A feces treatment method based on flora analysis

Abstract

The present application relates to the technical field of excrement treatment, and particularly relates to a feces treatment method based on flora analysis, comprising obtaining reaction temperature and material Ph value and determining gas production relative deviation based on gas production, determining device operation state based on characteristic parameters; in response to the device operation state, determining abnormal characteristic identification to obtain key functional flora parameters, determining flora balance index based on the key functional flora parameters to determine whether acid-producing bacteria and methanogenic bacteria are in flora balance; determining smoothness index according to the change curve of a plurality of actual flora balance indexes obtained in an initial detection period to determine the deviation degree of the flora balance index, and adjusting the correction sensitivity coefficient of the feed adjustment amount based on the smoothness index. The present application performs real-time monitoring and dynamic regulation on the excrement treatment process based on the flora analysis result to maintain optimal flora structure and metabolic activity, and improves treatment efficiency and stability.

Description

Technical Field

[0001] This invention relates to the field of fecal treatment technology, and in particular to a fecal treatment method based on microbial community analysis. Background Technology

[0002] Fecal waste treatment is a fundamental aspect of environmental sanitation and agricultural environmental protection. Currently, the mainstream methods for fecal waste treatment mainly fall into two categories: aerobic composting and anaerobic digestion. Aerobic composting utilizes aerobic microorganisms to decompose organic matter in feces, and the biological heat generated by microbial metabolism kills pathogens, achieving the harmlessness and stabilization of feces. Anaerobic digestion, on the other hand, uses anaerobic microorganisms to convert organic matter into methane under closed, anaerobic conditions, achieving energy recovery. However, existing technologies have the following shortcomings:

[0003] Traditional aerobic composting and anaerobic digestion processes typically employ fixed time periods or empirical parameters (such as temperature thresholds and fermentation days) for operational control, treating feces as a single chemical matrix and neglecting the core driving role of the microbial community in the treatment process. When environmental temperature fluctuates, material ratios change, or external pollutants invade, the microbial community structure within the treatment system alters, potentially leading to prolonged fermentation cycles, insufficient composting, decreased gas production efficiency, or even system collapse. While some studies have attempted to improve treatment outcomes by adding exogenous microbial agents, these agents are fixed formulations and cannot be dynamically adjusted based on the actual microbial community status, limiting their applicability. Therefore, a method is urgently needed that can monitor and dynamically regulate the fecal treatment process in real time based on microbial community analysis results to maintain optimal microbial structure and metabolic activity, thereby improving treatment efficiency and stability.

[0004] Chinese Patent Publication No. CN109182203A discloses a microbial flora for decomposing feces and a method for fecal treatment. The method includes the following steps: Step 1: Mixing the following components by mass fraction: Enterococcus faecalis 10%–15%, yeast 10%–15%, actinomycetes 18%–24%, rhizobium 5%–10%, Trichoderma viride freeze-dried powder 5%–10%, Bacillus spp. 15%–20%, mold 4%–6%, and Lactobacillus plantarum 18%–21%. Step 2: Further mixing the above-mentioned microbial flora with sawdust to form a microbial aggregate feedstock. Step 3: Uniformly and thoroughly mixing the feces with the microbial aggregate feedstock. Step 4: Fermenting and degrading the mixed feces and microbial aggregate feedstock for 10–15 days. Therefore, the aforementioned microbial flora for decomposing feces and the method for fecal treatment have the following problems:

[0005] The main approach relies on empirical judgment and fixed parameter control of the treatment process. It cannot perceive or accurately diagnose the root cause of the imbalance between acid-producing and methanogenic bacteria in real time at the microbial community level. Furthermore, it cannot adaptively predict and dynamically correct key parameters such as feed rate based on the dynamic changes of the microbial community, resulting in lagging, over-adjustment, or under-adjustment, making it difficult to guarantee treatment efficiency and operational stability. Summary of the Invention

[0006] Therefore, this invention provides a fecal treatment method based on microbial community analysis to overcome the problem in the prior art that it is impossible to adaptively predict and dynamically correct key parameters such as feed rate based on the dynamic changes of the microbial community.

[0007] To achieve the above objectives, the present invention provides a fecal treatment method based on microbial community analysis, comprising:

[0008] The reaction temperature and material pH value are obtained, and the relative deviation of gas production is determined based on the gas production rate. The operating status of the device is determined based on the characteristic parameters.

[0009] In response to the device's operating status, an abnormal feature identifier is determined and key functional microbial parameters are obtained. Based on the key functional microbial parameters, a microbial balance index is determined to determine whether acid-producing bacteria and methanogens are in microbial balance.

[0010] Based on the imbalance between acid-producing and methanogenic bacteria, the absolute abundance of methanogens or acid-producing bacteria is obtained, and the abundance levels of methanogens and acid-producing bacteria are determined based on the absolute abundance of methanogens or acid-producing bacteria.

[0011] In response to the microbial community balance, the abundance levels of methanogens and acidogens are combined to determine whether the feed load is too high or whether methanogens and acidogens are relatively excessive, and the absolute abundance of methanogens or the microbial community balance index is adjusted accordingly.

