High-temperature yeast dynamic grading method and device

Abstract

The invention provides a dynamic grading method and device for high-temperature yeast for making hard liquor. The method comprises the following steps: acquiring ecological data and multi-modal indexes of yeast for making hard liquor in a fermentation area; determining an index weight, and constructing a dynamic threshold grading model; according to a meteorological fluctuation adjustment threshold, calculating a comprehensive score and dividing Daqu grades; and optimizing process parameters based on the grading result. Compared with a traditional method, the method has the advantages that the ecological data and the multi-modal indexes are fused, the dynamic threshold value model responds to meteorological changes, objective grading of the quality of the yeast for making hard liquor and dynamic adjustment of technological parameters are achieved, the grading deviation rate is reduced, and the special-grade curvature is improved; rapid grading is achieved through a simple threshold rule and weight calculation, complex algorithm support is not needed, and remarkable scientificity, flexibility and operability are achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of liquor brewing, and in particular relates to a high-temperature Daqu dynamic grading method and device. Background Art

[0002] Traditional daqu grading methods rely primarily on sensory experience or single physical and chemical indicators, such as acidity and saccharification capacity. These methods lack systematic quantitative analysis of ecological conditions (such as vegetation cover and water mineral content) and fail to consider the impact of regional seasonal meteorological changes on microbial communities, resulting in poor adaptability of grading results. Summary of the Invention

[0003] In response to the problems that traditional grading relies on manual experience, ignores ecological factors and has rigid thresholds, the embodiments of the present application provide a dynamic grading method and device for high-temperature Daqu, which achieves objective grading of Daqu quality and dynamic adjustment of process parameters through a dynamic threshold model.

[0004] In the first aspect, an embodiment of the present application provides a dynamic grading method for high-temperature Daqu, including: S1, obtaining ecological data of the fermentation area and multimodal indicators of Daqu; S2, determining indicator weights and constructing a dynamic threshold grading model; S3, adjusting the threshold according to meteorological fluctuations, and calculating a comprehensive score to divide the Daqu grade; S4, optimizing process parameters based on the grading results.

[0005] The ecological data include vegetation data, water quality data, and meteorological data; the multimodal indicators of Daqu include physical and chemical indicators, microbial indicators, and sensory evaluation; the plant data include satellite remote sensing NDVI index ranging from 0.6 to 0.85, and the dominant plant type is oak or fern with a proportion of ≥60%; the water quality data include Ca 2+ Concentration 40-70mg / L and Mg 2+ The concentration is 20-35 mg / L, and the detection is performed by inductively coupled plasma mass spectrometry. Meteorological data include the average daily temperature of 25-35°C and relative humidity of 70-90% during the fermentation period. Physical and chemical indicators include acidity of 1.0-2.2 mmol / 10g, starch content of 55-68%, and saccharifying enzyme activity ≥800 U / g. Microbial indicators include Bacillus ≥1×10 6 CFU / g, lactic acid bacteria ≥5×10 5 CFU / g, determined by plate count method; sensory evaluation includes the intensity of burnt aroma or sauce aroma meeting the standard, and the proportion of yellow yeast in the cross section ≥70%.

[0006] Among them, in step S2, a judgment matrix is ​​constructed through the hierarchical analysis method, and the weight distribution is determined to be 35% for ecological data, 28% for physical and chemical indicators, 22% for microbial indicators, and 15% for sensory evaluation. A dynamic threshold classification model is constructed based on the weights; and the judgment matrix consistency test CR value is ≤0.1.

[0007] Among them, step S3 includes building a grading rule base based on the weights of step S2, dynamically adjusting the microbial threshold in combination with real-time meteorological data, calculating the comprehensive score and classifying the Daqu grades. The dynamic threshold grading rules include:

[0008] Special grade: NDVI ≥ 0.70, water quality Ca 2+ ≥50mg / L, Bacillus ≥1×10 7 CFU / g, and the comprehensive score is ≥90 points;

[0009] First-grade song: NDVI ≥ 0.65, Ca 2+ ≥45mg / L, Bacillus ≥5×10 6 CFU / g, comprehensive score 80-89 points;

[0010] Level 2: Does not meet the above conditions or has a comprehensive score of <80 points.

[0011] Among them, step S3 also includes that the dynamic threshold classification model adjusts the threshold in real time according to meteorological fluctuations. When the humidity in the meteorological data is continuously greater than 85%, the threshold of Bacillus spp. is automatically triggered to be lowered to 4×10 6 CFU / g.

