Tide level driven carbon flux measuring device and measuring method thereof

Abstract

The invention discloses a tide level driven carbon flux measuring device and a measuring method thereof, and relates to the technical field of environmental monitoring, and the measuring method comprises the following steps: monitoring real-time tide level data of an external environment; constructing a measurement opportunity sequence according to the tide level driving characteristics and a first regulation and control strategy; obtaining a water body exchange trigger signal in the current tide level period, judging the opening and closing state of the current measurement window according to the water body exchange trigger signal, and monitoring the greenhouse gas concentration change through a gas sensor under the steady-state measurement condition based on the opening and closing state of the current measurement window and the initial flux reading, calculating a time sequence carbon flux based on the concentration time sequence; and outputting a net carbon flux value according to the time sequence carbon flux in combination with environmental factor correction. According to the invention, the problems of incomplete carbon flux measurement time sequence data, serious interference and desynchrony with natural driving signals in a tidal dynamic environment are solved, and intelligent synchronization and adaptive optimization of a measurement process and a tidal period are realized.

Description

Technical Field

[0001] This invention relates to the technical field of environmental monitoring, and in particular to a tidal level-driven carbon flux measuring device and its measuring method. Background Technology

[0002] With the increasing severity of global climate change, accurately quantifying the carbon source and sink intensity of key ecosystems such as coastal zones and estuaries has become a crucial issue in global carbon cycle research and the implementation of the "carbon neutrality" strategy. Among these, the fluxes of greenhouse gases such as carbon dioxide and methane at the sediment-atmosphere interface are a key component of coastal blue carbon accounting. Traditional flux measurement methods, such as the static chamber method, typically involve fixing the measuring device and performing intermittent sampling. However, in areas periodically flooded by tides, traditional static measurement methods face fundamental challenges.

[0003] The intertidal environment is highly dynamic, with water levels changing drastically and rapidly with the ebb and flow of the tides. This dynamism presents several key technical bottlenecks for flux measurement: First, drastic water level changes can directly interrupt or disrupt the closed state of the measurement chamber, making it impossible to obtain continuous and complete concentration-time series. This results in fragmented and unrepresentative flux values, i.e., incomplete characteristic sequences. Second, tidal pressure changes, water exchange, and wave disturbances introduce significant environmental noise, severely interfering with the stable signal of gas concentration changes, leading to high background noise in feature extraction and distorted measurement results. Third, if the opening and closing of the measurement window is not synchronized with tidal changes, the impact of real water pressure on the gas release process from sediments cannot be simulated, causing misalignment between the system response and the external driving signal, and the measured flux failing to reflect the actual environmental process.

[0004] Existing technologies attempt to adapt to tides through timing or manual control, but lack the ability to intelligently and in real-time synchronize with actual tide level signals. In complex tidal environments, fixed timing strategies cannot adapt to tide table errors and weather-induced tidal anomalies, easily leading to incorrect measurement timing (such as measuring during the most intense ebb and flow of the tide) or equipment damage (such as the air chamber being flooded). Therefore, existing static or simple timing control methods struggle to reliably acquire high-quality, continuous, and synchronized carbon flux time-series data in the real dynamic intertidal environment.

[0005] Currently, no efficient and automated solution has been proposed to address the problems of incomplete carbon flux measurement sequences, severe interference, and difficulty in accurately synchronizing with tide levels in tidal dynamic environments. Summary of the Invention

[0006] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0007] In view of the problems existing in the prior art, the present invention is proposed.

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for measuring tidal-driven carbon flux, comprising: The system monitors real-time tide level data of the external environment and collects tide level change signals. Based on the real-time tide level data, it identifies the tide level phase state and generates tide level driving characteristics based on the tide level phase state and a pre-set tide level reference model. Based on the tidal level driving characteristics and the first control strategy, a measurement timing sequence is constructed; The water exchange trigger signal within the current tidal cycle is acquired. Based on the water exchange trigger signal, the opening and closing status of the current measurement window is determined. Based on the opening and closing status of the current measurement window and the initial flux reading, the current flux feature is extracted. The tidal level driving feature and the current flux feature correspond to the same monitoring point and tidal cycle. Based on the measurement timing sequence, the current flux characteristics, and the second control strategy, the operating parameters of the water exchange mechanism are adjusted to establish steady-state measurement conditions; Under the steady-state measurement conditions, greenhouse gas concentration changes are monitored by a gas sensor, and time-series carbon flux is calculated based on the concentration time series; based on the time-series carbon flux and combined with environmental factor correction, a net carbon flux value is output.

