System for assessing carbon sequestration by photosynthetic organisms under coking flue gas conditions

Abstract

The present application relates to the technical field of carbon fixation evaluation, in particular to a photosynthetic organism carbon fixation evaluation system under coking flue gas conditions, which comprises a carbon kinetic evolution monitoring module, a gas pressure penetration threshold extraction module, a spectral response adsorption evaluation module, a flux adaptive change control module and a carbon fixation stable fluctuation indication module.In the present application, by real-time tracking of the mutation amplitude and change direction of the carbon dioxide migration rate in the coking flue gas, the boundary pressure of the carbon migration reaction start is determined and extracted, so that the critical condition of the migration of the coking flue gas to the microalgae system can be more accurately mastered in the actual operation process, the trend comparison and analysis are carried out on the carbon fixation flux data and the carbon kinetic evolution trajectory in different stages, the range analysis is carried out on the carbon fixation rate change amplitude in different periods, the most violent fluctuation section of the carbon fixation reaction is determined, the biological carbon fixation process evaluation of the coking flue gas is more targeted, and the dynamic control ability of the carbon fixation reaction stability in the running process is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon fixation assessment, and in particular to a photosynthetic organism carbon fixation assessment system under coking flue gas conditions. Background Art

[0002] The field of carbon sequestration assessment technology lies at the intersection of environmental engineering and carbon emission reduction control, primarily involving systematic approaches to quantifying, modeling, and evaluating carbon fluxes, carbon removal efficiency, and environmental benefits during carbon capture, fixation, and conversion. This field typically encompasses assessment mechanisms for both biological and abiotic carbon sequestration processes, enabling comprehensive assessments of carbon flux, greenhouse gas reduction contributions, system energy efficiency, and environmental impacts through the construction of multi-parameter monitoring models and dynamic simulation algorithms. The core goal is to provide a scientific basis for optimizing carbon capture technology pathways, supporting carbon emission accounting mechanisms, and providing data support for policymaking and carbon market transactions.

[0003] Among them, the photosynthetic biological carbon fixation assessment system under coking flue gas conditions is a technical solution for evaluating the biological carbon fixation effect in industrial waste gas treatment scenarios. It aims to deploy photosynthetic organisms to absorb carbon dioxide emitted during the coking process and quantitatively evaluate the absorption process. The removal efficiency, carbon fixation capacity of photosynthetic organisms and their environmental purification potential are measured, and the carbon fixation efficiency is monitored and quantitatively evaluated in real time through multi-parameter detection methods, providing technical support and implementation basis for carbon emission reduction measures in the coking industry.

[0004] When traditional evaluation systems evaluate the carbon fixation effect of photosynthetic organisms under coking flue gas conditions, they tend to construct multi-parameter comprehensive models and dynamic simulation algorithms. They do not conduct detailed identification and definition of the critical pressure changes and migration rate inflection point characteristics during the actual migration of carbon dioxide, resulting in difficulty in clarifying the critical conditions for the specific occurrence of carbon migration reactions during actual operations. There is a lack of precise quantification and correspondence determination of spectral band response characteristics, and it is difficult to effectively distinguish the specific contributions to carbon fixation efficiency under different lighting conditions, which makes it easy to make errors when evaluating the adsorption capacity of microalgae and reduces the evaluation accuracy. Traditional systems do not conduct in-depth comparison and analysis of the differences in carbon fixation rate fluctuations caused by different flue gas injection cycles, making it difficult to identify and grasp the most drastic stages and corresponding characteristics of carbon fixation efficiency fluctuations, weakening the effective judgment of carbon fixation stability during actual monitoring, limiting the optimization of technical strategies and accurate decision-making capabilities during the implementation of carbon emission reduction technologies, and affecting the actual adaptability and effectiveness of carbon emission reduction measures in industrial environments. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a photosynthetic biological carbon fixation evaluation system under coking flue gas conditions.

[0006] To achieve the purpose, the present invention adopts the following technical solution: a photosynthetic biological carbon fixation evaluation system under coking flue gas conditions, the system comprising:

[0007] The carbon kinetic energy evolution monitoring module obtains the injection data of coking flue gas and, based on the carbon dioxide migration rate per unit volume in the microalgae cultivation system during the injection process, extracts the time point with the largest mutation amplitude in each injection cycle, constructs a trend trajectory, and generates a carbon kinetic energy evolution trajectory.

[0008] The pressure permeation threshold extraction module calls the carbon kinetic energy evolution trajectory, selects the inlet static pressure change curve within the injection cycle, monitors the fluctuation range of the curve per unit time before and after the inflection point, and combines the frequency and duration of the lower limit values ​​of multiple cycles to generate a carbon migration trigger boundary group;

[0009] The spectral response adsorption assessment module calls the carbon migration trigger boundary group, extracts the adsorption change trend and determines the reaction continuity under continuous bands, marks the adsorption active section in the curve, and evaluates the duration and response change performance to generate the light response adsorption section;

[0010] The flux adaptation change control module calls the light response adsorption section, performs trend control analysis based on the unit volume carbon fixation flux data curve within the covered time period, and combines the concurrent response in the carbon kinetic energy evolution trajectory in the coking flue gas injection response to generate carbon fixation adaptation change information.

[0011] As a further solution of the present invention, the carbon kinetic energy evolution trajectory includes the distribution of carbon migration critical time points, the gas injection cycle evolution trend curve and Migration response intensity level, the carbon migration trigger boundary group specifically includes the interface pressure limit set, pressure fluctuation threshold classification and injection cycle pressure activation mapping, the light response adsorption segment includes the active spectral bandwidth segment, adsorption intensity distribution label and light segment duration classification, and the carbon fixation adaptation change information specifically refers to the flux trend response offset set, the adsorption behavior response control group and the adaptability difference classification results.