[0012] Based on the deviation of the microbial community balance index and the proliferation rate of acid-producing bacteria obtained from excessive feed load, the feed adjustment coefficient is determined to determine the initial feed adjustment amount to reduce the current value of the feed amount, and the corresponding feed adjustment amount is dynamically corrected according to the microbial community recovery status according to the initial detection cycle.

[0013] Based on the initial detection cycle, several actual microbial community balance index change curves are obtained to determine the smoothness index, thereby determining the degree of deviation of the microbial community balance index. The correction sensitivity coefficient of the feed adjustment amount is then adjusted based on the smoothness index.

[0014] Furthermore, the characteristic parameters include reaction temperature, material pH value, and relative deviation of gas production;

[0015] When all characteristic parameters are within their corresponding threshold ranges, the determination device is in a stable operating state;

[0016] When any characteristic parameter is within the corresponding warning range, the device is in an emergency abnormal state, stops feeding and sends an alarm signal;

[0017] Specifically, when any feature parameter is not in the corresponding warning interval and any feature parameter is in the corresponding abnormal interval, the determination device is in a potential abnormal state, and the feature parameter in the corresponding abnormal interval is obtained as an abnormal feature identifier.

[0018] Furthermore, key functional microbial parameters include the copy number of acid-producing bacteria and the copy number of methanogens;

[0019] If the microbial balance index exceeds the preset index threshold range, it is judged that the balance index is too high, the acid-producing bacteria are relatively excessive, or the methanogens are relatively insufficient.

[0020] If the microbial balance index is lower than the preset index threshold range, it is judged that the balance index is low, the acid-producing bacteria are relatively insufficient, or the methanogenic bacteria are relatively excessive.

[0021] Furthermore, the process of determining whether the feed load is too high or whether there is a relative excess of methanogens and acidogens includes:

[0022] When the balance index is high, if the abundance level of methanogens is less than the abundance level threshold, it is judged that there is an absolute deficiency of methanogens, and the activity of methanogens is inhibited or their proliferation is hindered.

[0023] If the abundance level of methanogens is greater than or equal to the abundance level threshold, it is determined that acid-producing bacteria are relatively excessive, and the excessive feed load leads to excessive proliferation of acid-producing bacteria.

[0024] Furthermore, if the abundance level of acid-producing bacteria is less than the abundance level threshold when the balance index is low, it is determined that there is an absolute deficiency of acid-producing bacteria and insufficient easily degradable organic matter in the feed.

[0025] If the abundance level of acid-producing bacteria is greater than or equal to the abundance level threshold, it is determined that methanogens are relatively excessive, the material in the device is in an overstable state, and methanogens are excessively dominant.

[0026] Furthermore, when the activity of methanogens is inhibited, the absolute abundance of methanogens is regulated;

[0027] When the feed load is too high, or there is insufficient easily degradable organic matter, or when methanogenic bacteria are overly dominant, adjust the microbial balance index to restore the microbial balance index to within the preset index threshold range.

[0028] Furthermore, the process of regulating the gut microbiota balance index includes:

[0029] When the feed load is too high, determine the initial feed adjustment amount and reduce the current value of the feed amount, and adjust the microbial balance index by restoring the microbial balance.

[0030] When there is insufficient readily degradable organic matter, the microbial community balance index can be adjusted by supplementing the substrate to adjust the carbon-nitrogen ratio of the material.

[0031] When methanogens are excessively dominant, lowering the reaction temperature can regulate the microbial balance index by inhibiting methanogens.

[0032] Furthermore, the process of determining the initial feed adjustment amount includes:

[0033] The deviation of the microbial community balance index is measured to quantify the imbalance, and the proliferation rate of acid-producing bacteria is used to reflect the rate of deterioration of the imbalance.

[0034] The feed adjustment coefficient is determined based on the deviation of the microbial community balance index and the proliferation rate of the acid-producing bacteria, and the initial feed adjustment amount is determined based on the feed adjustment coefficient.

[0035] Furthermore, after determining the initial feed adjustment amount, the feed adjustment amount for the next initial testing cycle is adjusted based on the decrease in the microbial balance index during the initial testing cycle, combined with the correction sensitivity coefficient.

[0036] Furthermore, the ideal microbial balance index corresponding to several initial detection cycles is obtained, and the smoothness index is obtained by normalizing the sum of the absolute deviations between the actual microbial balance index and the ideal microbial balance index in each cycle.

[0037] If the smoothness index is greater than or equal to the preset index threshold, the recovery process is judged to be stable or the fluctuation of the recovery process is within an acceptable range.

[0038] If the smoothness index is lower than the preset index threshold, it is determined that there is a risk of over-adjustment in the recovery process fluctuation, and the correction sensitivity coefficient is reduced according to the ratio of the smoothness index to the preset index threshold.

