Grain storage and transportation environment monitoring and regulation method and system

Abstract

The invention relates to the technical field of grain monitoring, and particularly discloses a grain storage and transportation environment monitoring, regulation and control method and system, and the method comprises the steps: obtaining the preset ventilation information of a grain storage and transportation warehouse and the position information of a plurality of collection points, and obtaining the real-time environment information corresponding to the position information of each collection point, the real-time environment information comprises environment temperature, environment humidity and gas concentration, and the gas concentration comprises carbon dioxide concentration, oxygen concentration and volatile organic compound concentration; and obtaining an entomogenous and mildewed evaluation value of the corresponding acquisition point position information according to the carbon dioxide concentration, the oxygen concentration and the volatile organic compound concentration. Environmental information is acquired through a sensor in a grain storage and transportation space, a plurality of factors are integrated to calculate an entomophthole evaluation value, an entomophthole occurrence probability three-dimensional model diagram is drawn, a real-time hot area diffusion value is acquired in combination with preset ventilation information, an environment regulation and control three-dimensional model diagram is established, and entomophthole and hot area risks can be accurately evaluated.

Description

Technical Field

[0001] The present invention relates to the technical field of grain monitoring, and in particular to a method and system for monitoring and regulating the grain storage and transportation environment. Background Art

[0002] As an important strategic material for maintaining human survival and development, ensuring the quality of grain during storage and transportation is of crucial importance. During the storage and transportation of grain, the impact of environmental factors on grain quality cannot be underestimated. However, there are many deficiencies in traditional methods for monitoring and regulating the grain storage and transportation environment, making it difficult to meet the requirements of the modern grain industry.

[0003] Existing environmental regulation methods lack precision and systematicness. Regulation decisions mostly rely on manual experience, making it difficult to meet the different requirements of different regions, and it is easy to have a "one-size-fits-all" situation. In large grain warehouses, the environmental requirements for grain at different positions are different, but traditional regulation methods cannot accurately regulate according to the actual situation of each region, resulting in over-regulation in some regions and under-regulation in some regions, wasting resources and failing to effectively ensure grain quality. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for monitoring and regulating the grain storage and transportation environment to solve the technical problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions: A method for monitoring and regulating the grain storage and transportation environment, applied to a grain storage and transportation warehouse, includes: Obtaining the preset ventilation information and the position information of multiple collection points of the grain storage and transportation warehouse, and obtaining the real-time environmental information corresponding to each position information of the collection point, wherein the real-time environmental information includes environmental temperature, environmental humidity, and gas concentration, and the gas concentration includes carbon dioxide concentration, oxygen concentration, and volatile organic compound concentration; Obtaining a pest and mold evaluation value corresponding to the position information of the collection point according to the carbon dioxide concentration, oxygen concentration, and volatile organic compound concentration, and obtaining a three-dimensional model diagram of the pest and mold occurrence probability according to the pest and mold evaluation value corresponding to each position information of the collection point; Obtaining the ventilation rate of each position information of the collection point according to the preset ventilation information, and obtaining the real-time hot zone diffusion value according to the ventilation rate; Establishing a three-dimensional model diagram of environmental regulation according to the real-time hot zone diffusion value corresponding to each position information of the collection point; Obtaining regulation information according to the three-dimensional model diagram of the pest and mold occurrence probability and the three-dimensional model diagram of environmental regulation, wherein the regulation information includes environmental dehumidification adjustment, environmental ventilation adjustment, and environmental cooling adjustment, and performing dehumidification, ventilation, and cooling adjustments on the grain storage and transportation warehouse according to the regulation information.

[0006] Preferably, the step of obtaining the preset ventilation information and the position information of multiple collection points of the grain storage and transportation warehouse includes: Obtain the physical structure information of the grain storage and transportation warehouse, wherein the physical structure information includes the data of the length, width and height of the warehouse body, the position of the ventilation opening, and the shape of the grain pile distribution; Take the position of the ventilation opening as the origin of the coordinate system, and establish a space coordinate system of the warehouse body according to the origin of the coordinate system and the data of the length, width and height of the warehouse body; Obtain the positions of multiple signal collection points according to the shape of the grain pile distribution; Map the position of each signal collection point into the space coordinate system of the warehouse body to obtain the position information of multiple collection points; Obtain the type characteristic information inside the grain storage and transportation warehouse, wherein the type characteristic information includes the particle size, moisture content, and insect and mold sensitivity information; Obtain the preset ventilation information according to the particle size, moisture content, and insect and mold sensitivity information.

[0007] Preferably, the step of obtaining the insect and mold evaluation value of the corresponding collection point position information according to the carbon dioxide concentration, oxygen concentration and volatile organic compound concentration includes: Obtain the insect and mold respiration value according to the carbon dioxide concentration; Obtain the insect and mold activity value according to the oxygen concentration; Obtain the insect and mold metabolism value according to the volatile organic compound concentration; Obtain the insect and mold activity value according to the insect and mold respiration value, insect and mold activity value and insect and mold metabolism value; Obtain multiple coordinate information according to the multiple collection point position information; Obtain the insect and mold evaluation value corresponding to each collection point position information according to the distance difference and the insect and mold activity value.

[0008] Preferably, the step of obtaining the three-dimensional model diagram of the insect and mold occurrence probability according to the insect and mold evaluation value corresponding to each collection point position information includes: Generate a time sequence number according to the generation time of each collection point position information to obtain a collection point sequence list; Obtain the distance attenuation value of each collection point position information according to the collection point sequence list; Obtain the corrected insect and mold evaluation value corresponding to each collection point position information according to the distance attenuation value and the insect and mold evaluation value; Map the corrected insect and mold evaluation value into the collection space matrix to obtain the three-dimensional model diagram of the insect and mold occurrence probability.

[0009] Preferably, the step of obtaining the ventilation rate of each collection point position information according to the preset ventilation information and obtaining the real-time heat zone diffusion value according to the ventilation rate includes: Obtain the initial airflow velocity data of the ventilation equipment according to the preset ventilation information; Obtain the air dynamic viscosity corresponding to the position information of each collection point; Obtain the real-time wind speed value corresponding to the position information of each collection point; Obtain the ventilation rate corresponding to the position information of each collection point according to the initial airflow velocity data, air dynamic viscosity and real-time wind speed value; Obtain the real-time hot zone diffusion value according to the ventilation rate corresponding to the position information of each collection point and the probability of insect and mildew occurrence.

[0010] Preferably, the step of establishing the environmental control three-dimensional model diagram according to the real-time hot zone diffusion value corresponding to the position information of each collection point includes: Obtain the target point distance value from the position information of each collection point to the ventilation equipment; Obtain the target hot zone diffusion value according to the target point distance value; Obtain the regulated hot zone diffusion value according to the target hot zone diffusion value and the real-time hot zone diffusion value; Establish an environmental control three-dimensional model diagram according to the regulated hot zone diffusion value and the collection space matrix.

[0011] Preferably, the step of obtaining the regulation information according to the three-dimensional model diagram of the probability of insect and mildew occurrence and the three-dimensional model diagram of environmental control includes: Obtain the insect and mildew occurrence risk value corresponding to the position information of each collection point according to the three-dimensional model diagram of the probability of insect and mildew occurrence; Obtain the environmental control change value corresponding to the position information of each collection point according to the three-dimensional model diagram of environmental control; Obtain the sampling point risk value according to the insect and mildew occurrence risk value and the environmental control change value of each collection point position information; Judge whether the sampling point risk value is within the preset threshold range; If it is less, obtain the first regulation value according to the sampling point risk value, and perform environmental dehumidification adjustment according to the first regulation value; If it is within, obtain the second regulation value according to the sampling point risk value, and perform environmental dehumidification adjustment and environmental ventilation adjustment according to the second regulation value; If it is greater, obtain the third regulation value according to the sampling point risk value, and perform environmental cooling adjustment and environmental ventilation adjustment according to the third regulation value; Generate the collection point regulation information corresponding to the position information of each collection point according to the first regulation value, the second regulation value and the third regulation value; Sort the collection point regulation information based on the collection point sequence list to obtain the regulation information.

