Microbiological detection data analysis method based on electroactivity

Abstract

The invention relates to the technical field of detection data analysis, and discloses an electroactive microorganism-based detection data analysis method, which comprises the following steps: constructing an alternate circulation reaction system, inoculating electroactive microorganism mixed flora, recording environmental parameters, calculating time sequence data, and analyzing mixed flora interaction, drug-resistant gene transfer and pellicle characteristics. A four-factor coupling model is constructed, the multi-factor synergistic effect is quantified through index correction and ratio calculation, and the influence of active oxygen generation mechanism fragmentation and catalyst attenuation is solved; a diffusion degradation correlation model is constructed, a comprehensive efficiency index is obtained through analysis, efficiency quantification precision is improved through distribution uniformity correction, and carrier modification and in-situ regeneration double strategies are constructed; a reinforcement learning decision is carried out through carrier modification and in-situ regeneration double strategies, a comprehensive optimization coefficient is calculated to be combined with a near-end strategy optimization algorithm, dynamic decision is carried out by dividing different efficiency levels, metabolism and energy consumption are balanced, operation parameters are flexibly adjusted, and optimization of the overall performance is achieved.

Description

Technical Field

[0001] This invention relates to the field of detection data analysis technology, and specifically to a method for analyzing detection data based on electroactive microorganisms. Background Technology

[0002] Existing electroactive microbial detection data analysis technologies in the field of antibiotic resistance genes suffer from several problems, including insufficient adaptability to application scenarios, lack of multi-omics data fusion, blind spots in the analysis of gene horizontal transfer mechanisms, deficiencies in the standardization of operational procedures, and insufficient quantitative evaluation of the transfer inhibition effect. Specifically, these problems manifest as the lack of an antibiotic resistance gene-specific analysis system, ambiguity in the interaction mechanism between reactive oxygen species and genes, lack of detection of the gene transfer process, poor compatibility of multiple technical links, and incomplete evaluation of the inhibition effect, making it difficult to support the mechanism analysis and technology implementation in the control of antibiotic resistance gene pollution.

[0003] Existing technologies primarily focus on the removal of organic pollutants or heavy metals, and a specific analytical system for antibiotic resistance genes and their horizontal transfer has not yet been established. Traditional technologies only assess pollutant removal efficiency using electrochemical parameters such as current and power, without considering changes in the abundance of antibiotic resistance genes or the frequency of horizontal gene transfer as core analytical indicators. This fails to elucidate the dual effects of electroactive microorganisms on the degradation and controlled transfer of antibiotic resistance genes. Reactive oxygen species (ROS) generated by single-atom iron driven by electroactive microorganisms are key to the degradation of antibiotic resistance genes, but existing technologies have not quantified the dynamic correspondence between the types and concentrations of ROS and the degradation of antibiotic resistance genes, and cannot reveal the specific mechanisms by which ROS target and destroy gene structures. Electrochemical signals need to be acquired in real time, while antibiotic resistance gene detection is an offline operation. This time difference makes it impossible to establish a real-time correlation between the metabolic dynamics of electroactive microorganisms and gene changes, making it difficult to capture the instantaneous response of electron transfer and gene degradation.

[0004] Therefore, there is a need to provide a method for analyzing data from the detection of electroactive microorganisms. Summary of the Invention

[0005] The purpose of this invention is to provide a method for analyzing data from the detection of electroactive microorganisms. To solve the aforementioned problems in the prior art, this invention achieves this through the following technical solution:

[0006] The first part, an embodiment of the present invention, provides a method for analyzing data from the detection of electroactive microorganisms, specifically including the following steps:

[0007] Step 1: Construct an alternating cyclic reaction system, inoculate it with a mixed community of electroactive microorganisms, record environmental parameters, calculate time-series data, and analyze the interactions of the mixed community, drug resistance gene transfer, and biofilm characteristics;

[0008] Step 2: Construct a four-factor coupling model based on time-series data, and quantify the synergistic effect of multiple factors through exponential correction and ratio calculation to solve the impact of fragmentation of reactive oxygen generation mechanism and catalyst decay.

[0009] Step 3: Integrate the spatial distribution of reactive oxygen species and the heterogeneity of gene degradation data to construct a diffusion degradation association model to obtain a comprehensive efficiency index. Improve the efficiency quantification accuracy by correcting the distribution uniformity and construct a dual strategy of vector modification and in-situ regeneration.

[0010] Step 4: Reinforcement learning decision-making is carried out through a dual strategy of carrier modification and in-situ regeneration. The comprehensive optimization coefficient is calculated and combined with the near-end strategy optimization algorithm to divide different performance levels for dynamic decision-making.

[0011] Furthermore, the method for analyzing mixed microbial community interactions is as follows:

[0012] An anaerobic-aerobic alternating cyclic reaction system was constructed, environmental parameters were recorded in real time, and the concentration of electron shuttles secreted by the bacterial community was determined by differential pulse voltammetry to analyze the interspecific electron flux.

[0013] By combining 16S-rRNA high-throughput sequencing data, a co-occurrence network was constructed to obtain co-occurrence scores. The community symbiosis index of electroactive bacteria and resistant bacteria was calculated. The sum of all co-occurrence scores was divided by the total relative abundance of electroactive bacteria, and then divided by the total relative abundance of resistant bacteria to obtain the community symbiosis index.