[0009] As a preferred embodiment of the tidal level-driven carbon flux measuring device and method of the present invention, wherein: the first control strategy includes a tidal level phase analysis unit and a sequence generation unit, and the step of constructing a measurement timing sequence based on the tidal level-driven characteristics and the first control strategy includes: The tidal level driving characteristics are input into the tidal level phase analysis unit to determine the key tidal level phase characteristics, wherein the key tidal level phase characteristics include tidal level inflection points, phase duration, and phase change rate. The tidal level driving features are input into the sequence generation unit to determine the benchmark measurement sequence, wherein the benchmark measurement sequence is generated based on the historical tidal level data model and the initial distribution of the measurement window is defined; Based on the key phase characteristics of the tide level and the benchmark measurement sequence, the timing and interval of the measurement window are optimized by a dynamic adjustment algorithm to determine the measurement timing sequence.

[0010] In a preferred embodiment of the tidal level-driven carbon flux measuring device and method of the present invention, the second control strategy includes an operating parameter optimization unit and a steady-state determination unit. The step of adjusting the operating parameters of the water exchange mechanism according to the measurement timing sequence, the current flux characteristics, and the second control strategy to establish steady-state measurement conditions includes: The current throughput characteristics are input into the operation parameter optimization unit to determine the operation parameter adjustment amount, wherein the operation parameters include exchange duration, exchange frequency or exchange flow. The measurement timing sequence and the current flux characteristics are input into the steady-state determination unit to determine the system steady-state indicators, wherein the system steady-state indicators include concentration change rate, temperature stability or pressure balance value; Based on the adjustment amount of the operating parameters and the steady-state index of the system, the operating parameters of the water exchange mechanism are dynamically adjusted through a feedback control algorithm until the steady-state index of the system meets the preset threshold, thereby establishing the steady-state measurement conditions.

[0011] As a preferred embodiment of the tidal level-driven carbon flux measuring device and method of the present invention, the step of dynamically adjusting the operating parameters of the water exchange mechanism according to the adjustment amount of the operating parameters and the system steady-state index through a feedback control algorithm until the system steady-state index meets a preset threshold, thereby establishing the steady-state measurement conditions, includes: The control error value is calculated based on the adjustment amount of the operating parameters and the steady-state index of the system, wherein the control error value is based on the deviation of the steady-state index of the system from a preset threshold. Based on the control error value, a correction amount for the operating parameters is generated using a proportional-integral-derivative control algorithm; Based on the correction amount of the operating parameters, the operating parameters of the water exchange mechanism are adjusted, and the steady-state indicators of the system are monitored in real time. The above steps are iteratively executed until the control error value approaches zero, thereby establishing the steady-state measurement conditions.

[0012] As a preferred embodiment of the tidal level driven carbon flux measuring device and its measuring method according to the present invention, the method further includes: acquiring a measurement interruption signal, and when the measurement interruption signal indicates that the measurement window is abnormally closed due to environmental interference, determining the time-series carbon flux characteristic corresponding to the abnormally closed measurement window as the interruption flux characteristic; Acquire a measurement recovery signal, and if the measurement recovery signal indicates that the measurement window has reopened, determine the current time-series carbon flux characteristic as the recovered flux characteristic; If the similarity between the recovered flux feature and the interrupted flux feature is greater than or equal to the flux feature similarity threshold, the recovered measurement window and the interrupted measurement window are associated as a continuous measurement sequence, and the net carbon flux value is recalculated based on the associated sequence.

