Workshop management early warning method and system based on multi-source environmental bacteria information

Abstract

The invention discloses a workshop management early warning method and system based on multi-source environmental bacteria information, and relates to the technical field of workshop management. The method comprises the following steps: confirming m sampling areas in a current workshop, and obtaining real-time bacteria information of the current workshop from the sampling areas; judging whether the current workshop meets quality control conditions or not based on the real-time bacterial genus information; the quality control conditions comprise that each sampling area contains n types of characteristic bacteria, and the relative abundance value of the characteristic bacteria meets a threshold condition; the relative abundance value of each characteristic bacterial genus is in positive correlation or negative correlation with the ethanol content; and when the current workshop does not meet the quality control condition, sending an early warning instruction. According to the method, the quality control conditions are set, the bacteria information of multiple areas in the workshop is detected, the bacteria information of the workshop is compared with the quality control conditions, and whether the workshop environment meets the requirements of the quality control conditions or not is judged; if not, an alarm is given out in time, and a manager is reminded to pay attention to the workshop microorganism state in time after the alarm is given out.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of workshop management, in particular to a workshop management early warning method and system based on multi-source environmental genus information. BACKGROUND

[0002] The stacking fermentation is a key link in the brewing process of Maotai-flavor liquor and plays a decisive role in the formation of flavor substances. The stacking fermentation process promotes the generation of key flavor compounds such as pyrazines and esters through the dynamic succession of microbial communities in the fermented grains under high temperature environment. The process stability is directly related to the quality of the fermented grains entering the cellar, and further affects the fermentation efficiency in the cellar and the base liquor quality.

[0003] However, the current monitoring of the stacking fermentation process is mainly limited to the temperature, moisture, acidity and other physicochemical indicators of the fermented grains, and lacks systematic attention to the microbial ecology of the workshop environment. The existing technology relies on empirical apparent parameters such as "the pile temperature reaching above 55℃" for process judgment, ignoring the continuous inoculation of the fermented grains by the environment-derived microorganisms during the fermentation process.

[0004] Especially during the production process of new workshops, due to the lack of stable microbial ecological system accumulated for a long time in old workshops, the environmental genus structure fluctuates greatly, and the microbial community of the fermented grains is difficult to quickly establish balance, causing problems such as unstable flavor of base liquor, low rate of superior grade, etc. The existing technology has not established an environmental management standard and quantitative evaluation system based on the characteristics of microbial communities, and the identification of abnormal fermentation still highly depends on the subjective experience of the brewer, the early warning is lagging and the consistency is poor, which cannot meet the urgent needs of "quality stability and efficiency improvement" in large-scale production. SUMMARY

[0005] In order to solve at least one problem mentioned in the background art, the present application provides a workshop management early warning method and system based on multi-source environmental genus information, by setting quality control conditions and detecting the genus information of multiple regions in the workshop, comparing the genus information of the workshop with the quality control conditions, and determining whether the workshop environment meets the quality control condition requirements; when it does not meet, timely alarm is sent, and after the alarm is sent, the management personnel can be reminded to pay attention to the microbial state of the workshop in time, providing a scientific basis for production decision-making, which helps to intervene in potential fermentation risks in advance, avoids the quality fluctuation of fermented grains caused by microbial imbalance, and ensures the stability of the production process and the consistency of the product quality.

[0006] The specific technical solutions provided by the embodiments of the present application are as follows: In a first aspect, a workshop management early warning method based on multi-source environmental genus information is provided, and the method comprises: Confirming m sampling regions in a current workshop, and obtaining real-time genus information of the current workshop from the sampling regions; wherein the real-time genus information comprises genus species in the current workshop and relative abundance values of the genus; Based on the genus species in the current workshop and the relative abundance values of the genus, judging whether the current workshop meets a quality control condition; the quality control condition comprises that each of the sampling regions contains n characteristic genus species, and the relative abundance values of the characteristic genus species meet threshold conditions; wherein the relative abundance value of each of the characteristic genus species is positively or negatively correlated with the ethanol content, and n is the number of species of the characteristic genus; When the current workshop does not meet the quality control condition, issuing a warning instruction.

[0007] In a specific embodiment, the relative abundance values of the characteristic genus meet the threshold conditions, which comprises: The total relative abundance value of each of the characteristic genus is greater than or equal to a first threshold value, and the relative abundance value of each of the characteristic genus in at least one of the sampling regions is greater than or equal to a second threshold value; Wherein the total relative abundance value is the sum of the relative abundance values of each of the characteristic genus in all the sampling regions, and the first threshold value is greater than the second threshold value.

