Intelligent granary operation data supervision system and method based on Internet of Things

Abstract

The present invention belongs to the technical field of granary supervision, and specifically is an intelligent granary operation data supervision system and method based on the Internet of Things, wherein the system comprises a ventilation and temperature control detection module, a granary monitoring module, a mildew and pest analysis module, a granary management evaluation module and a granary supervision terminal; the present invention accurately judges the ventilation and temperature control performance status inside the granary through the ventilation and temperature control detection module, the mildew and pest analysis module judges the mildew and pest status in the granary based on the image recognition technology, and reasonably evaluates the management performance of the granary through the granary management evaluation module when a mildew and pest low risk signal is generated, and enables the grain supervision terminal to issue an early warning when a ventilation and temperature control abnormal signal, a mildew and pest high risk signal or a management optimization signal is generated, thereby realizing comprehensive monitoring and analysis of the granary operation and timely early warning, significantly reducing the management difficulty of supervisors and ensuring the storage safety of grain in the granary, and having a high degree of intelligence.

Description

Technical Field

[0001] The present invention relates to the technical field of granary supervision, and in particular to an intelligent granary operation data supervision system and method based on the Internet of Things. Background Art

[0002] Granaries refer to warehouses or buildings used to store grain, ensuring that the quality and quantity of grain are properly protected during storage. Granaries are important facilities for grain storage and are of great significance for ensuring food security and stabilizing market supply. Granaries need to be supervised during their operation;

[0003] Traditional grain silo supervision methods mainly rely on manual inspections, which make it difficult to accurately feedback the ventilation and temperature control performance and mold and pest risks of grain silos, and are unable to reasonably analyze and accurately evaluate the management performance of grain silos, increasing the difficulty of management for supervisors. The low level of intelligence is not conducive to ensuring the safety of grain stored in silos.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent granary operation data supervision system and method based on the Internet of Things, which solves the problem that the existing technology is difficult to accurately feedback the ventilation and temperature control performance and mildew and pest risks of the granary, and cannot reasonably analyze and accurately evaluate the management performance of the granary, the management is difficult and the degree of intelligence is low, which is not conducive to ensuring food safety.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The intelligent granary operation data supervision system based on the Internet of Things includes ventilation and temperature control detection module, granary monitoring module, mildew and pest analysis module, granary management evaluation module and granary supervision terminal;

[0008] The ventilation and temperature control detection module analyzes the internal environmental conditions of the granary, determines the ventilation and temperature control performance of the granary within a unit time, and generates a normal ventilation and temperature control signal or an abnormal ventilation and temperature control signal, and sends the abnormal ventilation and temperature control signal to the granary management evaluation module and the granary supervision terminal;

[0009] The granary monitoring module sets several monitoring locations in the granary, collects real-time monitoring images of all monitoring locations, and sends the real-time monitoring images of all monitoring locations to the mildew and pest analysis module and the granary monitoring terminal;

[0010] The mildew and pest analysis module uses real-time monitoring images of all monitoring locations and image recognition technology to determine the mildew and pest conditions in the granary, generates a high-risk mildew and pest signal or a low-risk mildew and pest signal through analysis, and sends the high-risk mildew and pest signal to the granary monitoring terminal, and sends the low-risk mildew and pest signal to the granary management evaluation module;

[0011] When the grain silo management assessment module receives a low-risk signal for mildew and pests, it will analyze the management performance of the grain silo during the detection period, generate a management optimization signal or a management qualification signal through the analysis, and send the management optimization signal or the management qualification signal to the grain supervision terminal; the grain supervision terminal will issue an early warning when it receives an abnormal ventilation and temperature control signal, a high-risk signal for mildew and pests, or a management optimization signal.

[0012] Furthermore, the specific operation process of the ventilation and temperature control detection module includes:

[0013] Through real-time detection and analysis of the environment in the granary, it is determined whether the granary is in a harmful storage state, the total duration of the granary in the harmful storage state per unit time is obtained and marked as the total harmful storage time value, and the single duration of each harmful storage state in the granary is marked as the harmful time, and the harmful time is numerically compared with the preset harmful time threshold, and the number of harmful time exceeding the preset harmful time threshold per unit time is marked as the harmful high-frequency value, and all ventilation and temperature control detection values ​​per unit time are averaged to obtain the granary internal condition value;

[0014] The ventilation and temperature control evaluation value is obtained by numerically calculating the harmful total time value, harmful high-frequency value and granary internal condition value, and the ventilation and temperature control evaluation value is numerically compared with the preset ventilation and temperature control evaluation threshold. If the ventilation and temperature control evaluation value exceeds the preset ventilation and temperature control evaluation threshold, a ventilation and temperature control abnormal signal is generated; if the ventilation and temperature control evaluation value does not exceed the preset ventilation and temperature control evaluation threshold, a ventilation and temperature control normal signal is generated.