[0013] As a preferred embodiment of the tidal-driven carbon flux measuring device and method of the present invention, the method further includes multiple monitoring points or measurement sequences, and after outputting the net carbon flux value based on the time-series carbon flux and environmental factor correction, it further includes: The data processing time corresponding to the net carbon flux value is obtained. If the data processing time is greater than the processing time threshold, non-critical measurement sequences are identified among the multiple monitoring points or measurement sequences. The non-critical measurement sequences are identified based on the carbon flux value being lower than the flux threshold or the environmental factor stability being lower than the stability threshold. The sequence other than the non-critical measurement sequence in the current monitoring point or measurement sequence is identified as the current priority measurement sequence, and the subsequent tidal level driven carbon flux measurement steps are performed only on the priority measurement sequence.

[0014] As a preferred embodiment of the tidal-driven carbon flux measuring device and method of the present invention, after outputting the net carbon flux value based on the time-series carbon flux and environmental factor correction, the method further includes: Obtain key carbon flux features from the target database. If the similarity between the time-series carbon flux features and the key carbon flux features is greater than the feature similarity threshold, determine the current measurement sequence as a suspected key measurement sequence. The tidal level driving features and measurement timing sequences corresponding to the suspected key measurement sequences are added to the historical tidal level driving feature queue and measurement timing sequence queue to update the current tidal level driving features and measurement timing sequences.

[0015] An apparatus used in the aforementioned method for measuring carbon flux driven by tidal level.

[0016] The present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method for measuring tidal level-driven carbon flux.

[0017] The present invention also discloses a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described method for measuring tidal level-driven carbon flux.

[0018] The beneficial effects of this invention are as follows: This invention generates tidal driving characteristics by monitoring real-time tidal data of the external environment, and constructs an adaptive measurement timing sequence based on these characteristics. By combining the state characteristics of the current measurement window with the historically constructed sequence and dynamically adjusting the operating parameters of the measurement system using a feedback control strategy, steady-state measurement conditions are established in complex tidal environments. Finally, continuous concentration time-series data are collected under steady-state conditions to calculate carbon flux. This application solves the problems of incomplete carbon flux measurement time-series data, severe interference, and asynchrony with natural driving signals in dynamic tidal environments. It achieves intelligent synchronization and adaptive optimization of the measurement process with the tidal cycle, significantly improving the accuracy, continuity, and automation of intertidal carbon flux measurement. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a schematic diagram illustrating the steps of a tidal level-driven carbon flux measurement method proposed in this invention. Figure 2 This is a logic block diagram of a tidal level-driven carbon flux measurement method proposed in this invention. Detailed Implementation

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0021] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0023] Reference Figures 1-2 As an embodiment of the present invention, a tidal level-driven carbon flux measuring device and a measuring method thereof are provided. The method includes the following steps: Step 1: Monitor real-time tide level data of the external environment and collect tide level change signals. Based on the real-time tide level data, identify the tide level phase state and generate tide level driving characteristics based on the tide level phase state and the pre-set tide level benchmark model. Specifically, the real-time tide data in this embodiment can be collected by tide sensors deployed on the coastline or estuary and transmitted in real time to a local or cloud-based data processing unit via IoT technology. Specifically, a tide phase recognition algorithm is used to process the collected tide change signals, outputting the tide phase state, which includes high tide, low tide, or slack tide stages, and recording the tide value sequence and phase timestamp. It should be noted that the tide phase recognition algorithm is a mature method based on signal processing and state machines. Its core logic typically includes the following steps: S101 Data preprocessing: Filtering the raw tide sensor signal (e.g., using a low-pass filter) to smooth high-frequency noise (e.g., wave effects). S102 Trend calculation: Within a sliding time window, calculating the first derivative of the tide level with respect to time (i.e., the rate of change, in centimeters per minute or meters per hour). S103 Phase determination rule (threshold-based state machine): High tide: When the rate of change of the tide level is continuously greater than a positive threshold (e.g., +0.5 cm / min), it is determined to be a high tide stage. Low tide: When the rate of change of tide level is consistently less than a negative threshold (e.g., -0.5 cm / min), it is considered a low tide phase. Slack tide (absence of high or low tide): When the absolute value of the rate of change of tide level is less than a minimum threshold (e.g., |0.1| cm / min) for a period of time, and the tide level is close to a local extreme (highest or lowest), it is considered a slack tide phase.