[0039] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention provides a fecal treatment method based on microbial community analysis to achieve precise adaptive control; it rapidly classifies the system status based on temperature, pH, and gas production rate, accurately diagnoses the root cause of imbalance through microbial community balance index and absolute abundance detection, and introduces deviation and proliferation rate in branches with excessive feed load to achieve dynamic and precise correction of feed volume; by calculating the recovery smoothness index, it performs negative feedback self-optimization on the correction sensitivity coefficient, allowing the control strategy to continuously evolve with operational experience; this invention upgrades traditional experience-based control to microbial community-driven intelligent control, enabling precise intervention in the early stages of microbial community imbalance, avoiding over-regulation or under-regulation, significantly improving treatment efficiency and operational stability, reducing operating costs, and possessing self-learning and adaptive capabilities, making it suitable for various fecal treatment scenarios.

[0040] Furthermore, this invention targets anaerobic reactors in livestock farms, enabling real-time online monitoring of fermentation temperature, pH, and gas production rate. By establishing a dynamic gas production benchmark value with a sliding window, it eliminates regular fluctuations caused by diurnal temperature differences and feeding cycles, making the determination of relative deviations in gas production more accurate. A three-level threshold division (normal zone, abnormal zone, and warning zone) is adopted to achieve rapid classification of system status: normal status maintains routine monitoring; potential abnormal status automatically increases the detection frequency and identifies abnormal characteristics; and emergency abnormal status immediately interlocks to stop feeding, activate emergency ventilation, and send an alarm. This method upgrades traditional single-point threshold judgment to multi-parameter hierarchical early warning, significantly improving the fault response speed and operational safety of anaerobic reactors.

[0041] Furthermore, this invention establishes a microbial community balance index by detecting the gene copy number of acid-producing and methanogenic bacteria, enabling precise analysis of the microbial ecology of the anaerobic digestion system. When the balance index deviates from a preset threshold, the absolute abundance of the target microbial community is further detected, and the abundance level is calculated in conjunction with the abundance benchmark value to accurately distinguish the root cause of the imbalance: whether it is methanogenic bacteria suppression, acid-producing bacteria overload, insufficient organic matter, or excessive dominance of methanogenic bacteria, avoiding the shortcomings of traditional methods that cannot locate the cause based solely on relative proportions. Targeted regulatory measures are taken for different causes, such as adding trace elements to activate methanogenic bacteria, reducing feed load, supplementing carbon sources, or temporarily lowering the temperature, and the intensity of regulation is dynamically optimized through multiple rounds of feedback. This method introduces microbiome analysis into engineering regulation, significantly improving the accuracy of anaerobic reactor fault diagnosis and the precision of regulation.

[0042] Furthermore, this invention addresses scenarios where excessive feed load leads to excessive proliferation of acid-producing bacteria. It introduces a feed adjustment coefficient prediction model based on the deviation of the microbial community balance index and the proliferation rate of acid-producing bacteria. This model enables precise calculation of the feed rate rather than fixed-ratio adjustment, avoiding wasted processing capacity due to over-adjustment or delayed recovery due to under-adjustment. During the control process, the feed adjustment amount is dynamically corrected every 4 hours based on the actual recovery of the microbial community balance index, forming a closed-loop control of "monitoring-calculation-adjustment-re-monitoring." Boundary conditions are set to ensure a smooth and safe recovery process. This method upgrades traditional empirical open-loop control to data-driven adaptive closed-loop control, significantly improving the recovery efficiency and operational stability of the anaerobic reactor under load shocks.

[0043] Furthermore, this invention constructs an ideal recovery curve, calculates the deviation between the actual microbial community balance index trajectory and the ideal trajectory, obtains a recovery smoothness index, and quantitatively assesses the stability of the control process. When the smoothness index is lower than a preset threshold, the correction sensitivity coefficient is automatically reduced, making subsequent control more conservative and avoiding secondary oscillations caused by excessively rapid recovery. This mechanism elevates the optimization of the control strategy from "post-event summary" to "in-event self-correction," identifying whether its own control behavior is too aggressive and dynamically adjusting control parameters based on actual results. This method effectively solves the problem of fixed parameters and inability to adapt to changes in material characteristics in traditional feedback control, significantly improving the adaptability and control robustness of anaerobic reactors in long-term operation. Attached Figure Description

[0044] Figure 1 This is a schematic flowchart of the fecal treatment method based on microbial community analysis in an embodiment of the present invention;

[0045] Figure 2 This is a schematic diagram of the process for determining whether the balance index is too high or too low in an embodiment of the present invention.

[0046] Figure 3 This is a schematic diagram of the process for determining whether the feed load is too high in an embodiment of the present invention;

[0047] Figure 4 This is a schematic diagram of the process for adjusting the correction sensitivity coefficient in an embodiment of the present invention. Detailed Implementation

[0048] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0049] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0050] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0051] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0052] Please see Figures 1-4 As shown, Figure 1 This is a schematic flowchart of the fecal treatment method based on microbial community analysis in an embodiment of the present invention; Figure 2 This is a schematic diagram of the process for determining whether the balance index is too high or too low in an embodiment of the present invention. Figure 3 This is a schematic diagram of the process for determining whether the feed load is too high in an embodiment of the present invention; Figure 4 This is a schematic diagram of the process for adjusting the correction sensitivity coefficient in an embodiment of the present invention.