[0015] Furthermore, the specific analysis process of real-time detection and analysis of the environment in the granary is as follows:

[0016] The temperature and humidity in the granary are collected, and the deviation value of the temperature compared to the median of the preset suitable temperature range is marked as the granary temperature detection value, and the deviation value of the humidity compared to the median of the preset suitable humidity range is marked as the granary humidity detection value;

[0017] And obtain the harmful gases that need to be monitored in the granary, collect the real-time concentration of the corresponding harmful gases in the granary, and sum up and calculate the real-time concentration of all the harmful gases that need to be monitored in the granary to obtain the granary gas inspection value;

[0018] The ventilation and temperature control detection value is obtained by numerically calculating the grain silo temperature detection value, the grain silo humidity detection value and the grain silo gas detection value, and the ventilation and temperature control detection value is numerically compared with the preset ventilation and temperature control detection threshold. If the ventilation and temperature control detection value exceeds the preset ventilation and temperature control detection threshold, it is judged that the granary is in a harmful storage state.

[0019] Furthermore, the ventilation and temperature control detection module is communicatively connected to the granary equipment diagnosis module, and the ventilation and temperature control detection module sends the ventilation and temperature control abnormality signal to the granary equipment diagnosis module. When the granary equipment diagnosis module receives the ventilation and temperature control abnormality signal, it performs an operation diagnosis on the ventilation and temperature control equipment of the granary to determine its abnormal condition, and generates a diagnosis qualification signal or a diagnosis abnormality signal, and sends the diagnosis qualification signal or the diagnosis failure signal to the granary monitoring terminal. When the granary monitoring terminal receives the diagnosis failure signal, it issues an early warning.

[0020] Furthermore, the specific operation process of the granary equipment diagnosis module includes:

[0021] During the operation of the ventilation and temperature control equipment, real-time data of various operating parameters are collected. If the real-time data of various operating parameters meet the corresponding data requirements, an operation judgment symbol YP-1 is assigned, otherwise an operation judgment symbol YP-2 is assigned; the number of times the ventilation and temperature control equipment is assigned the operation judgment symbol YP-2 per unit time is obtained and marked as the operation frequency difference value, and the ratio of the operation frequency difference value to the operation time of the ventilation and temperature control equipment per unit time is marked as the ventilation and temperature abnormality detection value;

[0022] The control execution delay time of the ventilation and temperature control equipment within a unit time is collected, and all vibration data during the operation of the ventilation and temperature control equipment within a unit time are averaged to obtain a vibration detection value, and all noise data generated during the operation of the ventilation and temperature control equipment within a unit time are averaged to obtain a noise detection value;

[0023] The granary equipment diagnostic value is obtained by numerically calculating the ventilation and temperature control abnormal detection value, the control execution delay time, the vibration detection value and the noise detection value, and the granary equipment diagnostic value is numerically compared with the preset granary equipment diagnostic threshold. If the granary equipment diagnostic value exceeds the preset granary equipment diagnostic threshold, a diagnosis failure signal is generated; if the granary equipment diagnostic value does not exceed the preset granary equipment diagnostic threshold, a diagnosis pass signal is generated.

[0024] Furthermore, the specific analysis process of the mildew and insect pest analysis module is as follows:

[0025] The mildewed grain area of ​​the corresponding monitoring position is obtained, the mildewed grain areas of all monitoring positions are summed up to obtain the mildewed grain detection value, and the mildewed grain pest detection value is obtained through pest identification and analysis, the mildewed grain pest detection value and the mildewed grain pest detection value are numerically calculated to obtain the mildewed and pest assessment value, and the mildewed and pest assessment value is numerically compared with the preset mildewed and pest assessment threshold value. If the mildewed and pest assessment value exceeds the preset mildewed and pest assessment threshold value, a high-risk signal for mildewed and pests is generated; if the mildewed and pest assessment value does not exceed the preset mildewed and pest assessment threshold value, a low-risk signal for mildewed and pests is generated.

[0026] Furthermore, the specific analysis process of pest identification analysis is as follows:

[0027] Set the detection period, obtain all the types of pests that need to be monitored, and mark the corresponding types of pests as target objects u, where u is a natural number greater than 1; if pests are identified at the corresponding monitoring part during the detection period, the corresponding monitoring part is marked as an infested part;

[0028] The pest parts where the target object u is distributed during the detection period are marked as matching parts, the number of matching parts corresponding to the target object u is marked as the matching number detection value, and the distribution density of the target object u in the corresponding matching parts during the detection period is collected, the distribution density of the target object u in all matching parts is averaged to obtain the distribution detection value, and the distribution detection value and the matching number detection value are numerically calculated to obtain the pest risk analysis value;

[0029] A set of preset risk weight values ​​is set in advance for each type of pest, and the product of the pest risk analysis value of the corresponding type of pest and the corresponding preset risk weight value is marked as the pest impact value; the pest impact values ​​of all types of pests existing during the detection period are summed up to obtain the pest comprehensive inspection value, and the number of pest-infested parts in the granary during the detection period is marked as the pest coverage value, and the grain pest detection value is obtained by numerically calculating the pest comprehensive inspection value and the pest coverage value.