[0012] The present invention also provides a grain storage and transportation environment monitoring and control system, including: A first acquisition module, configured to acquire preset ventilation information and multiple collection point location information of a grain storage and transportation warehouse, and acquire real-time environment information corresponding to each of the collection point location information, wherein the real-time environment information includes environmental temperature, environmental humidity, and gas concentration, and wherein the gas concentration includes carbon dioxide concentration, oxygen concentration, and volatile organic compound concentration; A second acquisition module, configured to acquire a pest and mold evaluation value corresponding to the collection point location information according to the carbon dioxide concentration, oxygen concentration, and volatile organic compound concentration, and acquire a three-dimensional model diagram of the pest and mold occurrence probability according to the pest and mold evaluation value corresponding to each collection point location information; A third acquisition module, configured to acquire the ventilation rate of each collection point location information according to the preset ventilation information, and acquire a real-time hot zone diffusion value according to the ventilation rate; A fourth acquisition module, configured to establish a three-dimensional model diagram of environmental control according to the real-time hot zone diffusion value corresponding to each collection point location information; An adjustment module, configured to acquire adjustment information according to the three-dimensional model diagram of the pest and mold occurrence probability and the three-dimensional model diagram of environmental control, wherein the adjustment information includes environmental dehumidification adjustment, environmental ventilation adjustment, and environmental cooling adjustment, and perform dehumidification, ventilation, and cooling adjustments on the grain storage and transportation warehouse according to the adjustment information.

[0013] Preferably, the first acquisition module includes: A first acquisition unit, configured to acquire physical structure information of the grain storage and transportation warehouse, wherein the physical structure information includes data of the length, width, and height of the warehouse body, the position of the ventilation opening, and the shape of the grain pile distribution; A second acquisition unit, configured to use the position of the ventilation opening as the origin of the coordinate system, and establish a warehouse body space coordinate system according to the origin of the coordinate system and the data of the length, width, and height of the warehouse body; A third acquisition unit, configured to acquire multiple signal collection point positions according to the shape of the grain pile distribution; A fourth acquisition unit, configured to map each signal collection point position to the warehouse body space coordinate system to obtain multiple collection point location information; A fifth acquisition unit, configured to acquire the type characteristic information inside the grain storage and transportation warehouse, wherein the type characteristic information includes particle size, moisture content, and pest and mold sensitivity information; A sixth acquisition unit, configured to acquire preset ventilation information according to the particle size, moisture content, and pest and mold sensitivity information.

[0014] Preferably, the second acquisition module includes: A seventh acquisition unit, configured to acquire a pest and mold respiration value according to the carbon dioxide concentration; An eighth acquisition unit, configured to obtain an entomophthora activity value according to the oxygen concentration; A ninth acquisition unit, configured to obtain an entomophthora metabolism value according to the volatile organic compound concentration; A tenth acquisition unit, configured to obtain an entomophthora activity value according to the entomophthora respiration value, the entomophthora activity value, and the entomophthora metabolism value; An eleventh acquisition unit, configured to obtain a plurality of coordinate information according to the position information of a plurality of the collection points; Obtain an entomophthora evaluation value corresponding to the position information of each collection point according to the distance difference and the entomophthora activity value.

[0015] The beneficial effects of this application are as follows: Through sensors in the grain storage and transportation space, the present invention obtains environmental information, comprehensively collects ventilation, position, and real-time environmental information (such as temperature, humidity, and various gas concentrations), makes up for the defect that traditional monitoring only focuses on a single index, can detect environmental changes in time, provides accurate data for subsequent regulation. In terms of risk assessment and early warning, it comprehensively calculates the entomophthora evaluation value by combining multiple factors, draws a three-dimensional model diagram of the probability of entomophthora occurrence, combines the preset ventilation information to obtain the real-time hot zone diffusion value, and establishes a three-dimensional model diagram of environmental regulation, which can accurately evaluate the risks of entomophthora and hot zones, and early warn potential problems, providing a scientific basis for prevention and control. In the regulation link, according to the model diagram, the regulation information is obtained, and differential regulation is implemented for different collection point position information, such as dehumidification, ventilation, and cooling, to achieve accurate regulation. It not only effectively prevents the occurrence of entomophthora, maintains appropriate temperature and humidity, but also improves the regulation efficiency and avoids waste of resources. Overall, it ensures the quality and safety of grain during storage and transportation, reduces grain losses, and improves the scientific and intelligent levels of grain storage and transportation environment monitoring and regulation. Description of the Drawings

[0016] Figure 1 It is a schematic flowchart of the method according to an embodiment of this application.

[0017] Figure 2 It is a schematic structural diagram of the system according to an embodiment of this application.

[0018] The realization, functional characteristics, and advantages of the purpose of this application will be further described with reference to the embodiments and the accompanying drawings. Detailed Embodiments

[0019] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0020] As Figure 1 shown, this application provides a method for monitoring and regulating the grain storage and transportation environment, including: S1. Obtain the preset ventilation information and the location information of multiple collection points of the grain storage and transportation warehouse, and obtain the real-time environmental information corresponding to each location information of the collection point. Among them, the real-time environmental information includes environmental temperature, environmental humidity, and gas concentration. Among them, the gas concentration includes carbon dioxide concentration, oxygen concentration, and volatile organic compound concentration; S2. Obtain the insect and mold evaluation value corresponding to the location information of the collection point according to the carbon dioxide concentration, oxygen concentration, and volatile organic compound concentration, and obtain the three-dimensional model diagram of the insect and mold occurrence probability according to the insect and mold evaluation value corresponding to each location information of the collection point; S3. Obtain the ventilation rate of each collection point location information according to the preset ventilation information, and obtain the real-time hot zone diffusion value according to the ventilation rate; S4. Establish a three-dimensional model diagram of environmental regulation according to the real-time hot zone diffusion value corresponding to each collection point location information; S5. Obtain the regulation information according to the three-dimensional model diagram of the insect and mold occurrence probability and the three-dimensional model diagram of environmental regulation. Among them, the regulation information includes environmental dehumidification adjustment, environmental ventilation adjustment, and environmental cooling adjustment. Dehumidify, ventilate, and cool the grain storage and transportation warehouse according to the regulation information.

