A heavy oil bio-bacteria recycling system

Abstract

The present invention relates to the field of bio-microbial consortium technology, and specifically to a heavy oil bio-microbial consortium recycling system. The system includes a microbial consortium screening module, a reaction delay identification module, a structural fatigue assessment module, a proliferation behavior calculation module, and a microbial replacement recommendation module. The present invention improves the efficiency and economy of heavy oil biodegradation by screening microbial consortia adapted to different heavy oil environments and optimizing their recycling. By obtaining detailed status parameters of heavy oil samples and dividing the microbial culture environment, the metabolic capacity and environmental adaptability of the bacterial strains can be accurately assessed. The start-up delay time and product concentration peak of the microbial consortium degradation reaction can be monitored, and strains with high response efficiency can be identified. The microbial consortium structure is optimized and the response speed of the biodegradation process is increased, thereby enhancing the biological efficiency of heavy oil treatment. Furthermore, a detailed assessment of strain structural fatigue and proliferation behavior is performed, enabling dynamic optimization of microbial consortium iteration.

Description

Technical Field

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

[0002] The field of bio-microbial consortium technology focuses on the use of microbial resources for environmental improvement, energy development, pollution control, and other applications. This field involves screening, cultivating, and optimizing microbial populations to form microbial consortium systems with specific metabolic functions, and controlling the growth environment and metabolic pathways of the consortium to achieve the goals of promoting organic matter decomposition, energy conversion, or environmental remediation. This includes, but is not limited to, consortium construction methods, microbial metabolic regulation technologies, research on the mechanisms of synergistic effects of bacterial strains, and the development of mechanisms for the long-term stable operation and recycling of consortiums. Bio-microbial consortium technology is widely used in industrial fields such as crude oil extraction, wastewater treatment, organic waste treatment, and biopharmaceuticals, and is of great value in improving resource utilization and reducing environmental burdens.

[0003] The heavy oil bio-bacteria recycling system is a resource reuse solution used in heavy oil development and processing. By constructing a microbial metabolic regulation and recycling culture system, it enables the continuous application of bacteria with strong heavy oil degradation capabilities and the systematic recovery of their metabolites. This system's uses include improving the biodegradation efficiency of heavy oil, extending the lifespan of the bacteria, reducing the cost of cultivating new strains, and enabling multiple rounds of utilization of the bacteria and their metabolites in oil reservoir development and crude oil processing, thereby improving the development efficiency and economic benefits of heavy oil resources.

[0004] Traditional utilization systems lack an understanding and control of the inherent dynamics of organisms. For example, in the biological treatment of heavy oil, traditional systems are unable to effectively distinguish and utilize the characteristics of microorganisms under different environmental conditions, resulting in low biodegradation efficiency and increased treatment costs. Traditional systems are generally not highly adaptable and targeted in selecting and optimizing microbial communities, and are unable to optimize the composition and function of microbial communities according to different reservoir conditions, limiting the application scope and effectiveness of biotreatment technologies. This leads to low resource utilization and increased environmental burdens during heavy oil development. For example, failure to effectively control the structural and functional stability of microbial communities leads to frequent interruptions in the biological treatment process, requiring frequent strain changes, increasing treatment time and economic costs. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a heavy oil biological flora recycling system.

[0006] In order to achieve the above-mentioned object, the present invention adopts the following technical solution: a heavy oil bio-bacteria recycling system, the system comprising:

[0007] 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 on the target component under each environment, eliminates circulating strains that do not meet the benchmark, and generates a multi-round adaptive bacterial community list;

[0008] The reaction delay identification module records the delay time for the bacterial community to initiate the degradation reaction based on the multi-round adaptive bacterial community list, monitors the time when the formic acid and phenylacetic acid concentrations in the product appear, 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;

[0009] 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 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;

[0010] The proliferation behavior calculation module collects the unit growth number of colony formation and the change value of metabolite generation concentration per unit time based on the structural stability identification table in the heavy oil reaction, evaluates the expansion ability of the strain, and generates information on the proliferation ability of the bacterial colony in the heavy oil environment.

[0011] As a further solution of the present invention, the multi-round adaptive bacterial community list includes a bacterial lineage classification number, an environmental adaptability index level, a heavy oil component response interval identifier, a bacterial species corresponding target degradation path number, and a cyclic dosing priority label; the heavy oil component reaction response label set includes a reaction start time marker 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 label, and a stability critical discrimination number; the bacterial community proliferation ability information in the heavy oil environment includes a bacterial proliferation activity interval, a metabolite unit output intensity identifier, a bacterial community relative growth rate code, a bacterial metabolic adaptability factor, and an activity expansion ability level.

[0012] As a further embodiment of the present invention, the bacterial flora screening module includes:

[0013] The state parameter identification submodule obtains the state parameters of the heavy oil sample, including dynamic viscosity, mass fraction of normal alkanes, and volume fraction of polycyclic aromatic hydrocarbons. Based on the three state parameters, the environment is divided into three types according to the interval range: high-viscosity layer, light layer, and intermediate layer. A mapping list is established between the number of each type of environment and the corresponding parameter value. The variation range of the sample state components is calculated based on the interval difference, and the value range of the three-layer parameter division of heavy oil is generated;

[0014] The metabolic capacity determination submodule uses the three-layer parameter division value range of the heavy oil to culture the screened bacterial population in three types of environments. The unit enzyme activity and corresponding degradation product concentration change data of the strain in each environment are collected. The degradation efficiency of the strain is judged based on the benchmark value of the reaction rate constant of the target component. Strains that do not meet the index are screened out to obtain the set of enzymatic reaction rates of the bacterial population in the target environment.

