Granary grain storage abnormity monitoring method, system, equipment and medium

Abstract

The invention relates to the technical field of grain storage monitoring, in particular to a granary grain storage abnormity monitoring method, system and device and a medium, and the method comprises the steps: obtaining the collection data of a multi-dimensional sensor in a granary, and extracting the drift feature value of the collection data of each sensor at the current time period; comparing the absolute value of the drift characteristic value of the data acquired by each sensor in the current time period with a preset threshold value, and establishing a sensor group comprising abnormal sensors and associated with the abnormal sensors; acquiring collected data or drift characteristic values of various types of sensors in the same time period in the sensor group, fusing multi-sensor data to carry out correlation analysis, and distinguishing environmental interference or real grain storage abnormity; and triggering grading response according to the abnormal type of the real grain storage abnormity. Abnormal sensors are determined by comparing the absolute values of the drift characteristic values, a sensor group associated with the abnormal sensors is established, and relevance between different abnormal types and other factors is considered, so that abnormal analysis can be more accurately carried out.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of grain storage monitoring, in particular to a grain storage abnormality monitoring method, system, device and medium. BACKGROUND

[0002] Grain storage management has high requirements on the environment, and grain temperature is one of the important indicators for ensuring the safe storage of grain. Only by timely and accurately measuring the grain temperature data of each layer of the grain pile and analyzing the environment according to the detected temperature and humidity data, can the loss caused by mold, unbalanced temperature and humidity, etc. during storage be avoided.

[0003] The existing grain storage environment monitoring system vertically inserts a temperature and humidity detector into the grain pile, and sets a temperature and humidity alarm value. When the temperature or humidity exceeds the threshold value, an alarm will be triggered. However, the placement positions of the temperature and humidity detectors are mostly spaced apart, and each temperature and humidity detector can only monitor the grain pile within the effective radius around it. The heat transfer of the grain pile is relatively slow, and the monitoring data of each sensor is detected and analyzed in isolation, lacking correlation between multiple monitoring data. Moreover, a single sensor is subject to environmental interference and sensor drift, and cannot effectively distinguish between real grain storage abnormalities and temporary environmental interference. The single threshold value detection and alarm has a high false alarm rate. SUMMARY

[0004] To solve the above problems, the present application provides a grain storage abnormality monitoring method in the first aspect, comprising: Obtaining the collection data of the multi-dimensional sensors in the grain storehouse, performing residual calculation on the data sequence of each sensor, and extracting the drift feature value of the current period of the collection data of each sensor; Comparing the absolute value of the drift feature value of the current period of the collection data of each sensor with the preset threshold value through the preset threshold value to determine the sensor equal to or greater than the threshold value; taking the sensor as an abnormal sensor, and establishing a sensor group containing the abnormal sensor and the sensors associated with the abnormal sensor; Based on the sensor group of the abnormal sensor, obtaining the collection data or drift feature value of each type of sensor in the same period in the sensor group, performing correlation analysis on the multi-sensor data, and distinguishing between environmental interference and real grain storage abnormalities; Triggering a hierarchical response according to the abnormal type of the real grain storage abnormality.

[0005] The residual calculation on the data sequence of each sensor and the extraction of the drift feature value of the current period of the collection data of each sensor are specifically as follows: According to the reference reference value corresponding to the sensor, the collection data of the current period of the sensor is subtracted from the reference reference value to obtain a residual value; Residual values of the same type of sensor in the current period are obtained respectively, and residual mean and residual standard deviation are calculated; the residual values are normalized based on the residual mean and the residual standard deviation; The residual values of the sensor in each period are linearly fitted, and a slope value is obtained, which is the drift characteristic value of the sensor in the current period.

[0006] The residual values are normalized, and the formula is:

[0007] In the formula, R t R represents the residual value of the sensor in the current period; t std R represents the normalized residual value of the sensor in the current period; μ R R represents the residual mean of the same type of sensor; σ R R represents the residual standard value of the same type of sensor.