[0008] In a specific embodiment, before the m sampling regions in the current workshop are confirmed, the method further comprises constructing the quality control condition; the construction of the quality control condition comprises: Setting m sampling regions in a standard workshop, sampling the sampling regions and obtaining quality control parameters, the quality control parameters comprising genus contained in the standard workshop and relative abundance values of the genus; Based on the genus contained in the standard workshop and the relative abundance values of the genus, screening a first dominant genus from the genus contained in the standard workshop; Obtaining the ethanol content in pit entry wine lees in the standard workshop, performing Spearman correlation analysis based on the ethanol content and the relative abundance value of the first dominant genus to obtain an analysis result, and screening the characteristic genus based on the analysis result; Based on the characteristic genus and the relative abundance values of the characteristic genus, the quality control condition is constructed.

[0009] In a specific embodiment, the screening of the characteristic genus based on the analysis result comprises: Performing Spearman correlation analysis based on the ethanol content and the relative abundance value of the first dominant genus to obtain a first analysis result, and screening a second dominant genus from the first dominant genus based on the first analysis result; performing Spearman correlation analysis on the second dominant genus contained in each of the sampling areas and the ethanol content to obtain a second analysis result, and screening the characteristic genus from the second dominant genus based on the second analysis result.

[0010] In a specific embodiment, the screening of the second dominant genus from the first dominant genus based on the first analysis result comprises: selecting a genus having a positive or negative correlation with the ethanol content from the first dominant genus as the second dominant genus.

[0011] In a specific embodiment, the screening of the characteristic genus from the second dominant genus based on the second analysis result comprises: selecting a genus having a positive or negative correlation with the ethanol content from the second dominant genus as the characteristic genus.

[0012] In a specific embodiment, the characteristic genus comprises Lactobacillus, Thermomyces, Thermoascus, Paecilomyces, and Nagelomyces.

[0013] In a specific embodiment, the sampling areas comprise an open-air piled ground, an operating tool, a surface of an object in a workshop, and air in the workshop.

[0014] In a specific embodiment, after the issuance of the early warning instruction, the method further comprises: controlling the genus in the current workshop to make the current workshop meet the quality control condition. The genus control comprises spraying the current workshop with Daqu before use of the current workshop.

[0015] In a second aspect, a workshop management early warning system based on multi-source environmental genus information is provided, which is used to implement the above-mentioned workshop management early warning method based on multi-source environmental genus information. The system comprises: A first module configured to confirm m sampling areas in a current workshop, and obtain real-time genus information of the current workshop from the sampling areas; wherein the real-time genus information comprises genus species in the current workshop and relative abundance values of the genus; A second module configured to judge whether the current workshop meets a quality control condition based on the genus species in the current workshop and the relative abundance values of the genus; the quality control condition comprises that each of the sampling areas contains n characteristic genera, and the relative abundance values of the characteristic genera meet threshold conditions; wherein each of the characteristic genera has a positive or negative correlation with the ethanol content, and n is the number of species of the characteristic genera. The third module is configured to issue a pre-warning instruction when the current workshop does not meet the quality control condition.

[0016] Advantages: The application sets a quality control condition, detects the bacterial genus information in multiple regions in the workshop, compares the bacterial genus information in the workshop with the quality control condition, judges whether the workshop environment meets the quality control condition requirement, and timely issues an alarm when the quality control condition is not met. After the alarm is issued, the management personnel can be reminded to timely pay attention to the microbial state in the workshop, scientific basis is provided for production decision, potential fermentation risk is intervened in advance, quality fluctuation of fermented grains caused by microbial imbalance is avoided, and the stability of the production process and the consistency of the product quality are ensured.