[0021] As described in the above steps S1 - S5, the present invention obtains the preset ventilation information, the position information of multiple collection points, and the real - time environmental information of each collection point (including gas concentrations such as environmental temperature, environmental humidity, carbon dioxide concentration, oxygen concentration, and volatile organic compound concentration) in the grain storage and transportation warehouse. By means of sensors, collection points are reasonably arranged in the grain storage and transportation space to comprehensively and accurately collect multi - aspect information. In a large - scale granary, temperature and humidity sensors and gas sensors are installed at regular intervals to be able to sense the environmental conditions in every corner in real time. These information provide a solid data basis for subsequent monitoring and regulation, avoiding the limitations of traditional monitoring methods that only focus on a single index (such as only monitoring temperature), and preventing the occurrence of grain spoilage due to ignoring factors such as changes in gas concentration. Based on the information obtained in S1, step S2 further conducts in - depth analysis. The respiration value of insects and mildew is obtained through the carbon dioxide concentration, the activity value of insects and mildew is obtained based on the oxygen concentration, and the metabolic value of insects and mildew is calculated using the volatile organic compound concentration. By synthesizing these values, the activity value of insects and mildew is obtained. At the same time, a space coordinate system and a collection space matrix are established in combination with the collection point coordinates, the distance difference is calculated, and thus the insect and mildew evaluation value is obtained. Then, according to the sequence number of the collection points, the distance attenuation value is obtained, the insect and mildew evaluation value is corrected and mapped to the collection space matrix, and a three - dimensional model diagram of the insect and mildew occurrence probability is drawn to accurately locate high - risk areas. For example, in a small - scale grain depot, through this step, it is found that the areas near the ground and walls have a relatively high probability of insect and mildew occurrence because these areas are relatively humid and have poor ventilation. Grain depot managers can then take targeted prevention and control measures in advance based on this result, such as strengthening moisture protection and ventilation, effectively reducing the risk of insect and mildew occurrence. Next, step S3 obtains the initial air flow velocity data of the ventilation equipment according to the preset ventilation information, calculates the ventilation rate in combination with the air dynamic viscosity and the real - time wind speed value of the collection point, and then obtains the real - time hot - zone diffusion value based on the ventilation rate and the insect and mildew occurrence probability. In the past, traditional ventilation regulation often relied on experience and was prone to problems such as over - ventilation or under - ventilation. This step provides a scientific basis for ventilation regulation. For example, in a grain drying workshop, through calculation, it is found that the ventilation rate in the corners of the workshop is low and the hot - zone diffusion is slow, resulting in uneven cooling of the grain. Subsequently, the angle and power of the ventilation equipment are adjusted to increase the ventilation rate in the corners, making the hot - zone diffusion more uniform and improving the quality of grain drying. On the basis of S3, by obtaining the distance value from the collection point to the target point of the ventilation equipment, the target hot - zone diffusion value is calculated, compared with the real - time hot - zone diffusion value to obtain the regulated hot - zone diffusion value, and finally an environmental regulation three - dimensional model diagram is established in combination with the collection space matrix. This model diagram closely associates the collection points with the ventilation equipment and visually shows the relationship between hot - zone diffusion and regulation requirements, providing visual support for formulating precise environmental regulation strategies.For example, in a newly built grain storage facility, using this model diagram, it is found that the heat zone diffusion is insufficient in the areas far from the ventilation openings, and it is difficult to control the temperature and humidity. Therefore, auxiliary ventilation equipment is added in these areas, optimizing the control effect and ensuring the safety of grain storage. Finally, step S5 is the key execution link of the entire control process. Obtain the risk value of insect and mold occurrence from the three-dimensional model diagram of insect and mold occurrence probability, and obtain the environmental control change value from the three-dimensional model diagram of environmental control. Then comprehensively obtain the risk value of the sampling point. According to the relationship between the risk value and the preset threshold interval, determine different control methods. If the risk value is less than the lower limit of the preset threshold, perform environmental dehumidification adjustment; if it is within the threshold interval, perform environmental dehumidification adjustment and environmental ventilation adjustment; if it is greater than the upper limit of the threshold, perform environmental cooling adjustment and environmental ventilation adjustment. Then generate the control information for the collection points and sort and execute it. This process realizes the precise control of the grain storage and transportation warehouse, effectively preventing the occurrence of insects and molds and maintaining suitable temperature and humidity. For example, in a certain grain transfer warehouse, the temperature is high and the humidity is high in summer. Through this step, the risk value of the sampling point is calculated to be relatively high, and cooling, ventilation and dehumidification treatments are carried out according to the control information, successfully avoiding the mildew and insect pests of the grain due to high temperature and high humidity, and ensuring the quality of the grain during the transfer process.

[0022] In one embodiment, the step of obtaining the preset ventilation information and the position information of multiple collection points of the grain storage and transportation warehouse includes: S101. Obtain the physical structure information of the grain storage and transportation warehouse, where the physical structure information includes the length, width and height data of the warehouse body, the position of the ventilation opening, and the shape of the grain pile distribution; S102. Take the position of the ventilation opening as the origin of the coordinate system, and establish a space coordinate system of the warehouse body according to the origin of the coordinate system and the length, width and height data of the warehouse body; S103. Obtain the positions of multiple signal collection points according to the shape of the grain pile distribution; S104. Map the position of each signal collection point to the space coordinate system of the warehouse body to obtain the position information of multiple collection points; S105. Obtain the variety characteristic information inside the grain storage and transportation warehouse, where the variety characteristic information includes the particle size, moisture content, and insect and mold sensitivity information; S106. Obtain the preset ventilation information according to the particle size, moisture content, and insect and mold sensitivity information.

[0023] As described in the above steps S101 - S106, the present invention obtains the physical structure information of the grain storage and transportation warehouse, including the length, width and height data of the warehouse body, the position of the ventilation openings, the shape of the grain pile distribution, etc. This step solves the problem of blindness in sensor layout and ventilation control caused by the lack of systematic analysis of the warehouse structure in traditional monitoring. For example, in a renovation project of a large - scale grain reserve depot, after obtaining the physical structure information by using a laser rangefinder, three - dimensional laser scanning technology, etc., the sensor layout and the installation of ventilation equipment were optimized, avoiding monitoring blind spots, improving the ventilation efficiency. Taking the position of the ventilation opening as the origin of the coordinate system and combining the length, width and height data of the warehouse body to establish a space coordinate system of the warehouse body solved the problems such as the confusion of the position information of the collection points and the difficulty in evaluating the ventilation effect caused by the non - uniform traditional coordinate system. Taking a grain storage enterprise as an example, after unifying the coordinate system, it is not only convenient to analyze the relationship between the collection points and the ventilation openings in a single warehouse, but also can compare and integrate the data of multiple warehouses, realizing the targeted optimization of the ventilation system. Determine the positions of multiple signal collection points according to the shape of the grain pile distribution. This process fully considers the influence of the grain pile shape on the distribution of environmental parameters and avoids the deficiencies of the traditional uniform point - setting method. For example, in a large - scale grain transfer depot, sensors were added in key areas according to the irregular shape of the grain pile, and the humidity anomaly in the easily - overlooked area was successfully monitored, and measures were taken in time to avoid grain mildew. Map the position of each signal collection point to the space coordinate system of the warehouse body, realizing the digitization and standardization of the collection point positions. This solves the problems of inaccurate recording of the traditional collection point positions and being unfavorable for data integration and analysis. In an intelligent grain storage system, when abnormal data of a certain sensor is found, the problem position can be quickly located through the coordinate information, and the cause can be accurately analyzed and timely regulated in combination with the surrounding data. Then, obtain the variety characteristics information inside the grain storage and transportation warehouse, including particle size, moisture content, susceptibility to insects and mildew, etc. This link formulates differential monitoring and control strategies according to the characteristics of different grain varieties. For example, in a grain reserve depot, different humidity thresholds and ventilation modes are set according to the different characteristics of paddy and wheat, effectively ensuring the grain quality. Finally, obtain the preset ventilation information according to the particle size, moisture content, susceptibility to insects and mildew information, realizing the personalized customization of the ventilation strategy. This solves the problems of lack of pertinence in traditional ventilation control, easy causing grain deterioration and energy waste. In a grain drying workshop, for paddy with a relatively high moisture content, the preset ventilation information obtained according to its characteristics effectively reduces the humidity, inhibits the growth of insects and mildew, and at the same time reduces energy consumption.

