Grain storage quality detection system

Abstract

The invention discloses a grain storage quality detection system, and relates to the technical field of grain storage, and the system comprises a sample collection module which is used for obtaining samples from grain storage, and employs an automatic sampling device to carry out multi-point sampling according to the size, shape and grain storage distribution condition of a granary and a preset sampling rule; the index detection module is used for carrying out index detection on screened samples in grain storage; and the data processing module is used for receiving the index detection data of the index detection module. According to the invention, various index information of grains can be detected at the same time, grains with potential safety hazards can be effectively identified, defective grains can be processed in time, mildewed and insect-attacked grains are prevented from flowing into the market, the body health of consumers is guaranteed, the stability and order of the grain market are maintained, the food safety of the grains is ensured from the source, the detection period is greatly shortened, and the detection efficiency is improved. Grain quality information can be fed back in time, managers can grasp the best processing opportunity, and grain loss is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of grain storage, and more specifically, to a grain storage quality detection system. Background Art

[0002] In the field of grain storage, ensuring grain quality and safety is crucial for guaranteeing grain supply, stabilizing the market, and maintaining people's livelihood. With the continuous growth of grain production and the increasing expansion of storage scale, many problems have emerged in traditional grain storage quality detection methods.

[0003] At present, on the one hand, existing grain quality detection methods mostly rely on manual sampling and laboratory testing, with cumbersome operation processes and low efficiency. Manual sampling is greatly affected by subjective factors, making it difficult to ensure the representativeness of samples, prone to deviations in test results, and resulting in misjudgments of grain storage quality. For example, in large granaries, manual sampling may not cover all corners of the granary, missing areas of locally deteriorated grain. And laboratory testing requires transporting samples to professional laboratories, with a long testing cycle, unable to provide timely feedback on grain quality information, missing the best treatment opportunity, and causing grain losses. On the other hand, during the grain storage process, environmental factors such as temperature, humidity, and ventilation conditions have a significant impact on grain quality. However, most current storage facilities lack real-time and comprehensive environmental monitoring means, unable to promptly grasp the impact of environmental changes on grain quality. When environmental parameters exceed the appropriate range, grains are prone to mildew, insect pests, etc., affecting their quality and food safety. For example, in high-temperature and high-humidity seasons, if the storage environment cannot be monitored and regulated in a timely manner, the risk of grain mildew increases sharply, not only reducing the nutritional value of grains but also possibly producing harmful toxins, threatening human health. In addition, the lack of a unified and efficient grain storage quality detection system makes it difficult for storage management departments to comprehensively evaluate and dynamically manage grain quality. Unable to adjust storage strategies such as ventilation, fumigation, etc. in a timely manner according to the grain quality situation, resulting in waste of resources or further deterioration of grain quality. Therefore, it is urgent to develop an accurate, efficient, and intelligent grain storage quality detection system to solve the above problems existing in the prior art, improve the level of grain storage management, and ensure grain safety.

[0004] Regarding the problems in the related art, no effective solutions have been proposed yet. Summary of the Invention

[0005] Regarding the problems in the related art, the present invention proposes a grain storage quality detection system to overcome the above technical problems existing in the related prior art.

[0006] The technical solution of the present invention is realized as follows:

[0007] A grain storage quality detection system includes:

[0008] Sample collection module: used to obtain samples from grain storage. Among them, it includes using an automated sampling device to perform multi-point sampling according to the size, shape of the granary and the distribution of grain storage, in accordance with the preset sampling rules.

[0009] Index detection module: used to detect the indexes of the selected samples in grain storage.

[0010] Data processing module: used to receive the index detection data from the index detection module, compare it with the preset index reference table, obtain the detection value corresponding to the index detection data, and obtain the grain quality value S according to the corresponding detection value, expressed as:

[0011] S = I SC ×W1 + I ZC ×W2 + I RC ×W3 + I KC ×W4 + I LC ×W5;

[0012] Among them, I SC is the moisture content detection value, I ZC is the impurity content detection value, I RC is the bulk density detection value, I KC is the fatty acid value detection value and I LC is the imperfect grain content detection value. W1, W2, W3, W4, and W5 are weight coefficients respectively, and W1 > W3 = W4 = W5 > W2;

[0013] Status evaluation module: used to evaluate the current grain status according to the grain quality value S and the preset grain quality scoring table.

[0014] Early warning decision module: used to give an early warning based on the grain status obtained from the status evaluation module.

[0015] Furthermore, it further includes: a storage and presentation module, used to store the index detection data and the calculated grain quality value S, establish a historical database, and present a grain quality change trend chart to provide data support for warehouse management decisions.