[0015] The strain screening generation submodule calibrates the adaptability of the strains to the corresponding heavy oil types based on the set of enzymatic reaction rates of the bacterial community in the target environment and the strains that meet the reaction rate benchmarks in multiple recorded environments. The bacteria with adaptation levels greater than the set proportion range are constructed as a core group set, and a multi-round list of adaptive bacterial communities is established.

[0016] As a further solution of the present invention, the reaction delay identification module includes:

[0017] The reaction environment setting submodule obtains the component parameters of the cycloalkane simulated liquid and the aromatic compound simulated liquid based on the multi-round adaptive bacterial community list, sets the dissolved oxygen concentration value and the constant temperature reaction temperature value in each liquid, inoculates the strain into the two simulated liquids respectively, records the starting reaction time point from the completion of bacterial community inoculation to the start of the decomposition reaction, and generates a bacterial community initial reaction time record table;

[0018] 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 based on the initial reaction time value of the strain, extracts the product peak appearance time corresponding to each strain in the two types of simulated liquids, calculates the difference between the product peak time and the corresponding initial reaction time as the lag reaction time, and obtains a set of bacterial colony reaction lag time values;

[0019] The rapid response screening submodule sets the reaction response time threshold as the average of the strain lag times in the two types of liquids based on the set of bacterial community reaction lag time values, marks strains with lag times 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.

[0020] As a further solution of the present invention, the structural fatigue assessment module includes:

[0021] The membrane thickness collection submodule calls the heavy oil component reaction response label set, connects the strain to the three-round 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 the data according to the round number to obtain a three-round membrane thickness change data table;

[0022] 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, identifies them as corresponding round morphological change records, aligns and archives the three types of parameter data, and establishes a set of bacterial community structure evolution parameters;

[0023] The structural stability calculation submodule is based on the set of bacterial community structure evolution parameters. According to the three rounds of change trajectories of each 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 parameters are normalized separately, 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.

[0024] As a further embodiment of the present invention, the formula for calculating the morphological index is:

[0025] ;

[0026] in, represents the morphological index, It 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.

[0027] As a further solution of the present invention, the proliferation behavior calculation module includes:

[0028] The colony growth collection submodule extracts strains with qualified stability based on the structural stability identification table in the heavy oil reaction, collects the change value of the colony formation unit number of each strain per unit time, and uniformly summarizes and counts them by time interval to generate the colony growth number value per unit period;

[0029] The metabolic rate acquisition submodule calls the colony growth number value per unit period, collects the metabolite production concentration change value of the corresponding strain in the same time period, and combines it with the colony change value to obtain the product production efficiency data per unit colony, and establishes a strain product production rate indicator set;

[0030] The expansion capacity determination submodule is based on the strain product generation rate indicator set. According to the unit growth number 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. The bacteria with an index lower than the lower limit of the benchmark range are marked as metabolically weak groups, and the information on the proliferation capacity of the bacterial population in a heavy oil environment is established.

[0031] As a further embodiment of the present invention, the formula for calculating the strain proliferation index of each strain is:

[0032] ;

[0033] in, represents the bacterial growth index, It represents the normalized value of colony growth per unit time. It represents the normalized value of the unit bacterial product production rate, 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.

[0034] As a further embodiment of the present invention, the system further comprises:

[0035] The bacterial cell replacement recommendation module, based on the information on the proliferation capacity of the bacterial community in the heavy oil environment, determines whether the proliferation capacity and structural stability of the low-activity strains are both below the critical value based on the identified low-activity strains and the structural stability of the low-activity strains in the heavy oil reaction, identifies the circulating bacterial cells that need to be replaced, and establishes a ranked list of circulating bacterial cell replacements;

[0036] The circulating bacterial community replacement ranking list specifically includes the replacement instruction sequence number, the replacement recommendation fitness level, the key metabolic pathway dependency factor, the candidate bacterial species functional classification code and the bacterial community structure reconstruction weight coefficient.

[0037] As a further embodiment of the present invention, the cell replacement suggestion module includes:

[0038] The low activity identification submodule extracts the strain numbers and corresponding proliferation capacity values based on the proliferation capacity information of the bacterial flora in the heavy oil environment and the strain numbers marked as having weak metabolic activity, and screens and summarizes the strains with a proliferation capacity below the benchmark value into a group to be reviewed, thereby establishing an identification list of weak activity strains;

[0039] 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 benchmark value, selects strains that simultaneously meet the critical lower limits of proliferation ability and stability, and establishes a replacement recommendation strain judgment result set;

[0040] The replacement sequence generation submodule calls the replacement recommendation strain determination result set, assigns replacement sorting label numbers based on the metabolic pathway category to which the strain belongs and the functional redundancy relationship within the group, sorts the strains that need to be replaced by the label numbers, and establishes a circulating bacterial population replacement sorting list.

[0041] Compared with the prior art, the advantages and positive effects of the present invention are:

[0042] In the present invention, the efficiency and economy of heavy oil biodegradation are improved by screening microbial flora adapted to different heavy oil environments and optimizing their recycling. By obtaining detailed status parameters of heavy oil samples and dividing the bacterial culture environment, the metabolic capacity and environmental adaptability of the bacterial species can be accurately evaluated, the start-up delay time and product concentration peak of the bacterial degradation reaction can be monitored, the strains with high-efficiency response can be identified, the bacterial flora structure can be optimized and the response speed of the biodegradation process can be increased, thereby ensuring that high-efficiency bacterial species are maintained in the system and improving the biological efficiency of heavy oil treatment. A detailed evaluation of strain structure fatigue and proliferation behavior is carried out, and inefficient bacterial species are identified and replaced by combining the dual standards of stability and proliferation ability, thereby realizing dynamic optimization of bacterial flora iteration, ensuring the biological activity and structural stability of the bacterial flora during the recycling process, effectively extending the service life and effect of the bacterial flora, and reducing the cultivation cost of new bacterial species. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0044] Figure 1 is a system flow chart of the present invention;