[0024] Furthermore, a tide data queue is constructed for each monitoring point to store the historical tide data for that point, and the tide phase state within it is determined as the historical tide phase. The tide data queue with point ID k is denoted as... Determine the tidal driving characteristics corresponding to the historical tidal phases. The queue of tidal driving characteristics for point ID k is denoted as... Where p is the length of the tidal data queue, which can be selected as 8 to cover typical tidal cycles. In addition, an environmental factor queue can be built for each location to store relevant environmental data, such as water temperature, salinity, or wind speed. The environmental factor queue for location ID k is denoted as... .

[0025] The tidal phase state from the tidal data queue at each location and a pre-defined tidal benchmark model (a mathematical model used to describe, predict, or characterize the tidal variation over time at a specific monitoring location under ideal or typical conditions) are input into the feature generation module. Through model calculation and feature fusion, the tidal driving features corresponding to that location can be obtained. Specifically, the core function of the feature generation module is comparison and fusion. It calculates the deviation, rate of change difference, phase advance / lag, etc., between the real-time phase state and the benchmark model prediction, and fuses these comparison results with the original phase state information (such as the current mid-high tide) to form a structured feature set.

[0026] Step 2: Construct a measurement timing sequence based on tidal level driving characteristics and the first regulation strategy; The control module of the tidal level-driven carbon flux measurement system includes a first regulation strategy. Tidal level-driven characteristics are input into the first regulation strategy to construct a measurement timing sequence. The length of the measurement timing sequence is consistent with the tidal level data queue to ensure coverage of the complete tidal cycle.

[0027] Step 3: Obtain the water exchange trigger signal within the current tidal cycle. Based on the water exchange trigger signal, determine the opening and closing status of the current measurement window. Based on the opening and closing status of the current measurement window and the initial flux reading, extract the current flux characteristics. The tidal level driving characteristics and the current flux characteristics correspond to the same monitoring point and tidal cycle. The system acquires the water exchange trigger signal within the current tidal cycle, which is generated based on real-time tidal data. It determines the opening and closing status of the current measurement window and inputs the opening and closing status of the current measurement window and the initial flux reading into the feature extraction module. Through data fusion and processing, the current flux characteristics can be obtained, while ensuring that the tidal level driving characteristics and the current flux characteristics correspond to the same monitoring point and tidal cycle.

[0028] Step 4: Based on the measurement timing sequence, current flux characteristics, and the second control strategy, adjust the operating parameters of the water exchange mechanism to establish steady-state measurement conditions; The control module of the tidal level driven carbon flux measurement system includes a second regulation strategy. The measurement timing sequence and current flux characteristics are input into the second regulation strategy to adjust the operating parameters of the water exchange mechanism in order to establish steady-state measurement conditions.

[0029] Step 5: Under steady-state measurement conditions, monitor changes in greenhouse gas concentrations using gas sensors and calculate time-series carbon flux based on the concentration time series; based on the time-series carbon flux and combined with environmental factor corrections, output the net carbon flux value.

[0030] Under steady-state measurement conditions, greenhouse gas concentration changes are monitored using high-precision gas sensors. When the concentration time series reaches the preset stability and length requirements, the time-series carbon flux is calculated based on the series.

[0031] The time-series carbon flux is matched and compared with the benchmark carbon flux library. The similarity is calculated using the correlation coefficient method. For time-series carbon fluxes whose similarity reaches the preset flux similarity threshold, environmental factors such as temperature, salinity, and air pressure are combined for correction, and the net carbon flux value is output.

[0032] For example, a high-precision nondispersive infrared (NDIR) gas analyzer is used to continuously monitor and determine the mole fraction of carbon dioxide (CO2) in the gas chamber at a frequency of 1 Hz. Monitoring continues until at least 30 consecutive and valid concentration data points are obtained, forming an initial concentration time series. .