[0008] The establishment of the sensor group containing the abnormal sensor and the sensors associated with the abnormal sensor includes: If the abnormal sensor is a temperature detection sensor, the temperature detection sensor is taken as the center, the distance between the temperature detection sensor and the temperature detection sensor closest to the right side is taken as the radius, and all temperature detection sensors in the circle are taken as sensor group one; If the abnormal detection sensor is a humidity detection sensor, the humidity detection sensor, the temperature detection sensor and the gas detection sensor are taken as sensor group two; If the abnormal detection sensor is a vibration detection sensor, the vibration detection sensor, the insect situation monitoring instrument and the infrared monitoring camera are taken as sensor group three; If the abnormal detection sensor is an insect situation monitoring instrument, the insect situation monitoring instrument, the temperature detection sensor and the humidity detection sensor are taken as sensor group four.

[0009] The correlation analysis of the fused multi-sensor data is carried out to distinguish the environmental interference or the real grain storage anomaly, which specifically includes: When the abnormal sensor is a temperature detection sensor, the drift characteristic values of each temperature detection sensor in the same period in the sensor group one are obtained, and compared with the temperature drift threshold value. If the absolute values of the drift characteristic values of two or more temperature detection sensors are greater than the temperature drift threshold value, it is a real grain storage anomaly, that is, the temperature imbalance in the grain depot. If only the absolute value of the drift characteristic value of the abnormal sensor is greater than the temperature drift threshold value, it is an environmental interference; When the abnormal sensor is a humidity detection sensor, if the humidity detection value of the humidity detection sensor decreases and the temperature detection value of the temperature detection sensor increases, the relative humidity decreases, the humidity detection sensor signal is distorted, and if the current time is in summer, it is a real grain storage abnormality; If the humidity detection value of the humidity detection sensor increases, the temperature detection value of the temperature detection sensor increases, and the CO2 detection concentration of the gas detection sensor increases and the O2 detection concentration decreases, it is a real grain storage abnormality.

[0010] The fusion multi-sensor data is associated for analysis, and the environment interference or real grain storage abnormality is distinguished, and the method further comprises: When the abnormal sensor is a vibration detection sensor, if the vibration detection value of the vibration detection sensor increases, the pest detection density of the pest monitoring instrument increases or the image recognition of the infrared monitoring camera is abnormal, it is a real grain storage abnormality, that is, there is a pest infestation; if only the absolute value of the drift characteristic value of the abnormal sensor is greater than the vibration drift threshold, it is an environmental interference; If the abnormal sensor is a pest monitoring instrument, if the pest detection density of the pest monitoring instrument increases, and the temperature detection value of the temperature detection sensor increases and the humidity detection value of the humidity detection sensor increases, it is a real grain storage abnormality; if only the absolute value of the drift characteristic value of the abnormal sensor is greater than the density drift threshold, it is an environmental interference.

[0011] The method further comprises the following steps of: If only one real grain storage abnormality exists, a three-level response is triggered; If two real grain storage abnormalities exist or the three-level response lasts for a long time without being eliminated, a two-level response is triggered; If more than two real grain storage abnormalities exist, a one-level response is triggered.

[0012] The second aspect of the application provides a grain storage abnormality monitoring system for a granary, which comprises: A feature extraction module is configured to acquire the collection data of the multi-dimensional sensors in the granary, perform residual calculation on the data sequences of the sensors, and extract the drift characteristic values of the collection data of the sensors in the current period; A comparison module is configured to compare the absolute values of the drift characteristic values of the collection data of the sensors in the current period with preset thresholds by using the preset thresholds, determine the sensors whose absolute values are greater than or equal to the thresholds, take the sensors as abnormal sensors, and establish a sensor group comprising the abnormal sensors and the sensors associated with the abnormal sensors; An analysis processing module is configured to acquire the collection data or drift characteristic values of the sensors of the same type in the sensor group based on the sensor group of the abnormal sensors, perform correlation analysis on the multi-sensor data, and distinguish the environment interference or real grain storage abnormality; An abnormal response module is configured to trigger a hierarchical response according to the abnormal type of the real grain storage abnormality.

[0013] The third aspect provides a grain storage abnormality monitoring device, comprising a processor and a memory, wherein the processor implements the grain storage abnormality monitoring method as described above when executing the computer program stored in the memory.

[0014] A computer readable storage medium is configured to store a computer program, wherein the computer program is executed by a processor to implement the grain storage abnormality monitoring method as described above.