[0030] Furthermore, the specific analysis process of the granary management assessment module includes:

[0031] The personnel entering and leaving the granary are verified, and non-staff entering the granary are marked as control personnel. The total time that control personnel exist in the granary during the detection period is collected and marked as the entry and exit detection value, and the number of times abnormal ventilation and temperature control signals are generated during the detection period is obtained and marked as the grain internal control frequency value. The granary management inspection value is obtained by numerically calculating the grain internal control frequency value, the entry and exit detection value and the mildew and pest assessment value. The granary management inspection value is numerically compared with the preset granary management inspection threshold. If the granary management inspection value exceeds the preset granary management inspection threshold, a management optimization signal is generated; if the granary management inspection value does not exceed the preset granary management inspection threshold, a management qualification signal is generated.

[0032] Furthermore, the present invention also proposes a method for supervising operation data of an intelligent granary based on the Internet of Things, comprising the following steps:

[0033] Step 1: Analyze the internal environmental conditions of the granary to determine the ventilation and temperature control performance inside the granary, and generate a normal ventilation and temperature control signal or an abnormal ventilation and temperature control signal;

[0034] Step 2: Set up several monitoring locations in the granary and collect real-time monitoring images of all monitoring locations;

[0035] Step 3: Using real-time monitoring images of all monitoring locations and image recognition technology to determine the mildew and insect pest conditions in the granary, and generating a high-risk signal for mildew and insect pests or a low-risk signal for mildew and insect pests through analysis;

[0036] Step 4: When a low-risk signal for mildew and pests is generated, the management performance of the granary during the detection period is analyzed, and a management optimization signal or a management qualification signal is generated through the analysis;

[0037] Step 5. When abnormal ventilation and temperature control signals, high-risk signals for mold and pests, or management optimization signals are generated, the grain supervision terminal issues an early warning.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] 1. In the present invention, the ventilation and temperature control detection module accurately judges the ventilation and temperature control performance status inside the granary, the mildew and pest analysis module judges the mildew and pest status in the granary based on the image recognition technology, and the granary management evaluation module reasonably evaluates the management performance of the granary when a low-risk signal for mildew and pest is generated, and the grain supervision terminal issues an early warning when an abnormal ventilation and temperature control signal, a high-risk signal for mildew and pest or a management optimization signal is generated, thereby realizing comprehensive monitoring and analysis of the granary operation and timely early warning, significantly reducing the management difficulty of supervisors and ensuring the storage safety of grain in the granary, and having a high degree of intelligence;

[0040] 2. In the present invention, the ventilation and temperature control abnormality signal is sent to the granary equipment diagnosis module through the ventilation and temperature control detection module. The granary equipment diagnosis module performs operation diagnosis on the ventilation and temperature control equipment of the granary to determine its abnormal condition. When a diagnosis failure signal is generated, the granary supervision terminal issues an early warning. When it is judged that the ventilation and temperature control effect of the granary is not good, it can automatically feedback the correlation with the abnormality of the ventilation and temperature control equipment, and promptly remind the supervisor to inspect and repair the ventilation and temperature control equipment of the granary, so as to ensure the subsequent ventilation and temperature control effect of the granary, which is beneficial to the safety of grain storage and reduces the difficulty of granary supervision. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0042] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0043] Figure 2 This is a system block diagram of Embodiment 2 of the present invention;

[0044] Figure 3 This is a flow chart of the method of Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] Embodiment 1: Figure 1 As shown, the intelligent granary operation data supervision system based on the Internet of Things proposed in the present invention includes a ventilation and temperature control detection module, a granary monitoring module, a mildew and pest analysis module, a granary management and evaluation module and a granary supervision terminal, and the grain supervision terminal is connected to the ventilation and temperature control detection module, the granary monitoring module, the mildew and pest analysis module and the granary management and evaluation module through Internet of Things communication;

