Thickened oil biological flora recycling system

Abstract

The invention relates to the technical field of biological flora, in particular to a thickened oil biological flora recycling system which comprises a flora screening module, a reaction delay recognition module, a structural fatigue evaluation module, a proliferation behavior calculation module and a thallus replacement suggestion module. According to the method, the efficiency and economical efficiency of heavy oil biodegradation are improved by screening the microbial flora adapting to different heavy oil environments and optimizing and recycling the microbial flora, and the metabolic capability and environmental adaptability of strains can be accurately evaluated by obtaining detailed state parameters of the heavy oil sample and dividing the thallus culture environment; according to the method, the starting delay time and the product concentration peak value of a flora degradation reaction are monitored, efficient response strains are recognized, the flora structure is optimized, the response speed of the biodegradation process is increased, the biological efficiency of heavy oil treatment is improved, fatigue and proliferation behaviors of the strain structure are carefully evaluated, and dynamic optimization of flora iteration is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of biological flora, and particularly to a system for recycling viscous oil biological flora in a cycle. Background Art

[0002] The technical field of biological flora focuses on the utilization of microbial resources for applications such as environmental improvement, energy development, and pollution treatment. This field involves screening, culturing, and optimizing microbial populations to form a flora system with specific metabolic functions, and by controlling the growth environment and metabolic pathways of the flora, achieving the purpose of promoting the decomposition of organic matter, energy conversion, or environmental remediation. It includes, but is not limited to, methods for constructing flora, technologies for regulating microbial metabolism, research on the mechanism of synergistic action of strains, and the development of mechanisms for long-term stable operation and recycling of flora. The technology of biological flora is widely applied in industrial fields such as crude oil extraction, wastewater treatment, treatment of organic waste, and biopharmaceuticals, and has important value for improving resource utilization efficiency and reducing the environmental burden.

[0003] Among them, the system for recycling viscous oil biological flora in a cycle is a resource recycling solution applied in the process of viscous oil development and treatment. Specifically, by constructing a system for regulating the metabolism and cyclic culture of microbial flora, continuous application of strains with strong ability to degrade viscous oil and systematic recovery of metabolites are achieved. The uses of this system include improving the biodegradation efficiency of viscous oil, extending the service life of the flora, reducing the cost of culturing new strains, and realizing multiple rounds of utilization of the flora and its metabolites in the oil reservoir exploitation and crude oil treatment links, thereby improving the development efficiency and economic benefits of viscous oil resources.

[0004] Traditional utilization systems lack an understanding and control of the internal dynamics of organisms. For example, in the biological treatment process of viscous oil, traditional systems cannot effectively distinguish and utilize the characteristics of microorganisms under different environmental conditions, resulting in low biodegradation efficiency and increased treatment costs. Traditional systems generally do not have high adaptability and pertinence in the selection and optimization of flora, and cannot optimize the composition and function of the flora according to different oil reservoir conditions, restricting the application scope and effect of biological treatment technologies. This leads to low resource utilization efficiency and increased environmental burden during the development of viscous oil. For example, failure to effectively control the structural and functional stability of the flora will cause frequent interruptions in the biological treatment process, requiring frequent replacement of strains, increasing the treatment time and economic costs. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art, and to propose a system for recycling viscous oil biological flora in a cycle.

[0006] To achieve the above purpose, the present invention adopts the following technical solution: A system for recycling viscous oil biological flora in a cycle, the system includes: The flora screening module obtains the state parameters of the viscous oil sample, collects the unit enzyme activity of the flora on the target component and the change in the concentration of the degradation product in each type of environment, eliminates the circulating strains that do not meet the benchmark, and generates a multi-round adaptive flora list; Based on the multi-round adaptive flora list, the reaction delay recognition module records the delay time for the flora to initiate the degradation reaction, monitors the moments when the concentration peaks of formic acid and phenylacetic acid appear in the product, calculates the time difference between the two as the lag time interval, constructs a set of fast-response strains, and outputs a viscous oil component reaction response tag set; The structural fatigue assessment module calls the viscous oil component reaction response tag set, places the strains in three rounds of simulated viscous oil degradation reactions, collects the cell membrane thickness, cell length-width ratio, and cytoplasmic region electron density before and after the action, calculates the morphological index, and uses exceeding the stability threshold as the screening condition to output a structural stability identification table in the viscous oil reaction; Based on the structural stability identification table in the viscous oil reaction, the proliferation behavior calculation module collects the change values of the colony-forming unit growth quantity per unit time and the metabolite generation concentration, evaluates the expansion ability of the strains, and generates information on the proliferation ability of the flora in the viscous oil environment.

[0007] As a further solution of the present invention, the multi-round adaptive flora list includes the bacterial strain pedigree classification number, the environmental adaptation index level, the viscous oil component response interval identifier, the target degradation path number corresponding to the bacterial species, and the cyclic dosing priority tag. The viscous oil component reaction response tag set includes the reaction start time marker code, the viscous oil component medium type identifier, the bacterial species response time interval level code, the target metabolite identification tag, and the degradation start phase sequence code. The structural stability identification table in the viscous oil reaction includes the structural degradation trend level, the membrane layer structure decline intensity distribution, the intracellular density dynamic distribution data, the bacterial cell structure evolution type label, and the stability critical discrimination number. The information on the proliferation ability of the flora in the viscous oil environment includes the bacterial cell proliferation activity interval, the metabolite unit output intensity identifier, the flora specific growth rate code, the bacterial cell metabolism adaptability factor, and the active expansion ability level.

[0008] As a further solution of the present invention, the flora screening module includes: The state parameter recognition sub-module obtains the state parameters of the viscous oil sample. The state parameters include the dynamic viscosity, the mass fraction of n-alkanes, and the volume fraction of polycyclic aromatic hydrocarbons. According to the three state parameters, they are divided into three types of environments: high-viscosity layer, light component layer, and intermediate component layer according to the interval range. A mapping list is established between the environmental number of each type and the corresponding parameter value, and the variation range of the sample state components is calculated according to the interval difference to generate the value range of the three-layer parameter division of the viscous oil; The metabolic capacity measurement sub-module calls the value range divided by the three-layer parameters of the viscous oil, cultures the screened bacterial flora in three types of environments respectively, collects the data of the unit enzyme activity of the strains and the changes in the concentration of the corresponding degradation products in each environment, judges the degradation efficiency of the strains according to the reference value of the reaction rate constant of the target component, screens out the strains that do not meet the standard of the index, and obtains the set of enzymatic reaction rates of the bacterial flora in the target environment; The strain screening and generation sub-module calibrates the adaptation of the strains to the corresponding viscous oil types according to the set of enzymatic reaction rates of the bacterial flora in the target environment and the strains that meet the reaction rate standard in multiple recorded environments, constructs a core population set from the cells with an adaptation level greater than the set proportion range, and establishes a list of adaptable bacterial flora for multiple rounds.