[0045] Figure 2 Schematic diagram of the system framework of the present invention;

[0046] Figure 3 This is a flow chart of the bacterial flora screening module of the present invention;

[0047] Figure 4 This is a flow chart of the reaction delay identification module of the present invention;

[0048] Figure 5 This is a flow chart of the structural fatigue assessment module of the present invention;

[0049] Figure 6 This is a flow chart of the proliferation behavior calculation module of the present invention;

[0050] Figure 7 Flowchart of the bacterial cell replacement suggestion module of the present invention. DETAILED DESCRIPTION

[0051] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0052] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0053] In the embodiments of the present invention, “image” and “picture” may sometimes be used interchangeably. It should be noted that when the distinction between them is not emphasized, the meanings they intend to express are the same.

[0054] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0055] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0056] See also Figure 1 A heavy oil bio-bacteria recycling system, the system comprising:

[0057] The bacterial community screening module obtains the state parameters of the heavy oil sample, including dynamic viscosity, mass fraction of normal alkanes, and volume fraction of polycyclic aromatic hydrocarbons. Based on each parameter, it divides the bacterial culture environment into three types: high-viscosity layer, light group layer, and intermediate group layer. The unit enzyme activity and degradation product concentration changes of the bacterial community against the target component in each environment are collected. The minimum reaction rate constant in each environment is set as the screening benchmark, and circulating strains that do not meet the benchmark are eliminated. The circulating core bacterial community set is established based on multi-environment adaptability, and a multi-round adaptive bacterial community list is generated;

[0058] The reaction delay identification module, based on a multi-round adaptive bacterial community list, sets dissolved oxygen concentration and constant temperature reaction conditions in two simulated liquids rich in cycloalkanes and aromatic compounds. It records the delay time for the bacterial community to initiate the degradation reaction and monitors the peak moments of formic acid and phenylacetic acid concentrations in the products. The difference between the two times is calculated as the lag time, and a fast-responding strain set is constructed to output a response label set for the heavy oil components.

[0059] The structural fatigue assessment module uses the heavy oil component reaction response label set and places the strain into three rounds of simulated heavy oil degradation reactions. The cell membrane thickness, cell aspect ratio, and cytoplasmic electron density before and after the reaction are collected. The change trend of each round is proportionally weighted and the morphological index is calculated. The exceeding of the stability threshold is used as the screening condition, and a structural stability indicator table in the heavy oil reaction is output.

[0060] The proliferation behavior calculation module extracts strains with qualified stability based on the structural stability identification table in the heavy oil reaction, collects the unit growth number of colony formation and the change value of metabolite production concentration per unit time, calculates the growth rate and product production rate respectively, normalizes the two data, evaluates the expansion ability of the strain, determines whether the bacteria are classified as a metabolically weak group, and generates information on the proliferation capacity of the bacterial population in the heavy oil environment;

[0061] The bacterial replacement recommendation module is based on the proliferation capacity information of the bacterial community in the heavy oil environment. Based on the identified low-activity strains and the structural stability of the low-activity strains in the heavy oil reaction, it determines whether the proliferation capacity and structural stability are both below the critical level. It identifies the circulating bacterial community that needs to be replaced and establishes a ranked list for the replacement of the circulating bacterial community.

[0062] Dynamic viscosity indicates the ability of a liquid to resist deformation while flowing and can be measured using a viscometer. The mass fraction of normal alkanes / volume fraction of polycyclic aromatic hydrocarbons is a common chemical composition indicator for heavy oil and a standard item in oil product analysis. Specific enzyme activity refers to the enzymatic reaction capacity exhibited by a unit mass of protein. The minimum reaction rate constant is a key parameter used to assess reaction efficiency in enzyme catalysis kinetics. Cell membrane thickness, cell aspect ratio, and cytoplasmic electron density are standard characterization indicators in transmission electron microscopy image analysis. Colony forming unit is a commonly used counting unit in 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.

[0063] The list of adaptive bacterial communities over multiple rounds includes the bacterial lineage classification number, environmental adaptability index level, heavy oil component response interval identifier, bacterial species corresponding target degradation pathway number, and cyclic dosing priority label. The heavy oil component reaction response label set includes the reaction start time marker code, heavy oil component medium type identifier, bacterial species response time interval level code, target metabolite identification label, and degradation starting phase sequence code. The structural stability identification table in heavy oil reactions includes the structural degradation trend level, membrane structure decay intensity distribution, intracellular density dynamic distribution data, bacterial structure evolution type label, and stability critical discrimination number. The information on bacterial community proliferation ability in heavy oil environments includes the bacterial proliferation activity range, metabolite unit output intensity identifier, bacterial community relative growth rate code, bacterial metabolic adaptability factor, and activity expansion ability level. The cyclic bacterial community replacement ranking list specifically includes the replacement instruction sequence number, replacement recommendation fitness level, key metabolic pathway dependency factor, candidate bacterial species functional classification code, and bacterial community structure reconstruction weight coefficient.