[0033] The linear slope of the concentration change within the sequence is calculated simultaneously. If the absolute value of the slope is less than a preset stability threshold (e.g., 0.5 ppm / min), the gas mixture inside the chamber is considered homogeneous and the exchange process is stable, and the sequence meets the calculation requirements. Based on the qualified concentration-time series, the time-series carbon flux is calculated using linear regression. Specifically, with time t as the independent variable and concentration C as the dependent variable, a linear equation is fitted: C = k * t + b. Here, the slope k represents the rate of change of concentration over time.

[0034] The rate of change is converted into area flux F using the following formula: F = k * (P / (R * T)) * (V / A); where: P is the measured atmospheric pressure (Pa) inside the chamber, read by an integrated pressure sensor. R is the ideal gas constant (8.314 J·mol⁻¹·K⁻¹). T is the measured Kelvin temperature (K) inside the chamber, read by a temperature sensor. V is the effective net volume of the measuring chamber (m³), a known design parameter. A is the contact area between the bottom of the chamber and the sediment / water body (m²), a known design parameter. The system repeats the above process for each measurement window that meets quality control requirements, obtaining a series of flux values ​​{F1, F2, ...} arranged in time sequence, which constitutes the time-series carbon flux.

[0035] In addition, the system has a built-in benchmark carbon flux feature library, which stores the range and variation patterns of carbon flux in different typical habitats (such as salt marshes, mangroves, and mudflats) under standard environmental conditions (such as daily flux curves and tidal cycle flux curves). The Pearson correlation coefficient (r) between the current time-series carbon flux {F1, F2, ...} and several benchmark curves in the library is calculated. If the r of a benchmark curve is ≥ 0.8 (a preset feature similarity threshold), the current measurement environment is considered comparable to that benchmark scenario, and the standard calibration parameter set corresponding to that benchmark curve is used. Environmental factor correction: To eliminate the difference between the environmental conditions at the time of measurement and the standard state, a multi-factor collaborative correction model is used to calculate the net carbon flux. In the formula: This refers to the average or median value of the original time-series carbon flux obtained from the above calculations. The temperature correction factor is typically used. Model: = ^ ((T_std -T_obs) / 10). Where T_std is the standard reference temperature, and T_obs is the average water temperature during the measurement period. This is the temperature sensitivity coefficient (typically 2.0, which can be selected based on the matching results of the benchmark library).

[0036] This is a salinity correction factor used to correct for the effect of salinity on CO2 solubility. = It can be obtained through empirical formulas or by looking up tables, where It is the solubility at a salinity of 0. It is the solubility at the measured salinity.

[0037] As a pressure correction factor, according to the ideal gas law: = P_std / P_obs, where P_std is standard atmospheric pressure and P_obs is the measured average atmospheric pressure. Finally, the system will use the corrected... The value, along with its corresponding measurement time, geographical location, tidal phase, and correction parameters used, is packaged into a complete net carbon flux record and output to a data storage unit or remote monitoring platform.

[0038] Through the above steps, this embodiment monitors real-time tidal data of the external environment, identifies the tidal phase state, and generates tidal driving characteristics based on the tidal phase state and tidal reference model, thereby establishing a driving mechanism synchronized with the tidal cycle. Based on the tidal driving characteristics and the first control strategy, a measurement timing sequence is constructed. This sequence optimizes the distribution of the measurement window, ensuring carbon flux measurement is performed at a suitable tidal phase. On this basis, the water exchange trigger signal within the current tidal cycle is acquired, the opening and closing state of the current measurement window is determined, and the current flux characteristics are extracted. Based on the measurement timing sequence, the current flux characteristics, and the second control strategy, the operating parameters of the water exchange mechanism are adjusted to establish steady-state measurement conditions. Finally, the time-series carbon flux is monitored and calculated using a gas sensor, and the net carbon flux value is output after environmental factor correction. This solves the problems of incomplete carbon flux measurement sequences and high interference in tidal dynamic environments, significantly improving the accuracy and environmental adaptability of carbon flux measurement.