[0009] As a further solution of the present invention, the reaction delay recognition module includes: The reaction environment setting sub-module, based on the list of adaptable bacterial flora for multiple rounds, obtains the component parameters of the naphthene simulated liquid and the aromatic compound simulated liquid, sets the dissolved oxygen concentration value and the constant temperature reaction temperature value in each liquid, inoculates the strains into the two simulated liquids respectively, records the starting reaction time point from the completion of inoculation of the bacterial flora to the start of the decomposition reaction, and generates a record table of the initial reaction time of the bacterial flora; The lag time calculation sub-module calls the record table of the initial reaction time of the bacterial flora, monitors the peak positions of the formic acid concentration and the phenylacetic acid concentration in the reaction system over time according to the starting reaction time values of the strains, extracts the product peak appearance times corresponding to each strain in the two simulated liquids, calculates the difference between the product peak time and the corresponding starting reaction time as the lag reaction time interval, and obtains the set of numerical values of the reaction lag time interval of the bacterial flora; The rapid response screening sub-module, according to the set of numerical values of the reaction lag time interval of the bacterial flora, sets the reaction response time threshold as the average value of the lag times of the strains in the two liquids, marks the strains with a lag time shorter than the threshold as rapid response bacteria, constructs a list of rapid response cell numbers, and establishes a reaction response label set for the viscous oil components.

[0010] As a further solution of the present invention, the structural fatigue assessment module includes: The membrane thickness collection sub-module calls the reaction response label set for the viscous oil components, inoculates the strains into a three-round viscous oil degradation simulation reaction system, collects the cell samples before and after each round of reaction respectively, detects the cell membrane thickness values of each strain in each round, and classifies and summarizes according to the round number to obtain a data table of the membrane thickness changes in three rounds; The morphological parameter extraction sub-module extracts the cell length-width ratio and the change value of the electron density in the cytoplasmic region of the same numbered strain before and after each round of reaction according to the data table of the membrane thickness changes in three rounds, respectively identifies them as the morphological change records of the corresponding rounds, aligns and archives the three types of parameter data, and establishes a set of structural evolution parameters of the bacterial flora; Based on the set of microbial community structure evolution parameters, the structural stability calculation sub-module calculates the cell membrane thickness difference sequence, the aspect ratio fluctuation amplitude, and the cytoplasmic density gradient change rate respectively according to the three-round change trajectories of each type of parameter value. The three types of parameters are respectively normalized, the morphological index is calculated, and comparison and screening are carried out according to the stability threshold. Strains with morphological indices exceeding the threshold are judged as structural fatigue objects, and a structural stability identification table in the heavy oil reaction is established.

[0011] As a further solution of the present invention, the formula for calculating the morphological index is: ; where represents the morphological index, represents the normalized value of the average difference in cell membrane thickness changes in three rounds, represents the standard deviation of the cell aspect ratio, represents the normalized gradient value of the change in electron density in the cytoplasmic region, represents the change adaptation factor of the aspect ratio fluctuation term, represents the response amplification coefficient of the cytoplasmic density change term.

[0012] As a further solution of the present invention, the proliferation behavior calculation module includes: The colony growth collection sub-module extracts the strains with qualified stability according to the structural stability identification table in the heavy oil reaction, collects the change value of the colony forming unit number of each strain per unit time, and summarizes and statistically analyzes it uniformly according to the time interval to generate the colony growth quantity value per unit cycle; The metabolic rate acquisition sub-module calls the colony growth quantity value per unit cycle, collects the change value of the metabolite generation concentration of the corresponding strain in the same time cycle, and combines and calculates it with the colony change value to obtain the product generation efficiency data per unit colony, and establishes a strain product generation rate index set; The expansion ability determination sub-module is based on the strain product generation rate index set. According to the unit growth quantity and the corresponding product generation value of each strain, the two types of data are respectively normalized and then jointly operated to calculate the strain proliferation index of each strain. According to the set index interval for classification and judgment, the bacteria with an index lower than the lower limit of the reference interval are marked as weak metabolic activity groups, and the information on the proliferation ability of the microbial community in the heavy oil environment is established.

[0013] As a further solution of the present invention, the formula for calculating the strain proliferation index of each strain is: ; where represents the strain proliferation index, represents the normalized value of colony growth per unit time, It represents the normalized value of the product formation rate per unit cell. It represents the normalized value of the average cell division cycle time. It represents the normalized value of the diffusion rate of metabolic intermediates. It represents the growth parameter adjustment coefficient. It represents the coefficient of the metabolic pressure regulation term. It represents the diffusion influence amplification coefficient.

[0014] As a further aspect of the present invention, the system further includes: Based on the information on the proliferation ability of the bacterial community in the heavy oil environment, the bacterial cell replacement suggestion module determines whether it is below the critical values of both the proliferation ability and the structural stability by combining the identified low-activity strains with the structural stability in the heavy oil reaction, marks the cyclic bacterial cells that need to be replaced, and establishes a cyclic bacterial community replacement sorting list. The cyclic bacterial community replacement sorting list specifically includes the replacement instruction sequence number, the replacement suggestion fitness level, the key metabolic pathway dependence factor, the candidate bacterial species function classification code, and the bacterial community structure reconstruction weight coefficient.

[0015] As a further aspect of the present invention, the bacterial cell replacement suggestion module includes: Based on the information on the proliferation ability of the bacterial community in the heavy oil environment, the low-activity identification sub-module extracts the strain numbers and the corresponding proliferation ability values according to the strain numbers marked as having weak metabolic activity, screens and summarizes the strains with proliferation ability values lower than the proliferation ability determination reference value into a group to be reviewed, and establishes a weak-activity strain identification list. The stability joint judgment sub-module calls the weak-activity strain identification list, matches the structural stability identification of the strains, performs a comparison operation to determine whether it is lower than the structural stability reference value, screens the strains that simultaneously meet the lower critical limits of the proliferation ability and the stability, and establishes a replacement suggestion strain judgment result set. The replacement sequence generation sub-module calls the replacement suggestion strain judgment result set, assigns replacement sorting tag numbers according to the metabolic pathway categories to which the strains belong and the functional redundancy relationship within the population, sorts the strains that need to be replaced according to the tag numbers, and establishes a cyclic bacterial community replacement sorting list.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In the present invention, by screening microbial flora adapted to different heavy oil environments and optimizing their recycling, the efficiency and economy of heavy oil biodegradation are improved. By obtaining detailed state parameters of heavy oil samples and dividing the bacterial culture environment, the metabolic capacity and environmental adaptability of bacterial strains can be accurately evaluated. Monitoring the start-up delay time and peak product concentration of the degradation reaction of the microbial flora, identifying highly responsive strains, optimizing the microbial flora structure and increasing the response speed of the biodegradation process ensure that high-performance bacterial strains are maintained in the system, enhancing the biological efficiency of heavy oil treatment. A detailed evaluation of the structural fatigue and proliferation behavior of the bacterial strains, combined with dual criteria of stability and proliferation ability to identify and replace low-efficiency microbial flora, realizes the dynamic optimization of microbial flora iteration, ensuring the biological activity and structural stability of the microbial flora during the recycling process, effectively extending the service life and effect of the microbial flora, and reducing the cultivation cost of newly added bacterial strains. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0018] Figure 1 is the system flowchart of the present invention; Figure 2 is the schematic diagram of the system framework of the present invention; Figure 3 is the flowchart of the microbial flora screening module of the present invention; Figure 4 is the flowchart of the reaction delay identification module of the present invention; Figure 5 is the flowchart of the structural fatigue assessment module of the present invention; Figure 6 is the flowchart of the proliferation behavior calculation module of the present invention; Figure 7 is the flowchart of the bacterial strain replacement suggestion module of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] The following describes the technical solutions in the present invention with reference to the drawings.