[0064] See also Figure 2 and Figure 3 ,The bacterial flora screening module includes a state parameter identification submodule, a metabolic capacity determination submodule, and a bacterial strain screening and generation submodule;

[0065] The state parameter identification submodule obtains the state parameters of the heavy oil sample, including dynamic viscosity, mass fraction of normal alkanes, and volume fraction of polycyclic aromatic hydrocarbons. Based on the three state parameters, the environment is divided into three types according to the interval range: high-viscosity layer, light layer, and intermediate layer. A mapping list is established between the number of each type of environment and the corresponding parameter value. The variation range of the sample state components is calculated based on the interval difference, and the value range of the three-layer parameter division of heavy oil is generated;

[0066] To obtain the state parameters of heavy oil samples, it is necessary to first 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 a set shear rate and constant temperature environment. For example, at a shear rate of 50 s -1The viscosity value is recorded under the conditions of 60℃ and temperature. The dynamic viscosity measurement unit is mPa·s. Then, the mass fraction of normal alkanes in the C10-C20 range in the sample is detected by gas chromatography. This mass fraction is often expressed as a percentage and is used as an indicator of light components in heavy oil. For example, the mass fraction of normal alkanes in a certain sample is 9.8%. At the same time, liquid chromatography combined with ultraviolet detection technology is used to quantify the volume fraction of polycyclic aromatic hydrocarbons such as naphthalene and phenanthrene in the sample. This value is expressed in ppm or μL / L, which provides a criterion for identifying high aromatic components in heavy oil. After completing the acquisition of the three types of parameter data, three interval standards are divided according to literature and measured statistics. The dynamic viscosity is divided into 1000~3000, 3000~6000, and 6000~10000 mPa·s, the mass fraction of normal alkanes is divided into less than 5%, 5%~10%, and greater than 10%, and the volume fraction of polycyclic aromatic hydrocarbons is set to less than 150ppm, 150~300ppm, and greater than 300 The three ppm segments correspond to the light group layer, the intermediate group layer and the high density layer respectively. The sample number is mapped to the three types of environmental numbers according to the intervals of the three state parameters, and a corresponding relationship table between the sample number and the three parameter values is established. By comparing the jump amplitude of each parameter in the sample at the boundaries of different intervals, the component variation range value ΔS is defined. ΔS can be calculated as the weighted sum of the deviation of the three parameters in the set standard interval, that is,

[0067] ;

[0068] in represent the dynamic viscosity, mass fraction of normal alkanes and volume fraction of polycyclic aromatic hydrocarbons of the sample, is the corresponding segment center value, The weight coefficient is set for experience (here it can be set to 1 / 3), and finally the environmental category corresponding to the archived sample is formed according to the ΔS value to form the three-layer parameter division value range of heavy oil. , , , , , represent the maximum and minimum values of the dynamic viscosity, mass fraction of n-alkanes and volume fraction of polycyclic aromatic hydrocarbons of the samples, respectively.

[0069] The metabolic capacity determination submodule uses the three-layer parameter division value range of heavy oil to culture the selected bacterial communities in three types of environments. The unit enzyme activity and corresponding degradation product concentration change data of the strains in each environment are collected. The degradation efficiency of the strains is judged based on the benchmark value of the reaction rate constant of the target component. Strains that do not meet the indicators are screened out to obtain the set of enzymatic reaction rates of the bacterial communities in the target environment.

[0070] After calling the three-layer parameter division range for heavy oil, the selected bacterial communities with preliminary heavy oil degradation potential were 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 range. For example, in the high-viscosity layer configuration, the viscosity was adjusted to 8000 mPa·s, the alkane volume fraction was controlled to 3%, and the aromatic hydrocarbon concentration was set to 400 ppm. The environment was adjusted to a stable state 24 hours before incubation. Then, the same concentration of bacterial suspension was added to each system, and a constant temperature and constant speed incubation device was set to maintain the reaction conditions. The unit enzyme activity of each strain after 24 hours in the system was recorded, and the enzyme activity unit was expressed in U / mg. The concentration change data of the target degradation products (such as formic acid and butenoic acid) in the reaction solution were collected in mg / L. The strain number, environmental type, unit enzyme activity value, and product concentration difference were uniformly integrated 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 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.

[0071] The strain screening submodule calibrates the strain's suitability for heavy oil types based on the set of enzymatic reaction rates of the bacterial community in the target environment and the strains that meet the reaction rate benchmark in multiple recorded environments. It constructs the core population set of bacteria with a fitness level greater than the set ratio range and establishes a multi-round list of adaptive bacterial communities.

[0072] According to the strain numbers screened from the set of enzymatic reaction rates of the bacterial flora in the target environment, whether each strain met the benchmark rate conditions in all three types of environments was extracted, and a multi-environment response table was established. Strains that were qualified in all three environments were defined as broad-spectrum adaptive strains. Then, according to the adaptation of heavy oil types, a fitness grade judgment criterion 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 judged to have a high fitness grade. All strains that met this ratio condition were selected as the core circulation population, and a core flora table was established based on the strain number, and their adaptation labels were recorded to form a complete multi-round adaptive flora list.

[0073] See also Figure 2 and Figure 4 ,The reaction delay identification module includes a reaction environment setting submodule, a lag time calculation submodule, and a quick response screening submodule;

[0074] The reaction environment setting submodule, based on a multi-round adaptive bacterial community list, obtains the component parameters of the cycloalkane and aromatic compound simulated liquids, sets the dissolved oxygen concentration and constant temperature of each liquid, inoculates the strains into the two simulated liquids, and records the starting time from the completion of bacterial inoculation to the start of the decomposition reaction, generating a bacterial community initial reaction time record table.