[0047] Among them, the ventilation and temperature control detection module analyzes the internal environmental conditions of the granary, and judges the ventilation and temperature control performance of the granary within a unit time through analysis, and generates a normal ventilation and temperature control signal or an abnormal ventilation and temperature control signal, and sends the abnormal ventilation and temperature control signal to the granary management evaluation module and the granary supervision terminal. When the grain supervision terminal receives the abnormal ventilation and temperature control signal, it issues an early warning, which can reasonably analyze and accurately judge the ventilation and temperature control conditions inside the granary, which is conducive to timely investigation of the cause and making corresponding improvement measures, thereby ensuring the safe storage of grain in the granary; the specific operation process of the ventilation and temperature control detection module is as follows:

[0048] Through real-time detection and analysis of the environment in the granary, it is determined whether the granary is in a harmful storage state. Specifically, the temperature and humidity in the granary are collected, and the deviation value of the temperature compared to the median of the preset suitable temperature range is marked as the granary temperature detection value, and the deviation value of the humidity compared to the median of the preset suitable humidity range is marked as the granary humidity detection value;

[0049] And obtain the harmful gases that need to be monitored in the granary (including sulfur dioxide, ammonia, phosphine, etc.), collect the real-time concentration of the corresponding harmful gases in the granary, and sum up the real-time concentrations of all the harmful gases that need to be monitored in the granary to obtain the granary gas inspection value;

[0050] The ventilation and temperature control detection value TK is obtained by numerically calculating the granary temperature detection value TR, the granary humidity detection value TS and the granary gas detection value TN through the formula TK=(cp1*TR+cp2*TS+cp3*TN) / 3, wherein cp1, cp2 and cp3 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the ventilation and temperature control detection value TK is, the worse the storage environment performance in the granary is at the corresponding moment;

[0051] The ventilation and temperature control detection value TK is numerically compared with the preset ventilation and temperature control detection threshold. If the ventilation and temperature control detection value TK exceeds the preset ventilation and temperature control detection threshold, it indicates that the storage environment in the granary at the corresponding moment is poor and the internal safety of the granary is not good, and it is judged that the granary is in a harmful storage state;

[0052] The total duration of the harmful storage state in the granary per unit time is obtained and marked as the total harmful storage time value, and the single duration of each harmful storage state in the granary is marked as the harmful time, and the harmful time is numerically compared with the preset harmful time threshold, and the number of harmful time exceeding the preset harmful time threshold per unit time is marked as the harmful high-frequency value, and all ventilation and temperature control detection values ​​per unit time are averaged to obtain the granary internal condition value;

[0053] By formula The total harmful time value QN, the harmful high-frequency value QM and the granary internal condition value QR are numerically calculated to obtain the ventilation and temperature control evaluation value QK; wherein hp1, hp2, and hp3 are preset proportional coefficients, hp2>hp3>hp1>0; and the larger the value of the ventilation and temperature control evaluation value QK, the worse the ventilation and temperature control performance of the granary in unit time is overall;

[0054] The ventilation and temperature control evaluation value QK is numerically compared with the preset ventilation and temperature control evaluation threshold. If the ventilation and temperature control evaluation value QK exceeds the preset ventilation and temperature control evaluation threshold, it indicates that the ventilation and temperature control performance of the granary in unit time is generally poor, and a ventilation and temperature control abnormal signal is generated; if the ventilation and temperature control evaluation value QK does not exceed the preset ventilation and temperature control evaluation threshold, it indicates that the ventilation and temperature control performance of the granary in unit time is generally good, and a ventilation and temperature control normal signal is generated.

[0055] The granary monitoring module sets up several monitoring locations in the granary, collects real-time monitoring images of all monitoring locations, and sends the real-time monitoring images of all monitoring locations to the mildew and pest analysis module and the granary monitoring terminal. The granary monitoring terminal displays the real-time monitoring images of all monitoring locations so that supervisors can observe the mildew and pest conditions of each monitoring location, which is convenient for later tracing.

[0056] The mold and pest analysis module uses real-time monitoring images of all monitoring parts and image recognition technology to determine the mold and pest conditions in the granary. It should be noted that the use of high-definition cameras and image recognition technology can monitor the color, texture and other characteristics of the grain surface. When the grain is moldy, its color, texture and other characteristics will change. These changes can be detected and analyzed through image recognition technology. By comparing the difference between the real-time image and the normal grain image, the moldy grain can be quickly identified. For example, the presence of mold in the grain and the moldy area can be quickly identified based on the depth of the image color change.

[0057] High-definition cameras and AI intelligent analysis technology are used to monitor pests. The camera captures real-time images inside the granary, and the images are analyzed using AI algorithms to identify the type, number, and activity of pests.