[0020] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly, the use of the word "exemplary" is intended to present concepts in a specific manner. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two.

[0021] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same.

[0022] In the embodiments of the present invention, sometimes subscripts such as W 1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meanings they express are the same.

[0023] To make the technical problems, technical solutions, and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0024] Please refer to Figure 1 , a system for recycling biotic flora of heavy oil. The system includes: The flora screening module obtains the state parameters of the heavy oil sample. The state parameters include dynamic viscosity, mass fraction of n-alkanes, and volume fraction of polycyclic aromatic hydrocarbons. According to each parameter, three types of bacterial culture environments, namely high-viscosity layer, light-component layer, and intermediate-component layer, are divided. The unit enzyme activity of the flora on the target component and the change in the concentration of degradation products are collected in each type of environment. The minimum reaction rate constant in each type of environment is set as the screening criterion, and the circulating strains that do not meet the criterion are eliminated. According to the multi-environment adaptability, a set of circulating core flora is established, and a multi-round adaptability flora list is generated. Based on the multi-round adaptability flora list, the reaction delay recognition module sets the dissolved oxygen concentration and constant temperature reaction conditions in two types of simulation liquids rich in naphthenes and aromatic compounds, records the delay time for the flora to initiate the degradation reaction, and monitors the occurrence time of the concentration peaks of formic acid and phenylacetic acid in the products. The time difference between the two is calculated as the lag time interval, a set of fast-response strains is constructed, and a reaction response tag set for heavy oil components is output. The structural fatigue assessment module calls the reaction response tag set for heavy oil components, places the strains in three rounds of simulated heavy oil degradation reactions, collects the cell membrane thickness, cell length-width ratio, and cytoplasmic region electron density before and after the action, weights the change trends in each round proportionally, calculates the morphological index, and uses exceeding the stability threshold as the screening condition to output a structural stability identification table in heavy oil reactions. Based on the structural stability identification table in heavy oil reactions, the proliferation behavior calculation module extracts the strains with qualified stability, collects the change values of the number of colony-forming units per unit time and the concentration of metabolite generation, calculates the growth rate and the product generation rate respectively, normalizes the two data, evaluates the expansion ability of the strains, determines whether the bacteria belong to the group with weak metabolic activity, and generates information on the proliferation ability of the flora in the heavy oil environment. Based on the information about the proliferation ability of the microbial community in the heavy oil environment, the microbial replacement suggestion module determines whether it is below the critical level of both proliferation ability and structural stability by identifying low-activity strains and combining the structural stability of low-activity strains in the heavy oil reaction, marks the circulating microbial cells that need to be replaced, and establishes a sorting list for the replacement of the circulating microbial community; Dynamic viscosity represents the ability of a liquid to resist deformation during flow and can be measured by a viscometer; the mass fraction of n-alkanes / volume fraction of polycyclic aromatic hydrocarbons is a common chemical composition index of heavy oil and is a standard item in oil product analysis; unit enzyme activity refers to the enzymatic reaction ability exhibited by unit mass of protein; the minimum reaction rate constant is a key parameter for evaluating reaction efficiency in enzyme-catalyzed kinetics; cell membrane thickness, cell length-width ratio, and cytoplasmic region electron density are standard characterization indicators in transmission electron microscope image analysis; colony-forming unit is a commonly used counting unit for microbial culture; specific growth rate and product formation rate are commonly used process control parameters in fermentation kinetics and can be directly calculated from experimental data; The multi-round adaptive microbial community list includes microbial lineage classification numbers, environmental adaptation index levels, heavy oil component response interval identifiers, strain corresponding target degradation path numbers, and circulating dosing priority tags. The heavy oil component reaction response tag set includes reaction start time marker codes, heavy oil component medium type identifiers, strain response time interval level codes, target metabolite identification tags, and degradation start phase sequence codes. The structural stability identification table in the heavy oil reaction includes structural degradation trend levels, membrane layer structure decline intensity distributions, intracellular density dynamic distribution data, microbial cell structure evolution type labels, and stability critical discrimination numbers. The information about the proliferation ability of the microbial community in the heavy oil environment includes microbial cell proliferation activity intervals, metabolite unit output intensity identifiers, microbial community specific growth rate codes, microbial cell metabolic adaptability factors, and activity expansion ability levels. The sorting list for the replacement of the circulating microbial community specifically includes replacement instruction sequence numbers, replacement suggestion adaptability levels, key metabolic path dependence factors, candidate strain functional classification codes, and microbial community structure reconstruction weight coefficients.