[0075] Based on the list of multi-round adaptive bacterial communities, the strain numbers and their groupings need to be clarified first, and each strain is inoculated into two different reaction systems, namely, a cycloalkane simulation liquid and an aromatic compound simulation liquid. When preparing the cycloalkane simulation liquid, a cyclohexane and methylcyclopentane mixture is selected as the reaction matrix, and the mass fraction is set to 1%. When preparing the aromatic compound simulation liquid, a naphthalene and xylene mixture is selected, and the volume fraction is set to 0.5%. Each type of reaction system needs to set constant dissolved oxygen concentration and temperature conditions. The dissolved oxygen concentration is uniformly set to 6.5 mg / L and the temperature is set to 42°C. The control is achieved through an oxygen electrode and a constant temperature water bath. When the strain is inoculated, the colony forming unit concentration of each bacterial liquid should be maintained at 1×10 6 CFU / mL, and start the timing at 0 minutes after inoculation to 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 colorimetric reagent to continuously take samples within 10 minutes, and measure once every minute whether the acidic product shows a color reaction. The time point of the initial color change is calibrated as the starting time point of the strain initiating the degradation reaction. For example, if the color reaction of a certain strain is significantly enhanced at the 6th minute of inoculation, the starting reaction time of the strain is recorded as 6 minutes. During the whole process, the starting time of each strain in the two systems is recorded separately and incorporated into the data table, and finally a record table of the initial reaction time of the bacterial population is generated.

[0076] The lag time calculation submodule calls the bacterial colony initial reaction time record table. Based on the strain's initial reaction time value, it monitors the peak positions of formic acid and phenylacetic acid concentrations in the reaction system over time. It extracts the product peak appearance time corresponding to each strain in the two types of simulated liquids. The difference between the product peak time and the corresponding initial reaction time is calculated as the lag reaction time interval, thereby obtaining a set of bacterial colony reaction lag time interval values.

[0077] After accessing the bacterial colony initial reaction time record table, the reaction progress should be monitored simultaneously in both simulation systems, ordered by strain number. Reaction liquid samples should be collected every two minutes. The detection wavelengths for formic acid and phenylacetic acid in the HPLC were set to 210 nm and 254 nm, with retention times of 2.1 minutes and 3.5 minutes, respectively. The peak position of each sample was detected and the concentration trend was recorded. The sampling endpoint was set to terminate if no concentration increase occurred within 30 minutes. In the analysis, if the formic acid peak of a certain strain reached its maximum at the 16th minute, corresponding to an initial reaction time of the 6th minute, the reaction lag time of this component was 10 minutes. If phenylacetic acid appeared at the 20th minute, with an initial time of 6 minutes, the reaction lag time of this component was 14 minutes. The lag times for the two components were calibrated and averaged, resulting in an average reaction lag time of 12 minutes for this strain. Similarly, the lag time data for all strains in the two simulation solutions were calculated and summarized to form a set of bacterial colony reaction lag time values.

[0078] The rapid response screening submodule sets the response time threshold as the average of the lag time of the strains in the two types of liquids based on the set of lag time values of the bacterial community response. Strains with lag time shorter than the threshold are marked as rapid response bacteria. A rapid response bacterial body number list is constructed, and a heavy oil component response label set is established.

[0079] The lag time data of all strains were extracted based on the bacterial community reaction lag time value set. The sum and average of the reaction lag time of each strain in the two types of simulated liquids were calculated based on the strain number. The reaction response time threshold was set as the arithmetic average of the lag time of all strains in the two types of simulated liquids. The calculation formula of the average value is:

[0080] ;

[0081] in For the The reaction lag time of each strain in cycloalkane simulated liquid, is the reaction lag time in the aromatic simulant, is the total number of strains. For example, if there are 5 strains, their lag times in cycloalkane simulated liquid are 9, 12, 11, 13, and 10 minutes, and their reaction lag times in aromatic simulated liquid are 10, 11, 13, 14, and 12 minutes, respectively. minutes, and then 11.5 minutes was used as the response time threshold. Strains with a lag time less than this value were screened out and marked as fast-responding bacteria. They were numbered and sorted, and finally a fast-responding bacteria number list was constructed to establish a heavy oil component reaction response label set.

[0082] See also Figure 2 and Figure 5,The structural fatigue assessment module includes a film thickness acquisition submodule, a morphological parameter extraction submodule, and a structural stability calculation submodule;

[0083] The membrane thickness acquisition submodule calls the heavy oil component reaction response label set, connects the strain to the three-round 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 the data according to the round number to obtain the three-round membrane thickness change data table;

[0084] After calling the heavy oil component reaction response label set, it is first necessary to extract the strain numbers identified as fast responses from the label set and insert the strains into three rounds of heavy oil degradation simulation systems according to the numbers. The source of heavy oil samples in each round of reaction system is consistent. The reaction liquid ratio is set to a heavy oil concentration of 5% volume fraction, and the supplementary nutrient source is 0.5% glucose solution. The reaction system is kept in suspension by a magnetic stirrer, and the constant temperature of the incubator is maintained at 40°C. Each round of reaction lasts for 24 hours. The concentration of the strain is controlled to be 1×10 6 CFU / mL. An initial sample must be collected before each round for structural control. Immediately after the end of the reaction, the bacteria are separated and collected by filtration and centrifugation. The bacteria are then fixed and pre-fixed with glutaraldehyde and stained with uranyl acetate. 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 reaction and the average value is taken. For example, the membrane thickness of a certain strain is 38 nm before the first round and 34 nm after the reaction. It changes from 37 nm to 33 nm in the second round and from 36 nm to 32 nm in the third round. All data are summarized and organized according to the round number. A membrane thickness change record matrix is established according to the difference before and after comparison to generate a three-round membrane thickness change data table.

[0085] The morphological parameter extraction submodule extracts the cell aspect ratio and cytoplasmic electron density change values of the same strain before and after each round of reaction based on the three-round membrane thickness change data table. These values are marked as corresponding round morphological change records, and the three types of parameter data are aligned and archived to establish a set of bacterial community structure evolution parameters.