[0053] This invention provides a fecal treatment method based on microbial community analysis, comprising:

[0054] Step S1: Obtain the reaction temperature and material pH value, determine the relative deviation of gas production based on the gas production rate, and determine the operating status of the device based on the characteristic parameters;

[0055] Step S2: In response to the device operating status, determine the abnormal feature identifier and obtain key functional microbial community parameters. Based on the key functional microbial community parameters, determine the microbial community balance index to determine whether the acid-producing bacteria and methanogenic bacteria are in microbial community balance.

[0056] Step S3: Based on the imbalance between acid-producing bacteria and methanogens, obtain the absolute abundance of methanogens or acid-producing bacteria, and determine the abundance level of methanogens and acid-producing bacteria based on the absolute abundance of methanogens or acid-producing bacteria.

[0057] Step S4: In response to the microbial community balance, combine the abundance levels of methanogens and acidogens to determine whether the feed load is too high or whether methanogens and acidogens are relatively excessive, and adjust the absolute abundance of methanogens or the microbial community balance index accordingly.

[0058] Step S5: Based on the deviation of the microbial community balance index and the proliferation rate of acid-producing bacteria obtained from the excessive feed load, determine the feed adjustment coefficient, so as to determine the initial feed adjustment amount to reduce the current value of the feed amount, and dynamically correct the corresponding feed adjustment amount according to the microbial community recovery according to the initial detection cycle.

[0059] Step S6: Obtain several actual microbial community balance index change curves according to the initial detection cycle to determine the smoothness index, so as to determine the degree of deviation of the microbial community balance index, and adjust the correction sensitivity coefficient of the feed adjustment amount based on the smoothness index.

[0060] Specifically, this invention provides a fecal treatment method based on microbial community analysis to achieve precise adaptive control. It rapidly classifies the system status based on temperature, pH, and gas production rate, accurately diagnoses the root cause of imbalance through microbial community balance index and absolute abundance detection, and introduces deviation and proliferation rate in branches with excessive feed load to achieve dynamic and precise correction of feed volume. By calculating the recovery smoothness index, it performs negative feedback self-optimization on the correction sensitivity coefficient, allowing the control strategy to continuously evolve with operational experience. This invention upgrades traditional experience-based control to microbial community-driven intelligent control, enabling precise intervention in the early stages of microbial community imbalance, avoiding over- or under-regulation, significantly improving treatment efficiency and operational stability, reducing operating costs, and possessing self-learning and adaptive capabilities, making it suitable for various fecal treatment scenarios.

[0061] In this embodiment, the fecal treatment device is an anaerobic reactor, and the application scenario is a livestock farm.

[0062] Sensors are installed at key locations in the fecal treatment device. Temperature sensors are placed in the middle section of the reactor and at the discharge port to monitor fermentation temperature and reflect microbial metabolic activity. pH sensors are placed in the middle section of the reactor and in the circulation pipeline to monitor acidity and alkalinity and reflect the system's buffering capacity. Gas flow meters are placed in the exhaust pipe to monitor gas production (anaerobic) or oxygen consumption (aerobic).

[0063] The gas production rate is determined based on the gas production volume. The gas production rate is calculated as (cumulative gas production volume within Δt time) / Δt. Δt = 10 minutes is used as a sliding window to eliminate the influence of instantaneous fluctuations.

[0064] For the gas production rate Q, a dynamic gas production baseline value needs to be established. Gas production benchmark value The calculation method is the average gas production rate in the same Δt time period over the past 72 hours;

[0065] Specifically, using a 72-hour sliding window can eliminate regular fluctuations caused by diurnal temperature differences and feed cycles, making the determination of normal fluctuations more accurate. During implementation, the gas production baseline value is automatically updated every 24 hours. .

[0066] The relative deviation of gas production is determined based on the gas production rate. The relative deviation of gas production = (gas production rate - gas production baseline value) / gas production baseline value × 100%.

[0067] The reaction temperature is obtained by a temperature sensor, and the pH value of the material is obtained by a pH sensor. When all characteristic parameters are within the corresponding threshold range, the device is determined to be in a stable operating state.

[0068] When any characteristic parameter is within the corresponding warning range, the device is in an emergency abnormal state, stops feeding and sends an alarm signal;

[0069] Specifically, when any feature parameter is not in the corresponding warning interval and any feature parameter is in the corresponding abnormal interval, the determination device is in a potential abnormal state, and the feature parameter in the corresponding abnormal interval is obtained as an abnormal feature identifier.

[0070] In practice, the corresponding threshold range for the reaction temperature is [50, 70], the corresponding threshold range for the pH value of the material is [6.5, 8.0], and the corresponding threshold range for the relative deviation of gas production is (-10%, 10%).