[0016] Furthermore, the index detection module includes: a moisture content detection unit, used to use a high-precision drying device to dry the sample according to the preset temperature and time, and calculate the moisture content I by using a sensor to monitor the quality change of the sample before and after drying in real time, s expressed as:

[0017] I s =(M a -M b )÷M a ×100%;

[0018] Among them, M a is the mass of the sample before drying, and M b is the mass of the sample after drying.

[0019] Furthermore, the index detection module includes: an impurity content detection unit, which is used to screen the sample with sieves of different sieve hole specifications, weigh the undersize material and the impurities on the sieve respectively through an automated weighing device, and calculate the impurity content I z , expressed as:

[0020] I z = Z x ÷ Z m × 100%;

[0021] Among them, Z x is the mass of the impurities, and Z m is the total mass of the sample.

[0022] Furthermore, the index detection module includes: a bulk density detection unit, which is used to put the sample into a bulk density meter with a set volume, accurately measure the mass of the sample through an electronic weighing device, and calculate the bulk density I R , expressed as:

[0023] I R = Y m ÷ V

[0024] Among them, Y m is the mass of the grain sample, and V is the volume of the bulk density meter.

[0025] Furthermore, the index detection module includes: a fatty acid value detection unit, which is used to first extract the fatty acids in the sample with anhydrous ethanol according to an automated extraction and titration device, then titrate with a potassium hydroxide standard solution, and record the volume of the potassium hydroxide solution consumed through a sensor, and calculate the fatty acid value I K , expressed as:

[0026] I K = (V - V0) × C × 56.11 ÷ m × 100;

[0027] Among them, V is the volume of the potassium hydroxide solution consumed in the titration of the sample, V0 is the volume of the potassium hydroxide solution consumed in the blank titration, C is the concentration of the potassium hydroxide standard solution, 56.11 is the molar mass of potassium hydroxide, and m is the mass of the sample.

[0028] Furthermore, the index detection module includes: an imperfect grain content detection unit, which is used to identify and count the imperfect grains in the grain sample by using an image recognition algorithm, and then obtain the mass of the imperfect grains through a weighing device, and calculate the imperfect grain content I L , expressed as:

[0029] I L = Y W ÷ Y m × 100%

[0030] where Y W is the mass of imperfect grains, and Y m is the mass of the grain sample.

[0031] Furthermore, the index detection module for evaluating the current state of the grain includes: high quality, good, qualified, poor, and inferior.

[0032] Furthermore, the grain state obtained by the state evaluation module is warned, including: when the grain quality value S ≥ the preset risk threshold, a warning message is issued, and corresponding decisions are provided according to the grain quality situation, including at least: for grains with too high moisture content, start the ventilation and drying equipment, and for grains with excessive impurity content, perform impurity removal treatment.

[0033] Advantages of the present invention:

[0034] 1. By using an automated sampling device, the present invention performs multi-point sampling according to the size, shape of the granary and the grain storage distribution, overcomes the problems of large influence of subjective factors on manual sampling and insufficient representativeness of samples, reduces the deviation of detection results, avoids misjudgment of the quality of grain storage, and at the same time detects various index information of the grain, can effectively identify grains with potential safety hazards, timely process the problematic grains, prevent mildewed and pest-infested grains from entering the market, ensure the physical health of consumers, maintain the stable order of the grain market, ensure the edible safety of grains from the source, greatly shorten the detection cycle, can timely feedback the grain quality information, enable the management personnel to seize the best treatment opportunity, reduce grain losses. In addition, by comparing the index detection data with the preset index reference table and combining the weights of each index, the grain quality value S is calculated; the state evaluation module evaluates the grain state based on the grain quality value S and the preset scoring table, covering various levels such as high quality, good, qualified, poor, and inferior, realizing the comprehensive evaluation of grain quality and providing a scientific basis for warehouse management.

[0035] 2. The present invention realizes the optimization of warehouse resource allocation, can accurately and in real time grasp the quality status of grains, and based on the grain quality value and the status evaluation result, the warehouse management department can scientifically plan warehouse resources. For high-quality grains, a long-term and stable storage environment is preferentially arranged to reduce unnecessary processing links; for grains with slightly lower quality, the warehouse storage strategy is adjusted in a timely manner, such as increasing the ventilation frequency and arranging processing in advance, to avoid excessive occupation of warehouse space and resources, improve the utilization rate of warehouse facilities, reduce warehouse costs, and at the same time can easily handle large-scale warehouse scenarios, and can efficiently detect and manage grains in multiple granaries and different storage areas, providing reliable technical support for modern large-scale grain storage and promoting the development of the grain storage industry towards intelligence and modernization. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0037] Figure 1 is a schematic block diagram of a grain storage quality detection system according to an embodiment of the present invention Figure I ;

[0038] Figure 2 is a schematic block diagram of a grain storage quality detection system according to an embodiment of the present invention Figure II . DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of the present invention.