[0017] The application breaks through the limitation of traditional surface monitoring relying on temperature and physicochemical indexes, for the first time, takes the microbial community in multiple regions in the workshop as a key monitoring index into the fermented grains fermentation process monitoring system, makes up for the neglect of the core driving action of the microbial community in the prior art, and realizes systematic analysis of the microorganisms in multiple source environments. The established quantitative quality control condition clearly shows the influence of the microbial community in the workshop environment on the ethanol content of the pit-entered fermented grains, provides a means for monitoring the microbial ecological differences of workshops in different production years, and establishes a quantitative basis for judging fermentation abnormalities. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0019] Figure 1 is a method flowchart in the application; Figure 2 is a characteristic bacterial community relative abundance value distribution diagram in an old workshop (production time > 20 years); Figure 3 is a characteristic bacterial community relative abundance value distribution diagram in a new workshop (production time < 3 years). DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the application more clear, the technical solutions in the embodiments of the application will be clearly and completely described in the following with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only some of the embodiments of the application, not all. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description herein is for describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0022] Embodiment 1 The embodiment provides a workshop management early warning method based on multi-source environmental bacteria information (hereinafter referred to as the method), as shown in the following formula: Figure 1 The method comprises the following steps: Confirming m sampling areas in the current workshop, and obtaining real-time bacteria information of the current workshop from the sampling areas; wherein the real-time bacteria information comprises bacteria species in the current workshop and relative abundance values of the bacteria species; Based on the bacteria species in the current workshop and the relative abundance values of the bacteria species, it is judged whether the current workshop meets the quality control condition; the quality control condition comprises that each sampling area contains n characteristic bacteria species, and the relative abundance values of the characteristic bacteria species meet the threshold condition; wherein the relative abundance value of each characteristic bacteria species is positively or negatively correlated with the ethanol content, and n is the number of characteristic bacteria species; When the current workshop does not meet the quality control condition, an early warning instruction is sent out.

[0023] In the above technical solution, by collecting microbial samples in different sampling areas in the current workshop, real-time bacteria information is formed, which includes bacteria species contained in the current workshop and relative abundance values of each bacteria species; the real-time bacteria information is compared with the quality control condition to judge whether the current workshop meets the quality control condition, if the current workshop meets the quality control condition, no early warning is sent out, if the current workshop does not meet the quality control condition, early warning is sent out. The sampling area includes: the ground of the airing hall, the operating appliance, the surface of the object in the workshop and the air in the workshop.

[0024] After early warning, the staff is reminded to take corresponding measures to regulate the bacteria in the current workshop, so that the current workshop meets the quality control condition. The corresponding measures are, for example, in production, the current workshop can be sprayed with Daqu and other materials before production to simulate the fermentation environment and enrich microorganisms, and attention should be paid to the hygiene management of the workshop to prevent the growth of miscellaneous bacteria.

[0025] By regulating the bacteria in the current workshop through early warning, the bacteria structure in the fermented grains tends to be reasonable when the fermented grains are stacked in the workshop in the later period, avoiding the adverse effects of abnormal bacteria on the ethanol content of the fermented grains, thereby stabilizing the quality of the fermented grains. Moreover, the reasonable bacteria environment in the workshop helps the fermentation process to proceed normally, reduces the production of undesirable flavor substances caused by miscellaneous bacteria, and improves the quality of the final product wine.

[0026] Further, the relative abundance value of the characteristic genus satisfies a threshold condition, including: the total relative abundance value of each characteristic genus is greater than or equal to a first threshold value, and the relative abundance value of each characteristic genus in at least one sampling area is greater than or equal to a second threshold value; wherein the total relative abundance value is the sum of the relative abundance values of each characteristic genus in all sampling areas, and the first threshold value is greater than the second threshold value.

[0027] It is worth noting that specific numerical values are set in this embodiment to facilitate understanding of the quality control condition. In this embodiment, the quality control condition is that each sampling area in the workshop contains 5 characteristic genera, the total relative abundance value of each characteristic genus in all sampling areas is ≥15%, and the relative abundance value of each characteristic genus in at least one sampling area is ≥1%. In other words, if the current workshop contains 5 characteristic genera in each sampling area (drying hall accumulation ground, operating tools, object surface in the workshop, and air in the workshop), and the total relative abundance value of each characteristic genus in all sampling areas is ≥15%, and the relative abundance value of each characteristic genus in at least one sampling area is ≥1%, it means that the current workshop meets the quality control condition; otherwise, the current workshop does not meet the quality control condition and a warning needs to be issued.

[0028] Further, the characteristic genera include Lactobacillus, Thermomyces, Thermoascus, Paecilomyces, and Starmerella.

[0029] For ease of understanding, the following detailed description is provided with specific operation examples: The above microbial samples in different sampling areas of the current workshop (new workshop with production time <3 years) are collected, and real-time genus information is formed, which is achieved by the following method: 1. Microbial collection in different sampling areas Prepare 100 mL of sterile physiological saline (with sterile cotton balls) and sterile forceps, and collect microbial samples from the drying hall accumulation ground, operating tools, object surface in the workshop, and air in the workshop after the end of the round of fermented grains fermentation.

[0030] (1) Drying hall accumulation ground: set 3 sampling points on the drying hall area in the workshop on average, wipe the ground with sterile cotton balls, and the collection area is 10×10 cm².

[0031] (2) Operating tools: wipe the surface of brooms, shovels, rakes, and other operating tools with sterile cotton balls, each with a sterile cotton ball wiping area ≥10 cm².

