Multi-element coupling evaluation system for spoilage and mildew of grain and oil food

Abstract

The invention discloses a multi-element coupling evaluation system for spoilage and mildewing of grain and oil foods. The multi-element coupling evaluation system comprises an identification capturing module, a model core operation layer, a pre-judgment look-ahead module, an alarm grading module and a disposal decision module. According to the invention, a food spoilage and mildewing multi-element coupling evaluation system based on recognition and grabbing, anticipation and look-ahead, alarm and grading, and disposal and decision-making is constructed, and mainly depends on four function modules of recognition, anticipation, alarm and disposal, so that the purpose of grabbing, look-ahead, grading and decision-making on spoilage and mildewing food generated in a whole chain is achieved. In order to realize the purpose from'grabbing 'to'look-ahead-grading', a'model core operation layer 'needs to be introduced, a multi-parameter coupling effect relationship is defined, a reference value and a threshold value are set, a link between'identification' and'pre-judgment-alarm 'is established, and an applicable and operable'disposal decision' suggestion is given through continuous dynamic correction. Therefore, a scientific, accurate and logic self-consistent'data information-spoilage and mildew 'relationship and a mathematical model are established.

Description

Technical Field

[0001] The invention relates to the field of spoilage and mildew of grains, oils and foods, and in particular to a multi-coupling evaluation system for spoilage and mildew of grains, oils and foods. Background Art

[0002] There are many kinds of grain and oil food, and the present invention mainly provides a specific research method for rice. Rice is one of the four major grain crops in my country, the main grain crop in my country, and currently an important strategic reserve grain in my country. Generally speaking, a considerable proportion of the rice circulation cycle is stored. Compared with wheat, rice is not resistant to storage. In order to meet the storage requirements of safe moisture and conventional grain storage temperature and humidity control, the reserve rotation period in the grain depot is generally 2-3 years.

[0003] As a living organism, rice has a respiration function. The nutrients in the endosperm, embryo, and endosperm of rice are oxidized and decomposed by enzymes. This respiration provides energy for the living organism and deteriorates the quality of rice. The change in the quality of rice during storage is irreversible. During this period, the activity of some enzymes is weakened, the respiration intensity is reduced, the vitality is weakened, and the changes in the physical and chemical properties of rice lead to changes in quality. The deterioration of rice quality involves changes in many aspects of rice, such as changes in its contents such as amylose and soluble protein. At the same time, the activities of various enzymes in rice change, such as the decrease in amylase activity and the increase in lipase and lipoxygenase activity, and the gradual accumulation of harmful substances, such as malondialdehyde and fungal toxins. Among them, the most important indicator of quality deterioration is the increase in the fatty acid value of rice. The production of free fatty acids in rice is mainly produced by the oxidation and decomposition of lipids in rice. Compared with the starch and protein in rice, which are more abundant, the lipids in rice are relatively few, but the oxidation and decomposition of lipids have a profound impact on the changes in rice quality.

[0004] There are many factors that affect the quality of rice, including the influence of temperature and humidity, as well as the influence of biological factors such as grain pests and mold during storage. The moisture content of rice is relatively low, and fungi can tolerate lower moisture content than bacteria. Therefore, for stored rice, the microorganisms on it are mainly fungi. In fact, rice will be contaminated by soil, water, pests and diseases, and animal excrement during the harvesting process.

[0005] In addition to the above-mentioned effects of stored grain insects, fungi, and various enzymes in rice grains, the moisture content of rice grains has the greatest impact on its quality. Water is an essential element for maintaining the life activities of grain grains and maintaining the color, aroma, and edible quality of grains. Generally, the higher the moisture content, the stronger the respiration of rice grains.

[0006] In short, as a living organism with breathing function, the strength of rice's respiration determines the speed of its deterioration. From the perspective of endogenous factors, the most important factor affecting the strength of rice's respiration is its water content. The external macro-environment directly or indirectly affects the respiration intensity by affecting the water content of rice. At the same time, microorganisms and insect pests greatly aggravate the speed and degree of rice deterioration.

[0007] Therefore, there is an urgent need to provide a multivariate coupling evaluation system for food spoilage and mildew, so as to establish a scientific, accurate, and logically self-consistent "data information-spoilage and mildew" relationship and mathematical model. Summary of the invention

[0008] In view of this, the present invention provides a multi-coupling evaluation system for spoilage and mildew of grain and oil foods.

