Real-time monitoring system and method for photosynthetic carbon sequestration data

Abstract

The invention relates to the technical field of photosynthetic carbon sequestration, in particular to a photosynthetic carbon sequestration data real-time monitoring system and method, and the system comprises an evapotranspiration gradient recognition module, a photosynthetic rate change capture module, a non-cooperative state judgment module, a carbon sequestration fluctuation trajectory extraction module and a monitoring stability output module. According to the method, the response time point of the evapotranspiration process to the illumination change is dynamically positioned by obtaining parameters such as stomatal conductance, leaf surface temperature, saturation vapor pressure difference and transpiration rate of a leaf layer, the time point of sudden change of the physiological activity is marked, and the change of the transpiration rate and the change of the net photosynthetic rate are compared according to the difference range between the change of the net photosynthetic rate and the change of the transpiration rate. According to the method, the non-collaborative evapotranspiration function state can be judged, the possible response imbalance situation between leaf photosynthesis and moisture adjustment can be effectively recognized, the recognition capacity for the dysfunction time period is enhanced, the time window with low output stability is screened, and the dynamic adaptability and abnormal response judgment capacity of carbon sequestration monitoring data are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of photosynthetic carbon fixation, and in particular to a system and method for real-time monitoring of photosynthetic carbon fixation data. Background Art

[0002] The field of photosynthetic carbon sequestration technology is based on the process by which photosynthetic organisms such as plants, algae, and photosynthetic microorganisms fix atmospheric carbon dioxide and convert it into organic matter through photosynthesis. This field aims to develop a systematic application of carbon sinks or biomass production. This technology involves plant physiology and ecology, carbon flux monitoring, ecosystem carbon budget modeling, remote sensing technology and geographic information system integration, carbon sequestration capacity assessment, and biomass conversion and utilization. It is widely used in scenarios such as addressing climate change, enhancing ecosystem carbon sinks, agricultural carbon sequestration and sink enhancement, forestry resource management, and carbon trading. Its core focus is to accurately quantify the amount of carbon sequestered during photosynthesis to support carbon sink assessment and ecological benefit analysis.

[0003] Among them, the photosynthetic carbon fixation data real-time monitoring system is a system used to collect and process data on the carbon dioxide absorption and conversion behavior of photosynthetic organisms in natural or artificial ecosystems. Its main purpose is to use sensor networks, carbon flux observation technology and data integration platforms to monitor the photosynthetic intensity, Flux, environmental parameters and other information, and then dynamically analyze carbon fixation efficiency and time change trends, providing accurate and continuous data support for carbon sink management, ecological regulation, agricultural efficiency improvement, carbon asset accounting, etc.

[0004] Traditional monitoring systems use an analysis method based on mean trends to analyze the physiological responses of vegetation and photosynthetic fluctuations. They lack the ability to identify fine-grained temporal and spatial variations in real time, and are unable to capture functional imbalances caused by nonlinear disturbances or sudden changes. For example, under high temperature or drought conditions, the closure of plant stomata can be recorded as a decrease in transpiration rate, but it is difficult to simultaneously identify its non-synergistic effects on photosynthetic efficiency. This can easily lead to misjudgment of actual changes in carbon fixation status, resulting in biased carbon sink assessments. Traditional systems fail to combine chlorophyll changes with The dynamic mapping processing of flux data cannot output the abnormal trajectory of carbon fixation within a specific time period, resulting in the monitoring results only reflecting the macro trend rather than the physiological micro mechanism. When responding to the dynamic assessment of carbon sinks under ecological disturbances, it is difficult to provide sufficient decision-making support basis, which is not conducive to the construction of a refined carbon flux management plan for intervention and regulation. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a real-time monitoring system and method for photosynthetic carbon fixation data.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a real-time monitoring system for photosynthetic carbon fixation data, the system comprising: The evapotranspiration gradient recognition module obtains the physiological data of photosynthetic plant leaves, calculates the transpiration rate change rate in adjacent time periods, selects the time points with offset changes as response nodes, and generates a list of evapotranspiration sensitive response nodes; The photosynthetic rate change capture module calculates the slope change of the net photosynthetic rate based on the evapotranspiration sensitive response node list, extracts the curve segment where the rate rises or falls beyond the mutation threshold, marks the corresponding time point as a photosynthetic mutation point, and generates a photosynthetic rate mutation node set; The non-synergistic state determination module extracts the transpiration rate change rate and the net photosynthetic rate change rate in the corresponding time period based on the photosynthetic rate mutation node set, calculates the extreme difference between the two, determines whether the photosynthetic transpiration response deviation limit of the canopy leaves is exceeded, and generates non-synergistic monitoring time series data information; The carbon fixation fluctuation trajectory extraction module obtains the net carbon fixation of the corresponding time period based on the non-cooperative monitoring time series data information. Flux data, extract continuous periods where the change rate is in a fluctuating state and record the time range to generate carbon fixation flux fluctuation trajectory information.

[0007] As a further solution of the present invention, the evapotranspiration sensitive response node list includes a time label sequence, a transpiration rate offset amplitude identifier, a photosynthetically active radiation response segment label and a synchronous evapotranspiration mutation mark, and the photosynthetic rate mutation node set is specifically a net photosynthetic rate drastic change node, a photosynthetic rate slope mutation index, a temperature anomaly response point label and The concentration difference drives the feature label. The non-synergistic monitoring time series data information includes the non-synergistic state time index set, the extreme amplitude of transpiration and photosynthetic response, the abnormal chlorophyll relative content mark and the photosynthetic function degradation judgment mark. The carbon fixation flux fluctuation trajectory information specifically refers to the carbon flux change trajectory line, the photosynthesis and transpiration difference mapping layer, the flux fluctuation frequency annotation index and the time continuity identification boundary.

[0008] As a further solution of the present invention, the evapotranspiration gradient identification module includes: The physiological data acquisition submodule obtains the physiological data of photosynthetic plant leaves, including stomatal conductance, leaf surface temperature, saturated water vapor pressure difference and transpiration rate, binds the physiological parameters with time tags, establishes a time series data set, and generates leaf evaporation time series data; The transpiration rate calculation submodule calls the transpiration rate data points in adjacent time periods based on the leaf evapotranspiration time series data, calculates the change rate values ​​between consecutive time points, and performs slope extraction on the trend curve of the rate change, extracts the transpiration rate change gradient segments, and generates a transpiration gradient change interval set; The gradient offset screening submodule obtains the photosynthetic active radiation change rate in the same time period according to the transpiration gradient change interval set, calculates the deviation value of the two types of change rates in each time period, screens the time nodes whose deviation values ​​exceed the photosynthetic response offset threshold, establishes a node index sequence, and generates a list of evapotranspiration sensitive response nodes.

