A research method for the law of change in the storage quality of grain based on grain condition data

Abstract

The present invention discloses a research method for the change law of grain storage quality based on grain condition data, including: arranging a plurality of temperature and humidity monitoring devices in each area of the monitored grain heap to obtain the temperature and humidity of the grain in the monitored area; calculating the effective accumulated temperature value and the effective accumulated humidity value of the grain in the monitored area, and calculating the effective accumulated temperature and humidity value of the monitored area through the effective accumulated temperature value and the effective accumulated humidity value of the monitored area; the calculation formula of the effective accumulated temperature and humidity is: #imgabs0# where J is the effective accumulated temperature and humidity of the stored grain, T is the effective accumulated temperature of the stored grain, S is the effective accumulated humidity of the stored grain, and N d is the number of days of the same single-day effective accumulated temperature / accumulated humidity; judging the safety of the stored grain according to the effective accumulated temperature and humidity value of the monitored area. During the grain storage period, real-time analysis and future prediction of the storage quality of the whole warehouse of grain can be realized, overcoming the defect of low accuracy in judging the storage quality of the whole warehouse of grain by single-point or mixed sampling with manual sampling.

Description

Technical Field

[0001] The present invention relates to a research method for the change law of grain storage quality based on grain condition data, belonging to the field of grain storage. Background Art

[0002] With the development of grain storage technology, it is difficult and challenging to model the real-time grain storage quality during the storage process, and pure mathematical models have gradually shown their limitations. For example, in most cases, it is necessary to first assume that the grains are identical and ideal before establishing a mathematical model; the individual differences and anisotropy of grains are ignored, thus affecting the accuracy of the model.

[0003] Since the 1970s, research scholars in the field of grain storage in China have carried out research on grain temperature detection and its impact on quality using information-based and automated detection technologies and methods. For example, in 1974, Chinese grain storage scholars first realized the automatic detection of grain moisture and temperature in the warehouse using pulse generator control technology. In addition, the impact of different storage environments on different grain varieties and different quality indicators is significantly different. For example, Zhang Yurong, Li He, and Yang Lu explored and summarized the change laws of the quality of stored corn in different grain storage ecological regions and different warehouse types. Yan Xingquan, Liu Huazhao, and Yu Qiuzhu explored and summarized the change laws of the storage quality of paddy rice in different grain storage ecological regions and different warehouse types. Wang Xiaoli, Zhang Chungui, and Wu Xinlian explored and summarized the change laws of the storage quality of wheat in different grain storage ecological regions and different warehouse types. The results all show that temperature is an important factor affecting the change of grain storage quality. Chen Ji, Wang Yue, etc. also explored the relationship between the annual accumulated temperature of paddy rice and its moisture, fatty acid value, and insect and mold growth. Song Wei, Ding Chao, etc. predicted the spoilage time of wheat by quantifying the change law of the accumulated storage temperature and the fatty acid value of wheat and scientifically guided the storage of wheat through the accumulated storage temperature.

[0004] Experts and scholars in developed countries such as the United States, Japan, and Canada have conducted early research on the basic research of grain storage. Due to the characteristics of short storage periods, high circulation, and single warehouse types, their research on grain storage automation measurement and control technology and quality changes during storage started earlier. Fuji Jian, etc. studied the temperature fluctuation and moisture migration laws of wheat stored in silos for 15 months in the Canadian region. U. Nithya, etc. studied the change laws of the quality of wheat during storage under different temperatures and different initial moisture contents. Summary of the Invention

[0005] The present invention designs and develops a research method for the change law of grain storage quality based on grain condition data, and judges the safety of grain storage based on the effective accumulated temperature and humidity values in the monitored area during the grain storage period, realizing real-time analysis and future prediction of the storage quality of the entire warehouse of grain.