[0015] Beneficial effects: The present application is a kind of grain storage abnormality monitoring method, by residual calculation to each sensor data sequence, and further extract drift characteristic value, can accurately capture the trend of data change over time, can effectively exclude interference, highlight the abnormal change characteristics of data, provide reliable basis for subsequent abnormal judgment; At the same time, by comparing the absolute value of the drift characteristic value to determine the abnormal sensor, establish the sensor group associated with it, consider the relevance of different abnormal types and other factors, which helps to more accurately analyze the abnormality;Obtain the correlation analysis of the data collected by each type of sensor in the group at the same time or the drift characteristic value, which can effectively distinguish environmental interference and real grain storage abnormality, and greatly improve the accuracy of abnormal judgment. BRIEF DESCRIPTION OF DRAWINGS

[0016] The scheme and advantages of the present application will become clear to those skilled in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not considered as limiting the present application.

[0017] In the drawings: Figure 1 A flowchart of the grain storage abnormality monitoring method. DETAILED DESCRIPTION

[0018] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings.

[0019] The present embodiment provides a grain storage abnormality monitoring method, applied to a grain storage environment monitoring system, the grain storage environment monitoring system comprising a temperature detection sensor, a humidity detection sensor, a gas detection sensor, a vibration detection sensor, a pest monitoring instrument and an infrared monitoring camera, and each sensor is provided with a plurality of sensors according to the measured grain storage environment. The grain storage environment monitoring system is deployed in the grain storage and connected with the Internet of Things management platform, and the environmental detection data in the warehouse is transmitted to the Internet of Things management platform through the Internet of Things technology, so as to facilitate remote operation and adjustment of abnormal environment.

[0020] Grain storage requires real-time monitoring of environmental information to prevent temperature and humidity imbalances, mold, and pest infestations. Therefore, aside from the need to open the grain silo openings during transport, the internal environmental information may change. When the grain is transported and placed under static storage, the silo is closed. Ideally, each silo needs to maintain a suitable low-temperature, low-oxygen environment based on the type of grain being stored, and the data from different sensors at different times should show minimal variation. However, the actual situation is much more complex.

[0021] To ensure the long-term effective preservation of grain in granaries, it is essential to promptly identify and monitor any abnormalities in grain storage facilities. (See [link to relevant documentation]). Figure 1 The specific implementation steps of the grain storage anomaly monitoring method are as follows: S1. Acquire the data collected by multi-dimensional sensors in the grain warehouse, perform residual calculation on the data sequence of each sensor, and extract the drift characteristic value of the data collected by each sensor in the current time period. Taking the data collected by any temperature sensor as an example, the residual calculation is performed on the data sequence of the temperature sensor to extract the drift characteristic value of the data collected by the temperature sensor in the current time period. The specific operation is as follows: The residual value is obtained by subtracting the sensor's current data collection value from the environmental reference benchmark value. Obtain the residual values ​​for the current time period from sensors of the same type, and calculate the mean and standard deviation of the residuals. Standardize the residual values ​​based on the mean and standard deviation of the residuals using the following formula:

[0022] In the formula, R t R represents the residual value of the sensor during the current time period. t std This represents the standardized residual value of the sensor for the current time period; μ R This represents the mean residual value of sensors of the same type. σ R This represents the standard residual value for sensors of the same type.

[0023] A linear fit is performed on the standardized residual values ​​of the sensor data sequences for each time period to obtain the slope value. This slope value is the drift characteristic value of the sensor data for the current time period. The slope value obtained by the linear fit is existing technology and will not be described in detail here.

[0024] Similarly, residual calculations were performed on the data sequences of other types of sensors in the grain storage environment monitoring system, and drift characteristic values ​​of the data collected by different types of sensors in the current time period were extracted.

[0025] S2. By using a preset threshold, compare the absolute value of the drift characteristic value of the data collected by each sensor in the current time period with the preset threshold, and determine the sensor that is greater than or equal to the threshold; treat the sensor as an abnormal sensor, and establish a sensor group that includes the abnormal sensor and the sensor associated with the abnormal sensor. If the abnormal sensor is a temperature detection sensor, then draw a sphere with the temperature detection sensor as the center and the distance between the temperature detection sensor and the nearest temperature detection sensor on one side as the radius, and take all the temperature detection sensors in the sphere as sensor group one; If the abnormality detection sensor is a humidity detection sensor, then the humidity detection sensor, temperature detection sensor, and gas detection sensor are considered as sensor group two. If the abnormality detection sensor is a vibration detection sensor, then the vibration detection sensor, the insect pest monitoring instrument, and the infrared monitoring camera will be used as sensor group three. If the abnormality detection sensor is an insect pest monitoring instrument, then the insect pest monitoring instrument, temperature detection sensor, and humidity detection sensor will be grouped as sensor group four.

