Q-value fluctuation coefficient threshold:

[0068] If the Q-value fluctuation coefficient is less than the Q-value fluctuation coefficient threshold, it means that the eddy center of the potential eddy region is stable in the time dimension, and the corresponding potential eddy region is recorded as a steady-state eddy region;

[0069] If the Q-value fluctuation coefficient is greater than or equal to the Q-value fluctuation coefficient threshold, it means that the eddy center of the potential eddy region is unstable in the time dimension, and the corresponding potential eddy region is recorded as an unsteady eddy region;

[0070] The technical solution of this embodiment is: deploying an Internet of Things sensor network in the grain storage area, collecting air flow data on the surface of the grain silo in real time based on a fluid mechanics algorithm, identifying potential vortex areas on the surface of the grain silo, and identifying steady-state vortex areas in the potential vortex areas by comprehensively analyzing the changes in the spatial position of the vortex center in the potential vortex area and the changes in the time dimension of the Q value in the potential vortex area; the present invention realizes the accurate identification of the vortex area in the grain silo and the dynamic evaluation of the grain breathing state by constructing a monitoring system that integrates the Internet of Things sensor network and the fluid mechanics algorithm: utilizing a micro wind speed sensor array in a Cartesian coordinate system and a central difference method to capture the velocity gradient changes caused by the separation of the airflow boundary layer in real time, determine the potential vortex area, and solve the problem that traditional methods are difficult to locate the airflow stagnation area; further, through the spatiotemporal joint analysis of the vortex center displacement and the Q value fluctuation, the steady-state and non-steady-state vortices are distinguished, providing a basis for targeted monitoring; combining Kalman filter denoising and the dynamic center method, the vortex center positioning accuracy is improved.

[0071] Example 2

[0072] See also Figure 1 As shown, an agricultural storage and freshness-keeping monitoring method based on the Internet of Things according to an embodiment of the present invention further includes the following steps:

[0073] Step 3: Extract the oxygen concentration of the grain in the steady-state eddy region during the monitoring period to determine the grain respiration rate. By analyzing the entropy change of the grain respiration rate in the steady-state eddy region, identify whether there is a potential respiratory jump phenomenon in the grain in the steady-state eddy region during the monitoring period, and evaluate the degree of the potential respiratory jump phenomenon.

[0074] The oxygen concentration in the steady-state eddy flow area is collected by an oxygen sensor, and the oxygen concentration in the steady-state eddy flow area within the monitoring period is integrated into an oxygen concentration sequence in chronological order;

[0075] Extract the oxygen concentrations of two adjacent monitoring time points from the oxygen concentration sequence as a time window, perform subtraction processing on the latter oxygen concentration and the previous oxygen concentration within the time window, take the absolute value, and obtain the oxygen concentration deviation value of the time window;

[0076] The oxygen concentration deviation value of the time window is ratioed with the duration corresponding to the time window to obtain the food respiration rate of the time window;

[0077] Integrate the food respiration rates of all time windows into a respiration rate sequence in chronological order. For the respiration rate sequence, take the embedding dimension and delay time to construct a subsequence. For each subsequence, sort the subsequences according to the numerical size to generate the arrangement pattern π.

[0078] For example, the values ​​of the subsequence [3,1,2] are 3, 1, 2, and the corresponding original indexes are 0, 1, 2. The sorted value list is 1, 2, 3, and the corresponding original indexes are 1, 2, 0. Therefore, the position of each original index in the sorted list is:

[0079] The value 3 at the original index 0 is ranked 2 after sorting.

[0080] The value 1 of the original index 1 is ranked 0 after sorting.

[0081] The value 2 at the original index 2 has a rank of 1 after sorting.

[0082] Therefore, the permutation pattern π is the ranking corresponding to each original position, that is, π[0]=2, π[1]=0, π[2]=1, so the permutation pattern π=[2,0,1];

[0083] Traverse all subsequences, count the number of times each permutation pattern appears, calculate the probability of each permutation pattern appearing in all subsequences, and according to the probability of each permutation pattern appearing in all subsequences, use: Calculate the entropy value H of the grain respiration rate during the monitoring period, where m represents the embedding dimension, Indicates the probability of each arrangement pattern appearing;

[0084] Compare the entropy value of the grain respiration rate during the monitoring period with the entropy threshold:

[0085] If the entropy value of the grain respiration rate during the monitoring period is greater than the entropy value threshold of the grain respiration rate during the monitoring period, then the grain in the steady-state eddy flow area has a potential respiratory jump phenomenon during the monitoring period;

[0086] If the entropy value of the grain respiration rate during the monitoring period is less than or equal to the entropy value threshold of the grain respiration rate during the monitoring period, then there is no potential respiratory jump phenomenon in the grain in the steady-state eddy flow area during the monitoring period;

[0087] Based on the potential respiratory jump phenomenon of grain in the steady-state eddy current area during the monitoring period, the maximum respiratory rate of grain during the monitoring period is extracted, and the absolute value is taken after subtracting it from the standard value of the grain respiratory rate to obtain the jump amplitude value.