[0040] According to an embodiment of the present invention, a grain storage quality detection system is provided.

[0041] As Figure 1 - Figure 2 shown, the grain storage quality detection system according to an embodiment of the present invention includes: a sample collection module 1: used to obtain representative samples from grain storage, wherein, an automated sampling device is adopted, and according to the size, shape of the granary and the grain storage distribution, multi-point sampling is carried out according to the preset sampling rules.

[0042] Specifically, during application, sampling probes installed at different heights and positions automatically collect grain samples and transport the samples to subsequent detection processes, ensuring the diversity and representativeness of the samples and reducing errors caused by manual sampling.

[0043] Index detection module 2: used to detect the indexes of the selected samples in grain storage;

[0044] Moisture content detection unit 21: used to dry the sample using a high-precision drying device according to a preset temperature and time, and use a sensor to monitor the mass change of the sample before and after drying in real time to calculate the moisture content I s , expressed as:

[0045] I s =(M a -M b )÷M a ×100%;

[0046] Among them, M a is the mass of the sample before drying, and M b is the mass of the sample after drying.

[0047] Impurity content detection unit 22: used to screen the sample with sieves of different sieve hole specifications, and use an automated weighing device to weigh the undersize material and the impurities on the sieve respectively to calculate the impurity content I z , expressed as:

[0048] I z =Z x ÷Z m ×100%;

[0049] Among them, Z x is the mass of the impurities, and Z m is the total mass of the sample.

[0050] Volume weight detection unit 23: used to put the sample into a volume weight meter with a set volume, and accurately measure the mass of the sample through an electronic weighing device to calculate the volume weight I R , expressed as:

[0051] I R =Y m ÷V

[0052] Among them, Y m is the mass of the grain sample, and V is the volume of the volume weight meter.

[0053] Fatty acid value detection unit 24: used to first extract the fatty acids in the sample with anhydrous ethanol according to an automated extraction and titration device, then titrate with a potassium hydroxide standard solution, and record the volume of the potassium hydroxide solution consumed through a sensor to calculate the fatty acid value I K, expressed as:

[0054] I K = (V - V0) × C × 56.11 ÷ m × 100;

[0055] Wherein, V is the volume of potassium hydroxide solution consumed in the titration of the sample, V0 is the volume of potassium hydroxide solution consumed in the blank titration (ml), C is the concentration of the potassium hydroxide standard solution (mol / L), 56.11 is the molar mass of potassium hydroxide (g / mol), and m is the mass of the sample (g).

[0056] The imperfect grain content detection unit 25 is used to identify and count the imperfect grains in the grain sample by using an image recognition algorithm, and then obtain the mass of the imperfect grains through a weighing device, and calculate the imperfect grain content I L , expressed as:

[0057] I L = Y W ÷ Y m × 100%

[0058] Wherein, Y W is the mass of the imperfect grains, and Y m is the mass of the grain sample.

[0059] In this technical solution, the image recognition algorithm identifies the imperfect grains in the grain sample, specifically picks out worm-eaten grains, diseased grains, broken grains, germinated grains, etc.

[0060] The data processing module 3 is used to receive the index detection data of the index detection module 2, compare it with a preset index reference table, obtain the detection value corresponding to the index detection data, and obtain the grain quality value S according to the corresponding detection value, expressed as:

[0061] S = I SC × W1 + I ZC × W2 + I RC × W3 + I KC × W4 + I LC × W5;

[0062] Wherein, I SC is the moisture content detection value, I ZC is the impurity content detection value, I RC is the bulk density detection value, I KC is the fatty acid value detection value, and I LC is the imperfect grain content detection value, and W1, W2, W3, W4, and W5 are weight coefficients respectively, and W1 > W3 = W4 = W5 > W2.

[0063] In this technical solution, corresponding weights are assigned according to the importance of the impact of each index information on grain quality. Excessive moisture content will directly lead to grain mildew, which has a greater impact on the safe storage of grain, so a higher weight can be assigned; while the impurity content has a relatively smaller impact on grain quality, so a lower weight can be assigned. Specifically, when applied, the value of W1 is 30%, the value of W2 is 10%, the value of W3 is 20%, the value of W4 is 20%, and the value of W5 is 20%.