[0025] Please refer to Figure 2 and Figure 3 The microbial screening module includes a state parameter identification sub-module, a metabolic ability determination sub-module, and a strain screening and generation sub-module; The state parameter identification sub-module obtains the state parameters of the heavy oil sample. The state parameters include dynamic viscosity, mass fraction of n-alkanes, and volume fraction of polycyclic aromatic hydrocarbons. According to the three state parameters, the environments are divided into three categories: high-viscosity layer, light-component layer, and intermediate-component layer according to the interval range. A mapping list is established for each category of environment number and the corresponding parameter values. The variation range of the sample state components is calculated based on the interval differences, and the value range of the three-layer parameter division of heavy oil is generated; To obtain the state parameters of heavy oil samples, it is first necessary to collect crude oil samples representing different reservoir conditions during the heavy oil sampling stage, and use a rotational viscometer to measure the dynamic viscosity of the samples under set shear rates and constant temperature environments. For example, record the viscosity value at a shear rate of 50 s -1 , temperature of 60 °C. The unit of dynamic viscosity measurement set here is mPa·s. Then, detect the mass fraction of n-alkanes in the range of C10-C20 in the sample by gas chromatography. This mass fraction is often expressed as a percentage and serves as an index for the light components of heavy oil. For example, the mass fraction of n-alkanes in a certain sample is 9.8%. At the same time, use liquid chromatography combined with ultraviolet detection technology to quantify the volume fraction of polycyclic aromatic hydrocarbons such as naphthalene and phenanthrene in the sample. This value is in ppm or μL / L and provides a criterion for identifying highly aromatic components in heavy oil. After obtaining the data of the three types of parameters, divide three interval standards according to the literature and actual measurements. Among them, the dynamic viscosity is segmented into 1000~3000, 3000~6000, 6000~10000 mPa·s, the mass fraction of n-alkanes is segmented into less than 5%, 5%~10%, greater than 10%, and the volume fraction of polycyclic aromatic hydrocarbons is set into three segments of less than 150 ppm, 150~300 ppm, and greater than 300 ppm, corresponding to the light component layer, the intermediate component layer, and the high-viscosity layer respectively. Map the sample numbers to three types of environment numbers according to the intervals to which the three state parameters belong, and establish a correspondence table between the sample numbers and the three parameter values. By comparing the jump amplitudes of each parameter in the sample at the boundaries of different intervals, define the component variation range value ΔS. ΔS can be calculated as the weighted sum of the deviation degrees of the three parameters in the set standard intervals, that is ; where respectively represent the dynamic viscosity, the mass fraction of n-alkanes, and the volume fraction of polycyclic aromatic hydrocarbons of the sample, is the central value of its corresponding segment, is the empirically set weight coefficient (here it can be set to 1 / 3 for all). Finally, archive the environmental category corresponding to the sample according to the ΔS value and form the value range of the three-layer parameter division of heavy oil, , , , , , respectively represent the maximum and minimum values of the dynamic viscosity, the mass fraction of n-alkanes, and the volume fraction of polycyclic aromatic hydrocarbons of the sample.

[0026] The metabolic capacity determination submodule calls the three-layer parameter division value range of heavy oil, cultivates the screened bacterial community in three types of environments, collects the unit enzyme activity of the strain and the corresponding degradation product concentration change data in each environment, judges the degradation efficiency of the strain according to the benchmark value of the reaction rate constant of the target component, screens out the strains that do not meet the index, and obtains the enzymatic reaction rate set of the bacterial community in the target environment; After calling the three-layer parameter division range of heavy oil, the selected bacterial communities with preliminary heavy oil degradation potential are assigned to three types of environmental simulation systems according to their numbers. The simulation systems should respectively prepare reaction solutions corresponding to the three-layer parameter ranges. For example, in the high-viscosity layer configuration, the viscosity is adjusted to 8000 mPa·s, the alkane volume fraction is controlled to 3%, and the aromatic concentration is set to 400 ppm. The environment is adjusted to a stable state 24 hours before cultivation. Then, bacterial suspensions of the same concentration are respectively connected to each system, and a constant temperature and constant speed culture device is set to maintain the reaction conditions. The unit enzyme activity of each strain after 24 hours in the system is recorded, and the enzyme activity unit is expressed in U / mg. The concentration change data of the target degradation products (such as formic acid and butenoic acid) in the reaction solution are collected in mg / L. The strain number, environmental type, unit enzyme activity value and product concentration difference are uniformly imported into the data set and compared with the reaction rate constant benchmark value. The benchmark value is the minimum effective conversion rate set for each component in the historical experiment. For example, the degradation rate k set in the aromatic hydrocarbon environment should be greater than 0.18 mol·L -1 ·h -1 , calculate the measured conversion rate of each strain in each type of environment and make judgments item by item with the benchmark value. When the unit enzymatic reaction value of a strain in any environment fails to reach the set rate, it will be marked as an elimination object, thereby constructing a set of enzymatic reaction rates of the bacterial flora in the target environment.

[0027] The strain screening submodule calibrates the adaptation of strains to heavy oil types based on the set of enzymatic reaction rates of the bacterial flora in the target environment and the strains that meet the reaction rate benchmark in multiple recorded environments. The bacterial flora with a fitness level greater than the set ratio range is constructed as a core population set, and a multi-round adaptive bacterial flora list is established. According to the strain numbers screened out from the set of enzymatic reaction rates of the flora under the target environment, the results were used to extract whether all strains in the three types of environments met the benchmark rate conditions, and a multi-environment response table was established. The strains that were qualified in all three environments were defined as broad-spectrum adaptive strains. Then, according to the adaptation of the heavy oil type, a criterion for determining the fitness level was constructed. The enzymatic reaction rates in the three types of environments were normalized to the interval [0,1], and the adaptation ratio range threshold was set to 0.75, that is, strains with at least two of the three items higher than the normalization threshold of 0.75 were determined to be high-fitness bacteria. All strains that met the ratio conditions were selected as the core circulation population, and a core flora table was established based on the strain numbers, and their adaptation labels were recorded to form a complete multi-round adaptive flora list.

[0028] Please refer to Figure 2 and Figure 4 , the reaction delay recognition module includes a reaction environment setting sub-module, a lag time calculation sub-module, and a quick response screening sub-module; The reaction environment setting sub-module obtains the component parameters of the naphthene simulated liquid and the aromatic compound simulated liquid based on the multi-round adaptive flora list, sets the dissolved oxygen concentration value and the constant temperature reaction temperature value in each liquid, inoculates the strains into the two simulated liquids respectively, and records the starting reaction time point from the completion of the inoculation of the flora to the start of the decomposition reaction, generating a flora initial reaction time record table; Based on the multi-round adaptive flora list, it is first necessary to clarify the strain numbers and their grouping situations, and inoculate each strain into two different reaction systems, namely the naphthene simulated liquid and the aromatic compound simulated liquid. When preparing the naphthene simulated liquid, a mixture of cyclohexane and methylcyclopentane is selected as the reaction matrix, and the mass fraction is set to 1%. When preparing the aromatic compound simulated liquid, a mixed solution of naphthalene and xylene is used, and the volume fraction is set to 0.5%. A constant dissolved oxygen concentration and temperature condition need to be set for each type of reaction system. The dissolved oxygen concentration is uniformly set to 6.5 mg / L, and the temperature is set to 42°C. The regulation is achieved through an oxygen electrode and a constant temperature water bath device. When inoculating the strains, the colony forming unit concentration in each bacterial liquid should be maintained at 1×10 6 CFU / mL, and start timing at 0 minutes after inoculation. Monitor whether there are signs of primary metabolites such as formic acid and phenylacetic acid in the reaction solution. At the same time, use a chromogenic reagent to continuously sample within 10 minutes, and measure whether there is a color reaction of the acidic product every minute. The time point of the initial color change is calibrated as the starting time point of the strain to initiate the degradation reaction. For example, if the color reaction of a certain strain significantly increases at the 6th minute after inoculation, record the starting reaction time of this strain as 6 minutes. Record the starting time of each strain in the two systems separately into the data table throughout the process, and finally generate a flora initial reaction time record table.