[0024] In one embodiment, the step of obtaining the insect and mildew evaluation value of the corresponding collection point position information according to the carbon dioxide concentration, oxygen concentration and volatile organic compound concentration includes: S201. Obtain the insect and mildew respiration value according to the carbon dioxide concentration; S202. Obtain the insect and mildew activity value according to the oxygen concentration; S203. Obtain the entomophthora metabolic value according to the concentration of the volatile organic compound; S204. Obtain the entomophthora activity value according to the entomophthora respiration value, the entomophthora activity value and the entomophthora metabolic value; S205. Obtain a plurality of coordinate information according to the position information of the plurality of collection points; S206. Obtain the distance difference corresponding to the position information of each collection point according to the coordinate information; S207. Obtain the entomophthora evaluation value corresponding to the position information of each collection point according to the distance difference and the entomophthora activity value.

[0025] As described in the above steps S201 - S207, the present invention obtains the respiratory value of Entomophthora by relying on the carbon dioxide concentration. During the process of grain storage and transportation, carbon dioxide, as the product of Entomophthora respiration, the change in its concentration directly reflects the respiratory intensity of Entomophthora. With the help of a special gas sensor to accurately measure the carbon dioxide concentration, and by establishing a corresponding relationship model based on fitting a large amount of experimental data, it is possible to timely capture the early weak signs of Entomophthora respiration activities. Just like in a large - scale grain storage depot, once the high - precision carbon dioxide sensor monitors an increase in the carbon dioxide concentration in a certain area, and the Entomophthora respiratory value is calculated to increase through an algorithm, the staff can quickly check and take measures such as ventilation and drying accordingly, preventing the large - scale growth of Entomophthora, avoiding serious damage to the grain, effectively ensuring the storage quality of the grain, and solving the problem that the traditional monitoring method is difficult to directly judge the respiratory situation of Entomophthora and is prone to miss the best prevention and control opportunity. Then, the activity value of Entomophthora is obtained through the oxygen concentration. Oxygen is a key factor affecting the life activities of Entomophthora, and different oxygen concentrations have different effects on the metabolism and reproduction speed of Entomophthora. The oxygen concentration is monitored in real - time using an oxygen sensor, and an association model between the oxygen concentration and the activity value of Entomophthora considering the synergistic effects of Entomophthora species, grain types, and other environmental factors is established based on experimental research. For example, in a small - scale grain depot, when the oxygen concentration in a certain corner is low in summer at high temperature, the calculated activity value of Entomophthora shows that although it has decreased, it is still in a certain active state. The staff strengthens ventilation accordingly, reduces the activity value of Entomophthora, and prevents the growth of Entomophthora, making up for the defect that the traditional monitoring method cannot accurately quantify the activity level of Entomophthora in different oxygen environments and is difficult to adjust ventilation measures in a timely manner according to the activity situation of Entomophthora. The metabolic value of Entomophthora is obtained according to the concentration of volatile organic compounds, which is an important link for early detection of Entomophthora infestation. Volatile organic compounds, as the metabolic products of Entomophthora, their concentration reflects the metabolic intensity of Entomophthora. Advanced gas detection technology is used to detect their concentration, and through a large number of experiments, the characteristics of volatile organic compounds produced by different Entomophthora at different growth stages are analyzed, and a corresponding model considering various factors is established. For example, in modern grain storage facilities, when the concentration of volatile organic compounds in a certain batch of grain storage area is detected to increase, the calculated metabolic value of Entomophthora indicates an increase in the metabolic activities of Entomophthora. The staff checks and processes it in a timely manner, curbing the development of Entomophthora, and overcoming the problem that the traditional monitoring means ignores the concentration of volatile organic compounds and cannot detect the early signs of Entomophthora infestation in a timely manner. Then, the respiratory value, activity value, and metabolic value of Entomophthora are comprehensively used to obtain the activity value of Entomophthora. This comprehensive index more comprehensively and accurately reflects the overall activity level of Entomophthora. Determine the weights of each index when calculating the activity value of Entomophthora, and use mathematical methods such as weighted average for comprehensive calculation.For example, in the raw material warehouse of a large grain processing enterprise, during a certain monitoring, although the respiration value of insects and mildew was normal, the activity value and metabolic value of insects and mildew were relatively high. The calculated activity value of insects and mildew showed that they were relatively active. Based on this, the enterprise comprehensively cleaned and disinfected the warehouse, strengthened ventilation, and controlled temperature and humidity, reducing the risk of insect and mildew infestation. This solved the problem that evaluating the activity of insects and mildew based on a single indicator alone was one-sided and prone to misjudging the risk of insect and mildew infestation. Multiple coordinate information was obtained according to the location information of multiple collection points, providing a basis for subsequent spatial analysis. When installing the sensors at the collection points, the coordinate information was recorded synchronously by using Geographic Information System (GIS) technology or other positioning means. In a large underground grain depot, with the help of positioning technology, the coordinates of the collection points were obtained. When the activity value of insects and mildew in a certain area was relatively high, accurate positioning could be achieved, facilitating the rapid arrival of staff at the scene for handling, avoiding the disadvantages of traditional monitoring, such as the lack of accurate recording and analysis of the precise location of the collection points and the inability to accurately judge the distribution of insects and mildew in space. A spatial coordinate system was established according to the grain storage and transportation warehouse, and the location information of the collection points was mapped into it to obtain the collection space matrix. This step digitized and structured the location information of the collection points. According to the actual shape and layout of the grain depot, the appropriate origin of the coordinate system and the direction of the coordinate axes were selected, and the coordinates of the collection points were converted into the positions of matrix elements. Taking a large open-air grain storage yard as an example, through the collection space matrix, it was found that the activity value of insects and mildew at the collection points in a certain area was high and showed an aggregated distribution. Based on this, the staff focused on prevention and control, effectively preventing the spread of insects and mildew, solving the problem that the traditional monitoring data recording lacked systematicness and spatial relevance and was difficult to grasp the distribution law of insects and mildew. The distance difference corresponding to the location information of each collection point was obtained according to the collection space matrix, which helped to analyze the spatial spread trend of insects and mildew. In the collection space matrix, the distance difference between any two collection points was calculated by using the spatial distance formula. In a multi-story grain warehouse, it was found through calculation that the distance difference between the collection points with similar positions in adjacent floors was small and the change trend of the activity value of insects and mildew was similar. The distance difference was mainly used to analyze the spread trend of insects and mildew in the grain storage and transportation space. Since the spread of insects and mildew was significantly affected by spatial distance, the possibility of insect and mildew spread was greater between collection points with closer distances. When the activity value of an insect and mildew at a collection point on a certain floor increased, it could be predicted that the collection points at similar positions on the adjacent floor might be affected, and the staff could take preventive measures in advance to control the spread of insects and mildew, making up for the deficiencies of traditional monitoring, such as the inability to quantify the impact of spatial differences between collection points on the distribution of insects and mildew and the difficulty in predicting the spread path and scope of insects and mildew. Finally, according to the distance difference and the activity value of insects and mildew, the insect and mildew evaluation value corresponding to the location information of each collection point was obtained. The first insect and mildew weight coefficient was obtained according to the distance difference, and the second insect and mildew weight coefficient was obtained according to the activity value of insects and mildew. Among them, the calculation formula is: ; where E represents the insect and mildew evaluation value, α represents the first insect and mildew weight coefficient, K represents the activity value of insects and mildew, β represents the second insect and mildew weight coefficient, and P represents the distance difference.