[0014] Furthermore, the method for transferring the drug resistance gene and obtaining the bacterial film characteristics is as follows:

[0015] The two-dimensional spatial distribution of hydroxyl radical concentration was scanned in real time to generate a spatiotemporal thermogram; Cryo-EM-EDS was used to capture the spatial correlation between iron element and reactive oxygen generation sites in biological membranes.

[0016] The concentrations and generation rates of hydroxyl radicals, hydrogen peroxide, and superoxide ions were determined separately.

[0017] qPCR was used to determine blavin tem The copy number C and transfer frequency T of the gene, tetA of the RP4 plasmid, and the mobile element IS26 were determined; the roughness and adhesion of the bacterial film were measured, and the relative change rate of roughness and the relative change rate of bacterial film adhesion were calculated.

[0018] Furthermore, the method for constructing the four-factor coupling model is as follows:

[0019] Based on the obtained charge transfer resistance and solution resistance, the electron transfer efficiency is obtained by dividing the extracellular current density by the product of the charge transfer resistance and solution resistance, combined with a preset correction coefficient.

[0020] The iron cycling rate is obtained by dividing the change in concentration of ferrous ions and ferric ions in the reaction system by the change time. The iron cycling activity is obtained by multiplying the ratio of ferrous ions to ferric ions measured by X-ray photoelectron spectroscopy with the iron cycling rate.

[0021] It integrates the generation rate and reactivity of three reactive oxygen species: hydroxyl radicals, hydrogen peroxide, and superoxide ions. It combines the preset reaction rate constants of hydroxyl radicals, hydrogen peroxide, and superoxide ions for typical pollutants and corrects the deviation of high generation rate but low reactivity through exponential terms.

[0022] Based on the iron circulation rate and specific surface area obtained from the analysis, the catalyst lifetime coefficient is obtained by multiplying the complement of the ratio of the iron circulation rate decay to the initial iron circulation rate with respect to 1 and the complement of the ratio of the specific surface area decay to the initial specific surface area with respect to 1.

[0023] Furthermore, the method for quantifying the synergistic effect of multiple factors is as follows:

[0024] Introducing the Community Symbiosis Index (CSI) and Catalyst Lifetime Coefficient (CLC), using the formula:

[0025] Analysis and calculation of the corrected reactive oxygen species-electron-iron coupling index Quantifying the synergistic efficiency of reactive oxygen species generation with four factors, among which, The preset interaction gain coefficient, This represents the overall potential for the formation of reactive oxygen species. For electron transfer efficiency, Iron cycling activity;

[0026] Furthermore, the method for constructing the diffusion degradation association model is as follows:

[0027] A diffusion degradation correlation model was established through spatiotemporal dynamics simulation, and the coupling equation between reactive oxygen diffusion reaction and gene degradation was established. The gene degradation efficiency in different regions was simulated using COMSOL software.

[0028] By analyzing the diffusion of reactive oxygen species in space, combining electron transfer efficiency and iron cycle activity, and using the corresponding preset reaction rate constant to remove dimensions, the reactive oxygen species concentration change rate is obtained by subtracting the reactive oxygen species concentration-related terms from the current reactive oxygen species concentration combined with the preset reaction rate constant.

[0029] The concentration of reactive oxygen species was multiplied by the concentration of degradation genes, and the change rate of resistance gene concentration was obtained by combining the product with the preset degradation rate constant. The degradation efficiency of different regions was simulated using COMSOL software.

[0030] Furthermore, the method for obtaining the comprehensive performance index through the analysis is as follows:

[0031] Obtain the initial copy number of the resistance gene, calculate the difference between the initial copy number and the current copy number, and then calculate the ratio with the initial copy number to obtain the gene degradation ratio;

[0032] The changes in the proportion of DNA α-helices and the changes in the peak intensity of protein amide I reflect molecular structural damage. The degree of gene breakage is quantified by the proportion of DNA fragmentation. The damage of resistance genes at the molecular level is shown from different perspectives, and the results are compared and calculated on the same scale after standardization.

[0033] Obtain the complement of the coefficient of variation of reactive oxygen species concentration with respect to 1 to obtain the uniformity correction coefficient, and correct the effect of distribution uniformity on degradation efficiency;

[0034] The comprehensive efficiency index was calculated by summing the changes in the proportion of DNA α-helices, the changes in the peak intensity of protein amide I band, and the proportion of DNA fragmentation, and then multiplying the sum with the gene degradation rate.

[0035] Furthermore, the method for correcting the distribution uniformity is as follows:

[0036] The transfer frequency, membrane lipid peroxidation degree, relative expression level of plasmid-transferred genes, and expression level after RecA inhibition and initial expression level were obtained between the untreated and treated groups. The dynamic resistance index of gene transfer was constructed by modifying the preset plasmid type coefficient, preset membrane damage gain coefficient, and bacterial membrane roughness change rate.

[0037] Furthermore, the method for constructing the dual strategy of carrier modification and in-situ regeneration is as follows:

[0038] A dual strategy of carrier modification and in-situ regeneration was developed, combined with dynamic triggering of regeneration using catalyst lifetime coefficient CLC.