[0071] The corresponding warning range for the reaction temperature is (-∞, 45) or (75, +∞), the corresponding warning range for the pH value of the material is (-∞, 5.5) or (9.0, +∞), and the corresponding warning range for the relative deviation of gas production is (-∞, -30%).

[0072] The corresponding abnormal range for the reaction temperature is [45, 50) or (70, 75], the corresponding abnormal range for the pH value of the material is [5.5, 6.5) or (8.0, 9.0], and the corresponding abnormal range for the relative deviation of gas production is (-30%, -10%) or [50%, +∞].

[0073] Example

[0074] The reaction temperature was 58.3℃ (normal range), the pH value of the material was 7.12 (normal range), the gas production rate was 12.8 m³ / h, and the relative deviation of gas production was +3.2% (normal range). The device was determined to be in a stable operating state, and the detection frequency was maintained at 30 minutes.

[0075] The reaction temperature is 48.2℃ (low temperature warning zone), the material pH value is 6.85 (normal zone), the gas production rate is 11.2 m³ / h, and the relative deviation of gas production is -15% (decline warning zone). The device is determined to be in a potentially abnormal state. The abnormal characteristics are identified as reaction temperature and gas production rate. The detection frequency is increased to 10 minutes / time.

[0076] The reaction temperature is 43.7℃ (low temperature danger zone), the pH value of the material is 6.12 (acidic warning zone), the gas production rate is 8.5 m³ / h, and the relative deviation of gas production is -35% (sudden drop danger zone). The device is determined to be in an emergency abnormal state. Feeding is stopped, emergency ventilation is turned on, and an alarm is sent.

[0077] Specifically, this invention targets anaerobic reactors in livestock farms, enabling real-time online monitoring of fermentation temperature, pH, and gas production rate. By establishing a dynamic gas production benchmark value with a sliding window, it eliminates regular fluctuations caused by diurnal temperature variations and feeding cycles, making the determination of relative deviations in gas production more accurate. A three-level threshold classification (normal zone, abnormal zone, and warning zone) is employed to achieve rapid classification of system status: normal status maintains routine monitoring; potential abnormal status automatically increases the detection frequency and identifies abnormal characteristics; and emergency abnormal status immediately triggers an interlock to stop feeding, activate emergency ventilation, and send an alarm. This method upgrades traditional single-point threshold judgment to multi-parameter hierarchical early warning, significantly improving the fault response speed and operational safety of anaerobic reactors.

[0078] When the device is in a potentially abnormal state, a fecal sample is extracted from the middle of the reactor using a sampling device to obtain the key functional microbial parameters of the sample, including the copy number of acid-producing bacteria and the copy number of methanogens.

[0079] Specifically, the copy number of acid-producing bacteria and the copy number of methanogens refer to the number of specific functional bacteria in a unit mass or unit volume of fecal sample, expressed in "gene copy number / gram sample" or "gene copy number / milliliter sample".

[0080] It is understandable that the copy number of acid-producing bacteria and the copy number of methanogens are not directly counted as individual bacteria, but rather the number of bacteria is estimated by detecting specific marker gene sequences in the bacterial genome. The biological basis for this is that each target bacterial group contains a specific number of marker gene copies inside its cells, and by detecting the copy number of the marker gene, the number of the corresponding bacterial group can be calculated.

[0081] The community balance index is determined based on the copy number of acid-producing bacteria and the copy number of methanogens. Community balance index = copy number of acid-producing bacteria / copy number of methanogens.

[0082] If the microbial balance index exceeds the preset index threshold range, it is judged that the balance index is too high, the acid-producing bacteria are relatively excessive, or the methanogens are relatively insufficient.

[0083] If the microbial balance index is lower than the preset index threshold range, it is judged that the balance index is low, the acid-producing bacteria are relatively insufficient, or the methanogenic bacteria are relatively excessive.

[0084] The preset index threshold range is [1.0, 3.0].

[0085] When the balance index is high, the absolute abundance of methanogens in the sample is detected.

[0086] When the balance index is low, the absolute abundance of acid-producing bacteria in the sample is detected.

[0087] Specifically, absolute abundance refers to the actual number of a specific microorganism in a unit mass (or unit volume) of sample, usually expressed as "gene copies / gram sample" or "cell count / gram sample". The absolute abundance of methanogens is the number of methanogen copies per gram of sample; the absolute abundance of acid-producing bacteria is the number of acid-producing copies per gram of sample.

[0088] The abundance level of methanogens is calculated based on the absolute abundance of methanogens, wherein the abundance level of methanogens = absolute abundance of methanogens / baseline abundance value of methanogens.

[0089] The abundance level of acid-producing bacteria is calculated based on the absolute abundance of acid-producing bacteria, wherein the abundance level of acid-producing bacteria = absolute abundance of acid-producing bacteria / baseline abundance value of acid-producing bacteria.

[0090] In practice, the selectable range for the methanogen abundance benchmark value is as follows: The selectable range for the abundance benchmark value of acid-producing bacteria is as follows: The abundance level threshold is 0.7.

[0091] When the balance index is high, if the abundance level of methanogens is less than the abundance level threshold, it is judged that there is an absolute deficiency of methanogens, and the activity of methanogens is inhibited or their proliferation is hindered.

