Granary environment analysis method based on industrial internet

Abstract

The invention discloses a granary environment analysis method based on an industrial internet, relates to the field of granary environment analysis, and solves the problem that the environment of different positions in a granary cannot be effectively monitored. The method comprises the steps of obtaining historical environment data in a cylindrical granary; according to the historical environment data, storage environments of different storage position points in the cylindrical granary are analyzed, and a storage position point sequence in the cylindrical granary is obtained through analysis; the interior of the cylindrical granary is analyzed according to different first storage position points, and a first effective monitoring area is obtained through analysis; different storage position points in the cylindrical granary are analyzed according to the preferred installation points, different effective monitoring areas are obtained through analysis, and plug-in sensors are installed at the center positions corresponding to the different effective monitoring areas; and analyzing the cylindrical granary after the installation position of the plug-in sensor is optimized. According to the invention, the environment of different positions in the granary can be effectively monitored.

Description

Technical Field

[0001] This invention belongs to the field of grain warehouse environmental analysis technology, specifically a grain warehouse environmental analysis method based on the Industrial Internet. Background Technology

[0002] During storage, grain is constantly in a micro-environment coupled with multiple factors such as temperature, humidity, oxygen, carbon dioxide, and moisture. Its physiological activity and external climate disturbances cause the parameters inside the grain pile to show continuous changes with uneven spatial and temporal distribution. As the tonnage of a single modern grain depot increases, the three-dimensional differences in the internal environment become more and more significant, which puts forward higher requirements for real-time and precise monitoring of grain conditions. In recent years, IoT sensing, edge computing, and industrial internet technologies have been gradually applied to the warehousing field, providing the hardware and data channel foundation for continuous collection of multi-dimensional environmental information of grain depots. In existing technologies, most methods for analyzing the environment of grain warehouses suffer from problems such as unreasonable installation locations of monitoring points and unreasonable allocation of monitoring points. Too many monitoring points can lead to waste of resources, while too few monitoring points can lead to monitoring lag. At the same time, existing technologies fail to effectively utilize historical environmental data inside the grain warehouse to analyze the earliest and most likely locations to experience anomalies, thus failing to complete the allocation of monitoring point locations. Therefore, this invention proposes a grain storage environment analysis method based on the Industrial Internet. Summary of the Invention

[0003] In view of the shortcomings of existing technologies, the purpose of this invention is to provide a grain warehouse environment analysis method based on the Industrial Internet.

[0004] The technical problem to be solved by this invention is: How to effectively monitor the environment in different locations inside a grain warehouse.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A method for analyzing the grain storage environment based on the Industrial Internet, comprising the following steps: Step S1: Obtain historical environmental data of the interior of the cylindrical grain silo; Step S2: Analyze the storage environment of different storage locations inside the cylindrical grain silo based on historical environmental data to obtain the sequence of storage locations inside the cylindrical grain silo. Step S3: Analyze the interior of the cylindrical grain silo based on different first storage locations to obtain the first effective monitoring area; Step S4: Analyze different storage locations inside the cylindrical grain silo based on the preferred installation point to obtain different effective monitoring areas, and install insertion sensors at the center of each effective monitoring area. Step S5: Analyze the cylindrical grain silo after optimizing the installation position of the insertion sensor.

[0006] Furthermore, the historical environmental data includes the historical real-time internal temperature and humidity at different storage locations inside the cylindrical grain silo.

[0007] Furthermore, the analysis process in step S2 includes the following sub-steps: Step S21: For any storage location inside the cylindrical grain silo, compare the historical real-time internal temperature at different time points with the standard storage temperature range, and compare the historical real-time internal humidity at different time points with the standard storage humidity range. If the historical internal real-time temperature corresponding to all time points is within the standard storage temperature range, and the historical internal real-time humidity corresponding to all time points is within the standard storage humidity range, then proceed to step S22. If the historical internal real-time temperature at any time point is not within the standard storage temperature range, or the historical internal real-time humidity at any time point is not within the standard storage humidity range, then the corresponding time point will be recorded as an abnormal time point, and the first abnormal time point among all abnormal time points will be recorded as the undetermined monitoring time point of the corresponding storage location. Step S22: Subtract the historical real-time internal temperature of any storage location point at adjacent time nodes, take the absolute value, and divide by a fixed time interval to obtain the historical internal temperature change rate of the corresponding storage location point at different time nodes. Similarly, by subtracting the historical real-time humidity of any storage location at adjacent time points and taking the absolute value, and then dividing by a fixed time interval, the historical internal humidity change rate of the corresponding storage location at different time points can be obtained. Step S23: Iterate through and compare the historical internal temperature change rate of any storage location at different time points, obtain the maximum value of the historical internal temperature change rate, and record the corresponding time point as the undetermined monitoring time point of the corresponding storage location point. Similarly, by iterating and comparing the historical internal humidity change rate of any storage location at different time points, the maximum value of the historical internal humidity change rate is obtained, and the corresponding time point is recorded as the undetermined monitoring time point of the corresponding storage location. Step S24: If there is one and only one undetermined monitoring time node for the storage location point, then the undetermined monitoring time node is taken as the starting monitoring time node for the corresponding storage location point. If there are multiple undetermined monitoring time nodes for any storage location, the first undetermined monitoring time node is selected as the starting monitoring time node for the corresponding storage location. Step S25: Sort all storage location points in ascending order according to the starting monitoring time node to obtain the storage location point sequence in the cylindrical grain silo, and record the storage location points in the storage location point sequence as the first storage location point, the second storage location point, the third storage location point, and the kth storage location point in sequence; where k is the number of the storage location point.