[0009] As a further embodiment of the present invention, the photosynthetic rate change capture module includes: The gas parameter extraction submodule obtains the net photosynthetic rate, leaf spacing, and The concentration difference and temperature change values ​​are integrated into a data group with the same time label in a time series format, a continuous data structure is established, and a photosynthetic climate joint parameter set is generated; The rate slope calculation submodule calls the continuous time point data of the net photosynthetic rate according to the photosynthetic climate joint parameter set, performs a difference operation on the net photosynthetic rate values ​​in adjacent time periods, calculates the change slope, and sorts the slope values ​​in time series, marking the intervals where the increase or decrease amplitude is higher than two standard deviations of the average change rate, and generates a photosynthetic slope change interval set; The mutation node screening submodule obtains the time tags corresponding to the change segments based on the photosynthetic slope change interval set, judges the monotonicity of the slope change direction, screens the nodes where the change direction turns and the slope mutation amplitude exceeds the slope mutation threshold as the photosynthetic abnormality time points, establishes a time point index structure, and generates a photosynthetic rate mutation node set.

[0010] As a further solution of the present invention, the non-cooperative state determination module includes: The response deviation extraction submodule extracts the transpiration rate change rate and the net photosynthetic rate change rate in each time period based on the photosynthetic rate mutation node set, performs point-by-point difference calculation on the change rates of the two, constructs a change difference sequence, and calculates the range between the maximum and minimum values, calculates the transpiration and photosynthetic change offset range value, determines whether the offset range value exceeds the canopy photosynthetic transpiration response deviation limit, screens the offset abnormal time period, and generates the evapotranspiration offset range interval; The functional status judgment submodule extracts the chlorophyll a relative content change rate and the chlorophyll b relative content change rate within the corresponding time period according to the evapotranspiration offset range interval, takes the absolute difference between the two change rates and compares them with the set chlorophyll attenuation and degradation judgment threshold, determines whether the chlorophyll physiological function has entered the degradation stage, obtains the time index when the change rate meets the degradation characteristics, and generates a photosynthetic function degradation node set; The variation node annotation submodule calls the photosynthetic function degradation node set, performs intersection judgment on the nodes in the distribution interval on the time axis and the evapotranspiration offset range interval, marks the intersection nodes as evapotranspiration function non-synergistic state marking points, establishes an index sequence and unifies the numbering and coding, and generates non-synergistic monitoring time series data information.

[0011] As a further solution of the present invention, the formula for calculating the extreme difference value of transpiration and photosynthesis change deviation is specifically: ; in, Indicates the extreme deviation of transpiration and photosynthesis changes. Indicates a time point The normalized value of the transpiration rate change rate, Indicates a time point The normalized value of the change rate of net photosynthetic rate, The normalized value of the leaf area index correction coefficient representing the canopy photosynthetic response, Represents the environment Concentration value normalized value, The photosynthetic rate Response inhibition coefficient for concentration changes.

[0012] As a further solution of the present invention, the carbon fixation fluctuation trajectory extraction module includes: The flux data acquisition submodule extracts the net flow rate of each time period based on the non-cooperative monitoring time series data information. The flux data are arranged in chronological order as a time flux series. The transpiration rate change rate and the net photosynthetic rate change rate are called to construct the three data sets corresponding to the time window and generate the flux three-parameter linkage series; The response difference mapping submodule calculates the difference between the net photosynthetic rate and the transpiration rate according to the flux three-parameter linkage sequence, and compares the difference with the corresponding net photosynthetic rate. The flux values ​​are mapped to establish a differential flux correspondence curve, and the overlapping sections of the flux fluctuation trend and the time point of the difference extreme value are screened to generate a differential driven flux offset curve; The trajectory segment extraction submodule calls the difference-driven flux offset curve, identifies the time period when the flux change rate continuously exceeds the fluctuation state judgment reference value, extracts the time index boundary corresponding to the time period, divides the continuity segment according to the sequence, establishes the fluctuation change continuous structure identifier, and generates the carbon fixation flux fluctuation trajectory information.

[0013] As a further embodiment of the present invention, the system further comprises: The monitoring stability output module obtains the proportion of time periods in a fluctuating state within the continuous monitoring period based on the carbon fixation flux fluctuation trajectory information, and performs a ratio judgment with the photosynthetically active radiation disturbance ratio of the corresponding time period, marks the time period where the ratio exceeds the stability reference factor as a carbon fixation unstable output area, and generates real-time carbon fixation monitoring stability identification information; The real-time carbon fixation monitoring stability identification information includes a fluctuation period positioning block, a photosynthetic active radiation disturbance rate partition label, an unstable response section boundary and a stability ratio grading identification.

[0014] As a further solution of the present invention, the monitoring stability output module includes: The time period ratio identification submodule obtains the time period marked as the fluctuation state in the continuous monitoring period based on the carbon fixation flux fluctuation trajectory information, calculates the total length of the time period in the entire period, and performs a proportional calculation on the total length with the total length of the monitoring period to obtain the fluctuation state time ratio and generate the fluctuation period ratio value; The disturbance ratio calculation submodule calls the fluctuation period proportion value, extracts the photosynthetically active radiation disturbance ratio data in the corresponding time period, performs disturbance ratio mean calculation in the hourly window, evaluates the matching degree between the disturbance value and the fluctuation ratio, and calculates the disturbance coupling evaluation coefficient; The formula for calculating the disturbance coupling evaluation coefficient is specifically: ; in, represents the disturbance coupling evaluation coefficient, Indicates the The photosynthetically active radiation disturbance ratio within a time period, Indicates the The normalized value of the duration of flux change in a time period, Indicates the The normalized value of the carbon fixation flux offset in the time period, Represents a very small constant introduced to avoid division by zero; The unstable segment marking submodule extracts the time period index whose evaluation coefficient exceeds the stability reference factor according to the disturbance coupling evaluation coefficient, uniformly marks the time period corresponding to the index as the carbon fixation unstable output area, sets the segment boundary in the time series information and numbers it, establishes the layer information structure, and generates real-time carbon fixation monitoring stability identification information.