[0012] Among them, in step S4, the brewing process parameters are reversely adjusted according to the classification results. If the special curvature is lower than 10%, the water quality Ca is supplemented. 2+ To ≥55 mg / L or increase the fermentation temperature by 2-3°C; after adjusting the parameters, re-execute steps S1-S3 for graded verification.

[0013] The comprehensive score is the weighted sum of ecological data, physical and chemical indicators, microbial indicators and sensory evaluation, and the calculation formula is:

[0014] S=(0.32×E)+(0.25×P)+(0.22×M)+(0.15×S e )

[0015] E is the standardized score of ecological data; P is the standardized score of physical and chemical indicators; M is the standardized score of microbial indicators; Se is the standardized score of sensory evaluation.

[0016] In a second aspect, the present application provides a high-temperature Daqu dynamic classification device, comprising:

[0017] An acquisition unit is used to obtain ecological data of the fermentation area and multimodal indicators of Daqu;

[0018] Construction unit, used to determine indicator weights and build a dynamic threshold classification model;

[0019] An adjustment unit is used to adjust the threshold according to meteorological fluctuations and calculate the comprehensive score to classify the Daqu level;

[0020] The optimization unit is used to optimize the process parameters based on the classification results.

[0021] In a third aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of any of the above methods when executed by a processor.

[0022] In a fourth aspect, an embodiment of the present application provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any of the above methods when executing the program.

[0023] The high-temperature Daqu dynamic classification method and device of the present application embodiment have the following beneficial effects:

[0024] The dynamic grading method for high-temperature Daqu provided in this application integrates ecological data and multimodal indicators, and the dynamic threshold model responds to meteorological changes. It realizes rapid grading through simple threshold rules and weight calculations without the support of complex algorithms, realizes objective grading of Daqu quality and dynamic adjustment of process parameters, reduces the grading deviation rate, increases the special grade curvature, and significantly improves the scientific nature of grading and the efficiency of process control. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a schematic flow chart of the dynamic classification method of high-temperature Daqu according to an embodiment of the present application;

[0026] Figure 2 This is a schematic structural diagram of the high-temperature Daqu dynamic classification device of this application. DETAILED DESCRIPTION

[0027] The present application will be further described below with reference to the accompanying drawings and embodiments.

[0028] In the following description, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance. The following description provides multiple embodiments of the present invention, and different embodiments can be replaced or combined, so this application can also be considered to include all possible combinations of the same and / or different embodiments described. Therefore, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then this application should also be considered to include embodiments containing one or more of all other possible combinations of features A, B, C, and D, even though such embodiments may not be explicitly described in the following text.

[0029] Example 1

[0030] like Figure 1 As shown, an embodiment of the present application provides a dynamic grading method for high-temperature Daqu, including: S1, obtaining ecological data of the fermentation area and multimodal indicators of Daqu; S2, determining indicator weights, and constructing a dynamic threshold grading model; S3, adjusting the threshold according to meteorological fluctuations, and calculating the comprehensive score to divide the Daqu grade; S4, optimizing the process parameters based on the grading results.

[0031] This application integrates ecological data and multimodal indicators, and the dynamic threshold model responds to meteorological changes. It achieves rapid grading through simple threshold rules and weight calculations without the need for complex algorithm support, and realizes objective grading of Daqu quality and dynamic adjustment of process parameters, reducing the grading deviation rate and improving the special grade curvature.

[0032] Example 2

[0033] This application provides a dynamic grading method for high-temperature Daqu, which integrates ecological data and multimodal indicators, and realizes objective grading of Daqu quality and dynamic adjustment of process parameters through a dynamic threshold model.

[0034] The specific implementation steps are as follows:

[0035] Step 1: Multi-source data collection

[0036] Ecological data:

[0037] Vegetation: Satellite remote sensing NDVI index (0.6-0.85), dominant plant types (oaks and ferns account for ≥60%);

[0038] Water quality: Inductively coupled plasma mass spectrometry (ICP-MS) detection of Ca 2+ (40-70mg / L), Mg 2+ (20-35mg / L);

[0039] Weather: Average daily temperature (25-35°C) and relative humidity (70-90%) during the fermentation period.

[0040] Daqu indicators:

[0041] Physical and chemical: acidity (1.0-2.2mmol / 10g), starch content (55-68%), saccharifying enzyme activity (≥800U / g);

[0042] Microorganisms: plate count method for determination of Bacillus (≥1×10 6 CFU / g), lactic acid bacteria (≥5×10 5 CFU / g);

[0043] Sensory: Experts evaluate aroma (intensity of burnt aroma and sauce aroma) and cross-section color (the proportion of yellow koji ≥ 70%).