[0008] Furthermore, the analysis process in step S3 includes the following sub-steps: Step S31: Record the circle at the bottom of the cylindrical grain silo as the outline circle, measure the diameter of the outline circle and record it as the grain silo diameter, construct a rectangle with the grain silo diameter as the side length, circumscribe the rectangle to the outline circle and record it as the circumscribed rectangle, and record the intersection of the circumscribed rectangle and the outline circle as the feature point of the outline circle. Step S32: Select any feature point as the origin of coordinates, and select a diameter passing through the origin of coordinates. Then move the corresponding diameter to the same height as the storage location point and use it as the reference diameter. Measure the vertical distance between the storage location point and the reference diameter and record it as the center distance. Step S33: The end that passes through the origin of the coordinate system and is perpendicular to the reference diameter is recorded as the side end of the cylindrical grain silo. The distance between the storage location point and the side end of the cylindrical grain silo is measured and recorded as the distance from the wall of the storage location point. The distance between the storage location point and the bottom end of the cylindrical grain silo is measured and recorded as the storage height of the storage location point. Similarly, the distance from the wall and the storage height of the first storage location point in different cylindrical grain silos were measured.

[0009] Furthermore, the analysis process in step S3 also includes the following sub-steps: Step S34: Construct a first rectangular coordinate system at the origin, with the straight line corresponding to the diameter at the bottom of the cylindrical grain silo as the horizontal axis and the side of the cylindrical grain silo as the vertical axis; where the horizontal axis represents the distance from the wall at different storage locations and the vertical axis represents the storage height at different storage locations. Similarly, with the straight line containing the reference diameter as the horizontal axis, and the straight line that is in the same plane as the bottom of the cylindrical grain silo and passes through the origin and is perpendicular to the reference diameter as the vertical axis, a second rectangular coordinate system is constructed at the origin; where the horizontal axis represents the distance from the wall of different storage locations, and the vertical axis represents the center distance of different storage locations. Step S35: Select any first storage location point as the initial analysis point. For the first rectangular coordinate system, obtain the coordinates of the initial analysis point and different first storage location points. Calculate the first distance between the initial analysis point and different first storage location points using the Euclidean formula. Similarly, for the second rectangular coordinate system, the coordinates of the initial analysis point and different first storage location points are obtained, and the second distance between the initial analysis point and different first storage location points is calculated using the Euclidean formula; Step S36: Obtain the monitoring distance of the insertion sensor, and compare the first distance, the second distance, and the monitoring distance between the initial analysis point and the first storage location point; When the first distance between the initial analysis point and all first storage location points is greater than or equal to the monitoring distance, or the second distance between the initial analysis point and all first storage location points is greater than or equal to the monitoring distance, the corresponding initial analysis point is recorded as an independent monitoring point. When the first distance between the initial analysis point and any first storage location point is less than the monitoring distance and the second distance between the initial analysis point and any first storage location point is less than the monitoring distance, the preferred installation point is constructed using the initial analysis point and the corresponding first storage location point.

[0010] Furthermore, the process of constructing the preferred installation point in step S36 includes the following sub-steps: Step S361: With the initial analysis point as the center and the monitoring distance as the radius, draw a sphere and record it as the first monitoring area. Include all first storage location points whose first distance and second distance from the initial analysis point are both less than the monitoring distance into the first monitoring area. Similarly, different first storage locations are selected as initial analysis points, and spheres are drawn with the initial analysis points as centers and the monitoring distance as the radius. These spheres are then sequentially labeled as the second monitoring area, the third monitoring area, and the nth monitoring area, where n is the number of the monitoring area. Step S362: Count the number of first storage location points in different monitoring areas and record them as the number of area points. Iterate and compare the number of area points in different monitoring areas to obtain the maximum number of area points. Record the monitoring area corresponding to the maximum number of area points as the first effective monitoring area and record the center of the circle corresponding to the first effective monitoring area as the preferred installation point.

[0011] Furthermore, the analysis process in step S4 includes the following sub-steps: Step S41: Obtain the second storage location points in different cylindrical grain silos, and calculate the first distance and second distance between the different second storage location points and the preferred installation point using the Euclidean formula; Step S42: Obtain the monitoring distance of the insertion sensor, and compare the first distance and the second distance between the second storage location point and the preferred installation point with the monitoring distance; If the first distance and the second distance between all second storage locations and the preferred installation point are both less than the monitoring distance, then the second storage locations are included in the first effective monitoring area. If any second storage location point has a first or second distance greater than or equal to the monitoring distance between it and the preferred installation point, then the corresponding storage location point is recorded as an independent monitoring point.