[0026] To comprehensively reflect the actual risk of Entomophthora, a comprehensive evaluation model is established. For example, the weighted summation algorithm is adopted to optimize the weight values through a large amount of data and calculate the Entomophthora evaluation value of each collection point. In a large grain storage park, the Entomophthora evaluation value of a collection point in a corner of a granary is high because its own Entomophthora activity value is high and the distance difference from the surrounding collection points with high activity values is small. The staff focused on treating this area, ensuring the quality and safety of the grain in the granary and solving the problem that relying solely on a single index to evaluate the Entomophthora risk in the past was likely to ignore spatial factors and lead to misjudgment.

[0027] In one embodiment, the step of obtaining the three-dimensional model diagram of the Entomophthora occurrence probability according to the Entomophthora evaluation value corresponding to the position information of each collection point includes: S209. Generate a sequence number according to the time corresponding to the position information of each collection point to obtain a collection point sequence list; S210. Obtain the distance attenuation value of the position information of each collection point according to the collection point sequence list; S211. Obtain the corrected Entomophthora evaluation value corresponding to the position information of each collection point according to the distance attenuation value and the Entomophthora evaluation value; S212. Map the corrected Entomophthora evaluation value into the collection space matrix to obtain the three-dimensional model diagram of the Entomophthora occurrence probability.

[0028] As described in steps S209-S212 above, the present invention generates a time series number based on the location information of each collection point, thereby generating a collection point sequence table, which adds a temporal dimension to the collected data. During grain storage and transportation, data from traditional monitoring methods is often fragmented and lacks temporal coherence, making it difficult to grasp the dynamics of insect and mold development. By generating a sequence table by numbering the collection points according to time, for example, in a large grain warehouse, data is collected and numbered daily. Based on this sequence table, staff can clearly observe the changes in insect and mold assessment values over time. If the insect and mold assessment values of certain corner collection points are found to be increasing, early warning can be provided, buying time for subsequent prevention and control measures, thereby effectively ensuring grain quality and safety. Based on the collection point sequence table, a distance decay value is further obtained for each collection point location information. In grain storage and transportation warehouses, the spread of insects and mold is significantly affected by spatial distance, but traditional monitoring often ignores this and treats the data of each collection point in isolation, resulting in inaccurate insect and mold risk assessments. By combining the location information in the sequence table with an established spatial coordinate system, the actual distance between each collection point is calculated, and the distance decay value is then derived using a pre-defined distance decay function. Taking a multi-story grain storage warehouse as an example, calculations revealed that the distance attenuation between adjacent collection points on the same floor is small, while the distance attenuation between collection points on different floors and at greater distances is large. This quantifies the impact of spatial distance on insect and mold transmission, providing a key basis for accurately assessing insect and mold risk. Next, a modified insect and mold assessment value corresponding to each collection point's location information is derived based on the distance attenuation and insect and mold assessment value. Previous risk assessments based solely on insect and mold assessment values failed to consider spatial transmission factors, leading to overestimation or underestimation of the actual risk. S211 uses mathematical methods such as weighted averaging to combine the insect and mold assessment value with the distance attenuation value. For example, in a large bulk grain warehouse, collection point A has a moderate insect and mold assessment value, but its proximity to an insect and mold active area significantly impacts its distance attenuation value. This combined calculation results in a higher modified insect and mold assessment value, reflecting the actual high risk at that point. This results in a more accurate and comprehensive assessment of insect and mold risk at each collection point, providing a reliable basis for prevention and control decisions and avoiding resource waste or inadequate prevention and control measures. Finally, the modified insect and mold assessment values are mapped onto the collection space matrix to produce a three-dimensional model of insect and mold occurrence probability. The traditional way of presenting monitoring data makes it difficult to intuitively display the spatial distribution of insect and mildew risks. It is time-consuming and laborious for staff to analyze and understand the data, and it is difficult to grasp the overall risk pattern. However, with the help of the previously established acquisition space matrix, the corrected insect and mildew assessment values are mapped to the corresponding positions, a three-dimensional data field is constructed, and then three-dimensional modeling and visualization technology are used to convert it into an intuitive model diagram. In modern large-scale grain logistics centers, staff can see the insect and mildew risk distribution of the entire area in real time by monitoring the model diagram on the large screen. Once a high-risk area is found in the corner of the warehouse, targeted prevention and control measures such as fumigation and ventilation can be quickly implemented, and strategies can be adjusted in a timely manner according to the changing trends of the risk areas, greatly improving the efficiency and pertinence of prevention and control work.

[0029] In one embodiment, the step of obtaining the ventilation rate of each collection point location information according to the preset ventilation information and obtaining the real-time hot zone diffusion value according to the ventilation rate includes: S301. Obtain the initial air flow velocity data of the ventilation equipment according to the preset ventilation information; S302. Obtain the air dynamic viscosity corresponding to each collection point location information; S303. Obtain the real-time wind speed value corresponding to each collection point location information; S304. Obtain the ventilation rate corresponding to each collection point location information according to the initial air flow velocity data, air dynamic viscosity and real-time wind speed value; S305. Obtain the real-time hot zone diffusion value according to the ventilation rate corresponding to each collection point location information and the probability of insect and mold occurrence.

[0030] As described in the above steps S301 - S305, the present invention obtains the initial air flow velocity data of the ventilation equipment. By installing a wind speed sensor on the ventilation equipment and regularly calibrating and maintaining it, and continuously recording the data, the initial power status of the ventilation system can be grasped in real time. For example, in a large grain storage depot, once it is found that the initial wind speed of a certain ventilation equipment drops, the staff can check it in time, such as cleaning the sundries in the ventilation duct, so as to avoid the mildew of grains caused by poor ventilation, ensure the normal air circulation in the grain storage environment. Based on the initial wind speed data, the air dynamic viscosity corresponding to each collection point location is obtained. The air dynamic viscosity is significantly affected by temperature and humidity. A special instrument is used to measure it on site at each collection point, and a relationship model is established in combination with temperature and humidity data. Taking a medium-sized granary as an example, in the high-temperature and humid summer, it is found through measurement that the air dynamic viscosity in the areas close to the walls and the ground is relatively high. When calculating the ventilation rate, this difference is considered, and the operation mode of the ventilation equipment is adjusted to increase the ventilation volume in the corresponding areas, effectively preventing the grains from getting damp and making up for the defect of inaccurate ventilation assessment caused by ignoring the change of air dynamic viscosity in the traditional method. Then, the real-time wind speed value of each collection point is obtained. Wind speed sensors are reasonably installed at each collection point, and the data is transmitted to the central monitoring system in real time, and a wind speed threshold is set for real-time analysis. In a newly built large grain storage center, this method was once used to find that the wind speed in a certain area was abnormally low. After inspection, it was found that the ventilation duct was leaking. After repair, the normal wind speed was restored, solving the problem that the traditional monitoring could not accurately grasp the real-time wind speed at each location and was difficult to find local ventilation problems, ensuring the uniform air circulation in the grain storage environment. Then, the ventilation rate is calculated by comprehensively considering the initial air flow velocity data, air dynamic viscosity and real-time wind speed value. The pressure difference is obtained according to the initial air flow velocity data and the real-time wind speed value, and the characteristic size of the ventilation channel and the ventilation path length are obtained. The calculation formula is: ; Among them, V represents the ventilation rate, ΔP represents the pressure difference, d represents the characteristic size of the ventilation channel, μ represents the air dynamic viscosity, and L represents the ventilation path length. A calculation model is established based on the principles of fluid mechanics, relevant parameters are substituted and numerical calculations are carried out, and the model is calibrated with actual data. In a large grain drying workshop, using this model, it is calculated that the low ventilation rate in the corner of the workshop leads to uneven grain drying. Subsequently, the angles and powers of the ventilation equipment are adjusted, the ventilation rate is increased, and the drying effect is improved, overcoming the problems of simple traditional methods for calculating the ventilation rate and inaccurate results, providing a scientific basis for optimizing the ventilation layout. Finally, the real-time hot zone diffusion value is obtained by combining the ventilation rate and the probability of insect and mildew occurrence. A calculation model including the weights of the ventilation rate and the probability of insect and mildew occurrence is constructed to calculate and analyze the values of each collection point in real time. In a large grain warehouse, when the real-time hot zone diffusion value is large in a certain area due to low ventilation rate and high probability of insect and mildew occurrence, the staff timely adjusts the ventilation mode and prevents insect and mildew, reducing the risk of hot zone formation and avoiding the damage to grain quality caused by heat accumulation, solving the problem that traditional monitoring does not comprehensively consider the influence of ventilation and insect and mildew on heat distribution.