[0064] In this technical solution, the index reference table is as shown in Table 1 specifically:

[0065]

[0066]

[0067] Table 1 Index Reference Table

[0068] The status evaluation module 4 is used to evaluate the current grain status according to the grain quality value S and the preset grain quality scoring table;

[0069] Among them, the grain quality scoring table is as shown in Table 2, specifically as follows:

[0070] Grain quality value Grain status 8 - 10 points High quality 6 - 8 points Good 4 - 6 points Qualified 2 - 4 points Poor 0 - 2 points Inferior

[0071] Table 2 Grain Quality Scoring Table

[0072] Specifically, with the help of Table 2 shown above:

[0073] 8 - 10 points: High quality, the grain quality is good, all indicators meet or exceed the set standards, suitable for long-term storage and processing.

[0074] 6 - 8 points: Good, the grain quality is good, all indicators basically meet the set standards, can be stored and processed normally.

[0075] 4 - 6 points: Qualified, the grain quality is average, some indicators are close to the lower limit of the set standards. Take corresponding measures for treatment, such as drying, impurity removal, etc., to ensure grain quality safety.

[0076] 2 - 4 points: Poor, the grain quality is poor, some indicators exceed the set standards, there are certain quality safety hazards. Can be used for downgrading or special processing.

[0077] 0 - 2 points: Inferior, the grain quality is seriously unqualified, multiple indicators exceed the set standards, and it is no longer suitable as edible grain. Used as industrial grain or for other purposes.

[0078] The early warning decision-making module 5 is used to obtain the grain status of the status evaluation module 4 for early warning, among which, including:

[0079] When the grain quality value S ≥ the preset risk threshold, a warning message is issued, and relevant management personnel are notified by means such as text messages, emails, or system pop-ups. According to the grain quality status, corresponding decisions are provided to help management personnel take timely measures to ensure grain quality safety.

[0080] Specifically, it includes the following decisions:

[0081] Among them, for grains with too high moisture content, it is recommended to start the ventilation and drying equipment;

[0082] Among them, for grains with excessive impurity content, impurity removal treatment is recommended.

[0083] The storage presentation module 6 is used to store the index detection data and the calculated grain quality value S, establish a historical database, and present the grain quality change trend chart, such as the change curve of moisture content over time, the distribution of impurity content in different storage areas, etc. So that the warehouse management personnel can timely grasp the dynamic changes of grain quality and provide data support for warehouse management decisions.

[0084] In summary, by means of the above technical solutions of the present invention, the following effects can be achieved:

[0085] 1. By adopting an automated sampling device, the present invention conducts multi-point sampling according to the size, shape of the granary and the grain storage distribution, overcomes the problems of large subjective influence and insufficient sample representativeness in manual sampling, reduces the deviation of detection results, avoids misjudgment of grain storage quality, and at the same time detects various index information of grains, can effectively identify grains with potential safety hazards, timely process the problematic grains, prevent mildewed and pest-infested grains from flowing into the market, ensure the physical health of consumers, maintain the stable order of the grain market, ensure the edible safety of grains from the source, and greatly shorten the detection cycle, can timely feedback grain quality information, enable management personnel to seize the best treatment opportunity, reduce grain losses. In addition, by comparing the index detection data with the preset index reference table and combining the weights of each index, the grain quality value S is calculated; the status evaluation module evaluates the grain status according to the grain quality value S and the preset scoring table, covering multiple levels such as high quality, good, qualified, poor, and inferior, realizing the comprehensive evaluation of grain quality and providing a scientific basis for warehouse management.

[0086] 2. The present invention realizes the optimization of warehouse resource allocation, can accurately and in real time grasp the quality status of grains, and based on the grain quality value and the status evaluation result, the warehouse management department can scientifically plan warehouse resources. For high-quality grains, a long-term and stable storage environment is preferentially arranged to reduce unnecessary processing links; for grains with slightly lower quality, the warehouse strategy is adjusted in a timely manner, such as increasing the ventilation frequency, arranging processing in advance, etc., to avoid excessive occupation of warehouse space and resources, improve the utilization rate of warehouse facilities, reduce warehouse costs, and at the same time can easily handle large-scale warehouse scenarios, and can efficiently detect and manage grains in multiple granaries and different storage areas, providing reliable technical support for modern large-scale grain storage and promoting the development of the grain storage industry towards intelligence and modernization.

[0087] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. After considering the disclosure in the specification and the embodiments, those skilled in the art will easily think of other implementation schemes of the present disclosure. This application aims to cover any variations, uses or adaptations of the present disclosure, and these variations, uses or adaptations follow the general principles of the present disclosure and include common general knowledge or conventional technical means in the technical field not disclosed in the present disclosure. The specification and the embodiments are only regarded as exemplary, and the true scope and spirit of the present disclosure are pointed out by the claims.