[0088] The grain respiration rate in the time window is compared with the standard value of grain respiration rate, which is set by those skilled in the art based on historical experience:

[0089] If the grain respiration rate in the time window is greater than or equal to the standard value of the grain respiration rate, the corresponding time window is recorded as a jump window;

[0090] If the grain respiration rate in the time window is less than the standard value of grain respiration rate, the corresponding time window is recorded as a non-jump window;

[0091] Count the number of transition windows in all time windows, calculate the ratio of transition windows in all time windows, and record it as the transition time value;

[0092] The weight is determined by the hierarchical analysis method, and the jump amplitude value and the jump time value are weighted and summed to obtain the jump degree index;

[0093] Compare the jump index to the jump index threshold:

[0094] If the jump degree index is greater than the jump index threshold, it means that the potential breathing jump phenomenon of grain in the steady-state eddy current area during the monitoring period is serious;

[0095] If the jump degree index is less than or equal to the jump index threshold, it means that the potential breathing jump phenomenon of grain in the steady-state eddy current area during the monitoring period is not serious;

[0096] Based on the serious potential respiratory jump phenomenon of grain in the steady-state eddy current area during the monitoring period, the steady-state eddy current area is turned over to prevent the grain from becoming moldy;

[0097] The technical solution of this embodiment is as follows: extracting the oxygen concentration of grain in the steady-state vortex area during the monitoring period, determining the grain respiration rate, identifying whether there is a potential respiratory jump phenomenon in the steady-state vortex area during the monitoring period by analyzing the entropy change of the grain respiration rate in the steady-state vortex area, and evaluating the degree of the potential respiratory jump phenomenon; the present invention uses an oxygen sensor to collect data in real time and converts it into a respiration rate sequence, and captures the disordered characteristics of the time series pattern of the respiration rate through a permutation entropy algorithm, thereby solving the problem that the traditional threshold method is difficult to identify early metabolic abnormalities; combining the jump amplitude and time proportion to construct a jump degree index, and assigning weights through the hierarchical analysis method to achieve quantitative risk grading; finally, triggering the warehouse turnover intervention based on the jump degree, and utilizing the sensitivity of entropy theory to nonlinear respiration signals to provide data support for precise mildew prevention in granaries, which can effectively reduce the risk of mildew caused by metabolic abnormalities in grain in the steady-state vortex area and improve the intelligence and refinement of warehouse management.

[0098] Example 3

[0099] See also Figure 2 As shown, an agricultural storage and freshness-keeping monitoring system based on the Internet of Things according to an embodiment of the present invention includes the following modules:

[0100] Potential eddy current area identification module: This module deploys an IoT sensor network in the grain storage area. Using fluid dynamics algorithms, it collects real-time air flow data on the grain silo surface and identifies potential eddy current areas on the grain silo surface.

[0101] Steady-state eddy current region determination module: This module identifies the steady-state eddy current region of a potential eddy current region by comprehensively analyzing the changes in the spatial position of the eddy current center in the potential eddy current region and the changes in the Q value in the time dimension of the potential eddy current region.

[0102] Respiratory jump analysis module: extracts the oxygen concentration of grain in the steady-state vortex area during the monitoring period, determines the grain respiration rate, and identifies whether there is a potential respiratory jump phenomenon in the steady-state vortex area during the monitoring period by analyzing the entropy change of the grain respiration rate in the steady-state vortex area, and evaluates the degree of the potential respiratory jump phenomenon.

[0103] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. An agricultural storage and preservation monitoring method based on the Internet of Things, characterized by: include: Deploy an IoT sensor network in the grain storage area to collect real-time air flow data on the grain silo surface using fluid dynamics algorithms to identify potential vortex areas on the grain silo surface. By comprehensively analyzing the changes in the spatial position of the eddy center in the potential eddy area and the changes in the time dimension of the Q value in the potential eddy area, the steady-state eddy area in the potential eddy area is identified. The oxygen concentration of grain in the steady-state vortex area during the monitoring period is extracted to determine the grain respiration rate. By analyzing the entropy change of the grain respiration rate in the steady-state vortex area, it is identified whether there is a potential respiratory jump phenomenon in the grain in the steady-state vortex area during the monitoring period, and the degree of the potential respiratory jump phenomenon is evaluated.

2. An agricultural storage and freshness-keeping monitoring method based on the Internet of Things according to claim 1, characterized in that: The specific process of identifying the potential eddy current area on the surface of the granary is as follows: Velocity data is collected by a micro wind speed sensor, and the velocity gradient tensor is calculated by the central difference method. The velocity gradient tensor is decomposed into the strain rate tensor and the rotation tensor, and the square of the Frobenius norm of the strain rate tensor is calculated. and the square of the Frobenius norm of the rotation tensor , through the formula The second-order invariant Q value of the velocity gradient tensor is calculated. If it is greater than the Q threshold, the grid area where the corresponding sensor node is located is recorded as a potential eddy current area.