[0031] In one embodiment, the step of establishing an environmental control three-dimensional model diagram according to the real-time hot zone diffusion value corresponding to the position information of each collection point includes: S401. Obtain the distance value from the position information of each collection point to the target point of the ventilation equipment; S402. Obtain the target hot zone diffusion value according to the target point distance value; S403. Obtain the regulated hot zone diffusion value according to the target hot zone diffusion value and the real-time hot zone diffusion value; S404. Establish an environmental control three-dimensional model diagram according to the regulated hot zone diffusion value and the collection space matrix.

[0032] As described in steps S401-S404 above, the present invention obtains the distance value from each collection point's location information to the target point of the ventilation equipment. This step is of great significance, as it utilizes a precise spatial coordinate system and distance calculation formula to clearly define the spatial relationship between the collection point and the ventilation equipment. Taking a large underground grain storage warehouse as an example, by establishing a coordinate system and calculating distance values, it was discovered that a certain corner was far from the ventilation equipment, and that this area indeed suffered from poor ventilation and abnormal temperature and humidity. This demonstrates that this step can accurately locate areas of poor ventilation, providing a key basis for subsequent adjustments to ventilation strategies. It addresses the problem of traditional methods ignoring distance factors and failing to accurately determine differences in ventilation effectiveness. It is an important foundation for optimizing air circulation in grain storage and transportation warehouses and reducing the risk of grain spoilage. Based on the distance values obtained in step S401, the target heat zone diffusion value is obtained based on the distance value to the target point. By establishing a calculation model that comprehensively considers ventilation equipment performance, grain physical properties, and spatial environmental factors, the heat zone diffusion of each collection point under ideal conditions is predicted. For example, in a large grain drying workshop, after calculating the target heat zone diffusion value for each point, it was discovered that the actual heat zone diffusion value in a certain area was too high. Upon inspection, it was found that the ventilation duct was clogged. This comparison provides a reference standard for assessing actual heat spread, helping staff promptly detect anomalies and scientifically plan ventilation layouts and control strategies. This overcomes the shortcomings of traditional assessment methods, which lack theoretical reference values and make it difficult to determine the rationality of heat spread. This effectively ensures temperature stability in grain storage environments. Next, the controlled heat spread value is derived based on the target heat spread value and the real-time heat spread value. This step uses a simple yet effective calculation method: the controlled heat spread value equals the real-time heat spread value minus the target heat spread value, accurately quantifying the degree of heat spread deviation. Based on this calculation, in a large grain warehouse, when the controlled heat spread values at certain collection points reached large positive values, staff promptly increased ventilation equipment power, successfully suppressing heat spread. This provides clear direction and intensity guidance for environmental control, overcoming the drawbacks of traditional control methods that rely on experience and are prone to over- or under-control, ensuring accuracy and timeliness of control. Finally, a three-dimensional model of environmental control is constructed based on the controlled heat spread values and the collection space matrix. This step combines the controlled heat spread values with the collection space matrix, utilizing 3D modeling technology to intuitively visualize the control needs and spatial distribution of heat spread. In modern, large-scale grain logistics centers, staff monitor models displayed on large screens to monitor the spread and control of hotspots in each warehouse in real time. They can quickly implement targeted measures for areas showing red (indicating high demand for control) and adjust strategies based on the effectiveness of control measures. This addresses the problem of traditional control decisions lacking intuitive tools and difficulty grasping the overall situation and control priorities, significantly improving the accuracy and efficiency of control.

[0033] In one embodiment, the step of obtaining regulation information according to the three-dimensional model diagram of entomophthora occurrence probability and the three-dimensional model diagram of environmental regulation includes: S501. Obtain the entomophthora occurrence risk value corresponding to the position information of each collection point according to the three-dimensional model diagram of entomophthora occurrence probability; S502. Obtain the environmental regulation change value corresponding to the position information of each collection point according to the three-dimensional model diagram of environmental regulation; S503. Obtain the sampling point risk value according to the entomophthora occurrence risk value and the environmental regulation change value of the position information of each collection point; S504. Determine whether the sampling point risk value is within a preset threshold range; If it is less, obtain a first regulation value according to the sampling point risk value, and perform environmental dehumidification adjustment according to the first regulation value; If it is within, obtain a second regulation value according to the sampling point risk value, and perform environmental dehumidification adjustment and environmental ventilation adjustment according to the second regulation value; If it is greater, obtain a third regulation value according to the sampling point risk value, and perform environmental cooling adjustment and environmental ventilation adjustment according to the third regulation value; S505. Generate the collection point regulation information corresponding to the position information of each collection point according to the first regulation value, the second regulation value, and the third regulation value; S506. Sort the collection point regulation information based on the collection point sequence list to obtain the regulation information.

[0034] As described in the above steps S501 - S506, the present invention obtains the risk value of Entomophthora occurrence at each collection point according to the three - dimensional model diagram of Entomophthora occurrence probability. In scenarios such as large - scale grain storage warehouses, traditional Entomophthora risk assessment relies on experience or simple detection, lacking accuracy and prone to missing potential high - risk areas. Through step S501, based on the three - dimensional model diagram of Entomophthora occurrence probability and according to the pre - set risk assessment criteria, the Entomophthora occurrence probability at each collection point is converted into an intuitive risk value. For example, in a certain storage warehouse, through this step, it is found that the risk values of the collection points near the walls and the ground are relatively high. The staff then focus on protecting these areas, arranging insect - proof nets in advance and putting in pesticides, successfully preventing the breeding of Entomophthora, achieving accurate quantitative assessment of Entomophthora risk and targeted prevention and control, and reasonably allocating prevention and control resources. Then, according to the three - dimensional model diagram of environmental regulation, the environmental regulation change value at each collection point is obtained. Traditional environmental regulation methods are often "one - size - fits - all" and difficult to meet the different needs of different regions. In a large - scale grain drying workshop, through the three - dimensional model diagram of environmental regulation, combined with various environmental information such as ventilation rate, heat zone diffusion value, and temperature and humidity, according to the established regulation rules and algorithms, the regulation change value at each collection point is calculated. For example, for a collection point in a certain corner, it is calculated that the ventilation volume needs to be increased and the cooling and dehumidification equipment needs to be started. The targeted regulation effectively improves the environment in this area, improves the quality of grain drying, and ensures that the grain can be stored in a suitable environment. Subsequently, the Entomophthora occurrence risk value and the environmental regulation change value are comprehensively calculated to obtain the risk value of the sampling point. Previously, considering Entomophthora risk and environmental regulation separately could not accurately measure the combined impact of the two. In a large - scale grain warehouse, the Entomophthora risk value of a certain collection point is 5, and the environmental regulation change value is 7. Through setting weights and calculation, the risk value of the sampling point is obtained as 5.8, indicating that the overall risk is relatively high. This enables the staff to comprehensively assess the risk, avoid decision - making mistakes, provides a scientific basis for formulating comprehensive prevention and control strategies, and more reasonably allocates prevention and control resources. Then, it is judged whether the risk value of the sampling point is within the preset threshold range. Traditional prevention and control decisions lack quantitative criteria, rely on subjective judgment, and are inefficient. In a large - scale grain transfer warehouse, the threshold range is preset according to grain storage requirements and experience, such as less than 4 being low risk, 4 - 6 being medium risk, and greater than 6 being high risk. The system monitors the risk value in real - time. When the risk value of a certain area reaches 7.5, an alarm is immediately issued to remind the staff to take corresponding measures, achieving rapid risk classification and accurate decision - making. Based on the judgment result of S504, regulation is carried out according to the risk value situation and the regulation information of the collection point is generated. Traditional regulation methods lack pertinence and systematicness, are prone to causing waste of resources and poor regulation effects. In a large - scale grain storage warehouse, for areas with a risk value less than 4, small - scale dehumidification equipment is started; for areas between 4 - 6, both dehumidification and ventilation adjustment are carried out; for areas greater than 6, emphasis is placed on cooling and ventilation adjustment.These differentiated control measures for different risk levels are sorted into detailed control information for collection points, providing an accurate guide for control implementation, effectively ensuring the quality of grain. Finally, based on the collection point sequence list, the control information for collection points is sorted to obtain the control information. If the control information is disordered, it is prone to confusion during execution, affecting the effect and being unfavorable for management and supervision. In a large grain logistics center, sorting the control information according to the collection point sequence list allows staff to operate in sequence, improving the control efficiency and avoiding omissions and duplicate operations. After the control is completed, the control effect can also be traced and evaluated through the sorted information, providing a basis for optimizing strategies.