[0039] In one embodiment, the first control strategy includes a tidal phase analysis unit and a sequence generation unit. Based on the tidal driving characteristics and the first control strategy, the measurement timing sequence is constructed as follows: The tidal level driving characteristics are input into the tidal level phase analysis unit to determine the key tidal level phase characteristics, which include the tidal level inflection point, phase duration and phase change rate. Tide-driven features are input into the sequence generation unit to determine the benchmark measurement sequence, which is generated based on a historical tide data model and defines the initial distribution of the measurement window. In this embodiment, the first control strategy includes a tide level phase analysis unit and a sequence generation unit. Specifically, the tide level phase analysis unit can employ a wavelet transform-based phase recognition algorithm to detect tide level inflection points, calculate phase duration and rate of change; the sequence generation unit can be an ARIMA time series model trained based on historical tide level data to generate a benchmark measurement sequence.

[0040] The tidal level driving features are input into the tidal level phase analysis unit and the sequence generation unit, respectively, to extract key tidal level phase features and benchmark measurement sequences, and the feature representation is optimized through data fusion processing.

[0041] Based on the key phase characteristics of the tide level and the benchmark measurement sequence, the timing and interval of the measurement window are optimized through a dynamic adjustment algorithm to determine the measurement timing sequence.

[0042] Based on the key phase characteristics of the tide level and the benchmark measurement sequence, the timing and interval of the measurement window are calculated by a dynamic adjustment algorithm (such as sliding window optimization), thereby determining the timing sequence of the measurement.

[0043] Through the above steps, this application embodiment provides a specific method for constructing measurement timing sequences based on the tidal phase analysis unit and the sequence generation unit. This method is highly feasible and has high sequence planning accuracy.

[0044] For example, let the key tidal phase features output by the tidal phase analysis unit be {stage: mid-high tide; attribute: accelerated high tide; expected transition to slack tide: 45 minutes later}; let the baseline measurement sequence output by the sequence generation unit be {within the next 3 hours, it is recommended to take measurements at relatively stable tidal levels predicted by the model, for example: T+30min, T+60min, T+120min}, where T represents the current moment, i.e., the specific time point at which the system completes the calculation of tidal driving features and begins planning the measurement sequence. T+30min represents the time point 30 minutes after the current moment, and so on. Detailed working steps of the dynamic adjustment algorithm: S201: Stage Identification and Importance Assessment: The algorithm first analyzes the tidal stage at each baseline measurement time point: T+30min: located in the high tide period (because the low tide is at T+45min). T+60min: Located during the slack tide period (assuming that the slack tide starts from T+45min and lasts for about 1 hour). T+120min: May be in the next high or low tide period; It is also important to understand that: during the rapid high tide period, water movement is intense, sediment-water interface disturbance is significant, and it is the peak period for carbon release, making it highly valuable for measurement. During the low tide period, the water body is relatively stable, facilitating the establishment of steady-state measurement conditions and ensuring stable baseline data. The phase transition period (high tide → low tide) involves dramatic changes in biogeochemical processes, making it a critical observation window.

[0045] S202: Problem Diagnosis and Adjustment Needs Analysis: The baseline sequence has only one measurement point (T+30min) during the critical high-value period (the accelerated high tide period from T+0 to T+45min). T+30min may miss the characteristics of the early high tide, while T+60min may be in the middle of the low tide, missing the subtle changes during the phase transition period.

[0046] S203: Specific adjustments to the calculation; the algorithm will be recalculated according to the following rules: Rule 1: Increase the density of measurements during critical periods of change: At least 2-3 measurement points should be arranged during the accelerated high tide period (T+0 to T+45min). 1-2 measurement points should be arranged during the phase transition period (around T+45min).

[0047] Rule 2: Timing Adjustment Strategy. The first measurement point should be moved from T+30 min to T+15 min to capture high tide characteristics earlier. An additional measurement point, T+40 min, should be added before low tide to specifically capture characteristics at the end of high tide. T+60 min should be adjusted to T+75 min to bring it closer to the middle of low tide rather than the beginning. T+120 min should be appropriately delayed to T+130 min to better cover the next period of change. Specific calculation process: Determine the densification interval: During the accelerated high tide period (45 minutes), use an interval of approximately 15-20 minutes.