[0012] As a further solution of the present invention, the carbon kinetic energy evolution monitoring module includes:

[0013] The injection parameter acquisition submodule obtains the injection data of coking flue gas, collects the carbon dioxide migration rate per unit volume in the microalgae cultivation system during the corresponding time period, and simultaneously monitors the rate of change of the carbon dioxide partial pressure during the injection cycle. Based on the numerical ratio between the carbon migration rate per unit volume and the partial pressure change rate, a ratio sequence is established to generate an integrated value of the ratio sequence;

[0014] The critical inflection point identification submodule extracts the time node with the largest mutation amplitude in each curve based on the integrated value of the ratio sequence, determines whether the direction is rising and is at the local high fluctuation level, integrates the time series distribution of the marked nodes, and obtains the migration inflection point distribution segment;

[0015] The trend trajectory construction submodule calls the migration inflection point distribution segment, performs a difference operation between the migration inflection point position time and the gas injection start time in the same cycle, and maps the trend change direction to the corresponding cycle group according to the load level classification, establishes the change evolution path under the injection conditions, and generates the carbon kinetic energy evolution trajectory.

[0016] As a further solution of the present invention, the air pressure osmotic threshold extraction module includes:

[0017] The pressure curve extraction submodule calls the carbon kinetic energy evolution trajectory and obtains the inlet static pressure variation curve within the injection cycle at each time point based on the marked migration inflection point time point. The symmetrical time segment centered on the inflection point is intercepted on each curve to establish a periodic static pressure fluctuation data set and generate a static pressure variation sequence group.

[0018] The fluctuation segment identification submodule determines whether the difference signs of adjacent pressure values ​​within a unit time are continuously positive based on each sequence in the static pressure change sequence group, calculates the length of the time segment with continuous positive signs, extracts the pressure value corresponding to the starting time of the segment, and obtains the starting pressure set of the continuous rising segment;

[0019] The boundary limit classification submodule calls the starting pressure set of the continuous rising segment, counts the frequency of occurrence in multiple injection cycles, and records the duration range of each pressure value. It constructs a combined feature sequence based on the frequency and duration, sorts and classifies the combined feature sequence according to its stability, and then groups it into a boundary condition set to generate a carbon migration trigger boundary group.

[0020] As a further embodiment of the present invention, the spectral response adsorption assessment module includes:

[0021] The flux density acquisition submodule obtains the carbon migration trigger boundary group, collects the photon flux density and algae surface unit area in multiple bands in each cycle Adsorption rate growth data, establish the corresponding relationship matrix between photon flux density and adsorption rate, extract the change curve of the corresponding relationship within the spectral band segment, and generate a spectral response curve group;

[0022] The adsorption trend identification submodule extracts the band segments that meet the continuous rising requirements based on the spectral response curve group, calculates the adsorption intensity response change value within the continuous band segments, and screens and marks the bands whose change value is greater than the continuous response benchmark intensity difference to generate an adsorption active segment set;

[0023] The response segment assessment submodule calls the adsorption active segment set, extracts the photon flux density change and the adsorption amount change trend per unit time of the segment corresponding to the time segment, constructs the active band adsorption curve set, establishes the active response band time coverage map, and obtains the light response adsorption segment.

[0024] As a further solution of the present invention, the formula for calculating the adsorption intensity response change value within the continuous band segment is specifically:

[0025] ;

[0026] in, represents the adsorption response change value, Representative The normalized value of the CO2 adsorption rate change corresponding to each band, Representative The normalized value of adsorption duration per unit area in each band, Representative The central wavelength of the band, Representative The normalized value of the photon flux density corresponding to the band, is the total number of bands extracted.

[0027] As a further embodiment of the present invention, the flux adaptation control module includes:

[0028] The carbon fixation data extraction submodule collects continuous data curves of carbon fixation flux per unit volume within the time period based on the light response adsorption section, constructs a flux change sequence, classifies and integrates each segment of data in combination with the timestamp information, establishes a flux behavior performance set, and generates a carbon fixation flux data set;

[0029] The trend response comparison submodule calls the carbon sequestration flux data set, extracts the flux change rate and kinetic energy fluctuation change rate series for the same period, matches the two types of series according to the time index, calculates the difference in change trends, and calculates the flux response offset value;

[0030] The adaptation performance merging submodule extracts the corresponding fluctuation interval data according to the flux response offset value, screens the continuous segments with a change amplitude less than the response tolerance threshold, performs duration statistics on the screened segments, constructs a flux response behavior classification set, and obtains carbon sequestration adaptation change information.

[0031] As a further solution of the present invention, the formula for calculating the flux response offset value is specifically:

[0032] ;

[0033] in, represents the flux response offset value, Indicates the The carbon sequestration flux per unit volume at time , represents the maximum carbon fixation flux in the same period, Indicates the The kinetic energy value of carbon per unit volume at the moment, Indicates the maximum value of carbon kinetic energy in the same period, Indicates the total number of time points involved in matching.

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

[0035] The carbon fixation stability fluctuation indication module calls the carbon fixation adaptation change information, extracts the amplitude values ​​corresponding to the five time points of each cycle according to the change amplitude of the carbon fixation rate in each stage, performs range extraction on the difference, marks the period with the most severe fluctuation, and calculates the proportion of fluctuation duration within the period. The proportion segments are classified and summarized as fluctuation indication segments to generate carbon fixation stability fluctuation segments;

[0036] The carbon fixation stable fluctuation section includes a stability grade marking section, a fluctuation duration ratio interval and a periodic fluctuation structure identification label.

[0037] As a further solution of the present invention, the carbon fixation stability fluctuation indication module includes:

[0038] The amplitude difference extraction submodule obtains the carbon sequestration adaptation change information, extracts the rate amplitude values ​​corresponding to the five fixed time points in each cycle, calculates the difference between every two time points, extracts the maximum and minimum values, performs the range operation between the differences, summarizes the range output values ​​of the cycle, and generates a carbon sequestration fluctuation range set;

[0039] The severe fluctuation identification submodule calls the carbon fixation fluctuation range set, screens periods that are greater than the fluctuation identification benchmark range, marks them as high-variation period segments, extracts the start and end times of the marked periods and carbon fixation response curve segments, and obtains a high-variation period index group;

[0040] The period segment aggregation submodule is based on the high-variability period index group. According to the corresponding time range of each period, it counts the proportion of the duration of carbon fixation rate fluctuation in each period to the total operating period, classifies the proportion data with the set fluctuation duration ratio threshold, and establishes a stable carbon fixation fluctuation segment.