[0092] If the abundance level of methanogens is greater than or equal to the abundance level threshold, it is determined that acid-producing bacteria are relatively excessive, and the feed load is too high, leading to excessive proliferation of acid-producing bacteria.

[0093] When the balance index is low, if the abundance level of acid-producing bacteria is less than the abundance level threshold, it is judged that there is an absolute deficiency of acid-producing bacteria and insufficient easily degradable organic matter in the feed.

[0094] If the abundance level of acid-producing bacteria is greater than or equal to the abundance level threshold, it is determined that methanogens are relatively excessive, the material in the device is in an overstable state, and methanogens are excessively dominant.

[0095] During implementation, when the activity of methanogens is inhibited, the absolute abundance of methanogens is adjusted by adding a mixture of trace elements (Ni, Co, Mo) to restore the absolute abundance of methanogens to above the baseline value.

[0096] When the feed load is too high, or there is insufficient easily degradable organic matter, or when methanogenic bacteria are over-dominant, adjust the microbial balance index to restore the microbial balance index to within the preset index threshold range.

[0097] Specifically, when the feed load is too high, the initial feed adjustment amount is determined to be reduced from the current feed amount and maintained for 24 hours to restore the microbial balance index.

[0098] When there is insufficient readily degradable organic matter, add readily degradable carbon sources (glucose or starch) to adjust the carbon-nitrogen ratio of the material and regulate the microbial community balance index by supplementing the substrate;

[0099] When methanogens become excessively dominant, briefly lower the temperature to 45°C and maintain it for 6 hours, while increasing stirring. This moderately inhibits methanogens and regulates the balance of the microbial community.

[0100] Taking the inhibition of methanogenic bacteria activity as an example,

[0101] Round 1 (t=0): Add trace elements, set the temperature to 55℃, and wait for 4 hours;

[0102] First round of feedback (t=4): Methanogen abundance increased from 0.64 to 0.72, and the community balance index decreased from 6.27 to 4.8, indicating a positive convergence maintenance strategy;

[0103] Round 2 (t=4): Continue to maintain the status quo and wait for 3 hours;

[0104] Second round of feedback (t=7): Methanogen abundance 0.81, community balance index = 3.2, approaching the target, reduce the intensity of regulation;

[0105] Round 3 (t=7): Stop adding trace elements, maintain the temperature, and wait for 4 hours;

[0106] Third round of feedback (t=11): Methanogen abundance 0.85, microbial balance index = 2.6, control was terminated upon reaching the target.

[0107] Specifically, this invention establishes a microbial community balance index by detecting the gene copy number of acid-producing and methanogenic bacteria, enabling precise analysis of the microbial ecology of the anaerobic digestion system. When the balance index deviates from a preset threshold, the absolute abundance of the target microbial community is further detected, and the abundance level is calculated in conjunction with the abundance benchmark value to accurately distinguish the root cause of the imbalance: whether it is suppressed methanogens, overloaded acid-producing bacteria, insufficient organic matter, or excessive dominance of methanogens, avoiding the shortcomings of traditional methods that cannot locate the cause based solely on relative proportions. Targeted regulatory measures are taken for different causes, such as adding trace elements to activate methanogens, reducing feed load, supplementing carbon sources, or temporarily lowering the temperature, and the intensity of regulation is dynamically optimized through multiple rounds of feedback. This method introduces microbiome analysis into engineering regulation, significantly improving the accuracy of anaerobic reactor fault diagnosis and the precision of regulation.

[0108] If the feed load is too high, determine the initial feed adjustment amount to reduce the current feed amount, obtain the deviation of the microbial community balance index from the quantitative imbalance, and obtain the acid-producing bacteria proliferation rate to reflect the speed of imbalance deterioration.

[0109] Specifically, the deviation of the microbial community balance index is the difference between the current microbial community balance index and the upper limit of the preset index threshold range, and the proliferation rate of acid-producing bacteria is the change rate of the abundance of acid-producing bacteria in the past 6 hours.

[0110] The feed adjustment coefficient is determined based on the deviation of the microbial community balance index and the proliferation rate of the acid-producing bacteria. The feed adjustment coefficient = min(1.0, α × deviation of microbial community balance index + β × proliferation rate of acid-producing bacteria / normal proliferation rate of acid-producing bacteria baseline value). The initial feed adjustment amount = current feed amount × (1 - feed adjustment coefficient).

[0111] In the formula, α is the deviation weighting coefficient, with a preset value of 0.15; β is the proliferation rate weighting coefficient, with a preset value of 0.2; and the baseline value for the normal proliferation rate of acid-producing bacteria is derived from historical data statistics, and is used in practice as follows: .

[0112] In this embodiment, the current microbial community balance index is 3.5, the microbial community balance index deviation is 0.5, and the acid-producing bacteria proliferation rate is 1.0 × 10⁻⁶. 7 The ratio of the acid-producing bacteria proliferation rate to the normal proliferation rate of acid-producing bacteria is 2.0. The feed adjustment coefficient is 0.15×0.5 + 0.2×2.0 = 0.075 + 0.4 = 0.475. The current feed rate is reduced to 52.5% of the current value (or reduced by 47.5%).