[0006] The technical solution provided by the present invention is as follows:

[0007] A research method for the change law of grain storage quality based on grain condition data, comprising:

[0008] In each area of the monitored grain heap, a plurality of temperature and humidity monitoring devices are arranged to obtain the temperature and humidity of the grain in the monitored area;

[0009] Calculate the effective accumulated temperature value and the effective accumulated humidity value of the grain in the monitored area, and calculate the effective accumulated temperature and humidity value of the monitored area through the effective accumulated temperature value and the effective accumulated humidity value of the monitored area;

[0010] The calculation formula for the effective accumulated temperature and humidity is:

[0011]

[0012] In the formula, J is the effective accumulated temperature and humidity of the stored grain, T is the effective accumulated temperature of the stored grain, S is the effective accumulated humidity of the stored grain, N d is the number of days with the same single-day effective accumulated temperature or accumulated humidity, and n is the natural number of days when the effective accumulated temperature or accumulated humidity accumulates to the end of storage;

[0013] Judge the safety of the stored grain according to the single-day effective accumulated temperature and humidity value of the monitored area:

[0014] When J < 0.03, the stored grain in the monitored area is in a safe state;

[0015] When 0.03 ≤ J < 0.1, the stored grain in the monitored area is in a semi-safe state;

[0016] When 0.1 ≤ J ≤ 0.2, the stored grain in the monitored area is in a critical state;

[0017] When 0.2 < J ≤ 0.4, the stored grain in the monitored area is in an unsafe state;

[0018] When J > 0.4, the stored grain in the monitored area is in a dangerous state;

[0019] When J > 1.4, the stored grain in the monitored area is in a severely dangerous state.

[0020] Preferably, the calculation formula for the effective accumulated temperature of the monitored area is:

[0021]

[0022] In the formula, T is the effective accumulated temperature of the stored grain, T j is the single-day effective accumulated temperature, and N d is the number of days with the same single-day effective accumulated temperature.

[0023] Preferably, the calculation formula for the daily effective accumulated temperature is as follows:

[0024]

[0025] In the formula, T j is the daily effective accumulated temperature, and t is the storage temperature.

[0026] Preferably, the calculation formula for the effective accumulated humidity in the monitoring area is as follows:

[0027]

[0028] In the formula, S is the effective accumulated humidity of the stored grain, S i is the daily effective accumulated humidity, and N d is the number of days with the same daily effective accumulated humidity.

[0029] Preferably, the calculation formula for the daily effective accumulated humidity is as follows:

[0030]

[0031] In the formula, S i is the daily effective accumulated humidity, and h is the storage humidity.

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

[0033] Firstly, in order to solve the applicability of the laboratory-type grain storage quality change model, the present invention chooses to conduct research mainly based on in-silo data;

[0034] The present invention deeply couples the basic data such as the temperature, humidity, and time of the whole silo stored grain with the grain storage quality data at multiple points in different regions to construct a mathematical model of the law of grain storage quality in the space-time region; according to the temperature and humidity of the grain in the monitored area, calculate the effective accumulated temperature value and the effective accumulated humidity value of the grain in the monitored area, and calculate the effective accumulated temperature and humidity value of the monitored area through the effective accumulated temperature value and the effective accumulated humidity value of the monitored area, and judge the safety of the stored grain according to the effective accumulated temperature and humidity value of the monitored area. Based on the grain situation monitoring system and data, realize the real-time state analysis and prediction of the grain storage quality of the whole silo, which helps to ensure the safety of grain storage and promote the construction of the "High-quality Grain Project" in China; BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1(a) is a relationship diagram between the storage temperature, moisture, and microbial growth time of wheat according to the present invention.

[0036] Figure 1(b) is a relationship diagram between the storage temperature, moisture, and microbial growth time of corn according to the present invention.

[0037] Figure 1(c) is a relationship diagram between the storage temperature, moisture, and microbial growth time of soybeans according to the present invention.

[0038] Figure 1(d) is a relationship diagram between the storage temperature, moisture content of paddy rice, and microbial growth time according to the present invention.

[0039] Figure 2(a) is a diagram showing the influence of the storage temperature on the effectiveness of the life activities of the biological field according to the present invention.

[0040] Figure 2(b) is a diagram showing the influence of the storage humidity on the effectiveness of the life activities of the biological field according to the present invention.