[0039] X-ray photoelectron spectroscopy was used to verify that the iron dissolution rate was controlled within the preset dissolution ratio.

[0040] If the catalyst lifetime coefficient is less than the preset lifetime threshold, the regeneration process is initiated: ascorbic acid solution is introduced into the system to reduce Fe. 3+ to Fe 2+ The regeneration recovery rate is obtained by calculating the difference between the regeneration life coefficient and the initial life coefficient and the decay life coefficient, respectively, and then calculating the ratio of the difference.

[0041] If the regeneration recovery rate is greater than or equal to the preset recovery ratio, continue with the current operating parameters; otherwise, adjust the catalyst dosage.

[0042] Furthermore, the method for classifying different performance levels is as follows:

[0043] Output the predicted comprehensive optimization coefficient for the next 3 cycles. Multiply the geometric mean of the obtained reactive oxygen-electron-iron coupling index, comprehensive efficiency index and dynamic resistance control index with the catalyst lifetime coefficient to obtain the core index term.

[0044] An environmental deviation term is introduced, and the ratio of the core index term to the environmental deviation term is calculated to obtain the comprehensive optimization coefficient.

[0045] Based on the comparison between the obtained comprehensive optimization coefficient and the preset efficiency threshold, different efficiency levels are divided, and dynamic decisions are made according to different efficiency levels.

[0046] The second part, an embodiment of the present invention, provides a data analysis system based on the detection of electroactive microorganisms, which specifically includes the following units:

[0047] Cyclic Reaction Unit: An alternating cyclic reaction system was constructed, inoculated with a mixed community of electroactive microorganisms, environmental parameters were recorded, time-series data were calculated, and the interactions of the mixed community, drug resistance gene transfer, and biofilm characteristics were analyzed.

[0048] Quantitative Synergistic Unit: Based on time-series data, a four-factor coupling model is constructed. Through exponential correction and ratio calculation, the synergistic effect of multiple factors is quantified to solve the fragmentation of reactive oxygen generation mechanism and the influence of catalyst decay.

[0049] Diffusion association unit: By integrating data on the spatial distribution of reactive oxygen species and the heterogeneity of gene degradation, a diffusion degradation association model is constructed to obtain a comprehensive efficiency index. The efficiency quantification accuracy is improved by correcting the distribution uniformity, and a dual strategy of vector modification and in-situ regeneration is constructed.

[0050] Decision adjustment unit: Reinforcement learning decision-making is carried out through a dual strategy of carrier modification and in-situ regeneration. The comprehensive optimization coefficient is calculated and combined with the proximal strategy optimization algorithm to divide different performance levels for dynamic decision-making.

[0051] The beneficial effects of this invention are as follows:

[0052] 1. An integrated online monitoring anaerobic-aerobic alternating cycle system enables dynamic coupling of electroactive microbial metabolism and catalytic processes; it breaks through the limitations of traditional single-environment cultivation, simulating natural water redox fluctuations to enhance the extracellular electron transfer efficiency of electroactive microorganisms; it simultaneously studies microbial community interactions, drug resistance gene transfer, and catalyst performance, expanding application scenarios from simple power generation to environmental remediation and pollution control; it synchronously monitors extracellular current density, oxidation peak potential, charge transfer resistance, and solution resistance to accurately quantify electron transfer efficiency; it establishes a joint determination method of iron cycling rate and valence state ratio to achieve dynamic evaluation of catalyst activity; it combines 16S-rRNA sequencing and Cytoscape co-occurrence network analysis to quantify the community symbiosis index of electroactive bacteria and resistant bacteria, revealing the intensity of interspecific interactions; it creates a synergistic analysis framework of four factors: electron transfer, iron cycling, reactive oxygen species, and catalyst lifetime, solving the fragmentation problem of isolated parameter analysis in traditional studies; it breaks through the limitations of traditional methods that only measure iron ion concentration, simultaneously considering iron redox activity and cycling rate, providing a new dimension for catalyst performance evaluation;

[0053] 2. Construct a multi-factor nonlinear coupling model to quantify the comprehensive impact of the synergistic effect of the four factors on system efficiency; introduce the community symbiosis index as a positive regulator and the catalyst lifetime coefficient as a negative constraint to form a balance regulation mechanism between promotion and inhibition; use a logarithmic function to suppress the masking effect caused by excessive iron cycling activity to ensure balanced expression of multiple factors; establish a full-chain monitoring system from gene copy number to transfer frequency, and reveal the transfer mechanism by combining membrane characteristic analysis; use AFM to measure changes in bacterial membrane roughness and adhesion to establish a correlation model between membrane physical properties and gene transfer efficiency, discover roughness, and improve transfer frequency; construct a time-series feature extraction network to capture long-distance dependencies and focus on key parameters, capture long-term dependencies in time-series data, automatically identify key parameters affecting system performance, and achieve optimal resource allocation; classify efficiency levels according to the comprehensive optimization coefficient, implement differentiated regulation strategies, and achieve precise intervention. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below:

[0055] Figure 1 This is a flowchart of the steps of a method for analyzing data from the detection of electroactive microorganisms provided in Embodiment 1 of the present invention;