[0026] S3. A sensor group based on anomaly sensors acquires the collected data or drift characteristic values ​​of various types of sensors within the sensor group during the same time period, fuses multi-sensor data for correlation analysis, and distinguishes between environmental interference and actual grain storage anomalies. Specifically, this includes: a. When the abnormal sensor is a temperature detection sensor, the drift characteristic values ​​of each temperature detection sensor in the same time period within sensor group 1 are obtained and compared with the temperature drift threshold. If the absolute values ​​of the drift characteristic values ​​of two or more temperature detection sensors are greater than the temperature drift threshold, it is a real grain storage abnormality, that is, the temperature in the grain warehouse is unbalanced; if the absolute value of the drift characteristic value of only the abnormal sensor is greater than the temperature drift threshold, it is an environmental disturbance.

[0027] b. When the abnormal sensor is a humidity detection sensor, acquire the collected data from each type of sensor in sensor group two, and perform correlation analysis by fusing the multi-sensor data. The specific operation is as follows: If the humidity sensor reading decreases while the temperature sensor reading increases, it indicates a decrease in relative humidity and a distortion in the humidity sensor signal. If the current time is summer, it indicates a genuine grain storage anomaly, as the grain temperature rises in summer, causing a false decrease in relative humidity and indicating an actual risk of the grain becoming damp. If the humidity detection value of the humidity sensor increases, the temperature detection value of the temperature sensor increases, and the CO2 detection concentration of the gas sensor increases while the O2 detection concentration decreases, then it indicates a real grain storage abnormality, i.e., grain mold has occurred. If only the absolute value of the drift characteristic value of the abnormal sensor is greater than the humidity drift threshold, then it is due to environmental interference.

[0028] c. When the abnormal sensor is a vibration detection sensor, acquire the data collected by each type of sensor in sensor group three, fuse the multi-sensor data for correlation analysis, and the specific operation is as follows: If the vibration detection value of the vibration detection sensor increases, the pest detection density of the pest monitoring instrument increases, or the image recognition of the infrared monitoring camera is abnormal, then it is a real grain storage abnormality, that is, there is pest infestation. If only the absolute value of the drift characteristic value of the abnormal sensor is greater than the vibration drift threshold, then it is due to environmental interference.

[0029] d. When the abnormal sensor is an insect pest monitoring instrument, the data collected by each type of sensor in sensor group four is acquired, and the multi-sensor data is fused for correlation analysis. The specific operation is as follows: If the pest detection density of the pest monitoring instrument increases, and the temperature detection value of the temperature sensor increases, and the humidity detection value of the humidity sensor increases, then it is a real grain storage abnormality, indicating that the pest outbreak is occurring. The pests reproduce and release heat, which leads to a rise in local temperature, which promotes the respiration of the grain and causes the humidity to rise. If only the absolute value of the drift characteristic value of the abnormal sensor is greater than the density drift threshold, then it is due to environmental interference.

[0030] S4. Trigger a tiered response based on the type of actual grain storage anomaly. When the grain storage environment monitoring system detects a genuine grain storage anomaly, the IoT management platform will initiate a tiered response based on the anomaly type and quantity, specifically including: If only one genuine grain storage anomaly exists, a Level 3 response will be triggered; The three-level response specifically involves automatically adjusting based on actual grain storage anomalies, such as activating directional ventilation or initiating temperature and humidity balancing methods; marking a yellow warning zone on the IoT management platform; and repeatedly monitoring the actual grain storage anomaly based on fixed time periods to verify the anomaly.

[0031] If two real grain storage anomalies exist or the Level 3 response remains unresolved, a Level 2 response is triggered. The Level 2 response involves the IoT management platform automatically dispatching inspection robots to collect grain samples from the abnormal grain storage area. The IoT management platform then generates a disposal recommendation report based on the collected grain samples and pushes it to the expert decision-making end.