[0044] Step 2: Assign indicator weights

[0045] Analytic Hierarchy Process (AHP):

[0046] Construct a judgment matrix and determine the weights through expert scoring:

[0047] index Ecological data Physical and chemical indicators microorganism Sensory evaluation Ecological data 1 1.5 2 3 Physical and chemical indicators 0.67 1 1.5 2 microorganism 0.5 0.67 1 1.5 Sensory evaluation 0.33 0.5 0.67 1

[0048] Consistency test (CR=0.028<0.1), final weights: ecological data (35%), physical and chemical (28%), microbiology (22%), sensory (15%).

[0049] Step 3: Dynamic threshold classification model

[0050] Hierarchical rule base:

[0051] Special Song:

[0052] Necessary conditions: NDVI ≥ 0.70, water quality Ca 2+ ≥50mg / L, Bacillus ≥1×10 7 CFU / g;

[0053] The comprehensive score is ≥90 points (calculated by weight).

[0054] First-level song:

[0055] Necessary conditions: NDVI ≥ 0.65, Ca 2+ ≥45mg / L, Bacillus ≥5×10 6 CFU / g;

[0056] The overall score is 80-89 points.

[0057] Level 2: below the threshold or overall score <80 points.

[0058] Dynamic Adjustment:

[0059] According to meteorological data (such as humidity > 85% in rainy season), the microbial threshold (Bacillus spores was lowered to 4 × 10 6 CFU / g).

[0060] Step 4: Process Optimization Feedback

[0061] If the curvature of a batch of special grade is less than 10%, it is necessary to adjust the water quality (supplement Ca 2+ to ≥55mg / L) or fermentation temperature (increase by 2-3℃).

[0062] This application is the first to include NDVI and water quality Ca 2+ Ecological indicators such as environmental indicators are included in the grading system, with a weight of 35%. The dynamic threshold model responds to meteorological changes, and the grading adaptability is improved by 30%. Rapid grading is achieved through simple threshold rules and weight calculations without the support of complex algorithms, which realizes the objective grading of Daqu quality and dynamic adjustment of process parameters, reduces the grading deviation rate, increases the special curvature, and significantly improves the scientific nature of grading and the efficiency of process control.

[0063] In some embodiments, the hierarchical rules verify:

[0064] Data: 2024 rainy season sample (n=100) of Panshui Industrial Park, Songbai Town, Shennongjia, NDVI 0.68-0.75, Ca 2+ 48-65mg / L;

[0065] result:

[0066] Special grade: 12% (all meet NDVI≥0.70, Ca 2+ ≥50mg / L, Bacillus ≥1×10 7 CFU / g);

[0067] First-class music: 68% (score 82-88 points), second-class music: 20%;

[0068] Comparison: The subjective deviation rate of traditional sensory evaluation grading results is 25%, while the deviation rate of this application is reduced to 8%.

[0069] In some embodiments, dynamic threshold adjustment:

[0070] Scenario: Humidity remains >85% during the rainy season, and the threshold for triggering Bacillus spores is lowered to 4×10 6 CFU / g;

[0071] Effect: 5% of the original first-grade songs are upgraded to special-grade songs, and the special-grade curvature rate is increased from 12% to 17%.

[0072] In some embodiments, process optimization applies:

[0073] Problem: The curvature of a batch of premium grade is only 8%, mainly due to Ca2+ Insufficient content (mean 48 mg / L);

[0074] Measures: Add food grade calcium carbonate to reduce the water quality Ca 2+ Increased to 52mg / L;

[0075] Result: The curvature of the next batch of special grade was increased to 15%, and the acidity was stabilized at 1.5±0.2mmol / 10g.

[0076] The dynamic grading method for high-temperature Daqu provided in this application realizes the objective grading of Daqu quality and dynamic adjustment of process parameters by integrating ecological data and multimodal indicators, and has significant scientificity, flexibility and operability.

[0077] like Figure 2 As shown, the present application also provides a high-temperature Daqu dynamic grading device, including: an acquisition unit 201, used to obtain ecological data of the fermentation area and Daqu multimodal indicators; a construction unit 202, used to determine the indicator weights and construct a dynamic threshold grading model; an adjustment unit 203, used to adjust the threshold according to meteorological fluctuations, calculate the comprehensive score to divide the Daqu grade; and an optimization unit 204, used to optimize the process parameters based on the grading results.

[0078] In this application, the embodiment of the high-temperature Daqu dynamic classification device is basically similar to the embodiment of the high-temperature Daqu dynamic classification method. For relevant details, please refer to the introduction of the embodiment of the high-temperature Daqu dynamic classification method.