[0029] The lag time calculation sub-module calls the flora initial reaction time record table, monitors the peak positions of the formic acid concentration and the phenylacetic acid concentration in the reaction system over time according to the starting reaction time values of the strains, extracts the product peak appearance times corresponding to each strain in the two types of simulated liquids, calculates the difference between the product peak time and the corresponding starting reaction time as the lag reaction time interval, and obtains a set of flora reaction lag time interval numerical values; After calling the record form of the initial reaction time of the microbial community, it is necessary to synchronously monitor the reaction process in two types of simulation systems in sequence according to the strain numbers. It is set to collect the reaction solution samples every two minutes. In high performance liquid chromatography (HPLC), the detection wavelengths of formic acid and phenylacetic acid are set to 210 nm and 254 nm respectively, and the retention times are set to 2.1 minutes and 3.5 minutes respectively. Detect the peak positions in each sampling and record the concentration change trend. Set the sampling end point to terminate the recording when the concentration does not increase within 30 minutes. In the analysis, if the formic acid peak of a certain strain reaches the maximum value at the 16th minute and its corresponding initial reaction time is the 6th minute, then the reaction lag time of this component is 10 minutes. Phenylacetic acid appears at the 20th minute and the starting time is the 6th minute, so the lag time is 14 minutes. Calibrate and average the lag times under the two types of components respectively to obtain the average reaction lag time of this strain as 12 minutes. In a similar way, calculate and summarize the lag time data of all strains in the two types of simulation liquids respectively, and finally form a numerical set of the reaction lag time of the microbial community.

[0030] The fast response screening sub-module sets the reaction response time threshold as the average of the strain lag times in the two types of liquids according to the numerical set of the reaction lag time of the microbial community, marks the strains with lag times shorter than the threshold as fast response bacteria, constructs a list of fast response bacterial strain numbers, and establishes a reaction response tag set for heavy oil components; Extract the lag time data of all strains according to the numerical set of the reaction lag time of the microbial community, calculate the sum and average of the reaction lag times of each strain in the two types of simulation liquids based on the strain numbers. Set the reaction response time threshold as the arithmetic average of the lag times of all strains in the two types of simulation liquids. The calculation formula for this average value is: ; where is the reaction lag time of the th strain in the naphthene simulation liquid, is the reaction lag time in the aromatic simulation liquid, is the total number of strains. For example, if there are 5 strains, their lag times in the naphthene simulation liquid are 9, 12, 11, 13, 10 minutes respectively, and their reaction lag times in the aromatic simulation liquid are 10, 11, 13, 14, 12 minutes respectively, then minutes. Then, take this 11.5 minutes as the response time threshold, screen out the strains with lag times less than this value and mark them as fast response bacteria, and sort out their numbers. Finally, construct a list of fast response bacterial strain numbers and establish a reaction response tag set for heavy oil components.

[0031] Please refer to Figure 2 and Figure 5 The structural fatigue assessment module includes a film thickness acquisition sub-module, a morphological parameter extraction sub-module, and a structural stability calculation sub-module; The film thickness acquisition sub-module calls the response label set of heavy oil component reactions, inoculates the strains into a three-round heavy oil degradation simulation reaction system, collects cell samples before and after each round of reaction respectively, detects the cell membrane thickness values of each strain in each round, and classifies and summarizes according to the round number to obtain a data table of film thickness changes in three rounds; After calling the response label set of heavy oil component reactions, it is first necessary to extract the strain numbers identified as rapid responses from the label set, and inoculate the strains into a three-round heavy oil degradation simulation system according to the numbers. The heavy oil samples in the reaction system of each round have the same source. The reaction liquid ratio is set with a heavy oil concentration of 5% by volume, and the supplementary nutrient source is 0.5% glucose solution. The reaction system is kept suspended by a magnetic stirrer, the temperature of the incubator is kept at 40 °C, and each round of reaction lasts for 24 hours. The concentration of the inoculated strains is controlled at 1×10 6 CFU / mL. An initial sample needs to be collected before each round for structural comparison. After the end, cell separation is immediately carried out. The cells are collected by filtration and centrifugation steps, and then the cells are fixed. After pre-fixation with glutaraldehyde, uranium acetate staining is carried out, and the cell membrane thickness is measured using a transmission electron microscope (TEM). The measurement unit is nm. Each strain is measured five times in three rounds of reactions and the average value is taken. For example, the pre-reaction membrane thickness of a certain strain in the first round is 38 nm, and after the reaction it is 34 nm, from 37 nm to 33 nm in the second round, and from 36 nm to 32 nm in the third round. After all the data are summarized, they are sorted according to the round number, and a film thickness change record matrix is established by comparing the differences before and after, generating a data table of film thickness changes in three rounds.

[0032] The morphological parameter extraction sub-module extracts the cell length-width ratio and the change value of the electron density in the cytoplasmic region of the strains with the same number before and after each round of reaction according to the data table of film thickness changes in three rounds, and respectively marks them as the morphological change records of the corresponding rounds. The three types of parameter data are aligned and archived to establish a set of parameters for the evolution of the microbial community structure; According to the strain numbers recorded in the three-round film thickness change data table, extract the morphological parameters of the corresponding strains before and after each round of reaction. Calibrate the pixel points through the TEM images of the same round, use image analysis software to measure the maximum cell length and maximum width, calculate the length-width ratio and record it. The length unit is in μm. For example, the length-width ratio of a certain strain before the first round is 1.8, and after is 2.2, 1.9 and 2.1 in the second round, and 2.0 and 2.4 in the third round. At the same time, perform gray value analysis on the cytoplasmic region, extract the gray difference between the central region and the edge of the gray value to calculate the density gradient, and normalize the obtained value to the data in the range of 0-1. For example, the density change in the first round is 0.23, 0.27 in the second round, and 0.31 in the third round. The three indicators are aligned in rows and columns according to the strain number and round number respectively, and the film thickness difference, length-width ratio change, and electron density change in the three rounds under the same strain are corresponded in sequence, construct a complete table for recording and archiving, and establish a set of parameters for the evolution of the microbial community structure.