[0009] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0010] A multi-coupling evaluation system for spoilage and mildew of grain and oil food includes an identification and capture module, a model core operation layer, a prediction and foresight module, an alarm classification module and a disposal decision module.

[0011] Preferably, the identification and capture module mainly includes a basic information module, a process information module and a quality information module.

[0012] Preferably, the identification and capture module is a capture and collection of information and indicators.

[0013] Preferably, the core computing layer of the model is used to analyze the coupling, synergy, antagonism and inhibition effects between various categories of multivariate information, establish the correlation between the captured information and corruption and mildew, and then construct a mathematical model to preliminarily determine the baseline value and threshold value, and provide a "predictive foresight-alarm classification".

[0014] Preferably, the predictive foresight module provides a forward-looking prediction of the potential spoilage and mildew of food through calculation and analysis of the model operation layer and reference thresholds.

[0015] Preferably, the predictive foresight module is divided into three levels: high concern, medium concern and no concern.

[0016] Preferably, the alarm classification module determines whether the food is spoiled or moldy through calculation and analysis of the model operation layer and reference thresholds, and issues an alarm.

[0017] Preferably, the alarm classification module is divided into three levels: high risk, medium risk and low risk.

[0018] Preferably, the disposal decision module provides auxiliary decision-making, intervention suggestions and disposal measures through the key focus suggestions and risk levels given by the predictive and prospective module and the alarm classification module.

[0019] Compared with the prior art, the present invention has achieved the following technical effects:

[0020] The present invention establishes a scientific, accurate and logically self-consistent "data information-corruption and mildew" relationship and mathematical model. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a main framework diagram of a multi-coupling evaluation system for spoilage and mildew of grain and oil food of the present invention, taking rice as an example;

[0022] Figure 2 It is a rice balance moisture fitting diagram of a multi-coupling evaluation system for spoilage and mildew of grain and oil food of the present invention;

[0023] Figure 3 A graph showing the change of fatty acid values ​​of indica rice with a moisture content of 16% at different temperatures in a multivariate coupling evaluation system for spoilage and mildew of grain and oil food of the present invention;

[0024] Figure 4 A relationship diagram between the 16% moisture content K value and temperature of a multi-coupling evaluation system for spoilage and mildew of grain and oil food of the present invention;

[0025] Figure 5 A graph showing the relationship between the K value of the change in the fatty acid value of indica rice and the temperature and water content in a multi-coupling evaluation system for spoilage and mildew of grain and oil food according to the present invention;

[0026] Figure 6 This is a relationship diagram between the change K value of japonica rice fatty acid value and temperature and water content in a multivariate coupling evaluation system for spoilage and mildew of grain and oil food of the present invention. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] Embodiment 1:

[0029] The invention discloses a multi-coupling evaluation system for corruption and mildew of grain and oil food, comprising an identification and capture module, a model core operation layer, a prejudgment and foresight module, an alarm classification module and a disposal and decision module.

[0030] Among them, the identification and capture module is composed of a basic information module, a process information module, and a quality information module. The three modules are divided into multi-level and multi-category information, and then perform the capture, collection, and coupling of information and indicators. Therefore, from the practical application level of this module, due to the diversity of indicators, multi-layer information, and multi-coupling, it is necessary to mine key characteristic indicators from all effective information related to spoilage and mildew according to different food types. The present invention takes stored rice as an example to illustrate.

[0031] After field visits and surveys of many rice storage and processing companies, it was found that the region, ecological environment, and storage technology capabilities are the basic prerequisites that affect the corruption and mildew of rice. Most storage companies have the ability to monitor grain conditions such as warehouse temperature, warehouse humidity, and grain temperature. Generally, the local rice safety moisture content and rice balance moisture content are used to manage rice storage. The moisture content of rice is used as an important process detection indicator. At the same time, the changing trend of grain moisture content is one of the key factors affecting the corruption and mildew of grain. The change of fatty acid value in rice is one of the important indicators of the corruption and mildew of grain, which is highly forward-looking and predictive. To this end, when constructing the "identification and capture" module of rice, the chain relationship of "ecological grain storage area"-"local safety moisture"-"warehouse temperature, warehouse humidity, grain temperature and moisture"-"balanced moisture"-"rice moisture content"-"fatty acid value / mycotoxin" is established around key characteristic indicators such as "moisture" and "fatty acid value", providing a strong basis for the terminal to determine the "corruption and mildew risk level" of rice.