[0056] Figure 2 This is a schematic diagram of a data analysis system for detecting electroactive microorganisms provided in Embodiment 2 of the present invention. Detailed Implementation

[0057] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0058] Example 1: As Figure 1 As shown in the figure, the present invention provides a method for analyzing data from the detection of electroactive microorganisms, which specifically includes the following steps:

[0059] Step 1: Construct an alternating cyclic reaction system, inoculate it with a mixed community of electroactive microorganisms, record environmental parameters, calculate time-series data, and analyze the interactions of the mixed community, drug resistance gene transfer, and biofilm characteristics;

[0060] In a specific embodiment, an anaerobic-aerobic alternating cyclic reaction system is constructed, and environmental parameters are recorded in real time. These environmental parameters include, but are not limited to, the pH value, dissolved oxygen concentration, and temperature of the reaction system.

[0061] For example, a culture solution with 50 mmol / L lactic acid as the electron donor was inoculated with a mixed flora of electroactive microorganisms, and a mesoporous SiO2-coated SA-Fe / g-C3N4 catalyst was added to construct an alternating anaerobic 2h-aerobic 1h cyclic reaction system. The system was run for 10 cycles in a constant temperature water bath shaker at 30℃ and 150 rpm. The reaction system integrated an online sensor, a microelectrode array, and a cryo-electron microscopy-energy dispersive spectroscopy sample fixation device. The online sensor monitored the pH value, dissolved oxygen concentration, and temperature of the reaction system, and environmental parameters were collected simultaneously.

[0062] Electrochemical and catalytic parameters were obtained, including extracellular current density, oxidation peak potential, charge transfer resistance, solution resistance, iron cycling rate, iron cycling activity, catalyst lifetime coefficient, electron shuttle concentration, interspecies electron flux, and electron transfer efficiency. The microbial membrane was scanned at the end of each cycle using cyclic voltammetry to record the extracellular current density and oxidation peak potential. The charge transfer resistance and solution resistance were determined using electrochemical impedance spectroscopy.

[0063] X-ray diffraction and X-ray photoelectron spectroscopy were used to analyze the crystal structure of the catalyst and Fe. 2+ / Fe 3+ Valence ratio; specific surface area was analyzed and measured using the BET method, and iron dissolution rate was quantified using X-ray photoelectron spectroscopy;

[0064] It should be noted that the BET method refers to the Brunauer-Emmett-Teller adsorption isotherm method, which indirectly calculates the specific surface area of ​​a solid material by measuring the amount of gas adsorbed on the solid surface and using a multilayer adsorption theory model.

[0065] The concentration of electron shuttles secreted by the bacterial community was determined by differential pulse voltammetry, and the interspecies electron flux was calculated using the formula: Analysis yielded interspecific electron flux ,in, The number of electrons transferred per molecule of shuttle. It is Faraday's constant. Let be the shuttle transport rate constant. The concentration of electron shuttles was used. Combined with 16S-rRNA high-throughput sequencing data, a co-occurrence network was constructed using Cytoscape to obtain co-occurrence scores. The community symbiosis index of electroactive bacteria and resistant bacteria was calculated. The sum of all co-occurrence scores was divided by the total relative abundance of electroactive bacteria, and then divided by the total relative abundance of resistant bacteria to obtain the community symbiosis index. The larger the community symbiosis index, the stronger the interaction between the two bacterial groups.

[0066] A two-dimensional spatial distribution of hydroxyl radical concentration was scanned in real time using a micro-electromechanical array to generate a spatiotemporal thermogram; Cryo-EM-EDS was used to capture the spatial correlation between iron and reactive oxygen species generation sites within the biomembrane.

[0067] It should be noted that Cytoscape is an open-source graph analysis and visualization software whose core function is to help users intuitively present and explore complex node-edge relationship networks and provide powerful network analysis algorithms; Cryo-EM-EDS is a technique that combines cryo-electron microscopy with energy-dispersive X-ray spectroscopy, mainly used to simultaneously analyze the high-resolution structure and elemental composition of samples at the atomic / nanoscale.

[0068] The concentrations and generation rates of hydroxyl radicals, hydrogen peroxide, and superoxide ions were determined using the 7-hydroxycoumarin method, a peroxidase kit, and a chemiluminescent probe, respectively.

[0069] qPCR was used to determine blavin tem The copy number C and transfer frequency T of the gene, tetA of the RP4 plasmid, and the mobile element IS26 were determined; the roughness and adhesion of the bacterial film were measured by atomic force microscopy (AFM), and the relative change rate of roughness and the relative change rate of bacterial film adhesion were calculated.

[0070] Specifically, the roughness of the treated bacterial film is subtracted from the initial bacterial film roughness to obtain the roughness change value. The roughness change value is then compared with the initial bacterial film roughness to obtain the relative roughness change rate. The adhesion force of the treated bacterial film is subtracted from the initial bacterial film adhesion force to obtain the adhesion force change value. The adhesion force change value is then compared with the initial bacterial film adhesion force to obtain the relative adhesion force change rate.

[0071] Local weighted smoothing and noise reduction are performed on time series data, outliers are removed using the 3σ criterion, and all parameters are normalized and dimensionless.