[0041] Figure 3 is a relationship diagram between the storage temperature, humidity, and storage safety of wheat according to the present invention.

[0042] Figure 4 is a relationship diagram between the effective accumulated temperature and humidity value of stored grain and the key indexes of stored grain quality according to the present invention.

[0043] Figure 5 is a schematic distribution diagram of seven major stored-grain ecological regions divided according to the climate characteristics, farming systems, main stored-grain pests, and annual accumulated temperature and humidity in various regions of China according to the present invention.

[0044] Figure 6 is a relationship diagram between the effective accumulated temperature and humidity value and time of different points according to the present invention

[0045] Figure 7 is a diagram of the effective accumulated temperature and humidity values at different positions at the same moment according to the present invention.

[0046] Figure 8 is a relationship diagram between the effective accumulated temperature and humidity value of stored grain at different depths and the fatty acid value. Detailed implementation manners

[0047] The following further describes the present invention in detail with reference to the accompanying drawings, so that those skilled in the art can implement it with reference to the description in the specification.

[0048] As shown in Figures 1-8, the present invention provides a research method for the change law of grain storage quality based on grain condition data, which constructs a mathematical model of the grain storage quality law in the time-space region by deeply coupling the basic data such as the temperature, humidity, and time of the whole warehouse of stored grain with the grain storage quality data at multiple points in different regions; on the basis of the grain condition detection system and data, it realizes the real-time state analysis and prediction of the grain storage quality of the whole warehouse, including:

[0049] In each area of the monitored grain pile, a plurality of temperature and humidity monitoring devices are arranged to obtain the temperature and humidity of the grain in the monitored area;

[0050] In the present invention, as a preference, the temperature and humidity monitoring devices are arranged in accordance with GB / T 26882.1 "Grain and oil storage - Grain condition monitoring and control system - Part 1: General rules".

[0051] Calculate the effective accumulated temperature value and the effective accumulated humidity value of the grain in the monitored area, and calculate the effective accumulated temperature and humidity value of the monitored area through the effective accumulated temperature value and the effective accumulated humidity value of the monitored area;

[0052] The calculation formula for the effective accumulated temperature and humidity is as follows:

[0053]

[0054] In the formula, J is the effective accumulated temperature and humidity of the stored grain, T is the effective accumulated temperature of the stored grain, S is the effective accumulated humidity of the stored grain, N d is the number of days of the same single - day effective accumulated temperature or accumulated humidity, and n is the natural number of days when the effective accumulated temperature or accumulated humidity accumulates to the natural number of days at the end of storage;

[0055] Judge the safety of the stored grain according to the single - day effective accumulated temperature and humidity value of the monitored area:

[0056] When J < 0.03, the stored grain in the monitored area is in a safe state;

[0057] When 0.03 ≤ J < 0.1, the stored grain in the monitored area is in a semi - safe state;

[0058] When 0.1 ≤ J ≤ 0.2, the stored grain in the monitored area is in a critical state;

[0059] When 0.2 < J ≤ 0.4, the stored grain in the monitored area is in an unsafe state;

[0060] When J > 0.4, the stored grain in the monitored area is in a dangerous state;

[0061] When J > 1.4, the stored grain in the monitored area is in a severely dangerous state.

[0062] As shown in Figures 1(a) - (d), study the relationship between temperature, humidity, moisture and stored - grain microorganisms and storage stability. It can be seen from the figures that the growth time of the main harmful microorganisms in grain storage will decrease with the increase of temperature and the increase of moisture; at the same time, the growth and metabolic products of microorganisms include heat, moisture, different metabolic products such as toxins, etc., which will also affect the quantity and quality of stored grain. It is necessary to keep the grain in the lowest viable state while reducing or avoiding the survival and infection of other organisms.