[0033] Based on the set of parameters for the evolution of the microbial community structure, according to the three-round change trajectories of each type of parameter value, calculate the cell membrane thickness difference sequence, the fluctuation range of the length-width ratio, and the change rate of the cytoplasmic density gradient respectively. Normalize the three types of parameters respectively, calculate the morphological index, and compare and screen according to the stability threshold. Determine the strains with morphological indices exceeding the threshold as the objects of structural fatigue, and establish a table for structural stability identification in the heavy oil reaction; The formula for calculating the morphological index is: ; Among them, represents the morphological index, represents the normalized value of the average difference in the cell membrane thickness change in three rounds, represents the standard deviation of the cell length-width ratio, represents the normalized gradient value of the electron density change in the cytoplasmic region, represents the change adaptation factor of the length-width ratio fluctuation term, represents the response amplification coefficient of the cytoplasmic density change term; Based on the set of parameters for the evolution of the microbial community structure, extract the three-round data of each strain from the table. First, calculate the average difference in the film thickness change, take the average difference before and after the reaction, and record it as , for example, the film thickness differences before and after the first to third rounds of a certain strain are 4 nm, 4 nm, and 4 nm, and the average is = 4 nm, and after normalization, it is set to 0.44. The standard deviation σr of the length-width ratio data is calculated as √[(0.42 + 0.22 + 0.42) / 3] ≈ 0.35, without units. The linear change gradient of the electron density change in the cytoplasmic region is calculated using the three-round gray difference = (0.31 - 0.23) / 2 = 0.04. After normalization, it is set to 0.25. Substitute the above parameters into the morphological index calculation formula: ; If we take , , then we get: ; According to the set threshold, the critical value is 0.45. = 0.460 > 0.45. It is determined that this strain is an object of structural fatigue. Judge the results of all samples in sequence according to the strain number, and establish an identification table of structural stability in the heavy oil reaction.

[0034] Please refer to Figure 2 and Figure 6 . The proliferation behavior calculation module includes a colony growth collection sub-module, a metabolic rate acquisition sub-module, and a expansion ability determination sub-module; The colony growth collection sub-module extracts the strains with qualified stability according to the identification table of structural stability in the heavy oil reaction, collects the change value of the colony forming unit number of each strain per unit time, and summarizes and statistically analyzes them uniformly according to the time interval to generate the colony growth quantity value per unit cycle; According to the identification table of structural stability in the heavy oil reaction, first extract the strain numbers marked as "structurally stable", and archive their corresponding stability evaluation values. Subsequently, enter the colony culture stage. Cultivate each strain in a standard LB liquid medium, and the initial inoculation concentration is uniformly 1×10 6 CFU / mL. Set three parallel experiments for each sample. Use a spectrophotometer to detect the absorbance at a wavelength of 600 nm every hour, record the OD 600 value, and take samples at 0 h, 6 h, and 12 h at the start of cultivation respectively. After ten-fold serial dilution, plate them on a solid medium, and count the number of colony units CFU formed after culturing for 24 hours. If the CFU values of a certain strain at 0 h, 6 h, and 12 h are 1×10 6 , 5×10 7 , 1×10 8 CFU / mL, then the colony growth amount per unit time can be expressed as the average growth rate ≈ 8.25×10 6 CFU / (mL·h). Then normalize the colony growth amount per unit time of all strains, set the maximum growth value as the standard 1.0, and calculate the normalized values of the growth rates of each strain, such as 0.82, 0.67, 0.91, and uniformly summarize and statistically analyze them within a time interval of 12 hours to establish the colony growth quantity value per unit cycle.

[0035] The metabolic rate acquisition sub-module calls the value of the number of colony growth per unit cycle, collects the change value of the metabolite generation concentration of the corresponding strain within the same time cycle, and combines and calculates it with the colony change value to obtain the product generation efficiency data per unit colony, and establishes a strain product generation rate index set; Call all the strain numbers in the value of the number of colony growth per unit cycle, and use the number as the query index to extract the change in the concentration of metabolites in the culture solution of the same strain within the same culture cycle. The metabolites are represented by volatile fatty acids such as acetic acid and butyric acid. The detection method is gas chromatography, and the unit is mg / L. Taking a certain strain as an example, the acetic acid concentration is 20 mg / L at 0 h and 145 mg / L at 12 h, so the generated concentration change is 125 mg / L. If the colony growth of this strain per unit cycle is 8.25×10 6 CFU / (mL·h), then the calculation of the product generation efficiency per unit colony is: 125 / (8.25×10 6 ) ≈1.515×10 -5 mg·h -1 ·CFU -1 , and the generation efficiency results of all strains are normalized. The maximum power generation efficiency corresponds to 1.0, and the values of other strains are scaled proportionally to standardized values such as 0.61 and 0.72. After sorting, they are uniformly archived and attached with the corresponding numbers to establish a strain product generation rate index set.

[0036] The expansion ability determination sub-module, based on the strain product generation rate index set, according to the unit growth quantity and the corresponding product generation value of each strain, respectively normalizes the two types of data and then performs a joint operation to calculate the strain proliferation index of each strain. According to the set index interval for classification and judgment, the bacteria with an index lower than the lower limit of the reference interval are marked as a group with weak metabolic activity, and the information on the proliferation ability of the bacterial community in the heavy oil environment is established; The formula for calculating the strain proliferation index of each strain is: ; Among them, represents the strain proliferation index, represents the normalized value of the colony growth per unit time, represents the normalized value of the product generation rate per unit cell, represents the normalized value of the average cell division cycle time, represents the normalized value of the diffusion rate of metabolic intermediates, represents the growth parameter adjustment coefficient, represents the metabolic pressure adjustment term coefficient, represents the diffusion influence amplification coefficient; Based on the normalized value of the unit growth of each strain in the strain product generation rate index set , the normalized product formation rate , and the average division cycle time derived from the experimental records , the time difference between the division peak and the start delay is obtained using the colony density time curve, and the normalized values are such as 0.31, 0.42, 0.28. At the same time, the diffusion rate of the metabolic intermediate product is measured , the method is to construct a Fourier diffusion model through the reaction solution concentration gradient, and the results after normalization are such as 0.15, 0.22, 0.17. Then substitute into the formula: ; where set , , , taking a certain strain as an example , , , , then: ; Set the strain proliferation index The lower limit of the interval is 0.75. Strains with values lower than this are judged as weak metabolic activity groups. Finally, based on the judgment results, the information on the proliferation ability of the microbial community in the heavy oil environment is established.

[0037] Please refer to Figure 2 and Figure 7 , the cell replacement suggestion module includes a low-activity recognition sub-module, a stability joint judgment sub-module, and a replacement sequence generation sub-module; Based on the information on the proliferation ability of the microbial community in the heavy oil environment, the low-activity recognition sub-module extracts the strain numbers and their corresponding proliferation ability values according to the strain numbers marked as weak metabolic activity, and screens and summarizes the strains with proliferation ability values lower than the judgment benchmark value into a group to be reviewed, and establishes a list of weak-activity strain recognition; Based on the information on the proliferation ability of the microbial community in the heavy oil environment, first extract the numbers of all strains and their corresponding strain proliferation indices , organize all the index data into a table, and perform a screening operation according to the set proliferation ability benchmark value. This benchmark value is determined by the 25% quantile of the strain proliferation index in the experiment. After arranging all the index values in ascending order, select the data at the corresponding position as the benchmark value. For example, if all the strain indices are 0.53, 0.61, 0.68, 0.72, 0.79, 0.81, 0.86, 0.93, 0.95, 0.97, then the 25% quantile value is 0.68. Set this value as the benchmark, and then compare the value of each strain in the table with the benchmark value item by item. When the index of the strain is less than 0.68, it is marked as "low activity". After screening and marking, reclassify its number into a new table and retain its corresponding Complete information supplementation and classification for numerical values and basic physiological information, such as the division cycle, optimal action temperature, etc., and finally establish a list for identifying weakly active strains.