[0048] Calculate the new time points: First point: T + 15 min (15 minutes earlier than the original T + 30 min). Second point: T + 40 min (equally divided between T + 15 min and T + 45 min, approximately 25 minutes apart, taking into account measurement duration). Third point: T + 75 min (15 minutes later than the original T + 60 min, to bring it closer to the middle of the slack tide). Fourth point: T + 130 min (10 minutes later than the original T + 120 min, adjusted to the next possible period of change).

[0049] S204: Verification and Output. The algorithm verifies that the adjusted sequence meets the following conditions: sufficient density during critical change periods (accelerated high tide, phase transition); sufficient time between adjacent measurement points to complete the full measurement process; and the overall sequence covers the complete tidal cycle characteristics.

[0050] Final output: Optimized measurement timing sequence = [T+15min, T+40min, T+75min, T+130min].

[0051] In one embodiment, the second control strategy includes an operating parameter optimization unit and a steady-state determination unit. Based on the measurement timing sequence, current flux characteristics, and the second control strategy, the operating parameters of the water exchange mechanism are adjusted to establish steady-state measurement conditions, including: The current throughput characteristics are input into the operation parameter optimization unit to determine the adjustment amount of the operation parameters, where the operation parameters include exchange duration, exchange frequency or exchange flow rate; The measurement timing sequence and current flux characteristics are input into the steady-state determination unit to determine the system steady-state indices, which include the concentration change rate, temperature stability, or pressure balance value. In this embodiment, the second control strategy includes an operating parameter optimization unit and a steady-state determination unit. Specifically, the operating parameter optimization unit can employ an optimizer based on a proportional-integral-derivative (PID) control algorithm to calculate the adjustment amount of the operating parameters; the steady-state determination unit can be a determination module based on multi-sensor data fusion to calculate the steady-state index of the system.

[0052] The current flux characteristics are input into the operating parameter optimization unit to extract flux change pattern characteristics, thereby determining the adjustment amount of the operating parameters; at the same time, the measurement timing sequence and the current flux characteristics are input into the steady-state determination unit to extract system dynamic characteristics, thereby determining the system steady-state index.

[0053] Based on the adjustment amount of the operating parameters and the steady-state index of the system, the operating parameters of the water exchange mechanism are dynamically adjusted through a feedback control algorithm until the steady-state index of the system meets the preset threshold, thereby establishing the steady-state measurement conditions.

[0054] Furthermore, based on the adjustment amount of the operating parameters and the steady-state index of the system, the operating parameters of the water exchange mechanism are dynamically adjusted through a feedback control algorithm until the steady-state index of the system meets the preset threshold, thereby establishing the steady-state measurement conditions.

[0055] Through the above steps, this application embodiment provides a specific method for adjusting operating parameters and establishing steady-state measurement conditions based on the operating parameter optimization unit and the steady-state determination unit. The method is highly feasible and the measurement conditions are stable and reliable.

[0056] In one embodiment, the operating parameters of the water exchange mechanism are dynamically adjusted using a feedback control algorithm based on the adjustment amount of the operating parameters and the system steady-state index, until the system steady-state index meets a preset threshold, thereby establishing the steady-state measurement conditions, including: The control error value is calculated based on the adjustment amount of the operating parameters and the steady-state index of the system, wherein the control error value is based on the deviation between the steady-state index of the system and the preset threshold. Based on the control error value, the operating parameter correction amount is generated using a proportional-integral-derivative control algorithm; Specifically, in this embodiment, a control error value is calculated based on the deviation between the system's steady-state indicators (such as concentration change rate and temperature stability) and a preset threshold. The control error value can be expressed as a weighted sum of the deviations of each steady-state indicator. Based on the control error value, a proportional-integral-derivative (PID) control algorithm is used to generate a correction amount for the operating parameters. The PID algorithm calculates the combined output of the proportional, integral, and derivative terms to eliminate the steady-state error.

[0057] Based on the correction amount of the operating parameters, adjust the operating parameters of the water exchange mechanism, and monitor the steady-state indicators of the system in real time. Iterate the above steps until the control error value approaches zero, thereby establishing the steady-state measurement conditions.