[0063] In the present invention, as a preference, wheat is selected as an example to show the relationship between storage temperature, storage humidity and storage stability, as Figure 3 shown;

[0064] From Figure 3 It can be seen that the topmost curve drawn with storage temperatures of 20°C, 10°C, and 0°C and corresponding equilibrium adsorption humidities of 60%, 70%, and 75% respectively is the first curve. The storage state of wheat above the first curve is the safe state; similarly, the middle curve drawn with storage temperatures of 30°C, 15°C, and 5°C and corresponding equilibrium adsorption humidities of 60%, 65%, and 75% respectively is the second curve. The storage state of wheat between the first curve and the second curve is the semi-safe state; the bottommost curve drawn with storage temperatures of 30°C, 20°C, and 5°C and corresponding equilibrium adsorption humidities of 65%, 70%, and 80% respectively is the third curve. The storage state of wheat below the second curve and between the second curve and the third curve is the critical state; the storage state of wheat below the third curve is the dangerous state. Thus, it can be seen that the storage stability (safety) of wheat is inversely related to the moisture content, storage temperature, and equilibrium humidity of wheat; that is, in order to achieve safe storage of wheat, the moisture content, storage temperature, and equilibrium humidity of wheat should be controlled as low as possible; however, this conflicts with maintaining the storage quality of wheat, etc.; therefore, a reasonable balance interval needs to be found between storage stability and maintaining quality, and thus the present invention conducts research on the law of grain storage quality based on grain condition data accordingly.

[0065] The present invention is based on the theory of multi-field coupling in a grain heap, and uses the physical fields mainly including the temperature field, humidity field, and moisture field of stored grain, and the mutual coupling effect with the biological fields of grain (mainly including the microbial field, pest biological field, and the self-life field of grain), and constructs a model for the change law of grain storage quality by using a "mechanism + data" dual-drive method.

[0066] Study the relationship between accumulated temperature and storage stability

[0067] Since the physical fields of stored grain will affect the field strengths of different biological fields, and the biological fields in turn affect the physical fields of stored grain, and the field strengths of biological fields will also reflect the changes in storage quality, but the starting points of accumulated temperature of different biological fields are different, as shown in Table 1,

[0068] Table 1

[0069]

[0070]

[0071] According to the starting points of effective accumulated temperature of the life activities of different biological fields, the effectiveness of grain temperature data in the general grain storage process is calculated in segments and different life compensation constants are assigned.

[0072] In the present invention, as a preference, a step-segmented distribution diagram is selected to describe the influence of storage temperature on the effectiveness of the life activities of biological fields, including:

[0073] As shown in Fig. 2(a), it represents a section of the storage temperature range, and the actual situation is not limited to this.

[0074] When the storage temperature does not exceed 0°C, the compensation coefficient of its effectiveness is 0; when the storage temperature is greater than 25°C, the compensation coefficient of its effectiveness is 2. Through the relationship between the storage temperature and the compensation coefficient, the influence weight ratio of the storage temperature on the grain quality is further reflected. Different storage temperatures have different influence weights on the change of grain storage quality, and the accumulated effects are also different.

[0075] Calculate the daily effective accumulated temperature:

[0076]

[0077] In the formula, T j is the daily effective accumulated temperature, and t is the storage temperature.

[0078] For example, when the daily storage temperature is 10°C, its daily effective accumulated temperature is:

[0079] T j =(5 - 0)×0.1+(10 - 5)×0.3 = 2.

[0080] Accumulated temperature refers to the sum of temperatures, which is related to temperature and time. Effective accumulated temperature refers to the sum of the differences between the daily average temperature and the biological zero degree within a certain period of time for crops. Based on the definition of effective accumulated temperature, it is extended and applied to the field of grain storage. In the present invention, it refers to the sum of the differences between the grain temperatures in each area of the grain pile and the effective temperature starting point of the life activities of different biological fields during the grain storage process. Then the calculation formula of the effective accumulated temperature is:

[0081]

[0082] In the formula, T is the effective accumulated temperature of stored grain, T j is the daily effective accumulated temperature, and N d is the number of days with the same daily effective accumulated temperature.