[0035] As Figure 2 shown, the present invention also provides a grain storage and transportation environment monitoring and control system, including: A first acquisition module 1, configured to acquire preset ventilation information and multiple collection point location information of a grain storage and transportation warehouse, and acquire real-time environment information corresponding to each of the collection point location information, where the real-time environment information includes environmental temperature, environmental humidity, and gas concentration, and the gas concentration includes carbon dioxide concentration, oxygen concentration, and volatile organic compound concentration; A second acquisition module 2, configured to acquire a pest and mold evaluation value of the corresponding collection point location information according to the carbon dioxide concentration, oxygen concentration, and volatile organic compound concentration, and acquire a three-dimensional model diagram of the pest and mold occurrence probability according to the pest and mold evaluation value corresponding to each collection point location information; A third acquisition module 3, configured to acquire the ventilation rate of each collection point location information according to the preset ventilation information, and acquire a real-time hot zone diffusion value according to the ventilation rate; A fourth acquisition module 4, configured to establish a three-dimensional model diagram of environmental control according to the real-time hot zone diffusion value corresponding to each collection point location information; An adjustment module 5, configured to acquire control information according to the three-dimensional model diagram of the pest and mold occurrence probability and the three-dimensional model diagram of environmental control, where the control information includes environmental dehumidification adjustment, environmental ventilation adjustment, and environmental cooling adjustment, and perform dehumidification, ventilation, and cooling adjustments on the grain storage and transportation warehouse according to the control information.

[0036] In one embodiment, the first acquisition module 1 includes: A first acquisition unit, configured to acquire physical structure information of the grain storage and transportation warehouse, where the physical structure information includes data of the length, width, and height of the warehouse body, the position of the ventilation opening, and the shape of the grain pile distribution; A second acquisition unit, configured to use the position of the ventilation opening as the origin of the coordinate system, and establish a warehouse body space coordinate system according to the origin of the coordinate system and the data of the length, width, and height of the warehouse body; A third acquisition unit, configured to acquire multiple signal collection point positions according to the shape of the grain pile distribution; A fourth acquisition unit, configured to map the position of each signal acquisition point into the bin space coordinate system to obtain a plurality of acquisition point position information; A fifth acquisition unit, configured to acquire the type characteristic information inside the grain storage bin, where the type characteristic information includes particle size, moisture content, and insect and mold sensitivity information; A sixth acquisition unit, configured to obtain preset ventilation information according to the particle size, moisture content, and insect and mold sensitivity information.

[0037] In one embodiment, the second acquisition module 2 includes: A seventh acquisition unit, configured to obtain an insect and mold respiration value according to the carbon dioxide concentration; An eighth acquisition unit, configured to obtain an insect and mold activity value according to the oxygen concentration; A ninth acquisition unit, configured to obtain an insect and mold metabolism value according to the volatile organic compound concentration; A tenth acquisition unit, configured to obtain an insect and mold activity value according to the insect and mold respiration value, the insect and mold activity value, and the insect and mold metabolism value; An eleventh acquisition unit, configured to obtain a plurality of coordinate information according to the plurality of acquisition point position information; Obtain an insect and mold evaluation value corresponding to each acquisition point position information according to the distance difference and the insect and mold activity value.

[0038] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above method embodiments. Among them, any reference to a memory, storage, value library, or other medium provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0039] It should be noted that in this text, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, apparatus, article or method comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such a process, apparatus, article or method. Without further limitation, an element defined by the statement "comprising one..." does not exclude the existence of additional identical elements in the process, apparatus, article or method comprising such an element.

[0040] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent results or equivalent process transformations made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, are similarly included in the patent protection scope of the present invention.

Claims

1. A method for monitoring and regulating the grain storage and transportation environment, which is applied to a grain storage and transportation warehouse, is characterized in that, Including: Obtain the preset ventilation information and the position information of multiple collection points of the grain storage and transportation warehouse, and obtain the real-time environmental information corresponding to each position information of the collection point, where the real-time environmental information includes environmental temperature, environmental humidity, and gas concentration, and the gas concentration includes carbon dioxide concentration, oxygen concentration, and volatile organic compound concentration; Obtain the insect and mold evaluation value of the corresponding collection point position information according to the carbon dioxide concentration, oxygen concentration, and volatile organic compound concentration, and obtain the three-dimensional model diagram of the insect and mold occurrence probability according to the insect and mold evaluation value corresponding to each collection point position information; Obtain the ventilation rate of each collection point position information according to the preset ventilation information, and obtain the real-time heat zone diffusion value according to the ventilation rate; Establish a three-dimensional environmental control model diagram according to the real-time heat zone diffusion value corresponding to each collection point position information; Obtain the control information according to the three-dimensional model diagram of the insect and mold occurrence probability and the three-dimensional environmental control model diagram, where the control information includes environmental dehumidification adjustment, environmental ventilation adjustment, and environmental cooling adjustment, and perform dehumidification, ventilation, and cooling adjustments on the grain storage and transportation warehouse according to the control information.

2. The method for monitoring and regulating the grain storage and transportation environment according to claim 1, characterized in that, The step of obtaining the preset ventilation information and the position information of multiple collection points of the grain storage and transportation warehouse includes: Obtain the physical structure information of the grain storage and transportation warehouse, where the physical structure information includes the length, width, and height data of the warehouse body, the position of the ventilation opening, and the shape of the grain pile distribution; Take the position of the ventilation opening as the origin of the coordinate system, and establish a three-dimensional coordinate system of the warehouse body according to the origin of the coordinate system and the length, width, and height data of the warehouse body; Obtain the positions of multiple signal collection points according to the shape of the grain pile distribution; Map the position of each signal collection point into the three-dimensional coordinate system of the warehouse body to obtain the position information of multiple collection points; Obtain the variety characteristic information inside the grain storage and transportation warehouse, where the variety characteristic information includes particle size, moisture content, and insect and mold sensitivity information; Obtain the preset ventilation information according to the particle size, moisture content, and insect and mold sensitivity information.