[0058] The mildew and pest analysis module generates a high-risk signal for mildew and pests or a low-risk signal for mildew and pests through analysis, and sends the high-risk signal for mildew and pests to the grain storage monitoring terminal. When the grain monitoring terminal receives the high-risk signal for mildew and pests, it issues an early warning, which can accurately feedback the storage risk level of grain in the grain storage, and remind the supervisor to take reasonable improvement measures in time to ensure the storage safety of grain in the grain storage, and reduce the difficulty of grain storage operation supervision. The specific analysis process of the mildew and pest analysis module is as follows:

[0059] The grain pest detection value is obtained through pest identification analysis, specifically: a detection period is set, preferably, the detection period is seven days; all types of pests that need to be monitored (including stored grain beetles, moths, cockroaches, etc.) are obtained, and the corresponding types of pests are marked as target objects u, where u is a natural number greater than 1; if the corresponding monitoring part identifies the pests to be monitored during the detection period, indicating that the corresponding monitoring part has pests, then the corresponding monitoring part is marked as an infested part;

[0060] The pest parts where the target object u is distributed during the detection period are marked as matching parts, the number of matching parts corresponding to the target object u is marked as the matching number detection value, and the distribution density of the target object u in the corresponding matching parts during the detection period is collected (that is, the data value of the average value of the distribution number of the target object u in the corresponding matching parts during the detection period), and the distribution density of the target object u in all matching parts is averaged to obtain the distribution detection value;

[0061] The distribution detection value HFu and the matching number detection value HSu are numerically calculated by the formula HXu=(sp1*HFu+sp2*HSu) / (sp1+sp2) to obtain the pest risk analysis value HXu; wherein sp1 and sp2 are preset weight coefficients, 0<sp1<sp2; and the larger the value of the pest risk analysis value HXu, the greater the potential harm caused by the target object u in the granary to the safety of grain storage;

[0062] A set of preset risk weight values ​​is set in advance for each type of pest (the preset risk weight value is set in advance by the supervisor and is greater than zero. The greater the adverse impact of the corresponding type of pest on food safety storage, the greater the value of the preset risk weight value adapted to it). The product of the pest risk analysis value of the corresponding type of pest and the corresponding preset risk weight value is marked as the pest impact value;

[0063] The pest impact values ​​of all types of pests existing during the detection period are summed up to obtain a pest comprehensive inspection value, and the number of pest-infested parts in the granary during the detection period is marked as a pest coverage value. The pest comprehensive inspection value YZ and the pest coverage value YF are numerically calculated by the formula YR=sq1*YZ+sq2*YF to obtain a grain pest detection value YR; wherein sq1 and sq2 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the grain pest detection value YR is, the more serious the pests in the granary are, and the more unfavorable it is for the safe storage of grain in the granary;

[0064] The grain moldy area of ​​the corresponding monitoring part is obtained, and the grain moldy areas of all the monitoring parts are summed up to obtain the grain moldy detection value; the grain moldy detection value MR and the grain pest detection value YR are numerically calculated by the formula MG=(b1*MR+b2*YR) / 2 to obtain the moldy pest assessment value MG; wherein b1 and b2 are preset weight coefficients with values ​​greater than zero, and the larger the value of the moldy pest assessment value MG is, the more serious the moldy pest situation in the granary is in general, and the more unsafe the storage of grain in the granary is;

[0065] The mold and pest assessment value MG is numerically compared with the preset mold and pest assessment threshold. If the mold and pest assessment value MG exceeds the preset mold and pest assessment threshold, it indicates that the mold and pest situation in the granary is generally serious and the storage of grain in the granary is unsafe, and a high-risk signal for mold and pest is generated; if the mold and pest assessment value MG does not exceed the preset mold and pest assessment threshold, it indicates that the mold and pest situation in the granary is generally light and the storage of grain in the granary is safe, and a low-risk signal for mold and pest is generated.

[0066] The mildew and pest analysis module sends the mildew and pest low risk signal to the granary management evaluation module. When the granary management evaluation module receives the mildew and pest low risk signal, it analyzes the management performance of the granary during the detection period, generates a management optimization signal or a management qualified signal through analysis, and sends the management optimization signal or the management qualified signal to the grain supervision terminal. When the grain supervision terminal receives the management optimization signal, it issues an early warning, which can reasonably analyze and accurately evaluate the management performance of the granary during the detection period and issue an early warning in time, so as to timely adjust the subsequent management plan of the granary, strengthen the subsequent supervision of the granary, and further ensure the safe storage of grain; the specific analysis process of the granary management evaluation module is as follows:

[0067] Verify the personnel entering and leaving the granary, mark the non-staff entering the granary as control personnel, collect the total time that the control personnel exist in the granary during the detection period and mark it as the entry and exit detection value, obtain the number of ventilation and temperature control abnormal signals generated during the detection period and mark it as the grain internal control abnormal frequency value;