[0083] As shown in Table 2, the relationship between moisture accumulation and storage stability is studied:

[0084] Table 2

[0085] Grain biological field activity Effective humidity starting point / RH% Field intensity Life activities of grain heap mites 65 Weak Life activities of main stored-grain insects 60 Weak Life activities of Aspergillus restrictus in stored grain 65 Weak Life activities of Penicillium in stored grain 60 Medium Life activities of Aspergillus flavus in stored grain 65 Weak Life activities of grain 55 Weak

[0086] According to the effective accumulated temperature starting point of the life activities of different biological fields, the effectiveness of the grain moisture data in the general grain storage process is calculated in segments and different life compensation constants are assigned.

[0087] In the present invention, as a preference, a stepped-segmented distribution diagram is selected to describe the influence of storage humidity on the effectiveness of the biological field life activities, including:

[0088] As shown in Figure 2(b), it represents a section of the storage humidity, and actually it is not limited to this;

[0089] When the storage humidity does not exceed 59%, the compensation coefficient of its effectiveness is 0; when the storage humidity is greater than 95%, the compensation coefficient of its effectiveness is 10.

[0090] Calculate the daily effective accumulated humidity value:

[0091]

[0092] In the formula, S i is the daily effective accumulated humidity, and h is the storage humidity.

[0093] For example, when the daily storage humidity is 70%, its daily effective accumulated humidity is:

[0094] S i = 0.005 + (0.70 - 0.65) × 0.3 = 0.02.

[0095] Accumulated humidity refers to the sum of humidity, which is related to humidity and time. Effective accumulated humidity refers to the sum of the difference between the daily average air humidity and the biological "zero degree" within a certain period of time for crops. Based on the definition of effective accumulated humidity and extending it to the field of grain storage, in the present invention, it refers to the sum of the difference between the relative humidity of the grain piles in each area of the grain pile and the effective humidity starting point of different biological field life activities during the grain storage process. Then the effective accumulated humidity

[0096]

[0097] In the formula, S is the effective accumulated humidity of the stored grain, S i is the daily effective accumulated humidity, and N d is the number of days with the same daily effective accumulated humidity.

[0098] According to the necessary physical factors such as temperature and humidity required for biological field activities in the multi-field coupling theory of the grain pile, only when the temperature and humidity reach their effective temperature starting point and effective humidity starting point, the field strength of different life activities in the biological field begins to increase, and obvious phenomena will occur macroscopically. Therefore, combining the above definitions of effective accumulated temperature and effective accumulated humidity, in the present invention, the two are combined and named effective accumulated temperature and humidity, that is, during the grain storage process, the sum of the differences between the grain temperature and grain humidity in each area of the grain pile at the same time and the effective temperature starting point and effective humidity starting point of different biological field life activities. The calculation formula of the effective accumulated temperature and humidity is:

[0099]

[0100] In the formula, J is the effective accumulated temperature and humidity of stored grain, T is the effective accumulated temperature of stored grain, S is the effective accumulated humidity of stored grain, N d is the number of days with the same single-day effective accumulated temperature or humidity, and n is the number of natural days when the effective accumulated temperature or humidity accumulates to the natural days at the end of storage.

[0101] As Figure 3 shown, the safety of stored grain is judged according to the single-day effective accumulated temperature and humidity value of the monitored area:

[0102] When J < 0.03, the stored grain in the monitored area is in a safe state;

[0103] When 0.03 ≤ J < 0.1, the stored grain in the monitored area is in a semi-safe state;

[0104] When 0.1 ≤ J ≤ 0.2, the stored grain in the monitored area is in a critical state;

[0105] When 0.2 < J ≤ 0.4, the stored grain in the monitored area is in an unsafe state;

[0106] When J > 0.4, the stored grain in the monitored area is in a dangerous state;

[0107] When J > 1.4, the stored grain in the monitored area is in a severely dangerous state.