[0012] Furthermore, the analysis process in step S4 also includes the following sub-steps: Step S43: Draw spheres with different independent monitoring points as centers and monitoring distances as radii, and record them as different independent monitoring areas in sequence; Step S44: Count the number of independent monitoring points in different independent monitoring areas, iterate through and compare the number of independent monitoring points in different independent monitoring areas, obtain the maximum number of independent monitoring points, and record the corresponding independent monitoring area as the second effective monitoring area; Similarly, the third storage location point, the fourth storage location point, and the kth storage location point are analyzed in sequence to obtain the third effective monitoring area, the fourth effective monitoring area, and the kth effective monitoring area; Step S45: Install insertion sensors at the center positions corresponding to different effective monitoring areas.

[0013] Furthermore, the analysis process in step S5 includes the following sub-steps: Step S51: Record the location of the insertion sensor as the data acquisition point, and obtain the real-time feature data of different data acquisition points at different time nodes and the standard feature data range inside the cylindrical grain silo. Step S52: Compare the real-time temperature at the data collection point with the standard temperature range at different time points, and compare the real-time humidity at the data collection point with the standard humidity range at different time points. If the real-time temperature at the data collection point is within the standard temperature range at all time points, and the real-time humidity at the data collection point is within the standard humidity range at all time points, then proceed to step S53. If the real-time temperature at any data collection point is outside the standard temperature range, or the real-time humidity at any data collection point is outside the standard humidity range, then the cylindrical grain silo should be inspected. Step S53: Subtract the real-time temperature at the data collection point at the current time node from the real-time temperature at the previous time node to obtain the real-time temperature change at the corresponding data collection point at the current time node. Similarly, the real-time humidity change at the corresponding data collection point at the current time point is calculated; Step S54: If the real-time temperature change or real-time humidity change at the data collection point is greater than or equal to zero at multiple time points, then check the cylindrical grain silo. If there is one and only one time point at which the real-time temperature change or real-time humidity change at the data collection point is greater than or equal to zero, then the data collection point will be continuously monitored.

[0014] Furthermore, the real-time feature data consists of the real-time temperature and humidity at different data collection points inside the cylindrical grain silo, while the standard feature data range consists of the standard temperature range and standard humidity range inside the cylindrical grain silo.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention first analyzes different locations inside cylindrical grain silos based on historical environmental data to identify abnormal storage locations. Then, the storage locations are arranged in chronological order of the abnormality time. Similarly, the same processing is applied to the storage locations in different cylindrical grain silos to obtain a sequence of storage locations arranged in chronological order of the abnormality time. This invention enables the analysis of the chronological order of the occurrence of abnormalities in storage locations inside cylindrical grain silos. 2. This invention also analyzes the first storage location points within different cylindrical grain silos to determine the preferred installation point for the insertable sensor. Similarly, by analyzing different storage location points, multiple effective monitoring areas or multiple independent monitoring points inside the cylindrical grain silo are determined. The center position of the effective monitoring area is used as the installation position for the insertable sensor. At the same time, the remaining installation positions for the insertable sensor are selected based on the monitoring range of different independent monitoring points, thereby optimizing the sensor deployment position. Finally, the insertable sensor with the optimized deployment position is used to effectively monitor the interior of the cylindrical grain silo. This invention achieves effective monitoring of the environment at different locations inside the cylindrical grain silo. Attached Figure Description

[0016] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0017] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a schematic diagram of the reference diameter in this invention; Figure 3 This is a schematic diagram showing the storage height, distance from the wall, and center distance in this invention; Figure 4 This is a schematic diagram of the first rectangular coordinate system in this invention; Figure 5 This is a schematic diagram of the second rectangular coordinate system in this invention; Figure 6 This is a schematic diagram of the effective monitoring area in this invention; Figure 7 This is a schematic diagram of the electronic device in this invention. Detailed Implementation

[0018] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1: Please refer to Figures 1-6 As shown, the technical solution provided by this invention is: a grain warehouse environment analysis method based on the Industrial Internet. This method is applicable to analyzing different locations inside cylindrical grain warehouses based on historical environmental data, identifying abnormal storage locations, and arranging these locations in chronological order based on the time of the anomalies. Similarly, the same processing is applied to storage locations in different cylindrical grain warehouses. Then, the first storage location in each cylindrical grain warehouse is analyzed to determine the preferred installation point for an insertable sensor. Likewise, different storage locations are analyzed to identify multiple effective monitoring areas or multiple independent monitoring points inside the cylindrical grain warehouse. The center of the effective monitoring area is used as the installation location for the insertable sensor. Simultaneously, the remaining installation locations for the insertable sensor are selected based on the monitoring range of different independent monitoring points, thereby optimizing the sensor deployment. Finally, the optimized insertion sensor is used to effectively monitor the interior of the cylindrical grain warehouse.

[0020] In this embodiment, the grain warehouse environment analysis method includes the following steps: Step S1: Obtain historical environmental data of the interior of the cylindrical grain silo; Among them, the historical environmental data are the historical real-time internal temperature and historical real-time internal humidity at different storage locations inside the cylindrical grain silo. In this embodiment, there are multiple sensors inside the cylindrical grain silo, which can collect historical real-time internal temperatures corresponding to different storage locations. It should be noted that the different storage locations are the positions where the sensors come into contact with the grain, and the sensors are insertion-type sensors.