[0086] According to the strain number recorded in the data table of membrane thickness changes in three rounds, the morphological parameters of the corresponding strain before and after each round of reaction were extracted, and pixel calibration was performed through TEM images of the same round. The maximum length and maximum width of the cells were determined using image analysis software, and the aspect ratio was calculated and recorded. The length unit was μm. For example, the aspect ratio of a certain strain was 1.8 before the first round, 2.2 after the first round, 1.9 and 2.1 in the second round, and 2.0 and 2.4 in the third round. At the same time, the grayscale value of the cytoplasmic region was analyzed, and the grayscale difference between the grayscale center and the edge was extracted to calculate the density gradient. The obtained value was standardized to the range of 0~1 data. For example, the density change in the first round was 0.23, the second round was 0.27, and the third round was 0.31. The three indicators were aligned in rows and columns according to the strain number and round number respectively. The membrane thickness difference, aspect ratio change, and electron density change of the three rounds under the same strain were corresponded in sequence. A complete table record was constructed and archived to establish a set of bacterial community structure evolution parameters.

[0087] The structural stability calculation submodule is based on a set of bacterial community structural evolution parameters. Based on the three-round change trajectory of each parameter value, it calculates the cell membrane thickness difference sequence, aspect ratio fluctuation amplitude, and cytoplasmic density gradient change rate. These three parameters are normalized separately, and the morphological index is calculated. Comparison and screening are performed based on the stability threshold. Strains with morphological indices exceeding the threshold are identified as structural fatigue strains, and a structural stability identification table for heavy oil reactions is established.

[0088] The formula for calculating the morphological index is:

[0089] ;

[0090] in, represents the morphological index, It 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;

[0091] Based on the bacterial community structure evolution parameter set, three rounds of data for each strain were extracted from the table. First, the mean difference of the membrane thickness change was calculated, and the average difference before and after the reaction was taken and recorded as For example, the membrane thickness difference between the first and third rounds of a certain strain is 4 nm, 4 nm, and 4 nm. The average is = 4 nm, which is normalized to 0.44. The standard deviation of the aspect ratio data is calculated as σr = √[(0.42 + 0.22 + 0.42) / 3] ≈ 0.35, without units. The linear gradient of the electron density change in the cytoplasmic region is calculated using the grayscale difference of three rounds. = (0.31 - 0.23) / 2 = 0.04, which is normalized to 0.25. Substitute the above parameters into the morphological index calculation formula:

[0092] ;

[0093] If you take , , then we get:

[0094] ;

[0095] According to the threshold value set to 0.45, = 0.460 > 0.45, the strain was determined to be a structural fatigue object, all sample results were judged in sequence according to the strain number, and a structural stability identification table for heavy oil reaction was established.

[0096] See also Figure 2 and Figure 6 ,The proliferation behavior calculation module includes the colony growth acquisition submodule, the metabolic rate acquisition submodule, and the ,expansion ability determination submodule;

[0097] The colony growth collection submodule extracts stable strains based on the structural stability identification table in the heavy oil reaction, collects the change value of the colony formation unit number of each strain per unit time, and uniformly summarizes and counts them by time interval to generate the colony growth number value per unit period;

[0098] According to the structural stability identification table in heavy oil reaction, the strain numbers marked as "structurally stable" were first extracted and their corresponding stability evaluation values were archived. Then, the colony culture stage was entered. Each strain was cultured in standard LB liquid medium with an initial inoculation concentration of 1×10 6 CFU / mL, three parallel experiments were set for each sample, and the absorbance was measured every hour at a wavelength of 600 nm using a spectrophotometer, and the OD was recorded. 600 The sample was taken at 0 h, 6 h and 12 h after the start of culture, and the sample was plated on solid culture medium after ten-fold gradient dilution. The number of colony forming units (CFU) was counted after 24 hours of culture. If the CFU value of a certain strain at 0 h, 6 h and 12 h was 1×10 6 , 5×10 7 , 1×10 8 CFU / mL, the colony growth per unit time can be expressed as the average growth rate ≈ 8.25×10 6 CFU / (mL·h), and then normalize the unit time growth of all strains, set the maximum growth value to 1.0, calculate the normalized value of the growth rate of each strain, for example, 0.82, 0.67, and 0.91, and summarize the statistics within a unified time interval of 12 hours to establish the colony growth number value per unit period.

[0099] The metabolic rate acquisition submodule calls the colony growth number value per unit period, collects the metabolite production concentration change value of the corresponding strain in the same time period, and combines it with the colony change value to obtain the product production efficiency data per unit colony and establish the strain product production rate indicator set;

[0100] Call all strain numbers in the colony growth value per unit cycle and use the number as the query index to extract the concentration changes of metabolites in the culture medium of the same strain in the same culture cycle. The metabolites are represented by volatile fatty acids such as acetic acid and butyric acid. The detection method uses gas chromatography and the unit is mg / L. For a certain strain, for example, the acetic acid concentration is 20 mg / L at 0 h and 145 mg / L at 12 h, the generated concentration change is 125 mg / L. If the colony growth per unit cycle of this strain is 8.25×10 6 CFU / (mL·h), the unit colony product generation efficiency is calculated as: 125 / (8.25×10 6 ) ≈1.515×10 -5 mg·h -1 CFU -1 ,All strain generation efficiency results were normalized, with the maximum power generation efficiency corresponding to 1.0, and the other strain values were scaled to standardized values such as 0.61 and 0.72. ,After sorting, they were uniformly archived and attached with the corresponding ,numbers to establish the strain product generation rate indicator set.

[0101] The expansion capacity determination submodule is based on the strain product generation rate indicator set. According to the unit growth number 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. According to the set index range, the strains are classified and judged. The strains with an index below the lower limit of the benchmark range are marked as metabolically inactive groups, thus establishing the information of the bacterial population proliferation capacity in the heavy oil environment.