[0108] The storage times of different grain types in China vary slightly. Generally, it is 2 - 3 years for paddy rice, 3 - 5 years for wheat, 2 - 3 years for corn, 1 - 2 years for soybeans, etc. In this invention, taking a storage cycle of 3 years (365×3 = 1095 days) as an example, the relationship between the effective accumulated temperature and humidity J value of the whole storage cycle and storage safety is as follows in the table:

[0109] Table 3

[0110] Serial number Effective accumulated temperature and humidity J value / ℃·RH%·d Stored-grain state 1 J<32.85 Safe 2 32.85≤J<109.5 Semi-safe 3 109.5≤J≤219 Critical 4 219<J≤438 Unsafe 5 J>438 Dangerous 6 J>1533 Seriously dangerous

[0111] As Figure 4 shown, the key indicators of stored grain quality increase with the increase of the J value. There is a positive correlation between them. And in most cases, the larger the key indicator value of stored grain quality, the worse the stored grain quality, that is, there is a negative correlation between the effective accumulated temperature and humidity J value of stored grain and the stored grain quality.

[0112] Because the key quality indicators of different grain types (such as corn, paddy rice, wheat, soybeans, etc.) are different, in this invention, as a preference, in - warehouse paddy rice and corn are selected for experiments.

[0113] China has a vast territory and abundant resources. According to the climate characteristics, farming systems, main stored-grain pests, annual accumulated temperature and humidity of each region, China is divided into seven stored-grain regions: the first stored-grain region (Qinghai-Tibet Plateau), the second stored-grain region (Inner Mongolia and Xinjiang), the third stored-grain region (Northeast China), the fourth stored-grain region (North China), the fifth stored-grain region (Central China), the sixth stored-grain region (Southwest China), and the seventh stored-grain region (South China).

[0114] Example 1

[0115] Select the fourth stored-grain region (North China), which is a medium-temperature and dry-wet region;

[0116] The grain stored in this warehouse is the paddy rice that was put into the warehouse on November 29, 2019. According to the formula, the effective accumulated temperature and humidity J values of the stored grain at different positions in the actual warehouse were calculated (the calculation time was up to April 25, 2021). In this figure, the relationship between the J values at two different heights, namely the grain surface and the grain bottom of a single point in the grain pile, changing with time was selected. It can be Figure 6 seen that the effective accumulated temperature and humidity J value of the stored grain increases with the prolonging of the storage time. There are obvious differences in the J values of different spatial regions. The change range and speed of the J value of the grain deep in the grain bottom are significantly smaller than those of the grain on the grain surface, indicating that the method of using a single point to represent the quality and storage state of the whole warehouse by the traditional manual sampling method is not advisable.

[0117] As Figure 6 shown, according to the deduction of the storage time and the selection of the storage state threshold (based on Table 3 above), it can be found that the grain at this point at the grain bottom was in a safe storage state between November 29, 2019 and May 18, 2020; it was in a semi-safe state between May 18, 2020 and March 8, 2021, while the grain at this point on the grain surface was in a semi-safe storage state between November 29, 2019 and May 18, 2020, and was in a critical storage state between May 18, 2020 and March 8, 2021. Thus, it can be known that the proposed method can analyze the storage safety and quality changes of different regions of the stored grain at different times.

[0118] Example 2

[0119] Select the fourth stored-grain region (North China), which is a medium-temperature and dry-wet region.

[0120] The grain stored in this warehouse is the corn that was put into the warehouse in November 2015. According to the formula, the effective accumulated temperature and humidity J values of the stored grain at different positions in the actual warehouse were calculated (the calculation time was up to August 28, 2017), Figure 7 and the J value diagram of different points 2m deep below the grain surface at a certain moment in the grain pile was selected in Figure 7 it. It can be seen that there are obvious differences in the J values of different spatial regions, and the change range of the J value of the grain near the wall is significantly greater than that of the grain inside the grain pile and at the center of the grain pile; in addition, from Figure 7It can be known that according to the selection of the sum and storage state threshold (according to the method in Table 3), it can be found that the grain between 15.5 m and 50.5 m from the east wall is in a safe and semi-safe state of grain storage, and the grain between 0 - 15.5 m from the east wall and 0 - 10.5 m from the west wall is in a critical state of grain storage; moreover, the degree of fluctuation of the J value of the grain near the south wall is greater than that near the north wall, which is affected by solar radiation and spatial position. Thus, it can be known that this method can be used to analyze the storage safety and quality changes of each area of stored grain at the same moment.