[0032] If two or more actual grain storage anomalies are detected, a Level 1 response is triggered, indicating a significant risk of a grain storage accident. Specifically, the IoT management platform automatically locks the doors of the abnormal grain storage facility, prohibiting entry and exit operations, and reports an early warning to the grain storage duty team and monitoring center. Depending on the type of anomaly, a corresponding response plan is initiated: for anomalies involving temperature imbalance within the grain storage facility, full-fledged ventilation is activated; for anomalies involving moldy grain, the dispatching and conveying equipment is activated to transfer the grain between storage areas; and for anomalies involving pest outbreaks, aluminum phosphide is released into the sealed compartments for pest control.

[0033] Then, this embodiment also provides a grain storage anomaly monitoring system, including: The feature extraction module is used to acquire data collected by multi-dimensional sensors in the grain warehouse, perform residual calculation on the data sequences of each sensor, and extract the drift feature values ​​of the data collected by each sensor in the current time period. The comparison module is used to compare the absolute value of the drift characteristic value of the data collected by each sensor in the current time period with the preset threshold, and to identify the sensor that is greater than or equal to the threshold; the sensor is identified as an abnormal sensor, and a sensor group containing the abnormal sensor and the sensor associated with the abnormal sensor is established. The analysis and processing module is used to acquire the collected data or drift characteristic values ​​of various types of sensors within the sensor group at the same time period based on the abnormal sensor, and to perform correlation analysis by fusing multi-sensor data to distinguish between environmental interference and real grain storage anomalies. The anomaly response module is used to trigger tiered responses based on the anomaly type of the actual grain storage anomaly. In addition, a grain storage anomaly monitoring device is provided, including a processor and a memory, wherein the processor executes a computer program stored in the memory to implement a grain storage anomaly monitoring method as described above.

[0034] Finally, a computer-readable storage medium is also provided for storing a computer program, wherein the computer program, when executed by a processor, implements a grain storage anomaly monitoring method as described above.

Claims

1. A method for monitoring abnormal grain storage in grain warehouses, characterized in that, include: Acquire data collected by multi-dimensional sensors in the grain warehouse, perform residual calculation on the data sequences of each sensor, and extract the drift characteristic values ​​of the data collected by each sensor in the current time period; By using a preset threshold, the absolute value of the drift characteristic value of the data collected by each sensor in the current time period is compared with the preset threshold to identify sensors that are greater than or equal to the threshold; these sensors are then identified as anomalous sensors, and a sensor group containing anomalous sensors and sensors associated with them is established. Based on the sensor group of anomaly sensors, the data collected or drift characteristic values ​​of various types of sensors in the same time period are obtained, and the data of multiple sensors are fused to perform correlation analysis to distinguish between environmental interference and real grain storage anomalies. A tiered response is triggered based on the type of actual grain storage anomaly.

2. The method for monitoring abnormal grain storage in a grain warehouse according to claim 1, characterized in that, The specific steps for performing residual calculations on the data sequences of each sensor and extracting the drift characteristic values ​​of the data collected by each sensor for the current time period are as follows: The residual value is obtained by subtracting the sensor's current data collection value from the reference value based on the sensor's corresponding reference value. Obtain the residual values ​​of the same type of sensors for the current time period, and calculate the mean and standard deviation of the residuals; The residual values ​​are standardized based on the residual mean and residual standard deviation; A linear fit is performed on the standardized residual values ​​of the sensor data sequences for each time period to obtain the slope value. The slope value is the drift characteristic value of the sensor data for the current time period.

3. The method for monitoring abnormal grain storage in a grain warehouse according to claim 2, characterized in that, The formula for standardizing the residual values ​​is as follows: In the formula, R t R represents the residual value of the sensor during the current time period. t std This represents the standardized residual value of the sensor for the current time period; μ R This represents the mean residual value of sensors of the same type. σ R This represents the standard residual value for sensors of the same type.

4. The method for monitoring abnormal grain storage in a grain warehouse according to claim 1, characterized in that, The establishment of a sensor group including anomaly sensors and sensor groups associated with the anomaly sensors includes: If the abnormal sensor is a temperature detection sensor, then draw a sphere with the temperature detection sensor as the center and the distance between the temperature detection sensor and the nearest temperature detection sensor on one side as the radius, and take all the temperature detection sensors in the sphere as sensor group one; If the abnormality detection sensor is a humidity detection sensor, then the humidity detection sensor, temperature detection sensor, and gas detection sensor are considered as sensor group two. If the abnormality detection sensor is a vibration detection sensor, then the vibration detection sensor, the insect pest monitoring instrument, and the infrared monitoring camera will be used as sensor group three. If the abnormality detection sensor is an insect pest monitoring instrument, then the insect pest monitoring instrument, temperature detection sensor, and humidity detection sensor will be grouped as sensor group four.