[0113] After determining the initial feed adjustment amount, the corresponding feed adjustment amount is dynamically adjusted according to the microbial community recovery status based on the initial detection cycle. The initial detection cycle mentioned in the implementation is 4 hours.

[0114] The feed adjustment amount for the next period = the feed adjustment amount for the current period × (1 - correction sensitivity coefficient × the decrease in the microbial balance index during the previous initial detection cycle).

[0115] In the formula, the preset value of the correction sensitivity coefficient is 0.3, which means that for every 1.0 improvement in the microbial community balance index, the regulation intensity is reduced by 30%.

[0116] Simultaneously, a correction boundary condition is set: when the microbial community balance index drops below 3.5, the feed is gradually restored, with each increase not exceeding 20% ​​of the original feed amount; when the microbial community balance index returns to the normal range (≤3.0), the control is immediately terminated.

[0117] Specifically, this invention addresses scenarios where excessive feed load leads to excessive proliferation of acid-producing bacteria. It introduces a feed adjustment coefficient prediction model based on the deviation of the microbial community balance index and the proliferation rate of acid-producing bacteria. This model enables precise calculation of the feed rate rather than fixed-ratio adjustment, avoiding wasted processing capacity due to over-adjustment or delayed recovery due to under-adjustment. During the control process, the feed adjustment amount is dynamically corrected every 4 hours based on the actual recovery of the microbial community balance index, forming a closed-loop control of "monitoring-calculation-adjustment-re-monitoring." Boundary conditions are set to ensure a smooth and safe recovery process. This method upgrades traditional empirical open-loop control to data-driven adaptive closed-loop control, significantly improving the recovery efficiency and operational stability of anaerobic reactors under load shocks.

[0118] Starting from the initial feed adjustment amount and reducing the current feed amount, several actual microbial balance indices are obtained according to the initial detection cycle, and the deviation between the curve of microbial balance index changing over time and the ideal recovery curve is determined.

[0119] Specifically, on the ideal recovery curve, the ideal microbial balance index for the kth initial testing cycle should be the initial microbial balance index - (initial microbial balance index - target microbial balance index) × (k / the total number of actual microbial balance indices), with a target microbial balance index of 3.0.

[0120] The sum of the absolute deviations between the actual microbial community balance index and the ideal microbial community balance index for each period is calculated as: Σ|actual microbial community balance index - ideal microbial community balance index|.

[0121] Normalization yields the smoothness index = 1 - {sum of absolute deviations / [total number of actual microbial community balance indices × (initial microbial community balance index - target microbial community balance index)]};

[0122] If the smoothness index is greater than or equal to the preset index threshold, the recovery process is judged to be stable or the fluctuation of the recovery process is within an acceptable range.

[0123] If the smoothness index is lower than the preset index threshold, it is determined that there is a risk of over-adjustment in the recovery process fluctuation, and the correction sensitivity coefficient is reduced according to the ratio of the smoothness index to the preset index threshold.

[0124] The preset index threshold can be selected from a value range of 0.6 to 0.8.

[0125] Specifically, this invention constructs an ideal recovery curve, calculates the deviation between the actual microbial community balance index trajectory and the ideal trajectory, obtains a recovery smoothness index, and quantitatively assesses the stability of the control process. When the smoothness index is lower than a preset threshold, the correction sensitivity coefficient is automatically reduced, making subsequent control more conservative and avoiding secondary oscillations caused by excessively rapid recovery. This mechanism elevates the optimization of the control strategy from "post-event summary" to "in-event self-correction," identifying whether its own control behavior is too aggressive and dynamically adjusting control parameters based on actual results. This method effectively solves the problem of fixed parameters and inability to adapt to changes in material characteristics in traditional feedback control, significantly improving the adaptability and control robustness of anaerobic reactors in long-term operation.

[0126] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0127] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A fecal treatment method based on microbial community analysis, characterized in that, include: The reaction temperature and material pH value are obtained, and the relative deviation of gas production is determined based on the gas production rate. The operating status of the device is determined based on the characteristic parameters. In response to the device's operating status, an abnormal feature identifier is determined and key functional microbial parameters are obtained. Based on the key functional microbial parameters, a microbial balance index is determined to determine whether acid-producing bacteria and methanogens are in microbial balance. Based on the imbalance between acid-producing and methanogenic bacteria, the absolute abundance of methanogens or acid-producing bacteria is obtained, and the abundance levels of methanogens and acid-producing bacteria are determined based on the absolute abundance of methanogens or acid-producing bacteria. In response to the microbial community balance, the abundance levels of methanogens and acidogens are combined to determine whether the feed load is too high or whether methanogens and acidogens are relatively excessive, and the absolute abundance of methanogens or the microbial community balance index is adjusted accordingly. Based on the deviation of the microbial community balance index and the proliferation rate of acid-producing bacteria obtained from excessive feed load, the feed adjustment coefficient is determined to determine the initial feed adjustment amount to reduce the current value of the feed amount, and the corresponding feed adjustment amount is dynamically corrected according to the microbial community recovery status according to the initial detection cycle. Based on the initial detection cycle, several actual microbial community balance index change curves are obtained to determine the smoothness index, thereby determining the degree of deviation of the microbial community balance index. The correction sensitivity coefficient of the feed adjustment amount is then adjusted based on the smoothness index.