[0015] A method for real-time monitoring of photosynthetic carbon fixation data, which is used to implement the above-mentioned real-time monitoring system for photosynthetic carbon fixation data, comprises the following steps: S1: Obtain the physiological data of photosynthetic plant leaves, calculate the transpiration rate change rate in adjacent time periods, select the time points where the change is offset as response nodes, and generate a list of evapotranspiration sensitive response nodes; S2: Based on the evapotranspiration sensitive response node list, calculating the slope change of the net photosynthetic rate, extracting the curve segments where the rate increases or decreases beyond the mutation threshold, marking the corresponding time points as photosynthetic mutation points, and generating a photosynthetic rate mutation node set; S3: extracting the transpiration rate change rate and the net photosynthetic rate change rate within the corresponding time period based on the photosynthetic rate mutation node set, calculating the range of the difference between the two, determining whether the deviation limit of the photosynthetic transpiration response of the canopy leaves is exceeded, and generating non-cooperative monitoring time series data information; S4: Based on the non-cooperative monitoring time series data information, obtain the net Flux data, extract continuous periods where the rate of change is in a fluctuating state and record the time range to generate carbon fixation flux fluctuation trajectory information; S5: According to the carbon fixation flux fluctuation trajectory information, the proportion of time periods in the fluctuating state within the continuous monitoring period is obtained, and the ratio is judged with the photosynthetic active radiation disturbance ratio of the corresponding time period, and the time period where the ratio exceeds the stability reference factor is marked as the carbon fixation unstable output area, and real-time carbon fixation monitoring stability identification information is generated.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by obtaining parameters such as stomatal conductance, leaf surface temperature, saturated water vapor pressure difference and transpiration rate at the leaf level, the change of transpiration rate in adjacent time periods is calculated, and the difference operation is performed with the change of photosynthetically active radiation. The response time point of the evaporation process to light changes can be dynamically located, and a key node sequence based on physiological response sensitivity is established. The net photosynthetic rate, interleaf photosynthesis and the relationship between the photosynthetic active radiation and the light intensity are introduced. The combined analysis of concentration difference and temperature change extracts the sudden change of photosynthetic efficiency slope and marks the time point of sharp change in physiological activity, so as to realize the precise spatiotemporal identification of photosynthetic dynamic fluctuations. By comparing the difference range between the net photosynthetic rate change and the transpiration rate change, combined with the degradation rate of the relative content of chlorophyll a and b, the non-cooperative evaporation function state can be determined, and the possible response imbalance between leaf photosynthesis and water regulation can be effectively identified, which enhances the ability to identify the functional disorder period and maps the non-cooperative state period to The flux change trend is used to extract the abnormal trajectory of continuously fluctuating carbon fixation. Combined with the photosynthetic active radiation disturbance ratio, the time window with low output stability is screened, which helps to improve the response and identification accuracy of carbon flux monitoring to short-term fluctuations, enhances the dynamic adaptability and abnormal response discrimination of carbon fixation monitoring data, supports the automatic identification and intervention management of unstable carbon sink periods, and improves the timeliness, continuity and reliability of carbon fixation behavior monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] 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.

[0018] Figure 1 is a system flow chart of the present invention; Figure 2 Schematic diagram of the system framework of the present invention; Figure 3 This is a flow chart of the evapotranspiration gradient identification module of the present invention; Figure 4 This is a flow chart of the photosynthetic rate change capture module of the present invention; Figure 5 This is a flow chart of the non-cooperative state determination module of the present invention; Figure 6 This is a flow chart of the carbon fixation fluctuation trajectory extraction module of the present invention; Figure 7 This is a flow chart of the stability monitoring output module of the present invention; Figure 8 Schematic diagram of the method steps of the present invention. DETAILED DESCRIPTION

[0019] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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.

[0024] See also Figure 1 The present invention provides a technical solution: a real-time monitoring system for photosynthetic carbon fixation data, the system comprising: The evapotranspiration gradient identification module obtains physiological data of photosynthetic plant leaves, including stomatal conductance, leaf surface temperature, saturated water vapor pressure difference, and transpiration rate. It calculates the transpiration rate change rate in adjacent time periods, extracts the transpiration rate change rate and performs difference analysis with the photosynthetically active radiation change rate in the time period. It selects the time points with offset changes as response nodes and generates a list of evapotranspiration sensitive response nodes. The photosynthetic rate change capture module obtains the net photosynthetic rate, leaf spacing and other parameters of each node based on the evapotranspiration sensitive response node list. The concentration difference and temperature change values ​​are used to calculate the slope change of the net photosynthetic rate, extract the curve segments where the rate rises or falls above the mutation threshold, mark the corresponding time points as photosynthetic mutation points, and generate a photosynthetic rate mutation node set; The non-synergistic state determination module extracts the transpiration rate change rate and the net photosynthetic rate change rate in the corresponding time period based on the photosynthetic rate mutation node set, calculates the extreme difference between the two, and determines whether it exceeds the canopy leaf photosynthetic transpiration response deviation limit. If it exceeds, the time point is marked as a non-synergistic state of evapotranspiration function, and the relative content change rate of chlorophyll a and b in the time period is obtained. If the change rate is in the degradation range, the time point is included in the non-synergistic marking time series to generate non-synergistic monitoring time series data information; The canopy leaf photosynthesis and transpiration response deviation limit refers to the upper limit of the normal variation range of the slope difference between the transpiration rate and the net photosynthetic rate under normal physiological activity conditions. This value is calculated by setting the transpiration-to-photosynthesis ratio and the stability standard of its change over time. The carbon fixation fluctuation trajectory extraction module obtains the net carbon fixation of the corresponding time period based on the non-cooperative monitoring time series data information. The flux data is mapped with the difference between transpiration rate and net photosynthetic rate in the same time period to construct Flux fluctuation curve, extracting continuous periods where the rate of change is in a fluctuating state and recording the time range to generate carbon fixation flux fluctuation trajectory information; The monitoring stability output module obtains the proportion of time periods in a fluctuating state within the continuous monitoring period based on the carbon fixation flux fluctuation trajectory information, and compares it with the photosynthetically active radiation disturbance ratio of the corresponding period. The time period where the ratio exceeds the stability reference factor is marked as a carbon fixation unstable output area, and real-time carbon fixation monitoring stability identification information is generated; The photosynthetically active radiation disturbance ratio refers to the relative deviation between the amplitude of photosynthetically active radiation change per unit time and the basic stable light level. This ratio is a key indicator for measuring the impact of light disturbance on plant photosynthetic activity and can be calculated by the difference ratio between quantum sensor data and the standard photosynthetic interval. The list of evapotranspiration sensitive response nodes includes time label sequence, transpiration rate offset amplitude identifier, photosynthetically active radiation response segment label and synchronous evapotranspiration mutation mark. The photosynthetic rate mutation node set is specifically the net photosynthetic rate drastic change node, photosynthetic rate slope mutation index, temperature anomaly response point label and Concentration difference drives characteristic labels. Non-synergistic monitoring time series data information includes non-synergistic state time index set, transpiration and photosynthetic response range, chlorophyll relative content abnormality mark and photosynthetic function degradation judgment mark. Carbon fixation flux fluctuation trajectory information specifically refers to the carbon flux change trajectory line, photosynthesis and transpiration difference mapping layer, flux fluctuation frequency annotation index and time continuity identification boundary. Real-time carbon fixation monitoring stability identification information includes fluctuation period positioning block, photosynthetic active radiation disturbance rate partition label, unstable response segment boundary and stability ratio grading mark.