5. The method for monitoring abnormal grain storage in a grain warehouse according to claim 4, characterized in that, The process of fusing multi-sensor data for correlation analysis to distinguish between environmental interference and actual grain storage anomalies specifically involves: When the abnormal sensor is a temperature detection sensor, the drift characteristic values ​​of each temperature detection sensor in the same time period within the sensor group are obtained and compared with the temperature drift threshold. If the absolute values ​​of the drift characteristic values ​​of two or more temperature detection sensors are greater than the temperature drift threshold, it is a real grain storage abnormality, that is, the temperature in the grain warehouse is unbalanced; if the absolute value of the drift characteristic value of only the abnormal sensor is greater than the temperature drift threshold, it is an environmental interference. When the abnormal sensor is a humidity detection sensor, if the humidity detection value of the humidity detection sensor decreases and the temperature detection value of the temperature detection sensor increases, it indicates a decrease in relative humidity and distortion of the humidity detection sensor signal. If the current time is summer, it indicates a real grain storage abnormality. If the humidity detection value of the humidity sensor increases, the temperature detection value of the temperature sensor increases, and the CO2 detection concentration of the gas sensor increases while the O2 detection concentration decreases, then it indicates a genuine grain storage anomaly.

6. The method for monitoring abnormal grain storage in a grain warehouse according to claim 4, characterized in that, The method of fusing multi-sensor data for correlation analysis to distinguish between environmental interference and actual grain storage anomalies also includes: When the abnormal sensor is a vibration detection sensor, if the vibration detection value of the vibration detection sensor increases, the pest detection density of the pest monitoring instrument increases, or the image recognition of the infrared monitoring camera is abnormal, then it is a real grain storage abnormality, that is, there is pest infestation; if only the absolute value of the drift characteristic value of the abnormal sensor is greater than the vibration drift threshold, then it is environmental interference. If the abnormal sensor is an insect pest monitoring instrument, and the insect pest detection density of the insect pest monitoring instrument increases, and the temperature detection value of the temperature detection sensor increases, and the humidity detection value of the humidity detection sensor increases, then it is a real grain storage abnormality; if only the absolute value of the drift characteristic value of the abnormal sensor is greater than the density drift threshold, then it is an environmental interference.

7. The method for monitoring abnormal grain storage in a grain warehouse according to claim 1, characterized in that, The specific operation of triggering a tiered response based on the anomaly type of the actual grain storage anomaly is as follows: If only one genuine grain storage anomaly exists, a Level 3 response will be triggered; If two genuine grain storage anomalies exist or the Level 3 response remains ineffective, a Level 2 response will be triggered. If two or more actual grain storage anomalies are found, a Level 1 response will be triggered.

8. A grain storage anomaly monitoring system, characterized in that, include: The feature extraction module is used to acquire data collected by multi-dimensional sensors in the grain warehouse, perform residual calculation on the data sequences of each sensor, and extract the drift feature values ​​of the data collected by each sensor in the current time period. The comparison module is used to compare the absolute value of the drift characteristic value of the data collected by each sensor in the current time period with the preset threshold, and to identify the sensor that is greater than or equal to the threshold; the sensor is identified as an abnormal sensor, and a sensor group containing the abnormal sensor and the sensor associated with the abnormal sensor is established. The analysis and processing module is used to acquire the collected data or drift characteristic values ​​of various types of sensors within the sensor group at the same time period based on the abnormal sensor, and to perform correlation analysis by fusing multi-sensor data to distinguish between environmental interference and real grain storage anomalies. The anomaly response module is used to trigger a tiered response based on the anomaly type of the actual grain storage anomaly.

9. A grain storage anomaly monitoring device, characterized in that, It includes a processor and a memory, wherein the processor executes a computer program stored in the memory to implement a grain storage anomaly monitoring method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, Used to store a computer program, wherein the computer program, when executed by a processor, implements a grain storage anomaly monitoring method as described in any one of claims 1-7.

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