[0072] It should be noted that time-series data include, but are not limited to: extracellular current density, oxidation peak potential, charge transfer resistance, solution resistance, interspecific electron flux, community symbiosis index, and antibiotic resistance gene blavin. tem The gene copy number and transfer frequency of the tetAt of the RP4 plasmid and the mobile element IS26, as well as the relative change rate of roughness and the relative change rate of bacterial film adhesion.

[0073] Step 2: Construct a four-factor coupling model based on time-series data, and quantify the synergistic effect of multiple factors through exponential correction and ratio calculation to solve the impact of fragmentation of reactive oxygen generation mechanism and catalyst decay.

[0074] In a specific embodiment, a four-factor coupling model is constructed based on time-series data. The four factors include: electron transfer efficiency, iron cycle activity, comprehensive reactive oxygen generation potential, and catalyst lifetime coefficient.

[0075] Specifically, based on the obtained charge transfer resistance and solution resistance, the extracellular current density is divided by the product of charge transfer resistance and solution resistance using a preset correction coefficient to obtain the electron transfer efficiency (ETE).

[0076] The iron cycling rate is obtained by dividing the change in concentration of ferrous ions and ferric ions in the reaction system by the change time. The iron cycling activity (FCA) is obtained by multiplying the ratio of ferrous ions to ferric ions measured by X-ray photoelectron spectroscopy with the iron cycling rate. The FCA characterizes the catalyst’s ability to cycle ferrous ions and ferric ions. The greater the FCA, the stronger the iron-mediated catalytic activity.

[0077] This study integrates the generation rates and reactivity of three reactive oxygen species: hydroxyl radicals, hydrogen peroxide, and superoxide ions. It also incorporates preset rate constants for the reactions of hydroxyl radicals, hydrogen peroxide, and superoxide ions to typical pollutants, with preset values ​​of 3.2 × 10⁻⁶. 9 1.8×10 7 and 2.1×10 8 By correcting the bias of high generation rate but low reactivity through the exponential term, the comprehensive reactive oxygen species generation potential (ROGP) is obtained through analysis.

[0078] Based on the iron circulation rate and specific surface area obtained from the analysis, the catalyst lifetime coefficient CLC is obtained by multiplying the complement of the ratio of the iron circulation rate decay to the initial iron circulation rate with respect to 1 and the complement of the ratio of the specific surface area decay to the initial specific surface area with respect to 1. The closer the catalyst lifetime coefficient is to 1, the better the catalyst stability.

[0079] By introducing the community symbiosis index (CSI) and catalyst lifetime coefficient (CLC), the corrected reactive oxygen-electron-iron coupling index is analyzed and calculated using the formula:

[0080] Analysis yielded the reactive oxygen species-electron-iron coupling index, in which... The preset interaction gain coefficient is the community symbiosis index positive regulation index, which reflects the promoting effect of interspecific interaction on reactive oxygen generation. The natural logarithm of the denominator avoids the excessive influence when the iron cycle activity is too high. The catalyst lifetime coefficient reflects the negative effect of catalyst decay. The larger the reactive oxygen-electron-iron coupling index, the higher the synergistic efficiency of reactive oxygen generation with the four factors.

[0081] Step 3: Integrate the spatial distribution of reactive oxygen species and the heterogeneity of gene degradation data to construct a diffusion degradation association model to obtain a comprehensive efficiency index. Improve the efficiency quantification accuracy by correcting the distribution uniformity and construct a dual strategy of vector modification and in-situ regeneration.

[0082] In a specific embodiment, a diffusion degradation correlation model is established through spatiotemporal dynamics simulation, and a coupling equation between reactive oxygen species diffusion reaction and gene degradation is established. COMSOL software is used to simulate the gene degradation efficiency in different regions. By analyzing the diffusion of reactive oxygen species in space, electron transfer efficiency and iron cycle activity are integrated, and the corresponding preset reaction rate constant is used to remove dimensions. At the same time, the reactive oxygen species concentration is subtracted from the reactive oxygen species concentration related terms combined with the current reactive oxygen species concentration and the preset reaction rate constant to obtain the reactive oxygen species concentration change rate, which reflects the dynamic changes in the concentration of reactive oxygen species during diffusion, generation and consumption.

[0083] It should be noted that COMSOL is a multiphysics simulation software whose core function is to solve a system of partial differential equations, thereby simulating the interaction of multiple physical fields in the real world.

[0084] The concentration of reactive oxygen species (ROS) and the concentration of degradation genes were multiplied together, and the change rate of resistance gene concentration was obtained by combining the product with the preset degradation rate constant. This reflects the dynamic degradation rate of resistance genes under the action of ROS. The degradation efficiency of different regions was simulated using COMSOL software to quantify the impact of microscale diffusion on macroscopic removal effect.

[0085] The initial copy number of the resistance gene is obtained, the difference between the initial copy number and the current copy number is calculated, and then the ratio with the initial copy number is calculated to obtain the gene degradation ratio, which reflects the degree of reduction in the number of resistance genes during the treatment process.

[0086] The changes in the proportion of DNA α-helices and the changes in the peak intensity of protein amide I reflect molecular structural damage. The degree of gene breakage is quantified by the proportion of DNA fragmentation. The damage of resistance genes at the molecular level is shown from different perspectives, and the results are compared and calculated on the same scale after standardization.