[0025] See also Figure 2 and Figure 3 ,The evapotranspiration gradient identification module includes a physiological data acquisition submodule, a transpiration rate calculation submodule, and a gradient offset screening submodule; The physiological data acquisition submodule obtains the physiological data of photosynthetic plant leaves, including stomatal conductance, leaf surface temperature, saturated water vapor pressure difference and transpiration rate, binds the physiological parameters with time tags, establishes a time series data set, and generates leaf evaporation time series data; To obtain the stomatal conductance, leaf surface temperature, saturated water vapor pressure difference and transpiration rate of photosynthetic plant functional leaves, it is necessary to select representative leaves in the natural environment of the field as the monitoring object, such as the middle expanded leaves of corn. By installing a leaf surface micro temperature sensor and an open leaf chamber structure, the leaf surface temperature and stomatal conductance values ​​are collected respectively. The stomatal conductance is recorded every 5 minutes in units of 100. Record the water vapor pressure inside and outside the leaf chamber using an infrared gas analyzer, and calculate the saturated water vapor pressure difference using the formula: ; in, represents the saturation vapor pressure difference, represents the saturated water vapor pressure corresponding to the leaf temperature, Indicates the actual air vapor pressure in units of , the instantaneous transpiration rate read by the evaporation dish and leaf microevaporation meter, in units Indicates that the data are recorded synchronously with the above three parameters, and then the four types of data are bound in chronological order according to the timestamp to generate a data set with a unified structure format. In this process, the time is marked in the format of YYYY-MM-DD HH:MM:SS, and a data table is established with each row representing a time point and each column representing a physiological indicator. At the same time, the missing values ​​are filled in according to the actual recording situation. For example, if there are records at 10:00, 10:05, and 10:15 but 10:10 is missing, the stomatal conductance is linearly interpolated to the average of the previous and next values. If 10:00 is 130 , 10:15 is 140 , then the interpolation value of 10:10 is 135 , and finally generate leaf evapotranspiration time series data; The transpiration rate calculation submodule is based on the leaf evapotranspiration time series data, calls the transpiration rate data points in adjacent time periods, calculates the change rate value between consecutive time points, and performs slope extraction on the trend curve of rate change, extracts the transpiration rate change gradient segment, and generates a transpiration gradient change interval set; Based on the generated leaf evapotranspiration time series data, call the transpiration rate data between two consecutive time points, for example, 10:00 is 130 , 140 at 10:05 , the rate of change is: ; The complete rate change sequence is obtained by differentiating all time periods. Then, every three consecutive slope data are selected to form a local trend segment. The trend change is calculated using the center point slope. For example, if the changes in the three time periods from 10:00 to 10:15 are 2, 3, and 2.5 respectively, then the center point trend slope is: ; The value between 0.1 and 0.5 is judged as a moderate rising segment. All trend slopes are divided into three categories: rising segment (>0.1), falling segment (<-0.1), and stable segment (-0.1 to 0.1). Adjacent trend segments are merged to form trend segments through traversal judgment. The start and end time of each segment are marked, and finally a set of transpiration gradient change intervals is generated. The gradient offset screening submodule obtains the photosynthetic active radiation change rate within the same time period based on the transpiration gradient change interval set, calculates the deviation value of the two types of change rates in each time period, screens the time nodes where the deviation value exceeds the photosynthetic response offset threshold, establishes a node index sequence, and generates a list of evapotranspiration sensitive response nodes; According to the extracted transpiration gradient change interval set, the photosynthetic active radiation change rate within the start and end time period of each section is called, in units of Record and calculate the absolute difference between the effective radiation difference and the transpiration rate difference every minute in the interval to form a deviation time series, and set the photosynthetic response offset threshold to 80 If the deviation value at a certain time point exceeds the threshold, it is considered as a response offset point. For example, the transpiration rate at 10:30 increases by 4 , while the effective radiation dropped by 120 , the deviation is 124, which exceeds the threshold, that is, 10:30 is marked as the offset point. Then, the adjacent offset points with an interval of less than 10 minutes are judged and merged into a single response event. The event number and index time series are established, and finally a list of evapotranspiration sensitive response nodes is generated.

[0026] See also Figure 2 and Figure 4 ,The photosynthetic rate change capture module includes a gas parameter extraction submodule, a rate slope calculation submodule, and a mutation node screening submodule; The gas parameter extraction submodule obtains the net photosynthetic rate, leaf spacing, and The concentration difference and temperature change values ​​are integrated into a data group with the same time label in a time series format, a continuous data structure is established, and a photosynthetic climate joint parameter set is generated; Based on the list of evapotranspiration sensitive response nodes, the net photosynthetic rate and leaf spacing of each node in the corresponding time period are obtained. Concentration difference and temperature change values, first set the evapotranspiration response nodes such as 10:00, 10:05, 10:10 as the mark time points, obtain the net photosynthetic rate value from 10:00 to 10:05, read the net photosynthetic rate at 10:00 as 12.3 , 13.1 at 10:05 , using the gas exchange system to measure the interleaf The concentration difference, during this time period, if the recorded value is 45 ppm, the temperature change value is read by the automatic weather station at 10:00 and 10:05, which are 27.2°C and 28.0°C respectively, with a change value of +0.8°C. The above three parameters are uniformly recorded as a joint parameter row with the time tag of 10:00, in the data format (timestamp, net photosynthetic rate, leaf spacing concentration difference, temperature change value) to form a matrix structure, and continuously append records using a fixed time step. For example, 10:05 is the second set of data, which is recorded as 13.1, 48 ppm, and +0.6°C. After continuous appending, the three types of parameters at each time node are organized into a data structure with the same time axis, and a three-dimensional structure table is constructed with the time column as the primary key. In the table, each column is a physiological indicator parameter, and each row corresponds to a time node. In this way, a one-to-one mapping relationship between photosynthetic parameters and climate parameters is established, and finally a photosynthetic and climate joint parameter set is generated; The rate slope calculation submodule calls the continuous time point data of the net photosynthetic rate according to the photosynthetic climate joint parameter set, performs the difference operation on the net photosynthetic rate values ​​in adjacent time periods, calculates the change slope, and sorts the slope values ​​in time series. It marks the intervals where the increase or decrease amplitude is higher than two standard deviations of the average change rate, and generates a photosynthetic slope change interval set; According to the photosynthetic climate joint parameter set, call the time series data of net photosynthetic rate, for example, 12.3 at 10:00 , 10:05 is 13.1, 10:10 is 12.7, 10:15 is 14.2, and the differences in net photosynthetic rate in adjacent time periods are calculated to be +0.8, -0.4, and +1.5, respectively. If the time interval is set to 5 minutes, the slopes of change are 0.16, -0.08, and 0.3, respectively. The slope calculation formula is: ; in, Indicates a time point The rate of change of the net photosynthetic rate, Indicates time The net photosynthetic rate value at Indicates the time interval in minutes. All slopes are sorted in chronological order and the slope mean is calculated. and standard deviation ,like 、 , then the decision boundary is the upper and lower , that is, the interval less than 0.0 or greater than 0.24 is the mutation warning interval, and the time period falling into this range is marked as the change segment, and the start and end time and the maximum change point number are attached, and finally the photosynthetic slope change interval set is generated; The mutation node screening submodule obtains the time tags corresponding to the change segments based on the photosynthetic slope change interval set, judges the monotonicity of the slope change direction, and selects nodes with a turning point in the change direction and a slope mutation amplitude exceeding the slope mutation threshold as photosynthetic anomaly points. It then establishes a time point index structure and generates a photosynthetic rate mutation node set. Based on the photosynthetic slope change interval set, the time tags corresponding to each change segment are obtained and recorded as segment 1: 10:05–10:15, segment 2: 10:40–10:55. The direction of the net photosynthetic rate slope trend in each segment is determined. The monotonicity is determined by judging whether the change direction between each three consecutive slope values ​​has the same sign. If the first two are positive and the last one is negative, it is marked as a turning point. The slope mutation threshold is further set as This value is set with reference to twice the standard deviation of the slope change in the entire sample as the standard for identifying significant mutations. In actual judgment, for example, at 10:10, the slope change values ​​before and after are +0.18 and -0.3 respectively, and the absolute value of the change is 0.48, which exceeds the mutation threshold and is marked as an abnormal mutation node. Nodes that meet the mutation judgment conditions are screened out from all change segments and uniformly coded, such as T1, T2, T3, etc. The timestamp and mutation number are stored in the data index table, and finally a set of photosynthetic rate mutation nodes is generated.