[0021] Step S2: Analyze the storage environment of different storage locations inside the cylindrical grain silo based on historical environmental data to obtain the sequence of storage locations inside the cylindrical grain silo. In this embodiment, the analysis process in step S2 includes the following sub-steps: Step S21: For any storage location inside the cylindrical grain silo, compare the historical real-time internal temperature at different time points with the standard storage temperature range, and compare the historical real-time internal humidity at different time points with the standard storage humidity range. If the historical internal real-time temperature corresponding to all time points is within the standard storage temperature range, and the historical internal real-time humidity corresponding to all time points is within the standard storage humidity range, then proceed to step S22. If the historical internal real-time temperature at any time point is not within the standard storage temperature range, or the historical internal real-time humidity at any time point is not within the standard storage humidity range, then the corresponding time point will be recorded as an abnormal time point, and the first abnormal time point among all abnormal time points will be recorded as the undetermined monitoring time point of the corresponding storage location. Step S22: Subtract the historical real-time internal temperature of any storage location point at adjacent time nodes, take the absolute value, and divide by a fixed time interval to obtain the historical internal temperature change rate of the corresponding storage location point at different time nodes. Similarly, by subtracting the historical real-time humidity of any storage location at adjacent time points and taking the absolute value, and then dividing by a fixed time interval, the historical internal humidity change rate of the corresponding storage location at different time points can be obtained. Step S23: Iterate through and compare the historical internal temperature change rate of any storage location at different time points, obtain the maximum value of the historical internal temperature change rate, and record the corresponding time point as the undetermined monitoring time point of the corresponding storage location point. Similarly, by iterating and comparing the historical internal humidity change rate of any storage location at different time points, the maximum value of the historical internal humidity change rate is obtained, and the corresponding time point is recorded as the undetermined monitoring time point of the corresponding storage location. Step S24: If there is one and only one undetermined monitoring time node for the storage location point, then the undetermined monitoring time node is taken as the starting monitoring time node for the corresponding storage location point. If there are multiple undetermined monitoring time nodes for any storage location, the first undetermined monitoring time node is selected as the starting monitoring time node for the corresponding storage location. Step S25: Sort all storage location points in ascending order according to the starting monitoring time node to obtain the storage location point sequence in the cylindrical grain silo, and record the storage location points in the storage location point sequence as the first storage location point, the second storage location point, the third storage location point, and the kth storage location point in sequence; where k is the number of the storage location point. It should be explained that if the time node corresponding to the maximum historical internal temperature change rate at a storage location is different from the time node corresponding to the maximum historical internal humidity change rate, then there will be multiple undetermined monitoring time nodes for the corresponding storage location. In this embodiment, the first undetermined monitoring time node is selected as the starting monitoring time node corresponding to the storage location. The starting monitoring time node is the first time node when there is an abnormal situation at the storage location. The first storage location is the first storage location in the cylindrical grain silo that has an abnormal situation.