[0068] By formula The grain internal control frequency difference value XK, the in-and-out detection value XW and the mildew and insect pest assessment value MG are numerically calculated to obtain the grain silo management inspection value XN; wherein rh1, rh2 and rh3 are preset proportional coefficients, rh1>rh3>rh2>0; and the larger the value of the grain silo management inspection value XN, the worse the operation management performance of the grain silo during the detection period;

[0069] The granary management inspection value XN is numerically compared with the preset granary management inspection threshold. If the granary management inspection value XN exceeds the preset granary management inspection threshold, it indicates that the operation management performance of the granary during the detection period is poor, and it is necessary to strengthen the granary supervision and optimize the management plan, then a management optimization signal is generated; if the granary management inspection value XN does not exceed the preset granary management inspection threshold, it indicates that the operation management performance of the granary during the detection period is good, then a management qualification signal is generated.

[0070] Embodiment 2: Figure 2 As shown, the difference between this embodiment and the first embodiment is that the ventilation and temperature control detection module is communicatively connected to the granary equipment diagnosis module, and the ventilation and temperature control detection module sends the ventilation and temperature control abnormality signal to the granary equipment diagnosis module. When the granary equipment diagnosis module receives the ventilation and temperature control abnormality signal, it performs operation diagnosis on the ventilation and temperature control equipment of the granary to determine its abnormal condition, and generates a diagnosis qualified signal or a diagnosis abnormality signal; it should be noted that the ventilation and temperature control equipment is used to ventilate and control the granary, and the ventilation and temperature control equipment automatically adjusts its own operation based on the control instructions;

[0071] The qualified diagnosis signal or unqualified diagnosis signal is sent to the granary monitoring terminal. When the granary monitoring terminal receives the unqualified diagnosis signal, it issues an early warning. When it determines that the ventilation and temperature control effect of the granary is not good, it can automatically feedback the correlation with the abnormality of the ventilation and temperature control equipment, so as to promptly remind the supervisor to inspect and repair the ventilation and temperature control equipment of the granary, ensure the subsequent ventilation and temperature control effect of the granary, which is beneficial to the safety of grain storage and reduces the difficulty of granary supervision. The specific operation process of the granary equipment diagnosis module is as follows:

[0072] During the operation of the ventilation and temperature control equipment, real-time data of its various operating parameters (such as ventilation speed, operating power, etc.) are collected. If the real-time data of various operating parameters meet the corresponding data requirements, an operation judgment symbol YP-1 is assigned; otherwise (that is, there are operating parameters whose real-time data do not meet the corresponding data requirements), an operation judgment symbol YP-2 is assigned; the number of times the ventilation and temperature control equipment is assigned the operation judgment symbol YP-2 per unit time is obtained and marked as the operation frequency difference value, and the ratio of the operation frequency difference value to the operation time of the ventilation and temperature control equipment per unit time is marked as the ventilation and temperature abnormality detection value;

[0073] The control execution delay time of the ventilation and temperature control equipment within a unit time is collected, wherein the control execution delay time is a data value indicating the average interval time for the ventilation and temperature control equipment to complete the corresponding execution operation for the corresponding control instruction within a unit time; and the larger the value of the control execution delay time, the slower the control execution efficiency of the ventilation and temperature control equipment;

[0074] And all vibration data generated during the operation of the ventilation and temperature control equipment in a unit time are averaged to obtain a vibration detection value, and all noise data generated during the operation of the ventilation and temperature control equipment in a unit time are averaged to obtain a noise detection value; wherein, the larger the values ​​of the vibration detection value and the noise detection value are, the more abnormal the operation of the ventilation and temperature control equipment is;

[0075] The formula LX=a1*LP+(a2*LS+a3*LZ+a4*LW) / a1 is used to numerically calculate the ventilation and temperature control abnormality detection value LP, the control execution delay time LS, the vibration detection value LZ and the noise detection value LW to obtain the granary equipment diagnostic value LX; wherein, a1, a2, a3, a4 are preset proportional coefficients, and the values ​​of a1, a2, a3, a4 are all positive numbers; and the larger the value of the granary equipment diagnostic value LX, the worse the operating condition of the ventilation and temperature control equipment, the greater the probability of abnormality, and the greater the possibility that the ventilation and temperature control condition in the granary is poor due to the abnormality of the ventilation and temperature control equipment;

[0076] The granary equipment diagnostic value LX is numerically compared with the preset granary equipment diagnostic threshold. If the granary equipment diagnostic value LX exceeds the preset granary equipment diagnostic threshold, it indicates that the ventilation and temperature control equipment is in poor operating condition and there is a high probability of abnormality. The supervisor should inspect and repair the ventilation and temperature control equipment in time, and a diagnosis failure signal is generated; if the granary equipment diagnostic value LX does not exceed the preset granary equipment diagnostic threshold, it indicates that the ventilation and temperature control equipment is in good operating condition. The supervisor can optimize the automatic control strategy of the ventilation and temperature control equipment as needed, and a diagnosis pass signal is generated.