[0027] See also Figure 2 and Figure 5 ,The non-cooperative state determination module includes the response deviation ,extraction submodule, the functional state judgment submodule, and the variant node ,annotation submodule; The response deviation extraction submodule extracts the transpiration rate change rate and the net photosynthetic rate change rate in each time period based on the photosynthetic rate mutation node set, performs point-by-point difference calculation on the two change rates, constructs a change difference sequence, and calculates the range between the maximum and minimum values. The transpiration and photosynthetic change offset range is calculated, and it is determined whether the offset range exceeds the canopy photosynthetic transpiration response deviation limit. The offset abnormal time period is screened to generate the evapotranspiration offset range interval; The formula for calculating the extreme deviation of transpiration and photosynthesis changes is as follows: ; in, Indicates the extreme deviation of transpiration and photosynthesis changes. Indicates a time point The normalized value of the transpiration rate change rate, Indicates a time point The normalized value of the change rate of net photosynthetic rate, The normalized value of the leaf area index correction coefficient representing the canopy photosynthetic response, Represents the environment Concentration value normalized value, The photosynthetic rate response inhibition coefficient for concentration changes; Based on the photosynthetic rate mutation node set, the transpiration rate change rate and net photosynthetic rate change rate in a fixed time window before and after each mutation node are extracted. The window length is set to 10 minutes, and a 5-minute time interval is used. That is, a time point is taken around each node, such as 10:00, 10:05, and 10:10 as a window, and the transpiration rate change rate in the window is obtained. and the rate of change of net photosynthetic rate , through normalization processing, the two index values ​​are unified to the [0,1] interval. For example, if the maximum transpiration rate is 12.0 and the current value is 6.0, the normalized value is 0.5, and the point-by-point difference is calculated. , forming a difference sequence , where the maximum value is 0.3, the minimum value is 0.1, the range of difference is 0.2, and the leaf area index correction coefficient is introduced The square root and logarithmic factor Calculating photosynthesis Inhibitory regulation term, where , set the response suppression coefficient , substitute into the formula: ; Calculated The value is 6.2, and the evapotranspiration response deviation limit is set to 5.0. It is judged that 6.2 > 5.0, so this time period is the deviation anomaly period. The time index is recorded, and the evapotranspiration deviation range interval is finally generated. The functional status judgment submodule extracts the chlorophyll a relative content change rate and the chlorophyll b relative content change rate within the corresponding time period based on the evapotranspiration offset range. The absolute difference between the two change rates is taken and compared with the set chlorophyll attenuation and degradation judgment threshold to determine whether the chlorophyll physiological function has entered the degradation stage. The time index at which the change rate meets the degradation characteristics is obtained, and a photosynthetic function degradation node set is generated. According to the evapotranspiration deviation range, the relative content change rate of chlorophyll a and chlorophyll b corresponding to the time period is extracted and expressed as a percentage. For example, at 10:05 and 10:10, the chlorophyll a content is 30.2 and 27.9 respectively. The calculated change rate is , chlorophyll b changes from 25.1 to 23.3, with a change rate of , take the absolute value of the two rates of change and find the difference , set the degradation judgment threshold to 1.5%. If the absolute difference is less than 1.5%, it is considered not to have entered the degradation state. Otherwise, it is marked as a degraded node. In this example, nodes that do not meet the degradation condition are not recorded. If the change rate difference in another time period is 2.3%, it is marked as the photosynthetic function degradation time point. The time period that meets the conditions is established as a time series identifier, and finally a photosynthetic function degradation node set is generated; The variation node annotation submodule calls the photosynthetic function degradation node set, makes an intersection judgment on the nodes in the distribution interval on the time axis and the evapotranspiration deviation range interval, marks the intersection nodes as evapotranspiration function non-cooperative state marking points, establishes an index sequence and unifies the numbering and coding, and generates non-cooperative monitoring time series data information; Call the photosynthetic function degradation node set, and make an intersection judgment based on the time series and the evapotranspiration offset range interval. Use the time label comparison method to match, for example, the photosynthetic function degradation node appears at 10:30, 10:35, and 10:40, and the evapotranspiration offset segment starts and ends between 10:20 and 10:40. Filter out 10:30 and 10:35 in the intersection, mark these two time points as non-cooperative state nodes, generate a marking structure, and unify the numbering code, such as N001 and N002. The time and number of the marked node are written into the data table structure, and the corresponding segment mapping index is established to finally generate non-cooperative monitoring time series data information.