[0022] Step S3: Analyze the interior of the cylindrical grain silo based on different first storage locations to obtain the first effective monitoring area; In this embodiment, the analysis process in step S3 includes the following sub-steps: Step S31, please refer to Figure 2 As shown, the circle at the bottom of the cylindrical grain silo is denoted as the outline circle. The diameter of the outline circle is measured and denoted as the grain silo diameter. A rectangle is constructed with the grain silo diameter as the side length. The rectangle is circumscribed to the outline circle and denoted as the circumscribed rectangle. The intersection of the circumscribed rectangle and the outline circle is denoted as the feature point of the outline circle. Step S32: Select any feature point as the origin of coordinates, and select a diameter passing through the origin of coordinates. Then move the corresponding diameter to the same height as the storage location point and use it as the reference diameter. Measure the vertical distance between the storage location point and the reference diameter and record it as the center distance. In this embodiment, the feature point on the left side of the circumscribed rectangle is selected as the origin of the coordinate system; Step S33, please refer to Figure 3 As shown, the end that passes through the origin of the coordinate system and is perpendicular to the reference diameter is recorded as the side end of the cylindrical grain silo. The distance between the storage location point and the side end of the cylindrical grain silo is measured and recorded as the distance from the wall of the storage location point. The distance between the storage location point and the bottom end of the cylindrical grain silo is measured and recorded as the storage height of the storage location point. Similarly, the distance from the wall and the storage height of the first storage location point in different cylindrical grain silos were measured. It should be explained that the storage height can be obtained by measuring the distance between the insertion sensor and the bottom of the cylindrical grain silo. The insertion sensor is inserted horizontally into the cylindrical grain silo. The distance between the storage location point and the side of the cylindrical grain silo can be read from the scale value of the insertion depth on the surface of the insertion sensor. In this embodiment, the definition of the distance from the wall is based on the insertion sensor being inserted from the left end of the cylindrical grain silo. If the insertion sensor is inserted from the right end of the cylindrical grain silo, the distance from the wall is calculated by subtracting the scale value of the insertion depth on the surface of the insertion sensor from the bottom diameter of the cylindrical grain silo. Here, multiple cylindrical grain silos are analyzed to obtain different first storage location points. Step S34, please refer to Figure 4As shown, a first rectangular coordinate system is constructed at the origin, with the straight line corresponding to the diameter at the bottom of the cylindrical grain silo as the horizontal axis and the side of the cylindrical grain silo as the vertical axis; where the horizontal axis represents the distance from the wall at different storage locations and the vertical axis represents the storage height at different storage locations. Similarly, please refer to Figure 5 As shown, a second rectangular coordinate system is constructed at the origin of the coordinate system, with the straight line containing the reference diameter as the horizontal axis and the straight line that is in the same plane as the bottom of the cylindrical grain silo and passes through the origin and is perpendicular to the reference diameter as the vertical axis; where the horizontal axis represents the distance from the wall of different storage locations and the vertical axis represents the center distance of different storage locations. Step S35: Select any first storage location point as the initial analysis point. For the first rectangular coordinate system, obtain the coordinates of the initial analysis point and different first storage location points. Calculate the first distance between the initial analysis point and different first storage location points using the Euclidean formula. Similarly, for the second rectangular coordinate system, the coordinates of the initial analysis point and different first storage location points are obtained, and the second distance between the initial analysis point and different first storage location points is calculated using the Euclidean formula; In this embodiment, the leftmost first storage location point is first selected as the initial analysis point. The first distance is the distance between the initial analysis point and the first storage location point when viewed from the cylindrical surface of the cylindrical grain silo. The second distance is the distance between the initial analysis point and the first storage location point when viewed from above the cylindrical grain silo. Step S36: Obtain the monitoring distance of the insertion sensor, and compare the first distance, the second distance, and the monitoring distance between the initial analysis point and the first storage location point; When the first distance between the initial analysis point and all first storage location points is greater than or equal to the monitoring distance, or the second distance between the initial analysis point and all first storage location points is greater than or equal to the monitoring distance, the corresponding initial analysis point is recorded as an independent monitoring point. When the first distance between the initial analysis point and any first storage location point is less than the monitoring distance and the second distance between the initial analysis point and any first storage location point is less than the monitoring distance, the preferred installation point is constructed using the initial analysis point and the corresponding first storage location point. In this embodiment, the monitoring distance can be obtained from the instruction manual of the corresponding insertable sensor; In this embodiment, the process of constructing the preferred installation point in step S36 includes the following sub-steps: Step S361: With the initial analysis point as the center and the monitoring distance as the radius, draw a sphere and record it as the first monitoring area. Include all first storage location points whose first distance and second distance from the initial analysis point are both less than the monitoring distance into the first monitoring area. Similarly, different first storage locations are selected as initial analysis points, and spheres are drawn with the initial analysis points as centers and the monitoring distance as the radius. These spheres are then sequentially labeled as the second monitoring area, the third monitoring area, and the nth monitoring area, where n is the number of the monitoring area. Step S362: Count the number of first storage location points in different monitoring areas and record them as the number of area points. Iterate and compare the number of area points in different monitoring areas to obtain the maximum number of area points. Record the monitoring area corresponding to the maximum number of area points as the first effective monitoring area and record the center of the circle corresponding to the first effective monitoring area as the preferred installation point.

[0023] Step S4: Analyze different storage locations inside the cylindrical grain silo based on the preferred installation point to obtain different effective monitoring areas, and install insertion sensors at the center of each effective monitoring area. In this embodiment, the analysis process in step S4 includes the following sub-steps: Step S41: Obtain the second storage location points in different cylindrical grain silos, and calculate the first distance and second distance between the different second storage location points and the preferred installation point using the Euclidean formula; Step S42: Obtain the monitoring distance of the insertion sensor, and compare the first distance and the second distance between the second storage location point and the preferred installation point with the monitoring distance; If the first distance and the second distance between all second storage locations and the preferred installation point are both less than the monitoring distance, then the second storage locations are included in the first effective monitoring area. If any second storage location point has a first distance or a second distance between it and the preferred installation point that is greater than or equal to the monitoring distance, then the corresponding storage location point is recorded as an independent monitoring point. Step S43, please refer to Figure 6 As shown, spheres are drawn with different independent monitoring points as centers and monitoring distances as radii, and these are sequentially recorded as different independent monitoring areas; Step S44: Count the number of independent monitoring points in different independent monitoring areas, iterate through and compare the number of independent monitoring points in different independent monitoring areas, obtain the maximum number of independent monitoring points, and record the corresponding independent monitoring area as the second effective monitoring area; Similarly, the third storage location point, the fourth storage location point, and the kth storage location point are analyzed in sequence to obtain the third effective monitoring area, the fourth effective monitoring area, and the kth effective monitoring area; In this embodiment, Figure 6The solid point is the preferred installation point, the hollow point is the second storage location point, and the solid circle area is the first effective monitoring area. Among them, there are two second storage location points whose first distance and second distance are both less than the monitoring distance. The corresponding two second storage location points are included in the first effective monitoring area. At the same time, there are three second storage location points as independent monitoring points. In this case, among the independent monitoring areas centered on the three independent monitoring points, the middle independent monitoring area covers the largest number of independent monitoring points. The corresponding independent monitoring area is recorded as the second effective monitoring area. It should be explained that when analyzing the third storage location point, it is necessary to simultaneously analyze whether the third storage location point belongs to the first effective monitoring area or the second effective monitoring area. Similarly, when analyzing the kth storage location point, it is necessary to simultaneously analyze whether the fourth storage location point belongs to the first effective monitoring area, the second effective monitoring area, or the third effective monitoring area. Step S45: Install insertion sensors at the center positions corresponding to different effective monitoring areas.