[0121] It can be seen from Embodiments 1 and 2 that the method of the present invention can be used to analyze the relationship between the J value of grain in different areas at different times and the grain storage state, so as to master the safety and quality state of the grain in the whole area.

[0122] Embodiment 3

[0123] The fifth grain storage area (central China) is selected, and this area is a medium-temperature and high-humidity area.

[0124] The grain stored in this warehouse is the paddy rice stored in 2018. The effective accumulated temperature and humidity J value of the stored grain at different points in the actual warehouse is calculated according to the formula (the calculation time is up to November 29, 2021), as Figure 8 shown. The relationship between the J value and the fatty acid value from the grain surface to the grain bottom of a single point in the grain pile is selected. According to the main judgment standard stipulated in GB 20569 - 2006 "Criterion for Judging the Storage Quality of Paddy Rice", the fatty acid value is selected as the key index for the storage quality of paddy rice. It can be Figure 8 known that as the J value increases, the fatty acid value of paddy rice also increases, so there is a positive correlation between the two, and the larger the fatty acid value, the worse the storage quality of paddy rice, and it should be processed by timely out-of-warehouse. Then it is proved by the example that there is a significant correlation between the J value and the key storage quality index, and it is a negative correlation.

[0125] Although the implementation scheme of the present invention has been disclosed above, it is not limited to the applications listed in the description and implementation mode. It can be fully applied to various fields suitable for the present invention. For those familiar with the field, additional modifications can be easily made. Therefore, without departing from the general concept defined by the claims and the equivalent scope, the present invention is not limited to specific details and the illustrations shown and described here.

Claims

1. A research method for the change law of grain storage quality based on grain condition data, characterized in that, Including: In each area of the monitored grain pile, a plurality of temperature and humidity monitoring devices are arranged to obtain the temperature and humidity of the grain in the monitored area; Calculate the effective accumulated temperature value and the effective accumulated humidity value of the grain in the monitored area, and calculate the effective accumulated temperature and humidity value of the monitored area through the effective accumulated temperature value and the effective accumulated humidity value of the monitored area; The calculation formula for the effective accumulated temperature and humidity is: Wherein, J is the effective accumulated temperature and humidity of stored grain, T is the effective accumulated temperature of stored grain, S is the effective accumulated humidity of stored grain, N d is the number of days with the same single-day effective accumulated temperature or effective accumulated humidity, and n is the natural number of days when the natural number of days of effective accumulated temperature or effective accumulated humidity accumulates to the end of storage; The calculation formula for the effective accumulated humidity of the monitored area is: Where S is the effective moisture accumulation of stored grain, S i is the daily effective moisture accumulation, and N d is the number of days with the same daily effective moisture accumulation; Judge the safety of the stored grain according to the daily effective accumulated temperature and humidity value of the monitored area: When J < 0.03, the stored grain in the monitored area is in a safe state; When 0.03 ≤ J < 0.1, the stored grain in the monitored area is in a semi-safe state; When 0.1 ≤ J ≤ 0.2, the stored grain in the monitored area is in a critical state; When 0.2 < J ≤ 0.4, the stored grain in the monitored area is in an unsafe state; When J > 0.4, the stored grain in the monitored area is in a dangerous state; When J > 1.4, the stored grain in the monitored area is in a serious dangerous state.

2. The research method for the change law of grain storage quality based on grain condition data according to claim 1, characterized in that The calculation formula for the effective accumulated temperature of the monitored area is: Where, T is the effective accumulated temperature of stored grain, T j is the daily effective accumulated temperature, and N d is the number of days with the same daily effective accumulated temperature.

3. The research method for the law of change in grain storage quality based on grain condition data according to claim 2, characterized in that, The calculation formula for the daily effective accumulated temperature is: where T j is the effective accumulated temperature per day, and t is the storage temperature.

4. The research method for the change law of grain storage quality based on grain condition data according to claim 3, characterized in that The calculation formula for the daily effective accumulated humidity is: Where S i is the effective accumulated humidity per day, and h is the storage humidity.

Images