[0087] The complement of the coefficient of variation of reactive oxygen species (ROS) concentration about 1 is obtained to obtain the uniformity correction coefficient, which corrects the effect of distribution uniformity on degradation efficiency. The ROS concentration coefficient of variation reflects the degree of dispersion of ROS in spatial distribution. The smaller the coefficient of variation, the more uniform the ROS distribution. The closer the uniformity correction coefficient is to 1, the greater the effect on improving gene removal efficiency.

[0088] The summation of changes in DNA α-helix ratio, protein amide I band intensity, and DNA fragmentation ratio was multiplied by the gene degradation rate, and the result was expressed by the formula: The overall efficiency index was calculated. ,in, The diffusion coefficient is... This represents a change in the α-helix ratio of DNA. This represents the change in peak intensity of protein amide I. The proportion of DNA fragmentation. For bacterial apoptosis rate, This is a uniformity correction factor;

[0089] It should be noted that through standardized processing and calculation, the synergistic quantification of multi-dimensional parameters is achieved. The larger the comprehensive efficiency index value, the better the gene removal efficiency.

[0090] The transfer frequency, membrane lipid peroxidation degree, relative expression level of plasmid-transferred genes, and expression level after RecA inhibition and initial expression level were obtained between the untreated and treated groups. The dynamic resistance index (GTRI) for gene transfer was constructed by modifying the preset plasmid type coefficient, preset membrane damage gain coefficient, and bacterial membrane roughness change rate. The larger the dynamic resistance index value, the more significant the resistance effect.

[0091] A dual strategy of carrier modification and in-situ regeneration was developed, which combined with dynamic triggering of regeneration by catalyst lifetime coefficient CLC to solve the problems of rapid iron dissolution and activity decay of catalyst;

[0092] A 5 nm thick mesoporous SiO2 layer was coated on the surface of g-C3N4 using the sol-gel method, and the iron dissolution rate was verified to be within the preset dissolution ratio by X-ray photoelectron spectroscopy.

[0093] If the catalyst lifetime coefficient is less than the preset lifetime threshold, the regeneration process is initiated: ascorbic acid solution is introduced into the system to reduce Fe. 3+ to Fe 2+ The regeneration recovery rate is obtained by calculating the difference between the regeneration life coefficient and the initial life coefficient and the decay life coefficient, respectively, and then calculating the ratio of the difference.

[0094] If the regeneration recovery rate is greater than or equal to the preset recovery ratio, continue with the current operating parameters; otherwise, adjust the catalyst dosage.

[0095] Step 4: Reinforcement learning decision-making is carried out through a dual strategy of carrier modification and in-situ regeneration. The comprehensive optimization coefficient is calculated and combined with the proximal strategy optimization algorithm to divide different performance levels for dynamic decision-making.

[0096] In a specific implementation, an LSTM layer is used to capture long-term temporal dependencies, and a Transformer attention mechanism is combined to focus on key parameters.

[0097] It should be noted that LSTM is a special type of recurrent neural network that effectively handles long-term dependencies in time series data. It controls the flow of information through a gating mechanism, thereby remembering important information in long-term series. Key parameters include, but are not limited to, environmental parameters, electrochemical and catalysis-related parameters, and time series data.

[0098] Based on the obtained key parameters, the predicted value of the comprehensive optimization coefficient for the next 3 cycles is output. The geometric mean of the obtained reactive oxygen-electron-iron coupling index, comprehensive efficiency index and dynamic resistance control index is multiplied with the catalyst lifetime coefficient to obtain the core index term.

[0099] By introducing an environmental deviation term, the actual operating conditions of the system are comprehensively reflected by considering environmental factors and energy consumption, thus guiding the system to operate efficiently, stably, and energy-savingly.

[0100] It should be noted that the environmental deviation item represents the sum of the absolute value of the deviation of the reaction system's pH value from the preset pH median value, the absolute value of the deviation of the reaction system's dissolved oxygen concentration from the preset dissolved oxygen concentration, and the energy consumption item.

[0101] The comprehensive optimization coefficient SCOC is obtained by comparing the core index item with the environmental deviation item.

[0102] Combining the proximal strategy optimization algorithm, the action space is set as catalyst dosage, anaerobic-aerobic cycle duration, and shaker speed, and the reward function is designed as follows: in, To comprehensively optimize the coefficients, This is the catalyst lifetime coefficient. This refers to energy consumption.

[0103] It should be noted that the proximal policy optimization algorithm is a reinforcement learning algorithm that combines policy gradients. It performs efficient policy optimization in a continuous action space. The setting of the action space clearly defines the adjustable parameter range of the system, and the reward function comprehensively considers the overall optimization coefficient, catalyst lifetime coefficient and energy consumption.

[0104] Based on the comparison between the obtained comprehensive optimization coefficient and the preset efficiency threshold, it is divided into different efficiency levels, and dynamic decision-making is made according to different efficiency levels to ensure the authenticity and stability of electroactive microbial detection data.