[0032] (3) Surface of objects in the workshop: sterile cotton balls were used to wipe the surface of the workshop window glass, windowsill, wall surface around the drying hall, corners, ladder and beam column surface, distribution box surface, fire box surface, wine tank and other corners that are not easy to clean, and all the sterile cotton balls after wiping were combined into one sample.

[0033] (4) Air in the workshop (using air sedimentation method): after placing sterile air flat plates in the workshop for 2 days, the flat plates were collected by wiping with sterile wet cotton balls, and the wiping liquid was collected to obtain the sample.

[0034] 2. Microbial genus identification and calculation of genus relative abundance value By extracting microbial DNA from the ground of the drying hall, operating tools, surface of objects in the workshop and air in the workshop and performing PCR amplification, high-throughput sequencing was performed based on the Illlumina MiSeq platform, and total genomic DNA in the sample was extracted using a soil DNA kit. The total genomic DNA sample obtained was stored at -20℃.

[0035] The 16S rRNA gene V3-V4 region fragment of bacteria was selected as the amplification primer, and 341F (5'-CCTACGGGNGGCWGCAG-3') and 805R (5'-GACTACHVGGGTATCTAATCC-3') were selected as the amplification primers. The ITS region of fungi was selected as the amplification primer, and ITS3 (GCATCGATGAAGAACGCAGC) and ITS4 (TCCTCCGCTTATTGATATGC) were selected as the amplification primers.

[0036] The PCR amplification conditions were as follows: 95℃ pre-denaturation for 3 min; 94℃ denaturation for 20 s; 55℃ annealing for 20 s; 72℃ extension for 30 s; 25 cycles; 72℃ final extension for 5 min, and then reduced to 4℃.

[0037] The PCR reaction system was as follows: 2×Hieff Robust PCR Master Mix 15 μL, 1 μL of forward and reverse primers, 20-30 ng of DNA template, and ddH2O was used to supplement the system to 30 μL.

[0038] The Illumina Miseq sequencing platform was used to analyze the 16S rRNA gene V3-V4 region of bacteria and the ITS3-ITS4 region of fungi, respectively. The data obtained after machine was processed and database was compared and analyzed. The bacteria used Silva database and the fungi used UNITE database. The types and relative abundance values of bacteria and fungi in the ground of the drying room, the operating tools, the surface of objects in the workshop and the air in the workshop were obtained, and real-time bacterial information was formed. The specific data of the real-time bacterial information of the current workshop (production life of 3 years) is shown in Table 1 and Figure 3 . The calculation formula of the relative abundance value is as follows: .

[0039] Table 1 Types and relative abundance values of characteristic bacterial genera in the current workshop (production life of 3 years)

[0040] As can be seen from Table 1, in the current workshop with a production life of 3 years, 5 characteristic bacterial genera were detected: Lactobacillus, Thermomyces, Thermoascus, Paecilomyces and Naginikhia.

[0041] The total relative abundance value of Lactobacillus in the ground of the drying room, the operating tools, the surface of objects in the workshop and the air in the workshop was 14.86. The total relative abundance value of Lactobacillus was obtained by adding the relative abundance values of Lactobacillus in the ground of the drying room, the operating tools, the surface of objects in the workshop and the air in the workshop, i.e. the total relative abundance value 14.86 = 1.68 + 1.24 + 8.70 + 3.24. The calculation of the total relative abundance value of other bacterial genera is the same as that of Lactobacillus, which will not be repeated here.

[0042] The total relative abundance value of Thermomyces in the ground of the drying room, the operating tools, the surface of objects in the workshop and the air in the workshop was 111.60. The total relative abundance value of Thermoascus in the ground of the drying room, the operating tools, the surface of objects in the workshop and the air in the workshop was 11.10. The total relative abundance value of Paecilomyces in the ground of the drying room, the operating tools, the surface of objects in the workshop and the air in the workshop was 13.56. The total relative abundance value of Naginikhia in the ground of the drying room, the operating tools, the surface of objects in the workshop and the air in the workshop was 0.74.

[0043] The total relative abundance value of the characteristic bacterial genera in the ground of the drying room, the operating tools, the surface of objects in the workshop and the air in the workshop in the current workshop was less than 15% except for Thermomyces.

[0044] The relative abundance values of Lactobacillus, Thermoascus, Thermoascus, and Paecilomyces single bacteria are greater than 1% in the ground of the drying room, the operating tools, the surface of the objects in the workshop, and the air in the workshop; the relative abundance values of the Nagelni yeast in the ground of the drying room, the operating tools, the surface of the objects in the workshop, and the air in the workshop are less than 1%.