[0024] Step S5: Analyze the cylindrical grain silo after optimizing the installation position of the insertion sensor; In this embodiment, the analysis process in step S5 includes the following sub-steps: Step S51: Record the location of the insertion sensor as the data acquisition point, and obtain the real-time feature data of different data acquisition points at different time nodes and the standard feature data range inside the cylindrical grain silo. Among them, the real-time feature data are the real-time temperature and humidity at different data collection points inside the cylindrical grain silo, and the standard feature data range is the standard temperature range and standard humidity range inside the cylindrical grain silo. Step S52: Compare the real-time temperature at the data collection point with the standard temperature range at different time points, and compare the real-time humidity at the data collection point with the standard humidity range at different time points. If the real-time temperature at the data collection point is within the standard temperature range at all time points, and the real-time humidity at the data collection point is within the standard humidity range at all time points, then proceed to step S53. If the real-time temperature at any data collection point is outside the standard temperature range, or the real-time humidity at any data collection point is outside the standard humidity range, then the cylindrical grain silo should be inspected. In this embodiment, the standard feature data range can be obtained by considering the storage environment requirements of the corresponding grain warehouse; Step S53: Subtract the real-time temperature at the data collection point at the current time node from the real-time temperature at the previous time node to obtain the real-time temperature change at the corresponding data collection point at the current time node. Similarly, the real-time humidity change at the corresponding data collection point at the current time point is calculated; Step S54: If the real-time temperature change or real-time humidity change at the data collection point is greater than or equal to zero at multiple time points, then check the cylindrical grain silo. If there is one and only one time point at which the real-time temperature change or real-time humidity change at the data collection point is greater than or equal to zero, then the data collection point will be continuously monitored.

[0025] Example 2: Figure 7 As shown, this embodiment provides an electronic device that may include a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The processor can call logical instructions in the memory to execute a grain silo environment analysis method based on the Industrial Internet. This method includes: acquiring historical environmental data inside a cylindrical grain silo; analyzing the storage environment of different storage locations inside the cylindrical grain silo based on the historical environmental data to obtain a sequence of storage locations within the cylindrical grain silo; analyzing the interior of the cylindrical grain silo based on different first storage locations to obtain a first effective monitoring area; analyzing different storage locations inside the cylindrical grain silo based on a preferred installation point to obtain different effective monitoring areas, and installing insertable sensors at the center positions corresponding to the different effective monitoring areas; and analyzing the cylindrical grain silo after optimizing the installation positions of the insertable sensors.

[0026] Furthermore, when the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0027] Example 3: This application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the industrial internet-based grain warehouse environment analysis method provided by the above methods. The method includes: acquiring historical environmental data inside a cylindrical grain warehouse; analyzing the storage environment of different storage locations inside the cylindrical grain warehouse based on the historical environmental data to obtain a sequence of storage locations inside the cylindrical grain warehouse; analyzing the inside of the cylindrical grain warehouse based on different first storage locations to obtain a first effective monitoring area; analyzing different storage locations inside the cylindrical grain warehouse based on a preferred installation point to obtain different effective monitoring areas, and installing an insertable sensor at the center position corresponding to the different effective monitoring areas; and analyzing the cylindrical grain warehouse after optimizing the installation position of the insertable sensor.

[0028] Example 4: This application also provides a computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program is implemented to perform the above-described industrial internet-based grain warehouse environment analysis methods. The method includes: acquiring historical environmental data inside a cylindrical grain warehouse; analyzing the storage environment of different storage locations inside the cylindrical grain warehouse based on the historical environmental data to obtain a sequence of storage locations inside the cylindrical grain warehouse; analyzing the inside of the cylindrical grain warehouse based on different first storage locations to obtain a first effective monitoring area; analyzing different storage locations inside the cylindrical grain warehouse based on a preferred installation point to obtain different effective monitoring areas, and installing insertable sensors at the center positions corresponding to the different effective monitoring areas; and analyzing the cylindrical grain warehouse after optimizing the installation positions of the insertable sensors.