[0105] For example, the efficiency level is divided into three levels: low efficiency, medium efficiency, and high efficiency, based on the comprehensive optimization coefficient. If the efficiency level is low efficiency, the catalyst dosage is increased, the anaerobic cycle is shortened to 1.5 hours, and the generation of reactive oxygen species is improved. If the efficiency level is medium efficiency, the shaking speed is adjusted to optimize mass transfer, mild regeneration is initiated, and the catalyst life coefficient is maintained. If the efficiency level is high efficiency, the catalyst dosage is reduced, the anaerobic cycle is extended to 2.5 hours, and metabolism and energy consumption are balanced. The operating parameters are flexibly adjusted to achieve overall performance optimization.

[0106] Example 2: As Figure 2 As shown in the figure, the present invention provides a data analysis system based on the detection of electroactive microorganisms, which specifically includes the following units:

[0107] Cyclic Reaction Unit: An alternating cyclic reaction system was constructed, inoculated with a mixed community of electroactive microorganisms, environmental parameters were recorded, time-series data were calculated, and the interactions of the mixed community, drug resistance gene transfer, and biofilm characteristics were analyzed.

[0108] Quantitative Synergistic Unit: Based on time-series data, a four-factor coupling model is constructed. Through exponential correction and ratio calculation, the synergistic effect of multiple factors is quantified to solve the fragmentation of reactive oxygen generation mechanism and the influence of catalyst decay.

[0109] Diffusion association unit: By integrating data on the spatial distribution of reactive oxygen species and the heterogeneity of gene degradation, a diffusion degradation association model is constructed to obtain a comprehensive efficiency index. The efficiency quantification accuracy is improved by correcting the distribution uniformity, and a dual strategy of vector modification and in-situ regeneration is constructed.

[0110] Decision adjustment unit: Reinforcement learning decision-making is carried out through a dual strategy of carrier modification and in-situ regeneration. The comprehensive optimization coefficient is calculated and combined with the proximal strategy optimization algorithm to divide different performance levels for dynamic decision-making.

[0111] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the present invention should still fall within the scope of the present invention.

Claims

1. A method for analyzing data from the detection of electroactive microorganisms, characterized in that, Includes the following steps: An alternating cyclic reaction system was constructed and inoculated with a mixed community of electroactive microorganisms. Environmental parameters were recorded, time-series data were calculated, and the interactions, drug resistance gene transfer, and biofilm characteristics of the mixed community were analyzed. A four-factor coupling model is constructed based on time-series data. Through exponential correction and ratio calculation, the synergistic effect of multiple factors is quantified to solve the fragmentation of reactive oxygen generation mechanism and the influence of catalyst decay. By integrating data on the spatial distribution of reactive oxygen species and the heterogeneity of gene degradation, a diffusion degradation association model was constructed to obtain a comprehensive efficiency index. The efficiency quantification accuracy was improved by correcting the distribution uniformity, and a dual strategy of vector modification and in-situ regeneration was constructed. Reinforcement learning decision-making is carried out through a dual strategy of carrier modification and in-situ regeneration. The comprehensive optimization coefficient is calculated and combined with the proximal strategy optimization algorithm to divide different performance levels for dynamic decision-making.

2. The method for analyzing data from electroactive microorganism detection according to claim 1, characterized in that, The method for analyzing mixed microbial community interactions is as follows: An anaerobic-aerobic alternating cyclic reaction system was constructed, environmental parameters were recorded in real time, and the concentration of electron shuttles secreted by the bacterial community was determined by differential pulse voltammetry to analyze the interspecific electron flux. By combining 16S-rRNA high-throughput sequencing data, a co-occurrence network was constructed to obtain co-occurrence scores. The community symbiosis index of electroactive bacteria and resistant bacteria was calculated. The community symbiosis index was obtained by summing all co-occurrence scores, dividing by the total relative abundance of electroactive bacteria, and then dividing by the total relative abundance of resistant bacteria.

3. The method for analyzing data from electroactive microorganism detection according to claim 1, characterized in that, The method for transferring the drug resistance gene and obtaining the bacterial film characteristics is as follows: The two-dimensional spatial distribution of hydroxyl radical concentration was scanned in real time to generate a spatiotemporal thermogram; Cryo-EM-EDS was used to capture the spatial correlation between iron element and reactive oxygen generation sites in biological membranes. The concentrations and generation rates of hydroxyl radicals, hydrogen peroxide, and superoxide ions were determined separately. qPCR was used to determine blavin tem The copy number C and transfer frequency T of the gene, tetA of the RP4 plasmid, and the mobile element IS26 were determined; the roughness and adhesion of the bacterial film were measured, and the relative change rate of roughness and the relative change rate of bacterial film adhesion were calculated.