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

[0042] In the present invention, by real-time tracking of the mutation amplitude and change direction of carbon dioxide migration rate in coking flue gas, the carbon migration inflection point is accurately captured. Based on the static pressure change fluctuation range at the inflection point, the boundary pressure for the start of carbon migration reaction is determined and extracted, and the migration pressure threshold of the gas-liquid interface is clearly defined, so that the carbon migration rate in coking flue gas can be more accurately grasped in actual operation. The critical conditions for migration to the microalgae system are analyzed by integrating the photon flux density in the spectral band with the microalgae The response relationship of the adsorption rate is clarified, the corresponding characteristics of the light conditions and algal adsorption reactions within the continuous band are clarified, the specific spectral adsorption response segment is determined, and the effective distinction and evaluation of carbon fixation efficiency under different light conditions are realized. The trend comparison analysis is carried out by combining the carbon fixation flux data of different stages with the carbon kinetic energy evolution trajectory. It can accurately judge the adaptive changes of the carbon fixation process to the coking flue gas injection cycle, conduct range analysis on the variation amplitude of the carbon fixation rate in different cycles, and clarify the most intense fluctuation segment of the carbon fixation reaction, so that the evaluation of the biological carbon fixation process of coking flue gas is more targeted, and the dynamic control capability of the stability of the carbon fixation reaction during operation is enhanced, the overall evaluation accuracy is improved, and an effective basis is provided for the technical optimization and policy formulation of carbon emission reduction effects. 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 carbon kinetic energy evolution monitoring module of the present invention;

[0047] Figure 4 This is a flow chart of the air pressure permeability threshold extraction module of the present invention;

[0048] Figure 5 This is a flow chart of the spectral response adsorption assessment module of the present invention;

[0049] Figure 6 This is a flow chart of the flux adaptation control module of the present invention;

[0050] Figure 7 This is a flow chart of the carbon fixation stabilization fluctuation indication 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, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same. The terms "of," "corresponding," and "corresponding" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey 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 , Photosynthetic biological carbon fixation evaluation system under coking flue gas conditions, the system includes:

[0057] The carbon kinetic energy evolution monitoring module obtains the injection data of coking flue gas, which includes the volume flow rate per unit time, the inlet pressure and the duration of the injection cycle. According to the carbon dioxide migration rate per unit volume in the microalgae cultivation system during the injection process, the partial pressure change rate is called, and the ratio of the two is judged. The time node with the largest mutation amplitude in each injection cycle is extracted and the node is obtained. The direction of migration rate change is combined with the change direction to determine whether there is an adsorption upper limit feature. If so, it is marked as an inflection point, and a trend trajectory is constructed. The change comparison of the trajectory under multiple flue gas load cycles is obtained to generate the carbon kinetic energy evolution trajectory;

[0058] The pressure permeation threshold extraction module uses the carbon kinetic energy evolution trajectory. Based on the pressure data segment corresponding to each inflection point, it selects the inlet static pressure change curve within the injection cycle, monitors the fluctuation range of the curve per unit time before and after the inflection point, extracts the gas-liquid interface pressure that first continuously increases and causes a change in the migration rate, and records it as the limit value. Combining the frequency and duration of the lower limit values ​​in multiple cycles, it generates a carbon migration trigger boundary group.

[0059] The spectral response adsorption assessment module calls the carbon migration trigger boundary group and obtains the photon flux density and algae surface unit area in multiple bands according to the corresponding gas injection cycle. The growth of adsorption rate, extraction of adsorption change trend and determination of reaction continuity under continuous bands, marking of adsorption active sections in the curve, evaluation based on duration and response change performance, and generation of light-responsive adsorption sections;

[0060] The flux adaptation change control module calls the light response adsorption section, and performs trend control analysis based on the unit volume carbon fixation flux data curve within the covered time period, combined with the concurrent response in the carbon kinetic energy evolution trajectory in the coking flue gas injection response, to generate carbon fixation adaptation change information;

[0061] The carbon fixation stability fluctuation indication module calls the carbon fixation adaptation change information, extracts the amplitude values ​​corresponding to the five time points of each cycle according to the change amplitude of the carbon fixation rate in each stage, performs range extraction on the difference, marks the period with the most severe fluctuation, and calculates the proportion of fluctuation duration within the period. The proportion segments are classified and summarized as fluctuation indication segments to generate carbon fixation stability fluctuation segments;

[0062] The carbon kinetic energy evolution trajectory includes the distribution of critical time points of carbon migration, the evolution trend curve of the gas injection cycle and Migration response intensity level, carbon migration trigger boundary group specifically includes interface pressure limit set, pressure fluctuation threshold classification and injection cycle pressure activation mapping, light response adsorption segment includes active spectral bandwidth segment, adsorption intensity distribution label and light segment duration classification, carbon fixation adaptation change information specifically refers to flux trend response offset set, adsorption behavior response control group and adaptability difference classification results, carbon fixation stable fluctuation segment includes stability level mark segment, fluctuation duration ratio interval and periodic fluctuation structure identification label.

[0063] See also Figure 2 and Figure 3 ,The carbon kinetic energy evolution monitoring module includes the injection ,parameter acquisition submodule, the critical inflection point identification submodule, and the ,trend trajectory construction submodule;

[0064] The injection parameter acquisition submodule obtains the injection data of the coking flue gas, including the volume flow rate per unit time, the inlet pressure, and the duration of the injection cycle. It collects the carbon dioxide migration rate per unit volume in the microalgae cultivation system during the corresponding time period, and simultaneously monitors the rate of change of the carbon dioxide partial pressure during the injection cycle. A ratio sequence is established based on the numerical ratio between the carbon migration rate per unit volume and the partial pressure change rate. The ratio sequence group of all injection cycles is screened to generate an integrated value of the ratio sequence.