[0079] The present application also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the above-mentioned high-temperature Daqu dynamic grading method steps are implemented.

[0080] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described high-temperature Daqu dynamic grading method. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a microdrive, a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0081] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A high-temperature Daqu dynamic classification method, characterized in that: include: S1, obtain ecological data of the fermentation area and Daqu multimodal indicators; S2, determine the indicator weights and build a dynamic threshold classification model; S3, adjust the threshold according to meteorological fluctuations, calculate the comprehensive score and classify the Daqu; S4, optimize process parameters based on the classification results.

2. The high-temperature Daqu dynamic classification method according to claim 1, characterized in that: In step S1, the ecological data includes vegetation data, water quality data, and meteorological data; the Daqu multimodal indicators include physical and chemical indicators, microbial indicators, and sensory evaluation; wherein, the plant data includes the satellite remote sensing NDVI index range of 0.6-0.85, the dominant plant type is oak or fern and the proportion is ≥60%; the water quality data includes Ca 2+ Concentration 40-70mg / L and Mg 2+ The concentration is 20-35 mg / L, and the detection is performed by inductively coupled plasma mass spectrometry. Meteorological data include the average daily temperature of 25-35°C and relative humidity of 70-90% during the fermentation period. Physical and chemical indicators include acidity of 1.0-2.2 mmol / 10g, starch content of 55-68%, and saccharifying enzyme activity ≥800 U / g. Microbial indicators include Bacillus ≥1×10 6 CFU / g, lactic acid bacteria ≥5×10 5 CFU / g, determined by plate count method; sensory evaluation includes the intensity of burnt aroma or sauce aroma meeting the standard, and the proportion of yellow yeast in the cross section ≥70%.

3. The high-temperature Daqu dynamic classification method according to claim 1 or 2, characterized in that: In step S2, a judgment matrix was constructed using the hierarchical analysis method, and the weight distribution was determined to be 35% for ecological data, 28% for physical and chemical indicators, 22% for microbial indicators, and 15% for sensory evaluation. A dynamic threshold classification model was constructed based on the weights; and the judgment matrix consistency test CR value was ≤0.

1.

4. The high-temperature Daqu dynamic classification method according to claim 1 or 2, characterized in that: Step S3 includes building a grading rule base based on the weights of step S2, dynamically adjusting the microbial threshold in combination with real-time meteorological data, calculating a comprehensive score and classifying the Daqu grades. The dynamic threshold grading rules include: Special grade: NDVI ≥ 0.70, water quality Ca 2+ ≥50mg / L, Bacillus ≥1×10 7 CFU / g, and the comprehensive score is ≥90 points; First-grade song: NDVI ≥ 0.65, Ca 2+ ≥45mg / L, Bacillus ≥5×10 6 CFU / g, comprehensive score 80-89 points; Level 2: Does not meet the above conditions or has a comprehensive score of <80 points.

5. The high-temperature Daqu dynamic classification method according to claim 1 or 2, characterized in that: In step S3, the dynamic threshold classification model adjusts the threshold in real time according to meteorological fluctuations. When the humidity in the meteorological data is continuously greater than 85%, the threshold of Bacillus spp. is automatically triggered to be lowered to 4×10 6 CFU / g.

6. The high-temperature Daqu dynamic classification method according to claim 1 or 2, characterized in that: In step S4, the brewing process parameters are reversely adjusted according to the classification results. If the special curvature is less than 10%, the water quality Ca is supplemented. 2+ To ≥55 mg / L or increase the fermentation temperature by 2-3°C; after adjusting the parameters, re-execute steps S1-S3 for graded verification.

7. The high-temperature Daqu dynamic classification method according to claim 1 or 2, characterized in that: The comprehensive score is the weighted sum of ecological data, physical and chemical indicators, microbiological indicators and sensory evaluation, and the calculation formula is: S=(0.35×E)+(0.28×P)+(0.22×M)+(0.15×S e ) E is the standardized score of ecological data; P is the standardized score of physical and chemical indicators; M is the standardized score of microbial indicators; Se is the standardized score of sensory evaluation.

8. A high-temperature Daqu dynamic classification device, characterized in that: include: An acquisition unit is used to obtain ecological data of the fermentation area and multimodal indicators of Daqu; Construction unit, used to determine indicator weights and build a dynamic threshold classification model; An adjustment unit is used to adjust the threshold according to meteorological fluctuations and calculate the comprehensive score to classify the Daqu level; The optimization unit is used to optimize the process parameters based on the classification results.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method according to any one of claims 1 to 7 are implemented.

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