[0029] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0030] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0031] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1.A method for analyzing a granary environment based on an industrial internet, characterized by, The method comprises the following steps: Step S1, obtaining historical environment data of the inside of the cylindrical silo; Step S2, analyzing the storage environment of different storage location points in the inside of the cylindrical silo according to the historical environment data, and obtaining a sequence of storage location points in the inside of the cylindrical silo; Step S3, analyzing the inside of the cylindrical silo according to different first storage location points, and obtaining a first effective monitoring area; Step S4, analyzing different storage location points in the inside of the cylindrical silo according to the first preferred installation point, obtaining different effective monitoring areas, and installing the plug-in sensor at the center position corresponding to the different effective monitoring areas; Step S5, analyzing the cylindrical silo after optimizing the installation position of the plug-in sensor. 2.The industrial internet of things based granary environment analysis method according to claim 1, characterized in that, The historical environment data is the historical internal real-time temperature and the historical internal real-time humidity corresponding to different storage location points in the inside of the cylindrical silo. 3.The industrial internet-based granary environment analysis method according to claim 2, characterized in that, The analysis process in step S2 comprises the following sub-steps: Step S21, for any storage location point in the inside of the cylindrical silo, comparing the historical internal real-time temperature corresponding to different time nodes with the standard storage temperature interval, and comparing the historical internal real-time humidity corresponding to different time nodes with the standard storage humidity interval; If the historical internal real-time temperature corresponding to all time nodes belongs to the standard storage temperature interval, and the historical internal real-time humidity corresponding to all time nodes belongs to the standard storage humidity interval, then step S22 is entered; If the historical internal real-time temperature corresponding to any time node does not belong to the standard storage temperature interval, or the historical internal real-time humidity corresponding to any time node does not belong to the standard storage humidity interval, then the corresponding time node is recorded as an abnormal time node, and the first abnormal time node in all abnormal time nodes is recorded as a to-be-determined monitoring time node of the corresponding storage location point; Step S22, subtracting the historical internal real-time temperature of any storage location point at adjacent time nodes and taking the absolute value, and then dividing by a fixed time interval, to obtain the historical internal temperature change rate of the corresponding storage location point at different time nodes; Similarly, subtracting the historical internal real-time humidity of any storage location point at adjacent time nodes and taking the absolute value, and then dividing by a fixed time interval, to obtain the historical internal humidity change rate of the corresponding storage location point at different time nodes; Step S23, traversing and comparing the historical internal temperature change rate of any storage location point at different time nodes to obtain the maximum value of the historical internal temperature change rate, and recording the corresponding time node as a to-be-determined monitoring time node of the corresponding storage location point; Similarly, traversing and comparing the historical internal humidity change rate of any storage location point at different time nodes to obtain the maximum value of the historical internal humidity change rate, and recording the corresponding time node as a to-be-determined monitoring time node of the corresponding storage location point; Step S24, if there is only one to-be-determined monitoring time node for a storage location point, then the only to-be-determined monitoring time node is taken as the starting monitoring time node of the corresponding storage location point; If there are multiple to-be-determined monitoring time nodes for any storage location point, then the first to-be-determined monitoring time node is selected as the starting monitoring time node of the corresponding storage location point; Step S25, arrange all the storage location points in ascending order according to the starting monitoring time node, and obtain a sequence of the storage location points in the cylindrical silo, and sequentially record the storage location points in the sequence as a first storage location point, a second storage location point, a third storage location point and a kth storage location point; wherein k is the number of the storage location points. 4.The industrial internet-based granary environment analysis method according to claim 3, characterized in that, The analysis process in the step S3 includes the following sub-steps: Step S31, record a circle at the bottom end of the cylindrical silo as a contour circle, measure the diameter of the contour circle and record it as a silo diameter, construct a rectangle with the silo diameter as the side length, circumscribe the rectangle to the contour circle and record it as a circumscribed rectangle, and record the intersection point of the circumscribed rectangle and the contour circle as a feature point of the contour circle; Step S32, select any feature point as the coordinate origin, select a diameter passing through the coordinate origin, and then move the corresponding diameter to the same height as the storage location point to serve as a reference diameter, measure the vertical distance between the storage location point and the reference diameter and record it as a center distance; Step S33, record the end of the diameter passing through the coordinate origin and perpendicular to the reference diameter as the side end of the cylindrical silo, measure the distance between the storage location point and the side end of the cylindrical silo and record it as the wall distance of the storage location point, and measure the distance between the storage location point and the bottom end of the cylindrical silo and record it as the storage height of the storage location point; Similarly, the wall distance and the storage height of the first storage location point in different cylindrical silos are measured. 5.The industrial internet-based granary environment analysis method according to claim 4, characterized in that, The analysis process in the step S3 also includes the following sub-steps: Step S34, construct a first rectangular coordinate system at the coordinate origin with the straight line where the diameter corresponding to the bottom end of the cylindrical silo is located as the horizontal axis and the side end of the cylindrical silo as the vertical axis; wherein the horizontal axis represents the wall distance of different storage location points, and the vertical axis represents the storage height of different storage location points; Similarly, construct a second rectangular coordinate system at the coordinate origin with the straight line where the reference diameter is located as the horizontal axis, and the straight line passing through the coordinate origin and perpendicular to the reference diameter and located in the same plane as the bottom end of the cylindrical silo as the vertical axis; wherein the horizontal axis represents the wall distance of different storage location points, and the vertical axis represents the center distance of different storage location points; Step S35, select any first storage location point as an initial analysis point, obtain the coordinates