[0045] According to the above comparison results, it can be known that the current workshop does not meet the quality control conditions, and a warning instruction needs to be issued. After the warning alarm is issued, the management personnel can be reminded to pay attention to the workshop microbial state in time, and a scientific basis can be provided for production decision-making, which is helpful to intervene in potential fermentation risks in advance, avoid the quality fluctuation of fermented grains caused by microbial imbalance, and ensure the stability of the production process and the consistency of product quality.

[0046] Embodiment 2 The embodiment provides a workshop management warning method based on multi-source environmental genus information, and further, before confirming m sampling areas in the current workshop, the method further comprises: constructing a quality control condition; the construction of the quality control condition comprises: setting m sampling areas in a standard workshop, sampling the sampling areas and obtaining quality control parameters, the quality control parameters comprising the genera contained in the standard workshop and the relative abundance values of the genera; based on the genera contained in the standard workshop and the relative abundance values of the genera, a first dominant genus is selected from the genera contained in the standard workshop; obtaining the ethanol content in the pit entry fermented grains of the standard workshop, performing Spearman correlation analysis based on the ethanol content and the relative abundance value of the first dominant genus to obtain an analysis result, and selecting a characteristic genus based on the analysis result; constructing the quality control condition based on the characteristic genus and the relative abundance value of the characteristic genus.

[0047] Further, the selection of the characteristic genus based on the analysis result comprises: performing Spearman correlation analysis based on the ethanol content and the relative abundance value of the first dominant genus and obtaining a first analysis result, selecting a second dominant genus from the first dominant genus based on the first analysis result; performing Spearman correlation analysis on the second dominant genus contained in each sampling area and the ethanol content respectively to obtain a second analysis result, and selecting a characteristic genus from the second dominant genus based on the second analysis result.

[0048] Further, the selection of the second dominant genus from the first dominant genus based on the first analysis result comprises: selecting a genus that is positively or negatively correlated with the ethanol content from the first dominant genus as the second dominant genus.

[0049] Furthermore, based on the results of the second analysis, characteristic genera were screened from the second dominant genera, including: Genera that show a positive or negative correlation with ethanol content are selected from the second dominant bacterial genera as characteristic genera.

[0050] In the above technical solution, an old workshop with a production history of more than 20 years is used as the standard workshop. m sampling areas are set up in the standard workshop, and the sampling areas in the standard workshop correspond to the sampling areas in the current workshop. That is, samples are also taken from the drying rack floor, operating equipment, surfaces of objects inside the workshop, and the air inside the workshop in the standard workshop. A total of 64 microbial samples were collected from 16 samplings across 7 production cycles in the standard workshop from 2024 to 2025 (16 samples from the drying rack floor, 16 from operating equipment, 16 from surfaces of objects inside the workshop, and 16 from the air inside the workshop) to obtain quality control parameters. The sampling areas in the standard workshop are the aforementioned drying rack floor, operating equipment, surfaces of objects inside the workshop, and air inside the workshop.

[0051] The methods for collecting microorganisms in the sampling area of ​​the standard workshop, as well as the methods for identifying microbial genera and calculating their relative abundance values, are the same as those used in the current workshop in Example 1, and will not be repeated here. After collecting, identifying, and calculating the relative abundance values ​​of microorganisms, the types of microorganisms contained in the standard workshop and their relative abundance values ​​are obtained, forming quality control parameters.

[0052] Bacteria and fungi were selected from the bacterial genera and their relative abundance values ​​in the standard workshop. From these two categories, bacterial genera were further selected as the dominant genera. The selection criteria were that the total relative abundance of the genera in the four sampling areas (drying area floor, operating equipment, surface of objects in the workshop, and air in the workshop) ≥15%, and the relative abundance of the genera in at least one sampling area ≥1%. The dominant genera included the bacterial genera *Lactobacillus*, *Mycobacterium*, *Leuconostoc*, *Cropstigma*, and *Staphylococcus*, and the fungal genera *Pichia pastoris*, *Thermophilic Fungi*, *Thermophilic Ascomycetes*, *Nagnige*, and *Penicillium*, totaling 10 microorganisms. The relative abundance data of the dominant genera obtained from one sampling of the four sampling areas are shown in Table 2. Table 2. Species and relative abundance of the dominant bacterial genus in the standard workshop.

[0053] The correlation between the selected dominant bacterial genera and the ethanol content of the mash entering the fermentation pit in the corresponding batches was analyzed. Specifically, the relative abundance data of all the dominant bacterial genera obtained from 16 samplings, along with the ethanol content data of the mash entering the fermentation pit in the standard workshop, were imported into SPSS software. The "Spearman correlation analysis" method was selected in the software, and the first analysis results were obtained, as shown in Table 3.