[0028] See also Figure 2 and Figure 6 ,The carbon fixation fluctuation trajectory extraction module includes the flux data acquisition submodule, the response difference mapping submodule, and the trajectory segment extraction submodule; The flux data acquisition submodule extracts the net flow rate of each time period based on the non-cooperative monitoring time series data information. The flux data are arranged in chronological order as a time flux series. The transpiration rate change rate and the net photosynthetic rate change rate are called to construct the three data sets corresponding to the time window and generate the flux three-parameter linkage series; Based on the non-cooperative monitoring time series data information, the net Flux data, set continuous time nodes such as 10:00, 10:05, 10:10, etc., and record the net flux value at each time point as unit , for example, 9.5, 10.2, and 11.3 respectively, to construct a time series flux vector At the same time, read the transpiration rate change rate during the time period, such as 1.6 from 10:00 to 10:05 , 10:05 to 10:10 is 1.2, and the net photosynthetic rate change rate is obtained synchronously. If the corresponding time periods are 1.9 and 0.9 respectively, they are marked as 、 , integrate the three types of parameters into a three-dimensional data set at each time point, such as the first window is , the second window is , construct the parameter linkage structure of the time series format, forming the structure: time index, net The linkage data table consists of four fields: flux value, transpiration rate change rate, and net photosynthetic rate change rate. The recording method is a sliding window continuous structure with seconds as the interval, and finally a flux three-parameter linkage sequence is generated; The response difference mapping submodule calculates the difference between the net photosynthetic rate and the transpiration rate according to the flux three-parameter linkage sequence, and compares the difference with the corresponding net The flux values ​​are mapped to establish a differential flux correspondence curve, and the overlapping sections of the flux fluctuation trend and the time point of the difference extreme value are screened to generate a differential driven flux offset curve; According to the flux three-parameter linkage sequence, the difference between the net photosynthetic rate and the transpiration rate in each time window is calculated. For example, in the 10:05 window, the photosynthetic change rate is 0.9 and the transpiration change rate is 1.2. The difference is Calculate the difference in all time periods and compare it with the corresponding net The flux values ​​are mapped item by item to form a set of difference-flux pairing structures, e.g. 、 、 On this basis, the corresponding relationship curve is drawn, with the time axis set as the horizontal axis and the vertical axis as the difference sequence and the flux value sequence. The time points where the extreme difference values ​​appear are identified, such as 10:15 and 10:30. At the same time, it is determined whether the flux value at the corresponding moment has a fluctuation turning point. If there is both a maximum difference and a flux value inflection point, it is regarded as the flux abnormal response time period driven by the difference. It is indexed as a valid identifier, and the start and end time points of each valid segment are recorded in sequence. Finally, the difference-driven flux offset curve is generated. The trajectory segment extraction submodule calls the difference-driven flux offset curve to identify the period when the flux change rate continuously exceeds the fluctuation state judgment reference value, extracts the time index boundary corresponding to the time period, divides the continuity segment according to the sequence, establishes the fluctuation change continuous structure identifier, and generates the carbon fixation flux fluctuation trajectory information; Call the difference drive flux offset curve to identify the slope of the flux change rate in the continuous time period and calculate the net Divide the flux difference by the time interval to determine whether it exceeds the flux fluctuation judgment reference value, and set the reference value to For example, if the flux rises from 10.2 to 11.0 from 10:10 to 10:15, the rate of change is 0.8 / 5=0.16, which does not exceed the benchmark; if the decrease reaches 1.5 from 10:20 to 10:25, the rate of change is -0.3, which meets the identification conditions. The time period from 10:20 to 10:25 is marked as a fluctuation state segment. Continuous data segments are processed accordingly, and the time segments of continuous fluctuations are screened out. The start and end time points and segment numbers of each segment are recorded. Finally, a multi-segment fluctuation interval index set is constructed under the time series to generate carbon fixation flux fluctuation trajectory information.

[0029] See also Figure 2 and Figure 7 ,The monitoring stability output module includes a period ratio identification submodule, a ,disturbance ratio calculation submodule, and an unstable section marking submodule; The time period ratio identification submodule obtains the time period marked as a fluctuation state within the continuous monitoring period based on the carbon fixation flux fluctuation trajectory information, calculates the total length of the time period in the entire period, and performs a proportional calculation on the total length with the total length of the monitoring period to obtain the fluctuation state time ratio and generate the fluctuation period ratio value; According to the carbon fixation flux fluctuation trajectory information, all time periods marked as fluctuation states were extracted. For example, 10:00–10:10, 10:25–10:40, and 11:00–11:15 were identified as fluctuation segments. The length of each time segment was calculated, which was 10 minutes, 15 minutes, and 15 minutes respectively, totaling 40 minutes. The continuous monitoring period was set from 10:00 to 12:00, for a total of 120 minutes. The proportion of the fluctuation period in the total monitoring period was calculated using the formula: ; in, represents the time ratio of the fluctuation state, Indicates the cumulative time of all fluctuation state segments, Indicates the length of the entire monitoring cycle, substitute the value , that is, the proportion of the fluctuating period is 33.3%. This value is used as the basic data for evaluating the persistence of fluctuations and ultimately generates the proportion of the fluctuating period; The disturbance ratio calculation submodule calls the fluctuation period ratio value, extracts the photosynthetically active radiation disturbance ratio data within the corresponding time period, performs the mean disturbance ratio calculation within the hourly window, evaluates the matching degree between the disturbance value and the fluctuation ratio, and calculates the disturbance coupling evaluation coefficient; The formula for calculating the disturbance coupling evaluation coefficient is as follows: ; in, represents the disturbance coupling evaluation coefficient, Indicates the The photosynthetically active radiation disturbance ratio within a time period, Indicates the The normalized value of the duration of flux change in a time period, Indicates the The normalized value of the carbon fixation flux offset in the time period, Represents a very small constant introduced to avoid division by zero; Call the value of the fluctuation period ratio to obtain the photosynthetically active radiation disturbance ratio data for all time points coinciding with the fluctuation period. For example, the disturbance ratio sequence measured in the 10:00–10:10 period is 0.21, 0.35, 0.29, and 0.33. Set every 5 minutes as a time window, then there are 2 windows in this section. The disturbance means are calculated as (0.21+0.35) / 2=0.28 and (0.29+0.33) / 2=0.31, respectively. The average disturbance ratio sequence in this section is obtained, and then the normalized value of the flux change duration in each corresponding window is read. Normalized value with flux offset amplitude ,like 、 、 , put it into the formula: ; Take three sets of sample data as an example, , , but: ; The disturbance coupling evaluation coefficient is obtained to be 0.647, and finally the disturbance coupling evaluation coefficient is generated; The unstable section marking submodule extracts the time period index where the evaluation coefficient exceeds the stability benchmark factor based on the disturbance coupling evaluation coefficient, uniformly marks the time period corresponding to the index as the carbon fixation unstable output area, sets the section boundary in the time series information and encodes the number, establishes the layer information structure, and generates real-time carbon fixation monitoring stability identification information; According to the disturbance coupling evaluation coefficient, the stability benchmark factor is set to 0.6 to judge the current Therefore, the current time period is marked as a stability imbalance interval, and all time periods with coupling values ​​exceeding the threshold, such as the 10:00–10:10 time period and the 10:25–10:40 time period, are summarized and numbered as S001 and S002, respectively. The start and end time, evaluation value, segment length, and number fields are set in the time series structure table to construct a layer display structure as the spatial boundary information of the unstable area, and finally generate real-time carbon sequestration monitoring stability identification information.