2. The fecal treatment method based on microbial community analysis according to claim 1, characterized in that, The characteristic parameters include reaction temperature, material pH value, and relative deviation of gas production; When all characteristic parameters are within their corresponding threshold ranges, the determination device is in a stable operating state; When any characteristic parameter is within the corresponding warning range, the device is in an emergency abnormal state, stops feeding and sends an alarm signal; Specifically, when any feature parameter is not in the corresponding warning interval and any feature parameter is in the corresponding abnormal interval, the determination device is in a potential abnormal state, and the feature parameter in the corresponding abnormal interval is obtained as an abnormal feature identifier.

3. The fecal treatment method based on microbial community analysis according to claim 2, characterized in that, Key functional microbial parameters include the copy number of acid-producing bacteria and the copy number of methanogens; If the microbial balance index exceeds the preset index threshold range, it is judged that the balance index is too high, the acid-producing bacteria are relatively excessive, or the methanogens are relatively insufficient. If the microbial balance index is lower than the preset index threshold range, it is judged that the balance index is low, the acid-producing bacteria are relatively insufficient, or the methanogenic bacteria are relatively excessive.

4. The fecal treatment method based on microbial community analysis according to claim 3, characterized in that, The process of determining whether the feed load is too high or whether there is a relative excess of methanogens and acidogens includes: When the balance index is high, if the abundance level of methanogens is less than the abundance level threshold, it is judged that there is an absolute deficiency of methanogens, and the activity of methanogens is inhibited or their proliferation is hindered. If the abundance level of methanogens is greater than or equal to the abundance level threshold, it is determined that acid-producing bacteria are relatively excessive, and the excessive feed load leads to excessive proliferation of acid-producing bacteria.

5. The fecal treatment method based on microbial community analysis according to claim 4, characterized in that, When the balance index is low, if the abundance level of acid-producing bacteria is less than the abundance level threshold, it is judged that there is an absolute deficiency of acid-producing bacteria and insufficient easily degradable organic matter in the feed. If the abundance level of acid-producing bacteria is greater than or equal to the abundance level threshold, it is determined that methanogens are relatively excessive, the material in the device is in an overstable state, and methanogens are excessively dominant.

6. The fecal treatment method based on microbial community analysis according to claim 5, characterized in that, When methanogenic activity is inhibited, the absolute abundance of methanogenic bacteria is regulated; When the feed load is too high, or there is insufficient easily degradable organic matter, or when methanogenic bacteria are overly dominant, adjust the microbial balance index to restore the microbial balance index to within the preset index threshold range.

7. The fecal treatment method based on microbial community analysis according to claim 6, characterized in that, The process of regulating the gut microbiota balance index includes: When the feed load is too high, determine the initial feed adjustment amount and reduce the current value of the feed amount, and adjust the microbial balance index by restoring the microbial balance. When there is insufficient readily degradable organic matter, the microbial community balance index can be adjusted by supplementing the substrate to adjust the carbon-nitrogen ratio of the material. When methanogens are excessively dominant, lowering the reaction temperature can regulate the bacterial balance index by inhibiting methanogens.

8. The fecal treatment method based on microbial community analysis according to claim 7, characterized in that, The process of determining the initial feed adjustment amount includes: The deviation of the microbial community balance index is measured to quantify the imbalance, and the proliferation rate of acid-producing bacteria is used to reflect the rate of deterioration of the imbalance. The feed adjustment coefficient is determined based on the deviation of the microbial community balance index and the proliferation rate of the acid-producing bacteria, and the initial feed adjustment amount is determined based on the feed adjustment coefficient.

9. The fecal treatment method based on microbial community analysis according to claim 8, characterized in that, After determining the initial feed adjustment amount, adjust the feed adjustment amount for the next initial testing cycle according to the decrease in the microbial balance index during the initial testing cycle and the correction sensitivity coefficient.

10. The fecal treatment method based on microbial community analysis according to claim 9, characterized in that, Obtain the ideal microbial community balance index corresponding to several initial detection cycles, and obtain the smoothness index by normalizing the sum of the absolute deviations between the actual microbial community balance index and the ideal microbial community balance index in each cycle. If the smoothness index is greater than or equal to the preset index threshold, the recovery process is judged to be stable or the fluctuation of the recovery process is within an acceptable range. If the smoothness index is lower than the preset index threshold, it is determined that there is a risk of over-adjustment in the fluctuation of the recovery process, and the correction sensitivity coefficient is reduced according to the ratio of the smoothness index to the preset index threshold.

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