[0102] The formula for calculating the strain proliferation index of each strain is:

[0103] ;

[0104] in, represents the bacterial growth index, It represents the normalized value of colony growth per unit time. It represents the normalized value of the unit bacterial product production rate, 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;

[0105] Normalized unit growth value of each strain based on the strain product production rate index set , product formation rate normalized value , and the average splitting cycle time derived from the experimental records The time difference between the split peak and the start delay was calculated using the colony density time curve. The normalized values were 0.31, 0.42, and 0.28. The diffusion rate of metabolic intermediates was also determined. The method is to construct a Fourier diffusion model based on the concentration gradient of the reaction solution. The normalized results are 0.15, 0.22, and 0.17, and then substituted into the formula:

[0106] ;

[0107] Among them , , , taking a certain strain as an example, , , , ,but:

[0108] ;

[0109] Setting the strain proliferation index The lower limit of the interval is 0.75. Values below this value are judged as metabolically weak groups. Finally, information on the proliferation capacity of the bacterial community in a heavy oil environment is established based on the judgment results.

[0110] See also Figure 2 and Figure 7 ,The bacterial replacement suggestion module includes a low activity recognition submodule, a ,stability joint judgment submodule, and a replacement sequence generation submodule;

[0111] The low-activity identification submodule is based on the bacterial proliferation capacity information in the heavy oil environment. According to the strain numbers marked as weak metabolic activity, the strain numbers and corresponding proliferation capacity values are extracted. The strains below the proliferation capacity determination benchmark value are screened and summarized into a group to be reviewed, and a weak activity strain identification list is established.

[0112] Based on the information of bacterial proliferation ability in heavy oil environment, first extract the numbers of all strains and their corresponding strain proliferation index , sort all index data into a table, and perform screening operations according to the set proliferation capacity benchmark value. The benchmark value is determined by the 25% quantile of the proliferation index of the strain in the experiment. After sorting all the index values in ascending order, select the data at the corresponding position as the benchmark value. For example, if the index of all strains is 0.53, 0.61, 0.68, 0.72, 0.79, 0.81, 0.86, 0.93, 0.95, and 0.97, the 25% quantile value is 0.68. Set this value as the benchmark, and then perform screening on each strain in the table. The values were compared with the benchmark values one by one. When the index of the strain was less than 0.68, it was marked as "low activity". After screening, its number was reclassified into the new table and its corresponding Numerical and basic physiological information, such as division cycle, optimal action temperature, etc., are used to complete information supplement and classification, and finally establish an identification list of weakly active strains.

[0113] The stability joint judgment submodule calls the weak activity 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 benchmark value, selects strains that meet the critical lower limits of proliferation ability and stability, and establishes a replacement recommendation strain judgment result set;

[0114] After calling the weak active strain identification list, extract all the strain numbers, match them one by one with the structural stability identification table in the heavy oil reaction, and extract the corresponding morphological index , and organize them into a new comparison table to set the structural stability benchmark value is 0.45, which is based on the fatigue strain in the previous structural stability evaluation experiment. The upper limit of the value is determined, and for each pair of numbered strains, a double-index judgment is performed: if and , the strain is simultaneously judged to be of low activity and structural fatigue, meeting the replacement conditions. The strains that meet the dual conditions are screened and numbered to be included in the replacement candidate set. All the basic parameters of proliferation, structure and metabolism of the screened strains are recorded, and the numbers are summarized and entered into a table. Finally, the replacement recommendation strain judgment result set is established.

[0115] The replacement sequence generation submodule calls the replacement recommendation strain determination result set, assigns replacement sorting label numbers based on the metabolic pathway category to which the strain belongs and the functional redundancy relationship within the group, sorts the strains that need to be replaced by label numbers, and establishes a replacement sorting list for the circulating bacterial population;

[0116] After calling the replacement recommendation strain determination result set, extract the metabolic pathway functional category corresponding to each strain. This information comes from the enzyme coding and metabolic site data of the strain, such as participation in the fatty acid β-oxidation pathway, aromatic compound cracking pathway, etc. At the same time, read the functional redundancy relationship value between the strains archived in the system This value is determined by whether the expression of key enzymes in the same pathway of the strain can be replaced by other strains. For example, if a strain is the only one expressing 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. The larger the redundancy relationship value, the lower the replacement cost. After grouping all strains according to functional categories, the redundancy relationship value is calculated in each group. The values are sorted from large to small, and replacement sorting label numbers are assigned in sequence, such as labels 001, 002, 003, etc. After integrating the sorting, a unified sorting list is generated, and finally a circulating flora replacement sorting list is established.

[0117] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0118] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0119] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0120] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0121] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0122] In the 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 merely a logical function division. In actual implementation, there may be other division methods, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms.

[0123] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0124] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0125] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or the portion of the 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 enabling a computer device (which can be a personal computer, server, or 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 media include various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical disks.