[0030] See also Figure 8 , a method for real-time monitoring of photosynthetic carbon fixation data, comprising the following steps: S1: Obtain the physiological data of photosynthetic plant leaves, calculate the transpiration rate change rate in adjacent time periods, select the time points where the change is offset as response nodes, and generate a list of evapotranspiration sensitive response nodes; S2: Based on the list of evapotranspiration-sensitive response nodes, calculate the slope change of the net photosynthetic rate, extract the curve segments where the rate rises or falls beyond the mutation threshold, mark the corresponding time points as photosynthetic mutation points, and generate a photosynthetic rate mutation node set; S3: Based on the photosynthetic rate mutation node set, extract the transpiration rate change rate and the net photosynthetic rate change rate in the corresponding time period, calculate the difference between the two, determine whether it exceeds the canopy leaf photosynthetic transpiration response deviation limit, and generate non-cooperative monitoring time series data information; S4: Based on the non-cooperative monitoring time series data information, obtain the net Flux data, extract continuous periods where the rate of change is in a fluctuating state and record the time range to generate carbon fixation flux fluctuation trajectory information; S5: Based on the carbon fixation flux fluctuation trajectory information, obtain the proportion of time periods in a fluctuating state within the continuous monitoring period, and make a ratio judgment with the photosynthetic active radiation disturbance ratio of the corresponding time period. Mark the time period where the ratio exceeds the stability reference factor as the carbon fixation unstable output area, and generate real-time carbon fixation monitoring stability identification information.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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 within 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 real-time monitoring system for photosynthetic carbon fixation data, characterized in that: The system comprises: The evapotranspiration gradient recognition module obtains the physiological data of photosynthetic plant leaves, calculates the transpiration rate change rate in adjacent time periods, selects the time points with offset changes as response nodes, and generates a list of evapotranspiration sensitive response nodes; The photosynthetic rate change capture module calculates the slope change of the net photosynthetic rate based on the evapotranspiration sensitive response node list, extracts the curve segment where the rate rises or falls beyond the mutation threshold, marks the corresponding time point as a photosynthetic mutation point, and generates a photosynthetic rate mutation node set; The non-synergistic state determination module extracts the transpiration rate change rate and the net photosynthetic rate change rate in the corresponding time period based on the photosynthetic rate mutation node set, calculates the extreme difference between the two, determines whether the photosynthetic transpiration response deviation limit of the canopy leaves is exceeded, and generates non-synergistic monitoring time series data information; The carbon fixation fluctuation trajectory extraction module obtains the net carbon fixation of the corresponding time period based on the non-cooperative monitoring time series data information. Flux data, extract continuous periods where the change rate is in a fluctuating state and record the time range to generate carbon fixation flux fluctuation trajectory information.

2. The real-time monitoring system for photosynthetic carbon fixation data according to claim 1, characterized in that: The evapotranspiration sensitive response node list includes a time tag sequence, a transpiration rate offset amplitude identifier, a photosynthetically active radiation response segment label, and a synchronous evapotranspiration mutation mark. The photosynthetic rate mutation node set specifically includes a net photosynthetic rate drastic change node, a photosynthetic rate slope mutation index, a temperature anomaly response point label, and The concentration difference drives the feature label. The non-synergistic monitoring time series data information includes the non-synergistic state time index set, the extreme amplitude of transpiration and photosynthetic response, the abnormal chlorophyll relative content mark and the photosynthetic function degradation judgment mark. The carbon fixation flux fluctuation trajectory information specifically refers to the carbon flux change trajectory line, the photosynthesis and transpiration difference mapping layer, the flux fluctuation frequency annotation index and the time continuity identification boundary.

3. The real-time monitoring system for photosynthetic carbon fixation data according to claim 2, characterized in that: The evapotranspiration gradient identification module includes: The physiological data acquisition submodule obtains the physiological data of photosynthetic plant leaves, including stomatal conductance, leaf surface temperature, saturated water vapor pressure difference and transpiration rate, binds the physiological parameters with time tags, establishes a time series data set, and generates leaf evaporation time series data; The transpiration rate calculation submodule calls the transpiration rate data points in adjacent time periods based on the leaf evapotranspiration time series data, calculates the change rate values ​​between consecutive time points, and performs slope extraction on the trend curve of the rate change, extracts the transpiration rate change gradient segments, and generates a transpiration gradient change interval set; The gradient offset screening submodule obtains the photosynthetic active radiation change rate in the same time period according to the transpiration gradient change interval set, calculates the deviation value of the two types of change rates in each time period, screens the time nodes whose deviation values ​​exceed the photosynthetic response offset threshold, establishes a node index sequence, and generates a list of evapotranspiration sensitive response nodes.

4. The real-time monitoring system for photosynthetic carbon fixation data according to claim 3, characterized in that: The photosynthetic rate change capture module includes: The gas parameter extraction submodule obtains the net photosynthetic rate, leaf spacing, and The concentration difference and temperature change values ​​are integrated into a data group with the same time label in a time series format, a continuous data structure is established, and a photosynthetic climate joint parameter set is generated; The rate slope calculation submodule calls the continuous time point data of the net photosynthetic rate according to the photosynthetic climate joint parameter set, performs a difference operation on the net photosynthetic rate values ​​in adjacent time periods, calculates the change slope, and sorts the slope values ​​in time series, marking the intervals where the increase or decrease amplitude is higher than two standard deviations of the average change rate, and generates a photosynthetic slope change interval set; The mutation node screening submodule obtains the time tags corresponding to the change segments based on the photosynthetic slope change interval set, judges the monotonicity of the slope change direction, screens the nodes where the change direction turns and the slope mutation amplitude exceeds the slope mutation threshold as the photosynthetic abnormality time points, establishes a time point index structure, and generates a photosynthetic rate mutation node set.