[0065] To obtain the volume flow rate per unit time, inlet pressure and cycle duration of the coking flue gas during the gas injection cycle, it is necessary to first deploy a standard flow sensor and a pressure acquisition device at the flue gas outlet of the coking section, and measure the volume flow data during different gas injection time periods using a standard orifice flow meter or ultrasonic gas flow meter, and record it as the sampling frequency per minute. , and then synchronously collect the instantaneous pressure of the inlet pipe section at the corresponding time point through the pressure transmitter , gas injection cycle length The automatic statistics are obtained by setting the start-stop signal flag. For example, in a typical coking exhaust cycle, the gas injection start time is 08:00:00 and the end time is 08:30:00. Seconds, in the process of gas injection, the carbon dioxide migration rate per unit volume in the microalgae culture system needs to be set in the photosynthetic bioreactor. Concentration difference measurement point, using laser infrared gas sensor to record inlet concentration and outlet concentration , through unit volume The culture medium space is calculated according to the formula:

[0066] ;

[0067] Migration rate , where all concentration units are converted to mol / m3, volume flow rate is measured in m3 / s, and the rate of change of partial pressure is calculated by the difference in inlet pressure value per second, recorded as , and then construct the ratio sequence at each moment , repeat this operation for all injection cycles to generate The series are collected and classified by period, and the corresponding The array is stored in a structured database to form a multi-period ratio integration, and finally the ratio sequence integration value is established;

[0068] The critical inflection point identification submodule is based on the ratio sequence integration value. According to the ratio change curve within each cycle, it extracts the time node with the largest mutation amplitude in each curve, obtains the direction of change of the carbon migration rate corresponding to the node, and determines whether the direction is upward and at the local high fluctuation level. If the judgment is met, it is marked as the migration upper limit trigger point. The time series distribution of the marked nodes is integrated to obtain the migration inflection point distribution segment;

[0069] Based on the ratio change curve in each period of the ratio sequence integration value, it is necessary to Perform first-order difference processing on all time points in the sequence to obtain the instantaneous increment value , search The index position with the largest absolute value The time corresponding to this index is the node with the largest mutation amplitude. At this time, the trend of the two ratios before and after this time point is judged. If it satisfies and , and the current value Higher than the average of all points in the previous 30 seconds , then the point is considered to be at the local high level of fluctuation and marked as the migration upper limit trigger point, such as: , , , then the point will be marked, and the operation will be repeated in each period sequence, integrating all marked nodes, extracting their corresponding time index sets, and constructing a cross-period time series mapping table, and finally forming the migration inflection point distribution segment;

[0070] The trend trajectory construction submodule calls the migration inflection point distribution segment. Based on the corresponding gas injection cycle information, it performs a difference operation between the migration inflection point position time and the gas injection start time in the same cycle, calculates the distribution trend of the difference between cycles, and maps the trend change direction to the corresponding cycle group according to the load level classification. It establishes the change evolution path under the injection conditions and generates the carbon kinetic energy evolution trajectory.

[0071] Call the gas injection cycle data of each time node in the migration inflection point distribution section and set the time of each migration inflection point , and its relationship with the start time of the gas injection cycle Do the difference operation, record it as , the distribution of the difference in different cycles is counted. For example, the inflection point in a certain cycle appears at 90 seconds, 120 seconds, and 150 seconds after gas injection, and the sequence is obtained. , compared with different periods The mean and standard deviation , calculate its changing trend direction, if a period group If the trend shows a continuous increase and the standard deviation offset interval is greater than 5 seconds, the trend direction is mapped as "fluctuation increase", and the load level of each cycle is classified. For example, the volume flow It is classified as high load, mapped and marked in combination with the trend direction, and finally a set of trend identification and trajectory paths for each cycle group is constructed to complete the summary of the evolution path of the inflection point distribution trend between all cycles and generate the carbon kinetic energy evolution trajectory.

[0072] See also Figure 2 and Figure 4 ,The air pressure penetration threshold extraction module includes a pressure curve extraction submodule, a ,fluctuation segment identification submodule, and a boundary limit classification submodule;

[0073] The pressure curve extraction submodule uses the carbon kinetic energy evolution trajectory to obtain the inlet static pressure change curve within the injection cycle at each time point based on the marked migration inflection point time point. On each curve, a symmetrical time segment centered on the inflection point is intercepted, and the pressure data points within the segment are extracted. These points are arranged in chronological order to form a continuous pressure change sequence, establish a periodic static pressure fluctuation data set, and generate a static pressure change sequence group.

[0074] When calling the migration inflection point time point marked in the carbon kinetic energy evolution trajectory, it is necessary to obtain the inflection point index in the established carbon kinetic energy evolution trajectory database , and extract and process the original inlet static pressure data of the gas injection cycle to which each inflection point belongs. The static pressure data should come from the high-precision pressure sensor arranged at the front end of the gas injection pipe section, and the sampling frequency is set to 1Hz. As the center, it extends 30 seconds before and after it to form a symmetrical time window , extract all data points within the time window from the original pressure series and record them as , totaling 61 data points. Each pressure curve sequence must be marked with the cycle number, inflection point number, and time window index in the data table to establish a structured mapping relationship. For example, if the inflection point number is #3 and the cycle is the fifth gas injection cycle, the corresponding entry is "C5_M3". The window pressure sequences corresponding to all inflection points are arranged in chronological order and stored in batches in the time series set to form a static pressure change sequence group: for example, if the static pressures in the cycle where the third inflection point is located under certain coking conditions are 121.3kPa, 122.0kPa, 123.1kPa, and so on to 125.0kPa, then this complete sequence is entered as a separate record in the static pressure fluctuation dataset for subsequent identification.