4. The method for analyzing data from electroactive microorganism detection according to claim 1, characterized in that, The method for constructing the four-factor coupling model is as follows: Based on the obtained charge transfer resistance and solution resistance, the electron transfer efficiency is obtained by dividing the extracellular current density by the product of the charge transfer resistance and solution resistance, combined with a preset correction coefficient. The iron cycling rate is obtained by dividing the change in concentration of ferrous ions and ferric ions in the reaction system by the change time. The iron cycling activity is obtained by multiplying the ratio of ferrous ions to ferric ions measured by X-ray photoelectron spectroscopy with the iron cycling rate. It integrates the generation rate and reactivity of three reactive oxygen species: hydroxyl radicals, hydrogen peroxide, and superoxide ions. It combines the preset reaction rate constants of hydroxyl radicals, hydrogen peroxide, and superoxide ions for typical pollutants and corrects the deviation of high generation rate but low reactivity through exponential terms. Based on the iron circulation rate and specific surface area obtained from the analysis, the catalyst lifetime coefficient is obtained by multiplying the complement of the ratio of the iron circulation rate decay to the initial iron circulation rate with respect to 1 and the complement of the ratio of the specific surface area decay to the initial specific surface area with respect to 1.

5. The method for analyzing data from electroactive microorganism detection according to claim 1, characterized in that, The method for quantifying the synergistic effect of multiple factors is as follows: Introducing the Community Symbiosis Index (CSI) and Catalyst Lifetime Coefficient (CLC), using the formula: Analysis and calculation of the corrected reactive oxygen species-electron-iron coupling index Quantifying the synergistic efficiency of reactive oxygen species generation with four factors, among which, The preset interaction gain coefficient, This represents the overall potential for the formation of reactive oxygen species. For electron transfer efficiency, It is active for iron cycling.

6. The method for analyzing data from electroactive microorganism detection according to claim 1, characterized in that, The method for constructing the diffusion degradation association model is as follows: A diffusion degradation correlation model was established through spatiotemporal dynamics simulation, and the coupling equation between reactive oxygen diffusion reaction and gene degradation was established. The gene degradation efficiency in different regions was simulated using COMSOL software. By analyzing the diffusion of reactive oxygen species in space, combining electron transfer efficiency and iron cycle activity, and using the corresponding preset reaction rate constant to remove dimensions, the reactive oxygen species concentration change rate is obtained by subtracting the reactive oxygen species concentration-related terms from the current reactive oxygen species concentration combined with the preset reaction rate constant. The concentration of reactive oxygen species was multiplied by the concentration of degradation genes, and the change rate of resistance gene concentration was obtained by combining the product with the preset degradation rate constant. The degradation efficiency of different regions was simulated using COMSOL software.

7. The method for analyzing data from electroactive microorganism detection according to claim 1, characterized in that, The method for obtaining the comprehensive performance index in the analysis is as follows: Obtain the initial copy number of the resistance gene, calculate the difference between the initial copy number and the current copy number, and then calculate the ratio with the initial copy number to obtain the gene degradation ratio; The changes in the proportion of DNA α-helices and the changes in the peak intensity of protein amide I reflect molecular structural damage. The degree of gene breakage is quantified by the proportion of DNA fragmentation. The damage of resistance genes at the molecular level is shown from different perspectives, and the results are compared and calculated on the same scale after standardization. Obtain the complement of the coefficient of variation of reactive oxygen species concentration with respect to 1 to obtain the uniformity correction coefficient, and correct the effect of distribution uniformity on degradation efficiency; The overall efficiency index was calculated by summing the changes in the proportion of DNA α-helices, the changes in the peak intensity of protein amide I, and the proportion of DNA fragmentation, and then multiplying the sum with the gene degradation rate.

8. The method for analyzing data from electroactive microorganism detection according to claim 1, characterized in that, The method for correcting the uniformity of distribution is as follows: The transfer frequency, membrane lipid peroxidation level, relative expression level of plasmid-transferred genes, and expression level after RecA inhibition were obtained between the untreated and treated groups. The dynamic resistance index of gene transfer was constructed by modifying the preset plasmid type coefficient, preset membrane damage gain coefficient, and bacterial membrane roughness change rate.

9. The method for analyzing data from electroactive microorganism detection according to claim 1, characterized in that, The method for constructing a dual strategy of carrier modification and in-situ regeneration is as follows: A dual strategy of carrier modification and in-situ regeneration was developed, combined with dynamic triggering of regeneration using catalyst lifetime coefficient CLC. X-ray photoelectron spectroscopy was used to verify that the iron dissolution rate was controlled within the preset dissolution ratio. If the catalyst lifetime coefficient is less than the preset lifetime threshold, the regeneration process is initiated: ascorbic acid solution is introduced into the system to reduce Fe. 3+ to Fe 2+ The regeneration recovery rate is obtained by calculating the difference between the regeneration life coefficient and the initial life coefficient and the decay life coefficient, respectively, and then calculating the ratio of the difference. If the regeneration recovery rate is greater than or equal to the preset recovery ratio, continue with the current operating parameters; otherwise, adjust the catalyst dosage.

10. The method for analyzing data from electroactive microorganism detection according to claim 1, characterized in that, The method for classifying different performance levels is as follows: Output the predicted comprehensive optimization coefficient for the next 3 cycles. Multiply the geometric mean of the obtained reactive oxygen-electron-iron coupling index, comprehensive efficiency index and dynamic resistance control index with the catalyst lifetime coefficient to obtain the core index term. An environmental deviation term is introduced, and the ratio of the core index term to the environmental deviation term is calculated to obtain the comprehensive optimization coefficient. Based on the comparison between the obtained comprehensive optimization coefficient and the preset efficiency threshold, different efficiency levels are divided, and dynamic decisions are made according to different efficiency levels.

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