[0077] Embodiment 3: Figure 3 As shown, the difference between this embodiment and the first and second embodiments is that the method for supervising the operation data of a smart granary based on the Internet of Things proposed in the present invention comprises the following steps:

[0078] Step 1: Analyze the internal environmental conditions of the granary to determine the ventilation and temperature control performance inside the granary, and generate a normal ventilation and temperature control signal or an abnormal ventilation and temperature control signal;

[0079] Step 2: Set up several monitoring locations in the granary and collect real-time monitoring images of all monitoring locations;

[0080] Step 3: Using real-time monitoring images of all monitoring locations and image recognition technology to determine the mildew and insect pest conditions in the granary, and generating a high-risk signal for mildew and insect pests or a low-risk signal for mildew and insect pests through analysis;

[0081] Step 4: When a low-risk signal for mildew and pests is generated, the management performance of the granary during the detection period is analyzed, and a management optimization signal or a management qualification signal is generated through the analysis;

[0082] Step 5. When abnormal ventilation and temperature control signals, high-risk signals for mold and pests, or management optimization signals are generated, the grain supervision terminal issues an early warning.

[0083] The working principle of the present invention is as follows: when in use, the internal environmental conditions of the granary are analyzed through the ventilation and temperature control detection module to determine the ventilation and temperature control performance conditions inside the granary, and generate a normal ventilation and temperature control signal or an abnormal ventilation and temperature control signal. The mildew and pest analysis module determines the mildew and pest conditions in the granary based on image recognition technology, and generates a high-risk mildew and pest signal or a low-risk mildew and pest signal through analysis. When the low-risk mildew and pest signal is generated, the management performance of the granary during the detection period is analyzed through the granary management evaluation module, and a management optimization signal or a qualified management signal is generated through analysis. When the abnormal ventilation and temperature control signal, the high-risk mildew and pest signal or the management optimization signal is generated, the grain supervision terminal issues an early warning, which can comprehensively monitor and analyze the operation of the granary and issue early warnings in time, significantly reducing the management difficulty of the supervisor and ensuring the storage safety of the grain in the granary, and has a high degree of intelligence.

[0084] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the contents of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. The intelligent granary operation data supervision system based on the Internet of Things is characterized by: It includes ventilation and temperature control detection module, granary monitoring module, mildew and pest analysis module, granary management evaluation module and granary supervision terminal; The ventilation and temperature control detection module analyzes the internal environmental conditions of the granary, determines the ventilation and temperature control performance of the granary within a unit time, and generates a normal ventilation and temperature control signal or an abnormal ventilation and temperature control signal, and sends the abnormal ventilation and temperature control signal to the granary management evaluation module and the granary supervision terminal; The granary monitoring module sets several monitoring locations in the granary, collects real-time monitoring images of all monitoring locations, and sends the real-time monitoring images of all monitoring locations to the mildew and pest analysis module and the granary monitoring terminal; The mildew and pest analysis module uses real-time monitoring images of all monitoring locations and image recognition technology to determine the mildew and pest conditions in the granary, generates a high-risk mildew and pest signal or a low-risk mildew and pest signal through analysis, and sends the high-risk mildew and pest signal to the granary monitoring terminal, and sends the low-risk mildew and pest signal to the granary management evaluation module; When the grain silo management assessment module receives a low-risk signal for mildew and pests, it will analyze the management performance of the grain silo during the detection period, generate a management optimization signal or a management qualification signal through the analysis, and send the management optimization signal or the management qualification signal to the grain supervision terminal; the grain supervision terminal will issue an early warning when it receives an abnormal ventilation and temperature control signal, a high-risk signal for mildew and pests, or a management optimization signal.

2. According to the Internet of Things-based intelligent granary operation data supervision system of claim 1, it is characterized in that: The specific operation process of the ventilation and temperature control detection module includes: Real-time detection and analysis of the environment inside the granary is used to determine whether the granary is in a harmful storage state. The total harmful time value, harmful high-frequency value and granary internal condition value are numerically calculated to obtain the ventilation and temperature control evaluation value. If the ventilation and temperature control evaluation value exceeds the preset ventilation and temperature control evaluation threshold, a ventilation and temperature control abnormal signal is generated; if the ventilation and temperature control evaluation value does not exceed the preset ventilation and temperature control evaluation threshold, a ventilation and temperature control normal signal is generated.