[0032] Among them, the core operation layer module of the model is the bridge between "identification" and "prediction-alarm". After the "identification and capture" is completed, it is necessary to analyze the coupling, synergy, antagonism, inhibition and other effects between the various types of multivariate information, establish the correlation between the captured information and corruption and mildew, and then build a mathematical model to preliminarily determine the benchmark value and threshold value, and give the "prediction-foresight-alarm classification".

[0033] Among them, the predictive foresight module performs the forward-looking prediction function of potential food spoilage and mildew. According to the threshold requirements, it is divided into three levels: high concern, moderate concern and no need for concern. It establishes the relationship between "basic information-process information-quality information" and constructs the corresponding mathematical model to achieve preliminary forward-looking prediction of food spoilage and mildew.

[0034] Among them, the alarm classification module determines the degree of food spoilage and mildew. Through the calculation and analysis of the model operation layer module and the reference threshold of specific indicators, it determines whether the food is spoiled and mildewed, and issues an alarm prompt, which is divided into three levels: high risk, medium risk and low risk.

[0035] Among them, the disposal decision module provides corresponding risk disposal suggestions to the reservoir / company based on the risk level determined by the previous level.

[0036] Example 2: Balanced moisture model based on warehouse temperature and humidity

[0037] The moisture content of raw grain in the granary mainly depends on the temperature and humidity of the granary. The water and heat conditions in the air can interact with the raw grain. The granary is a relatively closed environment. In the process of interaction with the environment, the raw grain will eventually reach a certain moisture balance through moisture diffusion and moisture migration. Under specific temperature and humidity conditions, when the rate at which the grain absorbs moisture from the surrounding environment is equal to the rate at which the grain loses moisture from the surrounding environment, the grain is in equilibrium with the environment. This moisture in equilibrium is called equilibrium moisture or humidity balance. The equilibrium moisture of rice at specific temperature and humidity is shown in Table 1.

[0038] Table 1: Balanced moisture of rice at specific temperature and humidity

[0039]

[0040] Only rice with a balance moisture content below the safe moisture content will not suffer from serious spoilage and mildew during storage, while rice with a balance moisture content above the safe moisture content is very likely to mildew. The spoilage and mildew of grain is a slow accumulation process, so constructing a safe storage time model for high-moisture grain is a prerequisite for determining grain storage safety during actual storage.

[0041] Example 3: Safe storage time model based on high moisture grain

[0042] In the actual storage process, due to different geographical locations and climatic conditions, it is inevitable that the moisture content of grain exceeds the safe moisture content range. At the same time, the spoilage and mildew of grain is a gradual accumulation process. Therefore, it is very important to study the number of days for safe storage of grain at a slightly higher moisture content, as shown in Table 2.

[0043] Table 2: Safe storage days for high moisture rice (days)

[0044] Moisture content 30℃ 25℃ 20℃ 15℃ 10℃ 13.5 150 days 14 145 14.5 80 110 15 70 90 130 15.5 60 80 100 16 40 60 90 200

[0045] The safe storage time model of rice with high moisture content is an important reference data for constructing the fatty acid value change model in rice. In the subsequent model construction, it is determined that the fatty acid value of rice with moisture content below the safe moisture content does not change during storage; if the moisture content is above the safe moisture content, it is considered that the fatty acid value of the rice has an increment. The specific increment is determined by the safe storage time model of rice with high moisture content. The specific content of the fatty acid value of rice after a specific number of days of storage is predicted by gradually accumulating the fatty acid value, and the storage state of the rice is determined based on the threshold value of the fatty acid value that determines whether it is suitable for storage. An intelligent evaluation mathematical model that integrates multiple factors in rice storage is constructed.

[0046] Example 4: Mathematical display of relevant models in early warning, i.e. construction and classification of core computing layer

[0047] To predict the quality change of rice through macro temperature and humidity information, it is necessary to predict the moisture content of rice and the index reflecting the quality of rice in rice, namely the fatty acid value. Among them, we can predict the moisture content of rice in the closed environment of the storage bin through the balance moisture table. When the relevant moisture content is predicted, it is compared with the balance moisture content of the storage location to determine whether the rice can be stored safely in the local area. When the balance moisture content is not higher than the local safe moisture, we believe that the rice can be stored safely at this time, which is reflected in the fact that the fatty acid value of the rice has not changed significantly in value; on the contrary, when the balance moisture content is higher than the local safe moisture, the rice has a great risk of mold, which is reflected in the significant change in the fatty acid value of the rice. This change is approximately simulated by the safe storage model of high-moisture grain, that is, at this time, the fatty acid value of rice gradually increases from the initial fatty acid value to an unsuitable state after the safe storage period. The balance moisture content can be regarded as a function of the ambient temperature and humidity. The mathematical description of the balance moisture content of rice can be known through the corresponding mathematical simulation.