of the initial analysis point and different first storage location points with respect to the first rectangular coordinate system, and calculate the first distances between the initial analysis point and different first storage location points by the Euclidean formula; Similarly, obtain the coordinates of the initial analysis point and different first storage location points with respect to the second rectangular coordinate system, and calculate the second distances between the initial analysis point and different first storage location points by the Euclidean formula; Step S36, obtain the monitoring distance of the plug-in sensor, and compare the first distance, the second distance and the monitoring distance between the initial analysis point and the first storage location point; When the first distance between the initial analysis point and all the first storage location points is greater than or equal to the monitoring distance, or the second distance between the initial analysis point and all the first storage location points is greater than or equal to the monitoring distance, then the corresponding initial analysis point is recorded as an independent monitoring point. When the first distance between the initial analysis point and any first storage location point is less than the monitoring distance and the second distance between the initial analysis point and any first storage location point is less than the monitoring distance, then the initial analysis point and the corresponding first storage location point are used to construct the preferred installation point. 6.The industrial internet of things based granary environment analysis method according to claim 5, characterized in that, The construction process of the preferred installation point in the step S36 includes the following sub-steps: Step S361: A ball is made with the initial analysis point as the center and the monitoring distance as the radius and is recorded as the first monitoring area, and the first storage location points with both the first distance and the second distance from the initial analysis point less than the monitoring distance are all included into the first monitoring area; Similarly, different first storage location points are selected as the initial analysis point, and a ball is made with the initial analysis point as the center and the monitoring distance as the radius and is recorded as the second monitoring area, the third monitoring area, and the nth monitoring area in turn, where n is the number of the monitoring area; Step S362: The number of the first storage location points in different monitoring areas is counted and is recorded as the area point number, and the maximum value of the area point number is obtained by comparing the area point numbers in different monitoring areas, and the monitoring area corresponding to the maximum value of the area point number is recorded as the first effective monitoring area, and the center of the first effective monitoring area is recorded as the preferred installation point. 7.The industrial internet of things based granary environment analysis method according to claim 6, characterized in that, The analysis process in the step S4 includes the following sub-steps: Step S41: The second storage location points in different cylindrical granaries are obtained, and the first distance and the second distance between different second storage location points and the preferred installation point are calculated by the Euclidean formula; Step S42: The monitoring distance of the plug-in sensor is obtained, and the first distance, the second distance between the second storage location points and the preferred installation point are compared with the monitoring distance; If the first distance and the second distance between all the second storage location points and the preferred installation point are less than the monitoring distance, then the second storage location points are included into the first effective monitoring area; If the first distance or the second distance between any second storage location point and the preferred installation point is greater than or equal to the monitoring distance, then the corresponding storage location point is recorded as an independent monitoring point. 8.The industrial internet of things based granary environment analysis method according to claim 7, characterized in that, The analysis process in the step S4 further includes the following sub-steps: Step S43: A ball is made with different independent monitoring points as the center and the monitoring distance as the radius and is recorded as different independent monitoring areas in turn; Step S44: The number of the independent monitoring points in different independent monitoring areas is counted, and the maximum value of the number of the independent monitoring points is obtained by comparing the number of the independent monitoring points in different independent monitoring areas, and the corresponding independent monitoring area is recorded as the second effective monitoring area; Similarly, the third storage location points, the fourth storage location points, and the kth storage location points are analyzed in turn to obtain the third effective monitoring area, the fourth effective monitoring area, and the kth effective monitoring area; Step S45: The plug-in sensor is installed at the center position corresponding to different effective monitoring areas. 9.The industrial internet of things based granary environment analysis method according to claim 8, characterized in that, The analysis process in the step S5 includes the following sub-steps: Step S51: The position of the plug-in sensor is recorded as the data acquisition point, and the real-time feature data at different data acquisition points at different time nodes and the standard feature data interval in the cylindrical granary are obtained; Step S52, comparing the real-time temperature at the data collection point at different time nodes with the standard temperature interval, and comparing the real-time humidity at the data collection point at different time nodes with the standard humidity interval; If the real-time temperature at the data collection point at all time nodes belongs to the standard temperature interval, and the real-time humidity at the data collection point at all time nodes belongs to the standard humidity interval, then step S53 is entered; If the real-time temperature at the data collection point at any time node does not belong to the standard temperature interval, or the real-time humidity at the data collection point at any time node does not belong to the standard humidity interval, then the cylindrical grain warehouse is checked; Step S53, subtracting the corresponding real-time temperature at the last time node from the real-time temperature at the data collection point at the current time node to obtain the real-time temperature change of the corresponding data collection point at the current time node; Similarly, the real-time humidity change of the corresponding data collection point at the current time node is calculated; Step S54, if there are multiple time nodes at which the real-time temperature change or the real-time humidity change of the data collection point is greater than or equal to zero, then the cylindrical grain warehouse is checked; If and only if there is only one time node at which the real-time temperature change or the real-time humidity change of the data collection point is greater than or equal to zero, then the data collection point is continuously monitored. 10.The industrial internet of things based granary environment analysis method according to claim 9, characterized in that, The real-time feature data is the real-time temperature and real-time humidity at different data collection points in the cylindrical grain warehouse, and the standard feature data interval is the standard temperature interval and the standard humidity interval in the cylindrical grain warehouse.

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