[0088] It should be understood that the present disclosure is not limited to the precise structures already described and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present disclosure is only limited by the appended claims.

Claims

1. A grain storage quality detection system, characterized in that: include: Sample collection module (1): used to obtain samples from grain storage, including using automated sampling equipment to perform multi-point sampling according to preset sampling rules based on the size and shape of the grain storage and distribution of the grain storage; Index detection module (2): used for index detection of samples screened in grain storage; The data processing module (3) is used to receive the index detection data of the index detection module (2), compare it with the preset index reference table, obtain the detection value corresponding to the index detection data, and obtain the grain quality value S according to the corresponding detection value, which is expressed as: S=I SC ×W1+I ZC ×W2+I RC ×W3+I KC ×W4+I LC ×W5; Among them, I SC is the moisture content detection value, I ZC is the impurity content detection value, I RC is the bulk density test value, I KC is the fatty acid value detection value and I LC is the detection value of imperfect particle content, W1, W2, W3, W4, and W5 are weight coefficients, and W1>W3=W4=W5>W2; A state evaluation module (4) is used to evaluate the current state of grain according to the grain quality value S and a preset grain quality scoring table; The early warning decision module (5) is used to obtain the food status of the status assessment module (4) for early warning.

2. The grain storage quality detection system according to claim 1 is characterized in that: Also includes: The storage and presentation module (6) is used to store the index detection data and the calculated grain quality value S, establish a historical database, and present a grain quality change trend chart to provide data support for storage management decisions.

3. The grain storage quality detection system according to claim 1 is characterized in that: The index detection module (2) comprises: a moisture content detection unit (21) for drying the sample using high-precision drying equipment according to a preset temperature and time, monitoring the mass change of the sample before and after drying in real time through a sensor, and calculating the moisture content I s , expressed as: I s =(M a -M b )÷M a ×100%; Among them, M a is the mass of the sample before drying, M b is the mass of the sample after drying.

4. The grain storage quality detection system according to claim 3 is characterized in that: The index detection module (2) comprises: an impurity content detection unit (22) for screening the sample using sieves with different mesh specifications, and weighing the impurities under the sieve and on the sieve respectively by an automatic weighing device to calculate the impurity content I z , expressed as: I z =Z x ÷Z m ×100%; Among them, Z x is the impurity mass, Z m is the total mass of the sample.

5. The grain storage quality detection system according to claim 4 is characterized in that: The index detection module (2) comprises: a bulk density detection unit (23) for placing a sample into a bulk density device of a set volume, accurately measuring the mass of the sample through an electronic weighing device, and calculating the bulk density I R , expressed as: I R =Y m ÷V Among them, Y m is the mass of grain sample, and V is the volume of bulk density device.

6. The grain storage quality detection system according to claim 5 is characterized in that: The index detection module (2) comprises: a fatty acid value detection unit (24) for first extracting the fatty acids in the sample with anhydrous ethanol according to an automated extraction and titration device, then titrating with a potassium hydroxide standard solution, and recording the volume of the consumed potassium hydroxide solution through a sensor to calculate the fatty acid value I K , expressed as: I K =(V-V0)×C×56.11÷m×100; Wherein, V is the volume of potassium hydroxide solution consumed by sample titration, V0 is the volume of potassium hydroxide solution consumed by blank titration, C is the concentration of potassium hydroxide standard solution, 56.11 is the molar mass of potassium hydroxide, and m is the sample mass.

7. The grain storage quality detection system according to claim 6, characterized in that: The index detection module (2) comprises: an imperfect grain content detection unit (25) for identifying and counting imperfect grains in a grain sample using an image recognition algorithm, and then obtaining the mass of the imperfect grains through a weighing device, and calculating the imperfect grain content I L , expressed as: I L =Y W ÷Y m ×100% Among them, Y W is the imperfect particle mass, Y m For food sample quality.

8. The grain storage quality detection system according to claim 1 or 7, characterized in that: The index detection module (2) evaluates the current grain status, including: high quality, good, qualified, poor and inferior.

9. The grain storage quality detection system according to claim 8, characterized in that: The grain status of the acquisition status assessment module (4) is warned, including: when the grain quality value S ≥ the preset risk threshold, a warning message is issued, and corresponding decisions are provided according to the grain quality status, which at least include: for grains with too high moisture content, the ventilation and drying equipment is started, and for grains with excessive impurity content, impurity removal treatment is performed.

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