[0048] The specific functional relationship is as follows:

[0049] Model Rice_Equil_warter_cont(User) equation <![CDATA[a+b*x+c*x - 2+d*y+e*y - 2+f*x*y]]> Drawing C a 10.25267±0.18567 b -0.0971±0.00957 c 5.04259E-4±2.59403E-4 d -0.01109±0.00585 e 0.00115±4.6941E-5 f 1.9907E-4±9.74865E-5 Reduced Chi-Sqr 0.02757 R-squared (OOD) 0.99588 Adjusted R-squared 0.99572

[0050] Where x is the ambient temperature (°C), and y is the ambient humidity (RH, %). For details of the simulation relationship diagram, see Figure 2 .

[0051] The fatty acid value of rice is easily affected by changes in the storage environment. High temperature and mold growth will cause the fatty acid value of rice to increase. Therefore, the fatty acid value of rice can well reflect the storage quality of rice. It is also one of the most sensitive indicators of rice spoilage and mildew. The change of the fatty acid value of rice during the actual storage process has a good indication effect on determining whether the rice is mildewed. The present invention constructs the change of fatty acid value according to the safe storage time of rice with high moisture content, such as Figure 3 shown.

[0052] The change rules of fatty acid values ​​corresponding to the safe storage time are shown in Tables 3 and 4. The initial fatty acid value of indica rice with a moisture content of 16% and stored at 30°C is 20 (KOH / dry basis) / (mg / 100g). The change of its fatty acid value is theoretically in accordance with y=20e 0.0154xTheoretically, after 26.3 days, the fatty acid content increases to 30 (KOH / dry basis) / (mg / 100g), reaching a moderately unsuitable state; after about 40 days, the fatty acid value increases to 37 (KOH / dry basis) / (mg / 100g), reaching a severely unsuitable state.

[0053] Table 3: Changes in fatty acid values ​​of high moisture content indica rice based on safe storage time

[0054] Moisture content 30℃ 25℃ 20℃ 15℃ 10℃ 13.50％ <![CDATA[y=20e 0.0041x ]]> - - - - 14％ <![CDATA[y=20e 0.0042x ]]> - - - - 14.50％ <![CDATA[y=20e 0.0077x ]]> <![CDATA[y=20e 0.0056x ]]> - - - 15％ <![CDATA[y=20e 0.0088x ]]> <![CDATA[y=20e 0.0068x ]]> <![CDATA[y=20e 0.0047x ]]> - - 15.50％ <![CDATA[y=20e 0.0103x ]]> <![CDATA[y=20e 0.0077x ]]> <![CDATA[y=20e 0.0062x ]]> - - 16％ <![CDATA[y=20e 0.0154x ]]> <![CDATA[y=20e 0.0103x ]]> <![CDATA[y=20e 0.0068x ]]> <![CDATA[y=20e 0.0 0 31x ]]> -

[0055] Table 4: Changes in fatty acid values ​​of high moisture content japonica rice based on safe storage time

[0056] Moisture content 30℃ 25℃ 20℃ 15℃ 10℃ 13.50％ <![CDATA[y=20e 0.0037x ]]> - - - - 14％ <![CDATA[y=20e 0.0039x ]]> - - - - 14.50％ <![CDATA[y=20e 0.007x ]]> <![CDATA[y=20e 0.0051x ]]> - - - 15％ <![CDATA[y=20e 0.008x ]]> <![CDATA[y=20e 0.0062x ]]> <![CDATA[y=20e 0.0043x ]]> - - 15.50％ <![CDATA[y=20e 0.0093x ]]> <![CDATA[y=20e 0.007x ]]> <![CDATA[y=20e 0.0056x ]]> - - 16％ <![CDATA[y=20e 0.014x ]]> <![CDATA[y=20e 0.0093x ]]> <![CDATA[y=20e 0.0062x ]]> <![CDATA[y=20e 0.0028x ]]> -

[0057] At the same time, the change model of fatty acid value of indica rice (water content of 16%) during storage at 30°C is constructed as y=20e 0.0154x , where the K value is 0.0154. Similarly, the K values ​​of indica rice with a moisture content of 16% at 25, 20, and 15°C are 0.0103, 0.0068, and 0.0031, respectively. At this time, the functional relationship between the K value and temperature can be determined by simulating different temperatures and K values.