[0038] The stability joint judgment sub-module calls the list for identifying weakly active strains, matches the structural stability identifiers of the strains, performs a comparison operation to determine whether it is lower than the structural stability benchmark value, screens out the strains that simultaneously meet the critical lower limits of proliferation ability and stability, and establishes a judgment result set for the proposed replacement strains; After calling the list for identifying weakly active strains, extract all the strain numbers, match them one by one with the structural stability identifier table in the heavy oil reaction according to the numbers, and extract their corresponding morphological indices and organize them into a new comparison table, and set the structural stability benchmark value to be 0.45. This value is determined based on the upper limit of the value of the fatigue strains in the previous structural stability evaluation experiment. For each pair of numbered strains, perform a dual-index judgment: if and , then the strain is simultaneously judged to be of low activity and structurally fatigued, meeting the replacement conditions. Screen out the strain numbers that meet the dual conditions and include them in the replacement candidate set. Record all the basic parameters of proliferation, structure, and metabolism of the screened strains, summarize the numbers and enter them into the table uniformly, and finally establish a judgment result set for the proposed replacement strains.

[0039] The replacement sequence generation sub-module calls the judgment result set for the proposed replacement strains, assigns replacement sorting tag numbers according to the metabolic pathway categories to which the strains belong and the functional redundancy relationship within the population, sorts the strains to be replaced according to the tag numbers, and establishes a replacement sorting list for the cyclic microbial community; After calling the judgment result set for the proposed replacement strains, extract the metabolic pathway function categories corresponding to each strain. This information is derived from the enzyme coding and metabolic site data of the strains, such as participating in the fatty acid β-oxidation pathway, aromatic compound cleavage pathway, etc. At the same time, read the functional redundancy relationship values archived in the system , which is determined by whether the key enzyme expression of the strain in the same pathway can be replaced by other strains. For example, if a strain is the only expressing strain of the enzyme CoA-transferase in pathway C4, the redundancy relationship value is 0. If the enzyme can also be expressed by another strain, the redundancy relationship value is set to 0.5 or 1. According to the fact that the greater the redundancy relationship value, the lower the replacement cost, after grouping all the strains according to the functional categories, sort them from large to small according to the value within each group, and assign replacement sorting tag numbers in sequence, such as tag 001, 002, 003, etc. After integrating the sorting, generate a unified sorting list, and finally establish a replacement sorting list for the cyclic microbial community.

[0040] It should be understood that the term "and / or" in this text is merely a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. Additionally, in this text, the character " / " generally represents an "or" relationship between the preceding and following associated objects, but it may also represent an "and / or" relationship, which can be specifically understood by referring to the context before and after.

[0041] In the present invention, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or its similar expressions refer to any combination of these items, including any combination of single item(s) or plural item(s). For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0042] It should be understood that in various embodiments of the present invention, the magnitudes of the sequence numbers of the above processes do not imply the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0043] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this text can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present invention.

[0044] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the devices, apparatuses, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0045] In several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be in an electrical, mechanical, or other forms.

[0046] The unit described as a separation component may or may not be physically separated. The component displayed as a unit may or may not be a physical unit, that is, it may be located in one place or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0047] In addition, each functional unit in various embodiments of the present invention may be integrated in a processing unit, may exist separately as individual physical units, or two or more units may be integrated in one unit.

[0048] If the described function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0049] As described above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A heavy oil bio-bacteria recycling system, characterized in that: The system comprises: The bacterial community screening module obtains the status parameters of the heavy oil sample, collects the unit enzyme activity and degradation product concentration changes of the bacterial community to the target component under each type of environment, removes the circulating strains that do not meet the benchmark, and generates a multi-round adaptive bacterial community list; The reaction delay identification module records the delay time of the bacterial community initiating the degradation reaction based on the multi-round adaptive bacterial community list, monitors the peak moments of formic acid and phenylacetic acid concentrations in the product, calculates the time difference between the two as the lag time, constructs a fast response strain set, and outputs a heavy oil component reaction response label set; The structural fatigue assessment module calls the heavy oil component reaction response label set, places the strain into three rounds of simulated heavy oil degradation reactions, collects the cell membrane thickness, cell aspect ratio and cytoplasmic electron density before and after the reaction, calculates the morphological index, uses exceeding the stability threshold as a screening condition, and outputs a structural stability identification table in the heavy oil reaction; The proliferation behavior calculation module collects the unit growth number of colony formation and the change value of metabolite generation concentration per unit time according to the structural stability identification table in the heavy oil reaction, evaluates the expansion ability of the strain, and generates the information of the proliferation ability of the bacterial colony in the heavy oil environment.

2. The heavy oil biological flora recycling system according to claim 1, characterized in that: The multi-round adaptive bacterial community list includes a bacterial lineage classification number, an environmental adaptation index level, a heavy oil component response interval identifier, a bacterial species corresponding to a target degradation path number and a cyclic dosing priority label; the heavy oil component reaction response label set includes a reaction start time mark code, a heavy oil component medium type identifier, a bacterial species response time interval level code, a target metabolite identification label and a degradation start phase sequence code; the structural stability identification table in the heavy oil reaction includes a structural degradation trend level, a membrane layer structure decay intensity distribution, an intracellular density dynamic distribution data, a bacterial structure evolution type number and a stability critical discrimination number; the bacterial community proliferation ability information under the heavy oil environment includes a bacterial proliferation activity interval, a metabolite unit output intensity identifier, a bacterial community ratio growth rate code, a bacterial metabolic adaptability factor and an activity expansion ability level.

3. The heavy oil biological flora recycling system according to claim 2, characterized in that: The bacterial colony screening module comprises: The state parameter identification submodule obtains the state parameters of the heavy oil sample, including dynamic viscosity, normal alkane mass fraction and polycyclic aromatic hydrocarbon volume fraction. According to the three state parameters, the environment is divided into three types: high-viscosity layer, light layer and intermediate layer according to the interval range. A mapping list is established between each type of environment number and the corresponding parameter value. The variation range of the sample state component is calculated according to the interval difference, and the value range of the three-layer parameter division of heavy oil is generated; The metabolic capacity determination submodule calls the three-layer parameter division value range of the heavy oil, cultivates the screened bacterial community in three types of environments respectively, collects the unit enzyme activity of the strain and the corresponding degradation product concentration change data in each environment, judges the degradation efficiency of the strain according to the benchmark value of the reaction rate constant of the target component, screens out the strains that do not meet the index, and obtains the enzymatic reaction rate set of the bacterial community in the target environment; The strain screening generation submodule calibrates the adaptation of the strains to the corresponding heavy oil types based on the set of enzymatic reaction rates of the bacterial flora in the target environment and the strains that meet the reaction rate benchmarks in multiple recorded environments, constructs the bacteria with adaptation levels greater than the set proportion range as a core population set, and establishes a multi-round list of adaptive bacterial flora.