3. An agricultural storage and freshness monitoring method based on the Internet of Things according to claim 2, characterized in that: The specific process of identifying the steady-state eddy current region of the potential eddy current region is as follows: Based on any potential eddy current area, a monitoring period is preset and the monitoring period is divided into several time points with equal time intervals. The coordinates of the eddy center of the potential eddy current area are extracted at the monitoring time points, and the maximum displacement of the eddy center of the potential eddy current area within the monitoring period is calculated; if it is less than or equal to the standard value of the maximum displacement, the Q values ​​of all time points are integrated into a Q value sequence in chronological order, and the coefficient of variation formula is used to calculate the coefficient of variation of the Q value in the Q value sequence, which is recorded as the Q value fluctuation coefficient. If it is less than the Q value fluctuation coefficient threshold, the corresponding potential eddy current area is recorded as a steady-state eddy current area.

4. The method for monitoring agricultural storage and freshness preservation based on the Internet of Things according to claim 3 is characterized by: The specific process of determining the grain respiration rate is as follows: The oxygen concentration in the steady-state eddy flow area is collected by an oxygen sensor, and the oxygen concentration in the steady-state eddy flow area within the monitoring period is integrated into an oxygen concentration sequence in chronological order; Extract the oxygen concentrations of two adjacent monitoring time points from the oxygen concentration sequence as a time window, perform subtraction processing on the latter oxygen concentration and the previous oxygen concentration within the time window, take the absolute value, and obtain the oxygen concentration deviation value of the time window; The oxygen concentration deviation value of the time window is ratioed to the duration corresponding to the time window to obtain the food respiration rate of the time window.

5. An agricultural storage and freshness monitoring method based on the Internet of Things according to claim 4, characterized in that: The process of identifying whether there is a potential respiratory jump phenomenon in the food in the steady-state eddy current area during the monitoring period is as follows: The food respiration rates of all time windows are integrated into a respiration rate sequence in chronological order. For the respiration rate sequence, the embedding dimension and delay time are taken to construct subsequences. For each subsequence, the subsequences are sorted by numerical size to generate a permutation pattern. All subsequences are traversed and the probability of each permutation pattern in all subsequences is calculated by: Calculate the entropy value H of the grain respiration rate during the monitoring period, where m represents the embedding dimension, It represents the probability of occurrence of each arrangement pattern. If it is greater than the entropy threshold of the grain respiration rate during the monitoring period, it indicates that there is a potential respiratory jump phenomenon in the grain in the steady-state eddy region during the monitoring period.

6. The method for monitoring agricultural storage and freshness preservation based on the Internet of Things according to claim 4, characterized in that: The process of evaluating the degree of potential respiratory climacteric phenomenon is as follows: The grain respiration rate during the monitoring period is analyzed to determine the jump amplitude value and jump time value. The jump amplitude value and the jump time value are weighted and summed to obtain the jump degree index. If it is greater than the jump index threshold, it means that the potential breathing jump phenomenon of the grain in the steady-state vortex area during the monitoring period is serious. Otherwise, it means that the potential breathing jump phenomenon of the grain in the steady-state vortex area during the monitoring period is not serious.

7. An agricultural storage and freshness monitoring method based on the Internet of Things according to claim 6, characterized in that: The process of determining the jump amplitude value is as follows: The maximum value of the grain respiration rate during the monitoring period is extracted, and the absolute value is taken after subtracting it from the standard value of the grain respiration rate to obtain the jump amplitude value.

8. An agricultural storage and freshness monitoring method based on the Internet of Things according to claim 6, characterized in that: The process of determining the transition time value is as follows: According to the grain respiration rate in the time window during the monitoring period, the transition window is determined, and the proportion of the transition windows in all time windows is calculated and recorded as the transition time value.

9. An agricultural storage and freshness monitoring method based on the Internet of Things according to claim 8, characterized in that: The process of determining the transition window is as follows: If the grain respiration rate in the time window is greater than or equal to the standard value of the grain respiration rate, the corresponding time window is recorded as a jump window.

10. An agricultural storage and preservation monitoring system based on the Internet of Things, characterized in that: The system is used to perform the method according to any one of claims 1 to 9, and the system comprises: Potential eddy current area identification module: This module deploys an IoT sensor network in the grain storage area. Using fluid dynamics algorithms, it collects real-time air flow data on the grain silo surface and identifies potential eddy current areas on the grain silo surface. Steady-state eddy current region determination module: This module identifies the steady-state eddy current region of a potential eddy current region by comprehensively analyzing the changes in the spatial position of the eddy current center in the potential eddy current region and the changes in the Q value in the time dimension of the potential eddy current region. Respiratory jump analysis module: extracts the oxygen concentration of grain in the steady-state vortex area during the monitoring period, determines the grain respiration rate, and identifies whether there is a potential respiratory jump phenomenon in the steady-state vortex area during the monitoring period by analyzing the entropy change of the grain respiration rate in the steady-state vortex area, and evaluates the degree of the potential respiratory jump phenomenon.

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