[0058] Using the simulated functional relationship, i.e., K = 0.0008*T-0.0093, the K value of the fatty acid value change function of rice with a moisture content of 16% at any temperature can be obtained. The simulated relationship diagram is shown in Figure 4 ; From the practical simulation point of view, the K value should be greater than 0, so when T is 11.625℃, the K value is zero, that is, theoretically when the storage temperature is 11.625℃, the rice can keep the fatty acid value unchanged, which is consistent with the fact that rice with 16% moisture content can be safely stored in an environment of 10℃. Based on the same idea, we know that the change of fatty acid value of rice with different moisture content has a specific K value at different temperatures. The mathematical model can be used to express that the specific K value is actually a function of moisture content and temperature. By constructing the corresponding function model, the fatty acid value change K value of rice with a specific moisture content at any temperature can be obtained.

[0059] The specific functional relationship is as follows:

[0060] Model Fatty acid changes K value equation a+b*x+c*x^2+d*y+e*y^2+f*x*y Drawing C a 0.3907±0.14525 b -0.00668±0.00232 c 8.87113E-6±1.25634E-5 d -0.04337±0.01621 e 0.00117±4.63923E-4 f 4.40264E-4±1.23074E-4 Reduced Chi-Sqr 5.77834E-7 R-squared (COD) 0.96576 Adjusted R-squared 0.94436

[0061] Where x is the ambient temperature (℃), and y is the moisture content of indica rice (%). For details of the simulated relationship diagram, see Figure 5 .

[0062] The specific functional relationship is as follows:

[0063] Model Fatty acid changes K value equation a+b*x+c*y+d*x*x+e*y*y+f*x*y Drawing C a 0.35209±0.13205 b -0.00606±0.00211 c -0.039±0.01474 d 8.08247E-6±1.14219E-5 e 0.00105±4.2177E-4 f 3.99567E-4±1.11891E-4 Reduced Chi-Sqr 4.77597E-7 R-squared (COD) 0.96546 Adjusted R-squared 0.94387

[0064] Where x is the ambient temperature (℃), and y is the moisture content of japonica rice (%). For details of the simulation relationship diagram, see Figure 6 .

[0065] After obtaining the changing pattern of fatty acid value of rice, it is very important to carry out early warning classification of the mildew status of rice. The change of fatty acid value is a very important indicator in the process of rice quality deterioration. Therefore, fatty acid value is used as the basis for classification to reflect the degree of rice spoilage and mildew. The specific threshold value of fatty acid value change is shown in Table 5.

[0066] Table 5: Rice storage quality criteria

[0067]

[0068] The risk level of rice spoilage and mildew is not only judged by the fatty acid value, but also by other indicators, such as the total amount of mold, toxin content, etc. Any indicator that reaches the corresponding set value will be defined as the corresponding risk level, that is, the determination of the risk level is a joint indicator of multiple factors.

[0069] Early warning risk classification provides corresponding risk disposal suggestions to the storage depot / company according to the risk level determined by the previous level. The risk level of rice and the corresponding disposal suggestions are shown in Table 6.

[0070] Table 6: Paddy early warning risk level setting and treatment measures

[0071]

[0072] Among them, the fitting data of fatty acid values ​​of indica rice are shown in Table 7:

[0073] Table 7: Fitting data of fatty acid values ​​of indica rice

[0074] Moisture content 30℃ 25℃ 20℃ 15℃ 10℃ 13.50％ <![CDATA[y=20e 0.0041x ]]> - - - - 14％ <![CDATA[y=20e 0.0042x ]]> - - - - 14.50％ <![CDATA[y=20e 0.0077x ]]> <![CDATA[y=20e 0.0056x ]]> - - - 15％ <![CDATA[y=20e 0.0088x ]]> <![CDATA[y=20e 0.0068x ]]> <![CDATA[y=20e 0.0047x ]]> - - 15.50％ <![CDATA[y=20e 0.0103x ]]> <![CDATA[y=20e 0.0077x ]]> <![CDATA[y=20e 0.0062 x]]> - - 16％ <![CDATA[y=20e 0.0154x ]]> <![CDATA[y=20e 0.0103x ]]> <![CDATA[y=20e 0.0068x ]]> <![CDATA[y=20e 0.0031x ]]> -

[0075] Where x is the storage time (days), and y is the fatty acid value of indica rice (mg / 100g, KOH / dry basis).