[0075] The fluctuation segment identification submodule determines whether the difference signs of adjacent pressure values ​​within a unit time are continuously positive based on each sequence in the static pressure change sequence group, calculates the length of the continuous positive time segment, selects the segment that first meets the continuous rise and lasts longer than the set fluctuation time threshold, extracts the pressure value corresponding to the starting time of the segment, integrates the starting points of the segments that meet the conditions to form an indicator set, and obtains the starting pressure set of the continuous rising segment;

[0076] According to each time series in the static pressure change series group , using 1 second as the unit time to determine the difference sign of adjacent pressure points If there is continuous , it is considered as a continuous upward trend of pressure. During the traversal process, the starting point, end point and duration of each continuous positive difference segment are recorded. The fluctuation time threshold is set to 5 seconds, that is, the difference value of at least 5 consecutive time points is greater than 0. If the difference value is greater than 0 within the 10th to 16th seconds of a sequence, , then this segment is recorded as a candidate segment, the starting time point is the 10th second, and the starting pressure value is Repeat this judgment on all inflection point window sequences, filter out the fragment that meets the condition for the first time, and extract its corresponding starting pressure value If a sequence rises continuously at t=14s and continues to t=21s, and the pressure increases from 122.3kPa to 124.1kPa, mark this segment and record it. All the starting pressure values ​​that meet the continuous positive increase condition are unified and merged to form the starting pressure set of the continuous rising segment, and their inflection points and cycle indexes are recorded to establish a one-to-one correspondence table structure for use in the next stage of classification calculation.

[0077] The boundary limit classification submodule calls the continuous rising segment starting pressure set, counts the frequency of occurrence in multiple injection cycles, and records the duration range of each pressure value. It constructs a combined feature sequence based on the frequency and duration, sorts and classifies the combined feature sequence according to its stability, and then aggregates it into a boundary condition set to generate a carbon migration trigger boundary group.

[0078] Call the starting pressure of the continuous rising section to concentrate the various pressure values , count the frequency of each value appearing in multiple periods , and calculate the duration of the corresponding paragraph , construct combined feature pairs by item , then all feature pairs are sorted for stability, and the stability weight is set ,by Sort and classify in order of size, Values ​​greater than the set stability reference value (for example, set to 100) are classified as "stable boundary segments", and values ​​lower than this value are classified as "secondary boundary segments". For example, if an initial pressure value of 122.8 kPa appears in 6 cycles and the average duration is 20 seconds, then , meeting the criteria for classification into the stable phase. Based on this classification result, the boundary condition set is organized and merged into a carbon migration trigger boundary group. Each record in the content structure should be accompanied by four fields: "Starting Pressure Value", "Frequency", "Duration" and "Classification Label". These serve as important basic parameters for the system to determine the active window of carbon response within the coking flue gas cycle.

[0079] See also Figure 2 and Figure 5 ,The spectral response adsorption assessment module includes a flux density acquisition submodule, an adsorption trend identification submodule, and a response section assessment submodule;

[0080] The flux density acquisition submodule obtains the carbon migration trigger boundary group and collects the photon flux density and algae surface unit area in multiple bands in each cycle according to the covered gas injection cycle. Adsorption rate growth data, establish the corresponding relationship matrix between photon flux density and adsorption rate, extract the change curve of the corresponding relationship within the spectral band segment, and generate a spectral response curve group;

[0081] After obtaining the gas injection cycles covered by the carbon migration trigger boundary group, it is necessary to extract the corresponding period of illumination control parameters and sensor data in the bioreactor according to the cycle number. In the illumination system, each gas injection cycle is equipped with an adjustable multi-band light source. The band range is set from 400nm to 700nm, with each 20nm segment being a segment, for a total of 16 bands. In each band, the photon flux density per unit area is measured using a radiometer. The unit is normalized and converted into relative value to eliminate the influence of energy input in different bands. Simultaneously, the gas exchange layer sensor array embedded on the surface of the reactor is used to obtain the gas exchange layer per unit time. Adsorption rate , after using the algal layer area as the normalization factor, we can get , and record the corresponding adsorption duration , with seconds as the time unit, is normalized for standardized comparison. The three sets of data are stored in a three-dimensional matrix, and the matrix dimensions correspond to the band number, flux value and adsorption response parameter respectively. Under actual experimental conditions, for example, the 7th band (520nm) is measured 、 、 , record the corresponding points in the matrix, and generate 16 change curves according to the band number, with the horizontal axis of the curve being the wavelength , the vertical axis is the adsorption rate per unit area, and finally a spectral response curve group is generated, which provides a basic data set for subsequent adsorption reaction trend identification;

[0082] The adsorption trend identification submodule determines whether the direction of adsorption rate change in adjacent bands is continuous and consistent based on the spectral response curve group, extracts the band segments that meet the continuous rising requirements, calculates the adsorption intensity response change value within the continuous band segments, and screens and marks the bands whose change values ​​are greater than the continuous response benchmark intensity difference to generate a set of adsorption active segments;

[0083] The formula for calculating the adsorption intensity response change value within the continuous band segment is as follows:

[0084] ;

[0085] in, represents the adsorption response change value, Representative The normalized value of the CO2 adsorption rate change corresponding to each band, Representative a normalized value of the adsorption duration per unit area in each waveband, a central wavelength representing the th waveband, a normalized value of the photon flux density corresponding to the th waveband, the total number of the extracted wavebands;

[0086] According to the data of each curve in the spectrum response curve group, first, the change direction of the adsorption rate between adjacent wavebands is determined, that is, the normalized adsorption rate difference in adjacent wavebands is calculated If the difference is positive, it is determined that the adsorption response in the current section presents a continuous growth trend. For example, in the three continuous wavebands of 520 nm, 540 nm and 560 nm, the normalized adsorption rates are , , The adsorption durations per unit area are , , The central wavelengths of the wavebands are , , (unit: nm), and the corresponding normalized photon flux densities are , , . The parameters are substituted into the adsorption response change value formula:

[0087] ;

[0088] First, the sum of the product of the adsorption rate and the duration is calculated:

[0089] ;

[0090] Then, the average of the product of the wavelength and the flux is calculated:

[0091] ;

[0092] Substituting the formula gives:

[0093] ;

[0094] If the continuous response reference intensity difference is set to 10.0, the calculation result of the waveband section is greater than the reference value, which meets the active adsorption response determination criterion, so the waveband section will be screened into the adsorption active section set. After screening in all spectrum response curves according to the same process, all continuous waveband sections whose formula output is greater than the reference value are retained, and their start and end waveband numbers, the period they are in, and the corresponding The values ​​are marked as the adsorption active segment set, which serves as the basic data set for subsequent segment response intensity assessment and spectrum generation. The structure of this set can be set as: band segment ID, start wavelength, end wavelength, gas injection cycle ID, and adsorption response intensity value, which facilitates graphical display and data backtracking.