[0054] The method for determining the ethanol content is as follows: when the mash is put into the cellar in the standard workshop, 10g of mash is weighed, 90mL of water is added, and the mixture is shaken on a shaker at 20℃ and 180 r / min for 20min. The supernatant is then filtered through an aqueous microporous membrane (0.22 μm) and measured using a biosensor analyzer (M-100 type) to obtain the ethanol content.

[0055] Table 3. Correlation between bacterial genera and ethanol

[0056] Note: ** indicates extremely significant correlation at the 0.01 significance level (two-tailed test); * indicates significant correlation at the 0.05 significance level (two-tailed test). The same applies below.

[0057] Table 3 shows that, except for *Leuconostoc*, all the dominant bacterial genera are associated with ethanol content. Specifically, *Lactobacillus*, *Mycobacterium*, *Croppensteinella*, *Staphylococcus*, *Pichia pastoris*, *Thermophilic Fungi*, *Thermophilic Ascomycetes*, and *Paecilomyces* show a significant positive correlation with ethanol content. *Nagnige* shows a significant negative correlation with ethanol content.

[0058] Based on the results in Table 3, second dominant genera were selected from the first dominant genera. The selection criteria were genera that showed a correlation with ethanol content (e.g., positive or negative correlation). The second dominant genera were *Lactobacillus*, *Mycobacterium*, *Croppensteinella*, *Staphylococcus*, *Pichia pastoris*, *Thermophilic Fungi*, *Thermophilic Ascomycetes*, *Penicillium*, and *Nagnige*.

[0059] To further screen for characteristic bacterial genera, the relative abundance of the second dominant bacterial genera (Lactobacillus, Mycobacterium, Croppensteinella, Staphylococcus, Pichia pastoris, Thermophilic Fungi, Thermophilic Ascomycetes, Paecilomyces, and Nagnach's yeast) in a single sampling area was used as the independent variable, and the ethanol content (g / kg) of the standard workshop mash entering the fermentation pit was used as the dependent variable. Spearman correlation analysis was used to analyze the relationship between the second dominant bacterial genera and the ethanol content. The results are shown in Tables 4, 5, 6, and 7.

[0060] Table 4. Correlation analysis of the core dominant microorganisms on the drying floor and the ethanol content of the mash when it is placed in the fermentation pit.

[0061] As shown in Table 4, the dominant bacteria of the Lactobacillus genus on the ground of the drying hall are significantly positively correlated with the ethanol content of the mash (r=0.638**), indicating that Lactobacillus acidogeneticus can promote the metabolism of yeast and other bacteria to a certain extent through metabolic regulation (such as creating a suitable environment for acid production), thereby indirectly increasing ethanol production.

[0062] The core advantages of the fungi *Thermophilus* showed a highly significant negative correlation with ethanol content (r=-0.645**), indicating that this fungus inhibits ethanol production, and its metabolic activity may have an antagonistic effect on key brewing processes; *Penicillium* showed a significant negative correlation with ethanol content (r=-0.494*), suggesting that this fungus may be detrimental to ethanol production, and its dynamic changes in the fermentation system need to be monitored; *Nagnishi* showed a significant positive correlation with ethanol content (r=0.598*), indicating that this fungus may promote ethanol synthesis by participating in metabolic flux regulation.

[0063] The correlation between other microorganisms, including *Croppensteinella*, *Mycobacterium*, *Staphylococcus*, *Pichia pastoris*, and thermophilic fungi, and ethanol content was not statistically significant.

[0064] Table 5. Correlation analysis of core microorganisms in the operating equipment and ethanol content when the mash is placed in the fermentation pit.

[0065] As shown in Table 5, the correlation between the second dominant bacterial genus and ethanol content in the operating equipment was not significant (p>0.05 or although there was a correlation coefficient, it did not meet the significance threshold).

[0066] Table 6. Correlation analysis of core potential microorganisms on the surface of objects in the workshop and ethanol content when the mash is placed in the fermentation pit.

[0067] As shown in Table 6, the correlation between the second dominant bacterial genus on the surface of objects in the workshop and the ethanol content was not significant (p>0.05 or although there was a correlation coefficient, it did not meet the significance threshold).

[0068] Table 7. Correlation analysis of core microorganisms in the workshop air and ethanol content when the mash is placed in the fermentation pit.