[0076] As can be seen from the table, the present invention can use the fitting equation to obtain the moisture content and fatty acid value change K value at any temperature and humidity. Specifically, the way to obtain the fitting curve is:

[0077] Changes in fatty acid value FAV = FAV 0 * e K*t ,

[0078] K=0.3907-0.00668*T+8.87113*10-6 *T 2 -0.04337*WC+0.00117*WC 2 +4.40264*10 -4 *T*WC, where FAV is fatty acid value (mg / 100g, KOH / dry basis), FAV 0 is the initial fatty acid value (mg / 100g, KOH / dry basis), K is the fatty acid value change K value, t is the storage time (days), T is the temperature (℃), WC is the moisture content (%), and * means multiplication.

[0079] Example 5: Construction of rice instant alarm module and construction of auxiliary decision-making suggestions

[0080] On the basis of the early warning classification model, the present invention constructs an instant alarm module, that is, through simple deduction and calculation of basic data and macro monitoring data, simple calculation results or part of the monitoring and detection data are directly compared with the set threshold to determine the actual storage risk. The alarm indicators are screened according to the national standard requirements for the rotten mold indicators related to rice food safety, mainly including macro temperature and humidity limits, important quality indicators, mycotoxin safety indicators, excessive intervention substances and other indicators, and the corresponding limits are specified and the operations that the storage management personnel or supervisors should perform after exceeding the limits are given, as well as the value source on the cloud platform or the applet APP. The monitoring and detection indicators and threshold settings in the rice instant alarm module are shown in Table 8.

[0081] Table 8: Monitoring and detection indicators and threshold settings in the rice instant alarm module

[0082]

[0083]

[0084] The above description is only a preferred embodiment of the present invention and does not limit the technical scope of the present invention. Therefore, any slight modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A multi-coupling evaluation system for spoilage and mildew of grain and oil food, characterized in that: It includes recognition and capture module, model core operation layer, prediction and foresight module, alarm classification module and disposal decision module.

2. A multi-coupling evaluation system for spoilage and mildew of grain and oil food according to claim 1, characterized in that: The identification and grasping module mainly includes a basic information module, a process information module and a quality information module.

3. A multi-coupling evaluation system for spoilage and mildew of grain and oil food according to claim 1, characterized in that: The identification and capture module captures and aggregates information and indicators.

4. A multi-coupling evaluation system for spoilage and mildew of grain and oil food according to claim 1, characterized in that: The core computing layer of the model is used to analyze the coupling, synergy, antagonism and inhibition effects between various categories of multivariate information, establish the correlation between captured information and corruption and mildew, and then construct a mathematical model to preliminarily determine the benchmark value and threshold value, and provide a "prediction-foresight-alarm classification".

5. A multi-coupling evaluation system for spoilage and mildew of grain and oil food according to claim 1, characterized in that: The prediction and foresight module provides a forward-looking prediction of the potential spoilage and mildew of food through calculation and analysis of the model operation layer and reference thresholds.

6. A multi-coupling evaluation system for spoilage and mildew of grain and oil food according to claim 5, characterized in that: The predictive foresight module is divided into three levels: high concern, medium concern and no concern.

7. A multi-coupling evaluation system for spoilage and mildew of grain and oil food according to claim 1, characterized in that: The alarm classification module determines whether the food is spoiled or moldy through calculation and analysis of the model operation layer and reference thresholds, and issues an alarm.

8. A multi-coupling evaluation system for spoilage and mildew of grain and oil food according to claim 7, characterized in that: The alarm classification module is divided into three levels: high risk, medium risk and low risk.

9. The multi-coupling evaluation system for spoilage and mildew of grain and oil food according to claim 1 is characterized in that: The disposal decision module provides auxiliary decision-making, intervention suggestions and disposal measures through the key focus suggestions and risk levels given by the pre-judgment and foresight module and the alarm classification module.

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