[0095] The response segment assessment submodule calls the adsorption active segment set. Based on the marked band segments, it extracts the photon flux density change and the adsorption amount per unit time change trend of the segment corresponding to the time segment, constructs the active band adsorption curve set, and judges the adsorption reaction performance level based on the curve fluctuation amplitude and adsorption increment. After grouping and classification, it establishes the active response band time coverage map to obtain the light response adsorption segment;

[0096] The index of each segment in the adsorption active segment set was retrieved, and the actual measured photon flux density and adsorption amount per unit time sequence within the corresponding time window were extracted. A set of adsorption trend curves within the segment was constructed, with time as the horizontal axis and adsorption rate as the vertical axis. Each curve was then classified based on its maximum fluctuation amplitude and total adsorption increment. The fluctuation amplitude level limit was set to 0.15, and the adsorption increment level limit was set to 0.4. A curve with a fluctuation of 0.18 and an increment of 0.45 was classified as "high response," while a curve with a fluctuation of 0.09 and an increment of 0.22 was classified as "low response," and the corresponding band segments were labeled accordingly. All classified active segments were mapped onto the corresponding injection cycle time axis based on their start and end time positions. All labels were integrated to generate a time coverage map of the active response bands. The map records the band time span and response intensity in a Gantt style, ultimately resulting in the light-responsive adsorption segments.

[0097] See also Figure 2 and Figure 6 ,The flux adaptation change comparison module includes the carbon sequestration data extraction submodule, the trend response comparison submodule, and the adaptation performance merging submodule;

[0098] The carbon sequestration data extraction submodule is based on the light response adsorption segment. According to the marked time period, it collects the continuous data curve of the carbon sequestration flux per unit volume within the time period, extracts the periodic carbon sequestration data segment based on the time axis, constructs the flux change sequence, and classifies and integrates each data segment based on the timestamp information to establish the flux behavior performance set and generate the carbon sequestration flux data group;

[0099] After obtaining the time period marked in the light response adsorption section, the corresponding time interval in each gas injection cycle should be partitioned and identified, and the unit volume carbon fixation flux recorded in seconds in the microalgae reactor should be read. The flux is calculated by combining the CO2 concentration difference with the gas exchange rate of the reactor, and the unit is usually mol / m3·s. Import and export concentration 、 , the flux is calculated using the formula ,in is the injection flow rate, is the effective volume of the culture system, if , , , ,but . All the Time series data are sorted by acquisition time and archived with system timestamps to form time-flux pairs, such as The data are then sliced ​​according to the light response adsorption segment coverage time and classified according to the cycle number. For example, if the light response segment in cycle C1 is from t=300s to t=600s, all the data in this interval are extracted. and included in the flux sequence , repeat the operation to form all periodic data sets, establish a structured carbon sequestration behavior database, and summarize to obtain the carbon sequestration flux data set;

[0100] The trend response comparison submodule calls the carbon sequestration flux data set, compares the flux curve of each time period with the corresponding response information marked in the carbon kinetic energy evolution trajectory, extracts the flux change rate and kinetic energy fluctuation change rate series, matches the two series by time index, calculates the difference in change trends, and obtains the flux response offset value.

[0101] The formula for calculating the flux response offset value is as follows:

[0102] ;

[0103] in, represents the flux response offset value, Indicates the The carbon sequestration flux per unit volume at time , represents the maximum carbon fixation flux in the same period, Indicates the The kinetic energy value of carbon per unit volume at the moment, Indicates the maximum value of carbon kinetic energy in the same period, Indicates the total number of time points involved in matching;

[0104] Call the carbon sequestration flux data set for each period Flux sequence, and call the unit time kinetic energy value sequence corresponding to the gas injection period in the same period in the carbon kinetic energy evolution trajectory , we need to align the two sequences by timestamp first, to ensure that each The corresponding time Match. Then the flux value and kinetic energy value at each moment j are normalized respectively, and the normalization rule is: 、 ,in 、 Let m be the total number of time points in the sequence. In each gas injection cycle, the following formula is executed:

[0105] ;

[0106] It is assumed that there are 5 time points in a certain cycle, namely:

[0107] , maximum value ;

[0108] , maximum value ;

[0109] Then after normalization:

[0110] ;

[0111] ;

[0112] The difference sequence is: , it is definitely worthwhile to find the average:

[0113] ;

[0114] The calculation results are recorded in the cycle index, and a flux response offset value set is formed for all cycles, which serves as the basic evaluation data for the flux-kinetic energy matching relationship in different load stages;

[0115] The adaptation performance merging submodule extracts the corresponding fluctuation interval data based on the flux response offset value, selects the continuous segments with a change amplitude less than the response tolerance threshold, performs duration statistics on the selected segments, extracts the response stability duration, and establishes adaptation level labels based on the response matching strength, constructs a flux response behavior classification set, and obtains carbon sequestration adaptation change information;

[0116] According to the flux response offset value recorded in Sequence, extract the fluctuation range corresponding to each period, that is, within a continuous time period The instantaneous difference does not exceed the given response tolerance threshold. The threshold is set to 0.05. In the previous example, , the time period that continuously meets the condition is from t2 to t5, corresponding to a duration of 4 seconds, and is recorded as a valid response segment. After screening all segments that meet this condition and extracting their duration within each cycle, a stable response duration sequence is constructed. Based on this, the response level standard is defined, with the level threshold set as: low adaptation <3 seconds, medium adaptation 3–6 seconds, and high adaptation >6 seconds. The above example is the medium adaptation level. After adding the adaptation level label to each segment, it is compiled into a response performance list, sorted by level to form a graded set of flux response behaviors, and outputs carbon sequestration adaptation change information.