3. The intelligent granary operation data monitoring system based on the Internet of Things according to claim 2 is characterized in that: The specific analysis process of real-time detection and analysis of the environment in the granary is as follows: The temperature and humidity in the granary are collected, and the deviation value of the temperature compared to the median of the preset suitable temperature range is marked as the granary temperature detection value, and the deviation value of the humidity compared to the median of the preset suitable humidity range is marked as the granary humidity detection value; And obtain the harmful gases that need to be monitored in the granary, collect the real-time concentration of the corresponding harmful gases in the granary, and sum up and calculate the real-time concentration of all the harmful gases that need to be monitored in the granary to obtain the granary gas inspection value; The ventilation and temperature control detection value is obtained by numerically calculating the grain silo temperature detection value, the grain silo humidity detection value and the grain silo gas detection value. If the ventilation and temperature control detection value exceeds the preset ventilation and temperature control detection threshold, it is judged that the grain silo is in a harmful storage state.

4. The intelligent granary operation data supervision system based on the Internet of Things according to claim 2 is characterized in that: The ventilation and temperature control detection module is communicatively connected to the granary equipment diagnosis module. The ventilation and temperature control detection module sends the ventilation and temperature control abnormality signal to the granary equipment diagnosis module. When the granary equipment diagnosis module receives the ventilation and temperature control abnormality signal, it performs an operation diagnosis on the ventilation and temperature control equipment of the granary to determine its abnormal condition, and generates a diagnosis qualification signal or a diagnosis abnormality signal, and sends the diagnosis qualification signal or the diagnosis failure signal to the granary supervision terminal. When the granary supervision terminal receives the diagnosis failure signal, it issues an early warning.

5. The intelligent granary operation data monitoring system based on the Internet of Things according to claim 4 is characterized in that: The specific operation process of the granary equipment diagnostic module includes: The granary equipment diagnostic value is obtained by numerically calculating the ventilation and temperature control abnormal detection value, the control execution delay time, the vibration detection value and the noise detection value. If the granary equipment diagnostic value exceeds the preset granary equipment diagnostic threshold, a diagnosis failure signal is generated; if the granary equipment diagnostic value does not exceed the preset granary equipment diagnostic threshold, a diagnosis pass signal is generated.

6. The intelligent granary operation data supervision system based on the Internet of Things according to claim 1 is characterized in that: The specific analysis process of the mildew and pest analysis module is as follows: The grain moldy area of ​​the corresponding monitoring part is obtained, the grain moldy areas of all the monitoring parts are summed up to obtain the grain moldy detection value, and the grain pest detection value is obtained through pest identification analysis, and the grain moldy detection value and the grain pest detection value are numerically calculated to obtain the mold and pest assessment value. If the mold and pest assessment value exceeds the preset mold and pest assessment threshold, a high-risk signal for mold and pest is generated; If the mold and pest assessment value does not exceed the preset mold and pest assessment threshold, a low mold and pest risk signal is generated.

7. The intelligent granary operation data monitoring system based on the Internet of Things according to claim 6 is characterized in that: The specific analysis process of pest identification analysis is as follows: Set the detection period, obtain all the types of pests that need to be monitored, and mark the corresponding types of pests as target objects u, where u is a natural number greater than 1; if pests are identified at the corresponding monitoring part during the detection period, the corresponding monitoring part is marked as an infested part; The pest parts where the target object u is distributed during the detection period are marked as matching parts, the number of matching parts corresponding to the target object u is marked as the matching number detection value, and the distribution density of the target object u in the corresponding matching parts during the detection period is collected, the distribution density of the target object u in all matching parts is averaged to obtain the distribution detection value, and the distribution detection value and the matching number detection value are numerically calculated to obtain the pest risk analysis value; A set of preset risk weight values ​​is set in advance for each type of pest, and the product of the pest risk analysis value of the corresponding type of pest and the corresponding preset risk weight value is marked as the pest impact value; the pest impact values ​​of all types of pests existing during the detection period are summed up to obtain the pest comprehensive inspection value, and the number of pest-infested parts in the granary during the detection period is marked as the pest coverage value, and the grain pest detection value is obtained by numerically calculating the pest comprehensive inspection value and the pest coverage value.

8. The intelligent granary operation data monitoring system based on the Internet of Things according to claim 1 is characterized in that: The specific analysis process of the grain silo management assessment module includes: The silo management inspection value is obtained by numerically calculating the grain internal control frequency difference value, entry and exit detection value and mildew and pest assessment value. If the silo management inspection value exceeds the preset silo management inspection threshold, a management optimization signal is generated; if the silo management inspection value does not exceed the preset silo management inspection threshold, a management qualification signal is generated.

9. The method for monitoring the operation data of smart granary based on the Internet of Things is characterized by: The method adopts the smart granary operation data monitoring system based on the Internet of Things as described in any one of claims 1-8.

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