5. The real-time monitoring system for photosynthetic carbon fixation data according to claim 4, characterized in that: The non-cooperative state determination module includes: The response deviation extraction submodule extracts the transpiration rate change rate and the net photosynthetic rate change rate in each time period based on the photosynthetic rate mutation node set, performs point-by-point difference calculation on the change rates of the two, constructs a change difference sequence, and calculates the range between the maximum and minimum values, calculates the transpiration and photosynthetic change offset range value, determines whether the offset range value exceeds the canopy photosynthetic transpiration response deviation limit, screens the offset abnormal time period, and generates the evapotranspiration offset range interval; The functional status judgment submodule extracts the chlorophyll a relative content change rate and the chlorophyll b relative content change rate within the corresponding time period according to the evapotranspiration offset range interval, takes the absolute difference between the two change rates and compares them with the set chlorophyll attenuation and degradation judgment threshold, determines whether the chlorophyll physiological function has entered the degradation stage, obtains the time index when the change rate meets the degradation characteristics, and generates a photosynthetic function degradation node set; The variation node annotation submodule calls the photosynthetic function degradation node set, performs intersection judgment on the nodes in the distribution interval on the time axis and the evapotranspiration offset range interval, marks the intersection nodes as evapotranspiration function non-synergistic state marking points, establishes an index sequence and unifies the numbering and coding, and generates non-synergistic monitoring time series data information.

6. The real-time monitoring system for photosynthetic carbon fixation data according to claim 5, characterized in that: The formula for calculating the extreme difference value of transpiration and photosynthesis change deviation is specifically: ； in, Indicates the extreme deviation of transpiration and photosynthesis changes. Indicates a time point The normalized value of the transpiration rate change rate, Indicates a time point The normalized value of the change rate of net photosynthetic rate, The normalized value of the leaf area index correction coefficient representing the canopy photosynthetic response, Represents the environment Concentration value normalized value, The photosynthetic rate Response inhibition coefficient for concentration changes.

7. The real-time monitoring system for photosynthetic carbon fixation data according to claim 6, characterized in that: The carbon fixation fluctuation trajectory extraction module includes: The flux data acquisition submodule extracts the net flow rate of each time period based on the non-cooperative monitoring time series data information. The flux data are arranged in chronological order as a time flux series. The transpiration rate change rate and the net photosynthetic rate change rate are called to construct the three data sets corresponding to the time window and generate the flux three-parameter linkage series; The response difference mapping submodule calculates the difference between the net photosynthetic rate and the transpiration rate according to the flux three-parameter linkage sequence, and compares the difference with the corresponding net photosynthetic rate. The flux values ​​are mapped to establish a differential flux correspondence curve, and the overlapping sections of the flux fluctuation trend and the time point of the difference extreme value are screened to generate a differential driven flux offset curve; The trajectory segment extraction submodule calls the difference-driven flux offset curve, identifies the time period when the flux change rate continuously exceeds the fluctuation state judgment reference value, extracts the time index boundary corresponding to the time period, divides the continuity segment according to the sequence, establishes the fluctuation change continuous structure identifier, and generates the carbon fixation flux fluctuation trajectory information.

8. The real-time monitoring system for photosynthetic carbon fixation data according to claim 7, characterized in that: The system further comprises: The monitoring stability output module obtains the proportion of time periods in a fluctuating state within the continuous monitoring period based on the carbon fixation flux fluctuation trajectory information, and performs a ratio judgment with the photosynthetically active radiation disturbance ratio of the corresponding time period, marks the time period where the ratio exceeds the stability reference factor as a carbon fixation unstable output area, and generates real-time carbon fixation monitoring stability identification information; The real-time carbon fixation monitoring stability identification information includes a fluctuation period positioning block, a photosynthetic active radiation disturbance rate partition label, an unstable response section boundary and a stability ratio grading identification.

9. The real-time monitoring system for photosynthetic carbon fixation data according to claim 8, characterized in that: The monitoring stability output module includes: The time period ratio identification submodule obtains the time period marked as the fluctuation state in the continuous monitoring period based on the carbon fixation flux fluctuation trajectory information, calculates the total length of the time period in the entire period, and performs a proportional calculation on the total length with the total length of the monitoring period to obtain the fluctuation state time ratio and generate the fluctuation period ratio value; The disturbance ratio calculation submodule calls the fluctuation period proportion value, extracts the photosynthetically active radiation disturbance ratio data in the corresponding time period, performs disturbance ratio mean calculation in the hourly window, evaluates the matching degree between the disturbance value and the fluctuation ratio, and calculates the disturbance coupling evaluation coefficient; The formula for calculating the disturbance coupling evaluation coefficient is specifically: ； in, represents the disturbance coupling evaluation coefficient, Indicates the The photosynthetically active radiation disturbance ratio within a time period, Indicates the The normalized value of the duration of flux change in a time period, Indicates the The normalized value of the carbon fixation flux offset in the time period, Represents a very small constant introduced to avoid division by zero; The unstable segment marking submodule extracts the time period index whose evaluation coefficient exceeds the stability reference factor according to the disturbance coupling evaluation coefficient, uniformly marks the time period corresponding to the index as the carbon fixation unstable output area, sets the segment boundary in the time series information and numbers it, establishes the layer information structure, and generates real-time carbon fixation monitoring stability identification information.

10. A method for real-time monitoring of photosynthetic carbon fixation data, characterized in that: The method is used to implement the real-time monitoring system for photosynthetic carbon fixation data according to any one of claims 1 to 9, comprising the following steps: S1: Obtain the physiological data of photosynthetic plant leaves, calculate the transpiration rate change rate in adjacent time periods, select the time points where the change is offset as response nodes, and generate a list of evapotranspiration sensitive response nodes; S2: Based on the evapotranspiration sensitive response node list, calculating the slope change of the net photosynthetic rate, extracting the curve segments where the rate increases or decreases beyond the mutation threshold, marking the corresponding time points as photosynthetic mutation points, and generating a photosynthetic rate mutation node set; S3: extracting the transpiration rate change rate and the net photosynthetic rate change rate within the corresponding time period based on the photosynthetic rate mutation node set, calculating the range of the difference between the two, determining whether the deviation limit of the photosynthetic transpiration response of the canopy leaves is exceeded, and generating non-cooperative monitoring time series data information; S4: Based on the non-cooperative monitoring time series data information, obtain the net Flux data, extract continuous periods where the rate of change is in a fluctuating state and record the time range to generate carbon fixation flux fluctuation trajectory information; S5: According to the carbon fixation flux fluctuation trajectory information, the proportion of time periods in the fluctuating state within the continuous monitoring period is obtained, and the ratio is judged with the photosynthetic active radiation disturbance ratio of the corresponding time period, and the time period where the ratio exceeds the stability reference factor is marked as the carbon fixation unstable output area, and real-time carbon fixation monitoring stability identification information is generated.

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