[0117] See also Figure 2 and Figure 7 ,The carbon fixation stability fluctuation indication module includes the ,amplitude difference extraction submodule, the fluctuation severity identification submodule, and the ,cycle segment aggregation submodule;

[0118] The amplitude difference extraction submodule obtains carbon sequestration adaptation change information. Based on the carbon sequestration rate change amplitude data of each cycle, it extracts the rate amplitude values ​​corresponding to the five fixed time points in each cycle, calculates the difference between every two time points, extracts the maximum and minimum values, performs range calculations between the differences, summarizes the range output values ​​of the cycle, and generates a carbon sequestration fluctuation range set.

[0119] When obtaining the rate change amplitude data of each cycle in the carbon sequestration adaptation change information, it is necessary to first confirm the selection rules of the five fixed time points in each cycle. Generally, the equidistant time points in each cycle are used as a reference. For example, if the total length of the cycle is 300 seconds, the five time points are set as 60s, 120s, 180s, 240s, and 300s respectively, and recorded as to Read the carbon fixation rate per unit volume at the corresponding time point from the carbon fixation behavior database to , for example, the data collected in a certain cycle is , calculate the difference sequence between adjacent time points in sequence , extract the maximum difference from , minimum difference , perform range calculation: range = maximum difference - minimum difference = 0.003 - (-0.002) = 0.005, record the range output value of the current cycle as 0.005, process the 5-point flux data of all cycles in the same way, summarize the range output values ​​under each cycle to form a standard data set, and establish the carbon fixation fluctuation range set;

[0120] The severe fluctuation identification submodule calls the carbon fixation fluctuation range set. Based on the range value of each cycle, it selects the cycles that are greater than the fluctuation identification benchmark range and marks them as high-variation cycle segments. It extracts the start and end times of the marked cycles and the carbon fixation response curve segments, constructs the high-variation curve distribution index set, and obtains the high-variation cycle index group.

[0121] Call each data in the carbon fixation fluctuation range set, set the fluctuation identification benchmark range to 0.004 mol / m3·s, and screen the range values ​​of all cycles based on this benchmark. If the range value of a cycle is greater than 0.004, it is classified as a high variation cycle. For example, if the range is 0.005 in the previous example, it meets the screening conditions. The cycle number, start and end time points, and corresponding carbon fixation response curves of all high variation cycles are extracted to form a high fluctuation identification index. Taking cycle C3 as an example, its fluctuation time range is t=60s to t=300s, and its response curve segment is marked The curve segment is encoded as C3_W1, and all the periodic curve index numbers that meet the conditions are summarized to establish a high-variability periodic index group, which will be used for periodic classification and stability band drawing in the future.

[0122] The period segment aggregation submodule is based on the high-variability period index group. According to the corresponding time range of each period, it calculates the proportion of the carbon fixation rate fluctuation duration in each period to the total operation period. The proportion data is classified into intervals with the set fluctuation duration ratio threshold. After integration of the classified time periods, a multi-level fluctuation time band spectrum is constructed to establish a stable carbon fixation fluctuation segment.

[0123] According to the time range of each cycle record in the high-variability cycle index group, the duration of carbon fixation rate fluctuation (i.e., the rate period higher than the average fluctuation of the cycle) in each cycle is calculated. and the total duration of the cycle Perform ratio calculation to obtain the fluctuation duration ratio For example, if the total duration of the C3 cycle is 300 seconds and the fluctuation period lasts for 75 seconds, then The fluctuation duration ratio thresholds are set to 0.2 and 0.4 as the level boundaries. It is marked as "low volatility segment". It is the "medium fluctuation section". The fluctuation levels and time periods of each cycle are aggregated to establish a multi-level fluctuation time distribution map covering all gas injection cycles, ultimately forming a carbon fixation stable fluctuation section, which is used to characterize the stability performance period of the system under different operating conditions.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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 photosynthetic biocarbon fixation evaluation system under coking flue gas conditions, characterized in that: The system comprises: The carbon kinetic energy evolution monitoring module obtains the injection data of coking flue gas and, based on the carbon dioxide migration rate per unit volume in the microalgae cultivation system during the injection process, extracts the time point with the largest mutation amplitude in each injection cycle, constructs a trend trajectory, and generates a carbon kinetic energy evolution trajectory. The pressure permeation threshold extraction module calls the carbon kinetic energy evolution trajectory, selects the inlet static pressure change curve within the injection cycle, monitors the fluctuation range of the curve per unit time before and after the inflection point, and combines the frequency and duration of the lower limit values ​​of multiple cycles to generate a carbon migration trigger boundary group; The spectral response adsorption assessment module calls the carbon migration trigger boundary group, extracts the adsorption change trend and determines the reaction continuity under continuous bands, marks the adsorption active section in the curve, and evaluates the duration and response change performance to generate the light response adsorption section; The flux adaptation change control module calls the light response adsorption section, performs trend control analysis based on the unit volume carbon fixation flux data curve within the covered time period, and combines the concurrent response in the carbon kinetic energy evolution trajectory in the coking flue gas injection response to generate carbon fixation adaptation change information; The carbon kinetic energy evolution trajectory includes the distribution of carbon migration critical time points, the gas injection cycle evolution trend curve and Migration response intensity level, the carbon migration trigger boundary group specifically includes the interface pressure limit set, pressure fluctuation threshold classification and injection cycle pressure activation mapping, the light response adsorption segment includes the active spectral bandwidth segment, adsorption intensity distribution label and light segment duration classification, and the carbon fixation adaptation change information specifically refers to the flux trend response offset set, the adsorption behavior response control group and the adaptability difference classification results.

2. The system for evaluating photosynthetic carbon fixation under coking flue gas conditions according to claim 1, characterized in that: The carbon kinetic energy evolution monitoring module includes: The injection parameter acquisition submodule obtains the injection data of coking flue gas, collects the carbon dioxide migration rate per unit volume in the microalgae cultivation system during the corresponding time period, and simultaneously monitors the rate of change of the carbon dioxide partial pressure during the injection cycle. Based on the numerical ratio between the carbon migration rate per unit volume and the partial pressure change rate, a ratio sequence is established to generate an integrated value of the ratio sequence; The critical inflection point identification submodule extracts the time node with the largest mutation amplitude in each curve based on the integrated value of the ratio sequence, determines whether the direction is rising and is at the local high fluctuation level, integrates the time series distribution of the marked nodes, and obtains the migration inflection point distribution segment; The trend trajectory construction submodule calls the migration inflection point distribution segment, performs a difference operation between the migration inflection point position time and the gas injection start time in the same cycle, and maps the trend change direction to the corresponding cycle group according to the load level classification, establishes the change evolution path under the injection conditions, and generates the carbon kinetic energy evolution trajectory.