4. The heavy oil biological flora recycling system according to claim 3, characterized in that: The reaction delay identification module comprises: The reaction environment setting submodule obtains the component parameters of the cycloalkane simulation liquid and the aromatic compound simulation liquid based on the multi-round adaptive bacterial flora list, sets the dissolved oxygen concentration value and the constant temperature reaction temperature value in each liquid, inoculates the strains into the two simulation liquids respectively, records the starting reaction time point from the completion of bacterial flora inoculation to the start of decomposition reaction, and generates a bacterial flora initial reaction time record table; The lag time calculation submodule calls the bacterial colony initial reaction time record table, monitors the peak positions of formic acid concentration and phenylacetic acid concentration in the reaction system over time according to the initial reaction time value of the strain, extracts the product peak appearance time corresponding to each strain in the two types of simulation liquids, calculates the difference between the product peak time and the corresponding initial reaction time as the lag reaction time interval, and obtains a bacterial colony reaction lag time interval value set; The rapid response screening submodule sets the reaction response time threshold as the average value of the strain lag time in the two types of liquids according to the bacterial community reaction lag time value set, marks the strains with lag time shorter than the threshold as rapid response bacteria, constructs a rapid response bacterial body number list, and establishes a heavy oil component reaction response label set.

5. The heavy oil biological flora recycling system according to claim 4, characterized in that: The structural fatigue assessment module includes: The membrane thickness collection submodule calls the heavy oil component reaction response label set, connects the strain to three rounds of heavy oil degradation simulation reaction system, collects cell samples before and after each round of reaction, detects the cell membrane thickness value of each strain in each round, and classifies and summarizes according to the round number to obtain the three rounds of membrane thickness change data table; The morphological parameter extraction submodule extracts the cell aspect ratio and cytoplasmic electron density change values ​​of the same numbered strain before and after each round of reaction based on the three-round membrane thickness change data table, and marks them as corresponding rounds of morphological change records, aligns and archives the three types of parameter data, and establishes a bacterial community structure evolution parameter set; The structural stability calculation submodule is based on the bacterial community structure evolution parameter set. According to the three rounds of change trajectories of each type of parameter value, the cell membrane thickness difference sequence, the aspect ratio fluctuation amplitude and the cytoplasmic density gradient change rate are calculated respectively. The three types of parameters are normalized respectively, and the morphological index is calculated. Comparison and screening are performed according to the stability threshold. The strains with morphological index exceeding the threshold are judged as structural fatigue objects, and a structural stability identification table in heavy oil reaction is established.

6. The heavy oil biological flora recycling system according to claim 5, characterized in that: The formula for calculating the morphological index is: ； in, represents the morphological index, Represents the normalized value of the mean difference of cell membrane thickness changes in three rounds, represents the standard deviation of the cell aspect ratio, Represents the normalized gradient value of the electron density change in the cytoplasmic region, represents the adaptation factor of the aspect ratio fluctuation term, Represents the response amplification factor of the cytoplasmic density change term.

7. The heavy oil biological flora recycling system according to claim 6, characterized in that: The proliferation behavior calculation module includes: The colony growth collection submodule extracts the strains with qualified stability according to the structural stability identification table in the heavy oil reaction, collects the change value of the colony formation unit quantity of each strain per unit time, and uniformly summarizes and counts them by time interval to generate the colony growth quantity value per unit period; The metabolic rate acquisition submodule calls the colony growth number value per unit period, collects the metabolite generation concentration change value of the corresponding strain in the same time period, and combines and calculates it with the colony change value to obtain the product generation efficiency data per unit colony, and establishes a strain product generation rate indicator set; The expansion capacity determination submodule is based on the strain product generation rate indicator set. According to the unit growth quantity of each strain and the corresponding product generation value, the two types of data are normalized and then jointly calculated to calculate the strain proliferation index of each strain. Classification and judgment are performed according to the set index range, and the bacteria with screening indexes below the lower limit of the benchmark range are marked as metabolically weak groups, so as to establish the proliferation capacity information of the bacterial community under the heavy oil environment.

8. The heavy oil biological flora recycling system according to claim 7, characterized in that: The formula for calculating the strain proliferation index of each strain is: ； in, represents the proliferation index of the strain, It represents the normalized value of colony growth per unit time. It represents the normalized value of the production rate of unit bacterial product. It represents the normalized value of the average cell division cycle time. represents the normalized value of the diffusion rate of metabolic intermediates, represents the growth parameter adjustment coefficient, represents the metabolic pressure regulation coefficient, Represents the diffusion effect amplification factor.

9. The heavy oil biological flora recycling system according to claim 8, characterized in that: The system further comprises: The bacterial replacement suggestion module is based on the bacterial proliferation ability information in the heavy oil environment, according to the identified low-activity strains, combined with the structural stability of the low-activity strains in the heavy oil reaction, to determine whether the proliferation ability and structural stability are both below the critical value, and marks the circulating bacterial bodies that need to be replaced, and establishes a circulating bacterial replacement ranking list; The circulating flora replacement ranking list specifically includes replacement instruction sequence number, replacement recommendation fitness level, key metabolic pathway dependency factor, candidate bacterial species functional classification code and flora structure reconstruction weight coefficient.

10. The heavy oil biological flora recycling system according to claim 9, characterized in that: The cell replacement suggestion module includes: The low activity identification submodule extracts the strain number and the corresponding proliferation ability value based on the proliferation ability information of the bacterial community in the heavy oil environment and the strain number marked as having weak metabolic activity, and screens and summarizes the strains below the proliferation ability determination benchmark value into a group to be reviewed, and establishes a weak activity strain identification list; The stability joint judgment submodule calls the weakly active strain identification list, matches the structural stability identifier of the strain, performs a comparison operation to determine whether it is lower than the structural stability reference value, screens strains that simultaneously meet the critical lower limits of proliferation ability and stability, and establishes a replacement recommendation strain judgment result set; The replacement sequence generation submodule calls the replacement recommended strain determination result set, assigns replacement sorting label numbers according to the metabolic pathway category to which the strain belongs and the functional redundancy relationship within the group, sorts the strains to be replaced according to the label numbers, and establishes a circulating flora replacement sorting list.

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