[0069] As shown in Table 7, thermophilic fungi showed a significant negative correlation with ethanol content (r=-0.557*), indicating that the higher the abundance of this fungus in the workshop air, the more likely it is to inhibit ethanol production; Nagnishi yeast showed a negative correlation with ethanol content (r=-0.346), but this correlation was not statistically significant.

[0070] Based on the results in Tables 4, 5, 6, and 7, genera showing significant or highly significant correlations (including positive and negative correlations) with ethanol content were screened from the second dominant bacterial genera to obtain characteristic genera. Characteristic genera include *Lactobacillus*, *Thermophilic Fungi*, *Thermophilic Ascomycetes*, *Nagnishi*, and *Penicillium*.

[0071] A clear and significant correlation was found between characteristic bacterial genera and ethanol content. As shown in Table 3, *Lactobacillus* showed a significant positive correlation with ethanol content when the mash was placed in the fermentation pit (r=0.638**). Higher abundance of this bacterium was more conducive to increasing ethanol content when the mash was placed in the pit, thus promoting ethanol production. *Thermophilic fungi* showed a significant negative correlation with ethanol content in the workshop air (r=-0.557*). Higher abundance of these fungi inhibited ethanol production from the mash, demonstrating a negative impact on ethanol production.

[0072] Characteristic bacterial genera are associated with ethanol content from different perspectives (positively correlated with promotion, negatively correlated with inhibition, and some correlations are significant), which can better reflect the impact of multi-source environment on ethanol production. Therefore, they are preferred as characteristic bacterial genera for management and monitoring workshops.

[0073] The relative abundance distribution of characteristic bacterial genera in the four sampling areas of the standard workshop is shown in Table 8. Figure 2 As shown.

[0074] Table 8. Distribution and total relative abundance values ​​of characteristic genera.

[0075] Based on the relative abundance values ​​of characteristic bacterial genera in each sampling region, and the total relative abundance values ​​in the four sampling regions, a quality control condition line is constructed.

[0076] The quality control criteria are as follows: each sampling area in the workshop contains 5 characteristic bacterial genera, and the total relative abundance of each characteristic bacterial genera in all sampling areas is ≥15%, and the relative abundance of each characteristic bacterial genera in at least one sampling area is ≥1%.

[0077] The overall bacterial distribution in the four sampling areas of the workshop was represented by the total relative abundance of each characteristic bacterial genera in the sampling areas. The total relative abundance comprehensively reflects the relative abundance of bacterial genera in different environments within the workshop, providing a macroscopic understanding of the overall proportion of bacterial genera in different locations within the workshop. A higher total relative abundance indicates that the bacterial genera are relatively abundant in the overall workshop environment; conversely, a lower total relative abundance indicates that they are relatively scarce.

[0078] The distribution of different bacterial genera in different environmental areas is affected by various factors, such as space, humidity, and temperature. By calculating the total relative abundance value and integrating the abundance of bacterial genera from different locations, the limitations of data from a single location can be eliminated, and the overall characteristics of bacterial genera in the workshop environment can be reflected more comprehensively.

[0079] Example 3 This embodiment provides a workshop management early warning system based on multi-source environmental microbial genus information, used to implement the workshop management early warning method based on multi-source environmental microbial genus information as described in Embodiment 1 or Embodiment 2 above. The system includes: The first module is configured to identify m sampling areas in the current workshop and obtain real-time bacterial genus information of the current workshop from the sampling areas; wherein, the real-time bacterial genus information includes the bacterial species and relative abundance values ​​of the bacterial genera in the current workshop. The second module is configured to determine whether the current workshop meets the quality control conditions based on the types of bacteria and their relative abundance values ​​in the current workshop. The quality control conditions include: each sampling area contains n characteristic bacterial genera, and the relative abundance values ​​of the characteristic bacterial genera meet the threshold conditions; wherein, the relative abundance value of each characteristic bacterial genera is positively or negatively correlated with the ethanol content, and n is the number of characteristic bacterial genera. The third module is configured to issue an early warning command when the current workshop does not meet the quality control conditions.

[0080] Although preferred embodiments have been described in this application, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of this application.

[0081] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A workshop management early warning method based on multi-source environmental microbial information, characterized in that, The method includes: In the current workshop, m sampling areas are identified, and real-time bacterial genus information of the current workshop is obtained from the sampling areas; wherein, the real-time bacterial genus information includes the bacterial species and relative abundance values ​​of the bacterial genera in the current workshop; Based on the types of bacteria and their relative abundance values ​​in the current workshop, it is determined whether the current workshop meets the quality control conditions. The quality control conditions include: each sampling area contains n characteristic bacterial genera, and the relative abundance value of the characteristic bacterial genera meets the threshold condition; wherein, the relative abundance value of each characteristic bacterial genera is positively or negatively correlated with the ethanol content, and n is the number of types of the characteristic bacterial genera. When the current workshop does not meet the quality control conditions, an early warning instruction is issued.