3. The system for evaluating photosynthetic carbon fixation under coking flue gas conditions according to claim 2, characterized in that: The air pressure permeability threshold extraction module includes: The pressure curve extraction submodule calls the carbon kinetic energy evolution trajectory and obtains the inlet static pressure variation curve within the injection cycle at each time point based on the marked migration inflection point time point. The symmetrical time segment centered on the inflection point is intercepted on each curve to establish a periodic static pressure fluctuation data set and generate a static pressure variation sequence group. The fluctuation segment identification submodule determines whether the difference signs of adjacent pressure values ​​within a unit time are continuously positive based on each sequence in the static pressure change sequence group, calculates the length of the time segment with continuous positive signs, extracts the pressure value corresponding to the starting time of the segment, and obtains the starting pressure set of the continuous rising segment; The boundary limit classification submodule calls the starting pressure set of the continuous rising segment, counts the frequency of occurrence in multiple injection cycles, and records the duration range of each pressure value. It constructs a combined feature sequence based on the frequency and duration, sorts and classifies the combined feature sequence according to its stability, and then groups it into a boundary condition set to generate a carbon migration trigger boundary group.

4. The system for evaluating photosynthetic carbon fixation under coking flue gas conditions according to claim 3, characterized in that: The spectral response adsorption assessment module includes: The flux density acquisition submodule obtains the carbon migration trigger boundary group, collects the photon flux density and algae surface unit area in multiple bands in each cycle Adsorption rate growth data, establish the corresponding relationship matrix between photon flux density and adsorption rate, extract the change curve of the corresponding relationship within the spectral band segment, and generate a spectral response curve group; The adsorption trend identification submodule extracts the band segments that meet the continuous rising requirements based on the spectral response curve group, calculates the adsorption intensity response change value within the continuous band segments, and screens and marks the bands whose change value is greater than the continuous response benchmark intensity difference to generate an adsorption active segment set; The response segment assessment submodule calls the adsorption active segment set, extracts the photon flux density change and the adsorption amount change trend per unit time of the segment corresponding to the time segment, constructs the active band adsorption curve set, establishes the active response band time coverage map, and obtains the light response adsorption segment.

5. The system for evaluating photosynthetic carbon fixation under coking flue gas conditions according to claim 4, characterized in that: The formula for calculating the adsorption intensity response change value within the continuous band section is specifically: ； in, represents the adsorption response change value, Representative The corresponding band The normalized value of the adsorption rate change, Representative The normalized value of adsorption duration per unit area in each band, Representative The central wavelength of the band, Representative The normalized value of the photon flux density corresponding to the band, is the total number of bands extracted.

6. The system for evaluating photosynthetic carbon fixation under coking flue gas conditions according to claim 5, characterized in that: The flux adaptation change control module includes: The carbon fixation data extraction submodule collects continuous data curves of carbon fixation flux per unit volume within the time period based on the light response adsorption section, constructs a flux change sequence, classifies and integrates each segment of data in combination with the timestamp information, establishes a flux behavior performance set, and generates a carbon fixation flux data set; The trend response comparison submodule calls the carbon sequestration flux data set, extracts the flux change rate and kinetic energy fluctuation change rate series for the same period, matches the two types of series according to the time index, calculates the difference in change trends, and calculates the flux response offset value; The adaptation performance merging submodule extracts the corresponding fluctuation interval data according to the flux response offset value, screens the continuous segments with a change amplitude less than the response tolerance threshold, performs duration statistics on the screened segments, constructs a flux response behavior classification set, and obtains carbon sequestration adaptation change information.

7. The system for evaluating photosynthetic carbon fixation under coking flue gas conditions according to claim 6, characterized in that: The formula for calculating the flux response offset value is specifically: ； in, represents the flux response offset value, Indicates the The carbon sequestration flux per unit volume at time , represents the maximum carbon fixation flux in the same period, Indicates the The kinetic energy value of carbon per unit volume at the moment, Indicates the maximum value of carbon kinetic energy in the same period, Indicates the total number of time points involved in matching.

8. The system for evaluating photosynthetic carbon fixation under coking flue gas conditions according to claim 7, characterized in that: The system further comprises: The carbon fixation stability fluctuation indication module calls the carbon fixation adaptation change information, extracts the amplitude values ​​corresponding to the five time points of each cycle according to the change amplitude of the carbon fixation rate in each stage, performs range extraction on the difference, marks the period with the most severe fluctuation, and calculates the proportion of fluctuation duration within the period. The proportion segments are classified and summarized as fluctuation indication segments to generate carbon fixation stability fluctuation segments; The carbon fixation stable fluctuation section includes a stability grade marking section, a fluctuation duration ratio interval and a periodic fluctuation structure identification label.

9. The system for evaluating photosynthetic carbon fixation under coking flue gas conditions according to claim 8, characterized in that: The carbon fixation stability fluctuation indication module includes: The amplitude difference extraction submodule obtains the carbon sequestration adaptation change information, extracts the rate amplitude values ​​corresponding to the five fixed time points in each cycle, calculates the difference between every two time points, extracts the maximum and minimum values, performs the range operation between the differences, summarizes the range output values ​​of the cycle, and generates a carbon sequestration fluctuation range set; The severe fluctuation identification submodule calls the carbon fixation fluctuation range set, screens periods that are greater than the fluctuation identification benchmark range, marks them as high-variation period segments, extracts the start and end times of the marked periods and carbon fixation response curve segments, and obtains a high-variation period index group; The period segment aggregation submodule is based on the high-variability period index group. According to the corresponding time range of each period, it counts the proportion of the duration of carbon fixation rate fluctuation in each period to the total operating period, classifies the proportion data with the set fluctuation duration ratio threshold, and establishes a stable carbon fixation fluctuation segment.

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