[0126] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection 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 on the target component under each environment, eliminates 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 for the bacterial community to initiate the degradation reaction based on the multi-round adaptive bacterial community list, monitors the time when the formic acid and phenylacetic acid concentrations in the product appear, 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 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 formula for calculating the morphological index is: ； in, represents the morphological index, It 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; The proliferation behavior calculation module collects the unit growth number of colony formation and the change value of metabolite production concentration per unit time based on the structural stability identification table in the heavy oil reaction, evaluates the expansion ability of the strain, and generates information on the proliferation ability of the bacterial colony in the heavy oil environment; The proliferation behavior calculation module includes: The colony growth collection submodule extracts strains with qualified stability based on the structural stability identification table in the heavy oil reaction, collects the change value of the colony formation unit number of each strain per unit time, and uniformly summarizes and counts them by time interval to generate the colony growth number value per unit period; The metabolic rate acquisition submodule calls the colony growth number value per unit period, collects the metabolite production concentration change value of the corresponding strain in the same time period, and combines it with the colony change value to obtain the product production efficiency data per unit colony, and establishes a strain product production rate indicator set; The expansion capacity determination submodule is based on the product production rate index set of the strains. According to the unit growth number of each strain and the corresponding product production value, the two types of data are normalized and then jointly calculated to calculate the strain proliferation index of each strain. The strains are classified and judged according to the set index range. The strains with an index below the lower limit of the benchmark range are marked as metabolically inactive groups, thereby establishing information on the proliferation capacity of the bacterial population in the heavy oil environment. The bacterial cell replacement recommendation module, based on the information on the proliferation capacity of the bacterial community in the heavy oil environment, determines whether the proliferation capacity and structural stability of the low-activity strains are both below the critical value based on the identified low-activity strains and the structural stability of the low-activity strains in the heavy oil reaction, identifies the circulating bacterial cells that need to be replaced, and establishes a ranked list of circulating bacterial cell replacements; The circulating bacterial community replacement ranking list specifically includes the replacement instruction sequence number, the replacement recommendation fitness level, the key metabolic pathway dependency factor, the candidate bacterial species functional classification code and the bacterial community structure reconstruction weight coefficient.

2. The heavy oil bio-bacteria recycling system according to claim 1, characterized in that: The multi-round adaptive bacterial community list includes a bacterial lineage classification number, an environmental adaptability index level, a heavy oil component response interval identifier, a bacterial species corresponding target degradation path number, and a cyclic dosing priority label. The heavy oil component reaction response label set includes a reaction start time marker 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 in the heavy oil environment includes a bacterial proliferation activity interval, a metabolite unit output intensity identifier, a bacterial community relative growth rate code, a bacterial metabolic adaptability factor, and an activity expansion ability level.

3. The heavy oil bio-bacteria recycling system according to claim 2, characterized in that: The bacterial colony screening module includes: The state parameter identification submodule obtains the state parameters of the heavy oil sample, including dynamic viscosity, mass fraction of normal alkanes, and volume fraction of polycyclic aromatic hydrocarbons. Based on the three state parameters, the environment is divided into three types according to the interval range: high-viscosity layer, light layer, and intermediate layer. A mapping list is established between the number of each type of environment and the corresponding parameter value. The variation range of the sample state components is calculated based on the interval difference, and the value range of the three-layer parameter division of heavy oil is generated; The metabolic capacity determination submodule uses the three-layer parameter division value range of the heavy oil to culture the screened bacterial population in three types of environments. The unit enzyme activity and corresponding degradation product concentration change data of the strain in each environment are collected. The degradation efficiency of the strain is judged based on the benchmark value of the reaction rate constant of the target component. Strains that do not meet the index are screened out to obtain the set of enzymatic reaction rates of the bacterial population in the target environment. The strain screening generation submodule calibrates the adaptability of the strains to the corresponding heavy oil types based on the set of enzymatic reaction rates of the bacterial community in the target environment and the strains that meet the reaction rate benchmarks in multiple recorded environments. The bacteria with adaptation levels greater than the set proportion range are constructed as a core group set, and a multi-round list of adaptive bacterial communities is established.

4. The heavy oil bio-bacteria recycling system according to claim 3, characterized in that: The reaction delay identification module includes: The reaction environment setting submodule obtains the component parameters of the cycloalkane simulated liquid and the aromatic compound simulated liquid based on the multi-round adaptive bacterial community list, sets the dissolved oxygen concentration value and the constant temperature reaction temperature value in each liquid, inoculates the strain into the two simulated liquids respectively, records the starting reaction time point from the completion of bacterial community inoculation to the start of the decomposition reaction, and generates a bacterial community 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 based on the initial reaction time value of the strain, extracts the product peak appearance time corresponding to each strain in the two types of simulated liquids, calculates the difference between the product peak time and the corresponding initial reaction time as the lag reaction time, and obtains a set of bacterial colony reaction lag time values; The rapid response screening submodule sets the reaction response time threshold as the average of the strain lag times in the two types of liquids based on the set of bacterial community reaction lag time values, marks strains with lag times 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 bio-bacteria 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 the three-round 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 the data according to the round number to obtain a three-round 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, identifies them as corresponding round morphological change records, aligns and archives the three types of parameter data, and establishes a set of bacterial community structure evolution parameters; The structural stability calculation submodule is based on the set of bacterial community structure evolution parameters. According to the three rounds of change trajectories of each 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 parameters are normalized separately, 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 bio-bacteria recycling system according to claim 1, characterized in that: The formula for calculating the strain proliferation index of each strain is: ； in, represents the bacterial growth index, It represents the normalized value of colony growth per unit time. It represents the normalized value of the unit bacterial product production rate, 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.

7. The heavy oil bio-bacteria recycling system according to claim 1, characterized in that: The cell replacement suggestion module includes: The low activity identification submodule extracts the strain numbers and corresponding proliferation capacity values based on the proliferation capacity information of the bacterial flora in the heavy oil environment and the strain numbers marked as having weak metabolic activity, and screens and summarizes the strains with a proliferation capacity below the benchmark value into a group to be reviewed, thereby establishing an identification list of weak activity strains; 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 benchmark value, selects 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 recommendation strain determination result set, assigns replacement sorting label numbers based on the metabolic pathway category to which the strain belongs and the functional redundancy relationship within the group, sorts the strains that need to be replaced by the label numbers, and establishes a circulating bacterial population replacement sorting list.

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