2. The workshop management early warning method based on multi-source environmental microbial information as described in claim 1, characterized in that, The relative abundance values ​​of the characteristic bacterial genera satisfy the threshold conditions including: The total relative abundance of each of the characteristic bacterial genera is greater than or equal to a first threshold, and the relative abundance of each of the characteristic bacterial genera in at least one of the sampling regions is greater than or equal to a second threshold; The total relative abundance value is the sum of the relative abundance values ​​of each of the characteristic bacterial genera in all the sampling regions, and the first threshold is greater than the second threshold.

3. The workshop management early warning method based on multi-source environmental microbial information as described in claim 1, characterized in that, Before confirming m sampling areas in the current workshop, the method further includes: constructing the quality control conditions; constructing the quality control conditions includes: In a standard workshop, m sampling areas are set up. The sampling areas are sampled and quality control parameters are obtained. The quality control parameters include the bacterial genera contained in the standard workshop and the relative abundance value of the bacterial genera. Based on the bacterial genera contained in the standard workshop and their relative abundance values, the first dominant bacterial genera were screened from the bacterial genera contained in the standard workshop. The ethanol content in the standard workshop mash entering the cellar is obtained, and Spearman correlation analysis is performed based on the ethanol content and the relative abundance value of the first dominant bacterial genus to obtain the analysis results. The characteristic bacterial genus is then screened based on the analysis results. The quality control conditions are constructed based on the characteristic bacterial genera and their relative abundance values.

4. The workshop management early warning method based on multi-source environmental microbial information as described in claim 3, characterized in that, The characteristic bacterial genera obtained by screening based on the analysis results include: Spearman correlation analysis was performed based on the ethanol content and the relative abundance value of the first dominant bacterial genus to obtain the first analysis result. Based on the first analysis result, a second dominant bacterial genus was screened from the first dominant bacterial genus. Spearman correlation analysis was performed on the second dominant bacterial genus contained in each of the sampling areas with the ethanol content to obtain a second analysis result. Based on the second analysis result, the characteristic bacterial genus was screened from the second dominant bacterial genus.

5. The workshop management early warning method based on multi-source environmental microbial information as described in claim 4, characterized in that, The second dominant bacterial genus selected from the first dominant bacterial genus based on the first analysis results includes: Select from the first dominant bacterial genus a bacterial genus that shows a positive or negative correlation with the ethanol content as the second dominant bacterial genus.

6. The workshop management early warning method based on multi-source environmental microbial information as described in claim 4, characterized in that, The characteristic genera selected from the second dominant genera based on the second analysis results include: Select from the second dominant bacterial genera those genera that show a positive or negative correlation with the ethanol content as the characteristic bacterial genera.

7. The workshop management early warning method based on multi-source environmental microbial information as described in claim 1, characterized in that, The characteristic genera include: Lactobacillus, thermophilic fungi, thermophilic ascomycetes, Paecilomyces, and Nagnich yeast.

8. The workshop management early warning method based on multi-source environmental microbial information as described in claim 1, characterized in that, The sampling areas include: the floor of the drying hall, operating tools, the surfaces of objects in the workshop, and the air in the workshop.

9. The workshop management early warning method based on multi-source environmental microbial information as described in claim 1, characterized in that, After issuing the warning command, the method further includes: The microbial genus of the current workshop is adjusted to ensure that the current workshop meets the quality control conditions. The microbial regulation includes: spraying the current workshop with Daqu (a type of Chinese liquor) before use.

10. A workshop management early warning system based on multi-source environmental microbial information, characterized in that, For implementing the workshop management early warning method based on multi-source environmental microbial information as described in claim 1, the system includes: The first module is configured to identify m sampling areas in the current workshop and obtain real-time bacterial genus information of the current workshop from the sampling areas; wherein, the real-time bacterial genus information includes the bacterial species and relative abundance values ​​of the bacterial genera in the current workshop. The second module is configured to determine whether the current workshop meets quality control conditions based on the types of bacterial genera and their relative abundance values ​​in the current workshop. The quality control conditions include: each sampling area contains n characteristic bacterial genera, and the relative abundance values ​​of the characteristic bacterial genera meet a threshold condition; wherein the relative abundance value of each characteristic bacterial genera is positively or negatively correlated with the ethanol content, and n is the number of types of the characteristic bacterial genera. The third module is configured to issue an early warning command when the current workshop does not meet the quality control conditions.

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