3. The method for monitoring and regulating the grain storage and transportation environment according to claim 1, wherein, The step of obtaining the insect and mold evaluation value of the corresponding collection point position information according to the carbon dioxide concentration, oxygen concentration, and volatile organic compound concentration includes: Obtain the insect and mold respiration value according to the carbon dioxide concentration; Obtain the insect and mold activity value according to the oxygen concentration; Obtain the insect and mold metabolism value according to the volatile organic compound concentration; Obtain the insect and mold activity value according to the insect and mold respiration value, insect and mold activity value, and insect and mold metabolism value; Obtain multiple coordinate information according to the multiple collection point position information; Obtain the insect and mold evaluation value corresponding to each collection point position information according to the distance difference and the insect and mold activity value.

4. A method for monitoring and regulating the grain storage and transportation environment according to claim 1, characterized in that The step of obtaining the three-dimensional model diagram of the insect and mold occurrence probability according to the insect and mold evaluation value corresponding to each collection point position information includes: Generate a time sequence number according to the position information of each collection point to obtain a collection point sequence list; Obtain the distance attenuation value of each collection point position information according to the collection point sequence list; Obtain the corrected insect and mold evaluation value corresponding to each collection point position information according to the distance attenuation value and the insect and mold evaluation value; Map the corrected insect and mold evaluation value into the collection space matrix to obtain the three-dimensional model diagram of the insect and mold occurrence probability.

5. A method for monitoring and regulating the grain storage and transportation environment according to claim 1, characterized in that, The steps of obtaining the ventilation rate of each collection point location information according to the preset ventilation information and obtaining the real-time hot zone diffusion value according to the ventilation rate include: Obtaining the initial air flow wind speed data of the ventilation equipment according to the preset ventilation information; Obtaining the air dynamic viscosity corresponding to each collection point location information; Obtaining the real-time wind speed value corresponding to each collection point location information; Obtaining the ventilation rate corresponding to each collection point location information according to the initial air flow wind speed data, air dynamic viscosity and real-time wind speed value; Obtaining the real-time hot zone diffusion value according to the ventilation rate corresponding to each collection point location information and the probability of insect and mold occurrence.

6. The method for monitoring and regulating the grain storage and transportation environment according to claim 1, wherein, The steps of establishing an environmental control three-dimensional model diagram according to the real-time hot zone diffusion value corresponding to each collection point location information include: Obtaining the target point distance value from each collection point location information to the ventilation equipment; Obtaining the target hot zone diffusion value according to the target point distance value; Obtaining the regulated hot zone diffusion value according to the target hot zone diffusion value and the real-time hot zone diffusion value; Establishing an environmental control three-dimensional model diagram according to the regulated hot zone diffusion value and the collection space matrix.

7. A method for monitoring and regulating the grain storage and transportation environment according to claim 1, characterized in that, The steps of obtaining the regulation information according to the three-dimensional model diagram of the probability of insect and mold occurrence and the environmental control three-dimensional model diagram include: Obtaining the insect and mold occurrence risk value corresponding to each collection point location information according to the three-dimensional model diagram of the probability of insect and mold occurrence; Obtaining the environmental control change value corresponding to each collection point location information according to the environmental control three-dimensional model diagram; Obtaining the sampling point risk value according to the insect and mold occurrence risk value and the environmental control change value of each collection point location information; Judging whether the sampling point risk value is within a preset threshold range; If it is less, obtaining a first regulation value according to the sampling point risk value and performing environmental dehumidification regulation according to the first regulation value; If it is within, obtaining a second regulation value according to the sampling point risk value and performing environmental dehumidification regulation and environmental ventilation regulation according to the second regulation value; If it is greater, obtaining a third regulation value according to the sampling point risk value and performing environmental cooling regulation and environmental ventilation regulation according to the third regulation value; Generating the collection point regulation information corresponding to each collection point location information according to the first regulation value, the second regulation value and the third regulation value; Sorting the collection point regulation information based on the collection point sequence list to obtain the regulation information.

8. A grain storage and transportation environment monitoring and control system, characterized in that, Including: A first acquisition module for acquiring the preset ventilation information of the grain storage and transportation warehouse and the location information of a plurality of collection points, and acquiring the real-time environmental information corresponding to each collection point location information, wherein the real-time environmental information includes environmental temperature, environmental humidity, gas concentration, and the gas concentration includes carbon dioxide concentration, oxygen concentration and volatile organic compound concentration; A second acquisition module for obtaining the insect and mold evaluation value of the corresponding collection point location information according to the carbon dioxide concentration, oxygen concentration and volatile organic compound concentration, and obtaining the three-dimensional model diagram of the probability of insect and mold occurrence according to the insect and mold evaluation value corresponding to each collection point location information; A third acquisition module for obtaining the ventilation rate of each collection point location information according to the preset ventilation information and obtaining the real-time hot zone diffusion value according to the ventilation rate; A fourth acquisition module, configured to establish an environmental regulation three-dimensional model diagram according to the real-time hot zone diffusion value corresponding to each collection point location information; An adjustment module, configured to obtain adjustment information according to the three-dimensional model diagram of the entomophthora occurrence probability and the three-dimensional model diagram of environmental regulation, wherein the adjustment information includes environmental dehumidification adjustment, environmental ventilation adjustment, and environmental cooling adjustment, and perform dehumidification, ventilation, and cooling adjustments on the grain storage and transportation warehouse according to the adjustment information.

9. The grain storage and transportation environment monitoring and control system according to claim 8, characterized in that, The first acquisition module includes: A first acquisition unit, configured to acquire the physical structure information of the grain storage and transportation warehouse, wherein the physical structure information includes the data of the length, width, and height of the warehouse body, the position of the ventilation opening, and the distribution shape of the grain pile; A second acquisition unit, configured to use the position of the ventilation opening as the origin of the coordinate system, and establish a space coordinate system of the warehouse body according to the origin of the coordinate system and the data of the length, width, and height of the warehouse body; A third acquisition unit, configured to acquire the positions of a plurality of signal collection points according to the distribution shape of the grain pile; A fourth acquisition unit, configured to map the position of each signal collection point into the space coordinate system of the warehouse body to obtain a plurality of collection point position information; A fifth acquisition unit, configured to acquire the variety characteristic information inside the grain storage and transportation warehouse, wherein the variety characteristic information includes particle size, moisture content, and entomophthora sensitivity information; A sixth acquisition unit, configured to obtain preset ventilation information according to the particle size, moisture content, and entomophthora sensitivity information; 10. A grain storage and transportation environment monitoring and control system according to claim 8, characterized in that, The second acquisition module includes: A seventh acquisition unit, configured to obtain the entomophthora respiration value according to the carbon dioxide concentration; An eighth acquisition unit, configured to obtain the entomophthora activity value according to the oxygen concentration; A ninth acquisition unit, configured to obtain the entomophthora metabolism value according to the concentration of volatile organic compounds; A tenth acquisition unit, configured to obtain the entomophthora activity value according to the entomophthora respiration value, the entomophthora activity value, and the entomophthora metabolism value; An eleventh acquisition unit, configured to obtain a plurality of coordinate information according to the plurality of collection point position information; Obtain the entomophthora evaluation value corresponding to each collection point position information according to the distance difference and the entomophthora activity value.

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