[0058] Through the above steps, this application embodiment introduces a feedback control mechanism to dynamically optimize operating parameters, thereby obtaining stable measurement conditions and further improving the accuracy and reliability of carbon flux measurement.

[0059] For example, in the embodiments of this application, establishing steady-state measurement conditions requires monitoring multiple system steady-state indicators and setting specific preset thresholds (i.e., desired steady-state values ​​or allowable ranges) for each indicator. For example: concentration change rate threshold: (e.g., 0.1% / min) indicates that, under steady-state conditions, the rate of change in the concentration of greenhouse gases (e.g., CO2) within the measuring chamber should not exceed 0.1% per minute. Temperature stability threshold: (e.g., ±0.5 °C); This means that under steady-state conditions, the water temperature fluctuation in the measuring chamber should be within ±0.5 °C. Pressure balance threshold: (e.g., ±10 Pa) indicates that under steady-state conditions, the pressure difference between the inside and outside of the gas chamber should be maintained within ±10 Pascals.

[0060] The control error (E) is a quantification of the degree to which the current state of the system deviates from the desired steady state. In calculation, the real-time measured values ​​of each steady-state indicator are typically compared with preset thresholds to obtain the deviation of each indicator, and then the total error is obtained through weighted summation. For example: Real-time data collection: At time point t, the concentration change rate is monitored. Temperature fluctuations Pressure difference Calculate the deviations of each indicator: concentration deviation = ( Temperature deviation Take the absolute value; pressure deviation = (Negative values ​​indicate that the error is within the acceptable range and can be considered as 0); the total error is obtained by weighted summation: weights are assigned according to the degree of influence of each indicator on the system stability (e.g., ...). , , ),but: ; this This represents the control error value at the current moment. A value greater than 0 indicates that the system has not reached steady state. The Proportional-Integral-Derivative (PID) control algorithm uses the current error... The cumulative error over time (integral term) and the trend of error change (differential term) are used to calculate a comprehensive operating parameter correction. This is to adjust the actuator (water exchange mechanism). The discrete form of the PID algorithm (implemented in a microcontroller): ; in: , , These are the proportional, integral, and derivative coefficients, respectively, which need to be tuned based on the system response (e.g., using the Ziegler-Nichols method). ∑ This represents the cumulative error (integral term) from the start of control to time t, used to eliminate persistent small deviations. This represents the rate of change of error (differential term), used to predict trends and suppress oscillations. For the control period (e.g., 1 second). The generated correction amount. This will be used to adjust the operating parameters of the water exchange mechanism (such as exchange flow rate). Assuming current traffic... The adjustment amount is: + Furthermore, a positive correction is defined as increasing the flow rate to accelerate equilibrium, while a negative correction is defined as decreasing the flow rate to reduce disturbances. When If the value remains below a minimum (e.g., 0.001) or 0 for several cycles, it is considered to have entered a steady-state measurement condition, at which point adjustment should be stopped or the system should enter a maintenance mode.

[0061] In one embodiment, the method of the present invention further includes: acquiring a measurement interruption signal, and when the measurement interruption signal indicates that the measurement window is abnormally closed due to environmental interference, determining the time-series carbon flux characteristic corresponding to the abnormally closed measurement window as the interruption flux characteristic; Acquire the measurement recovery signal, and when the measurement recovery signal indicates that the measurement window has reopened, determine the current time-series carbon flux characteristic as the recovered flux characteristic; In this embodiment, when the measurement window is abnormally closed due to environmental interference (such as sudden tidal changes or equipment failure), historical flux features are invoked to supplement the continuity of the measurement sequence and maintain data integrity. A measurement interruption signal is acquired; this signal can be generated during continuous monitoring, indicating the unexpected closure of the measurement window. The closure of the measurement window indicated by the interruption signal results in the number of currently valid measurement windows being less than the number of windows in the expected measurement window sequence, meaning that data acquisition for some measurement windows is interrupted. At this time, the time-series carbon flux features corresponding to the measurement windows with missing data due to abnormal closure are identified as interrupted flux features and recorded in the recent interrupted measurement queue. The set of interrupted measurement window IDs can be denoted as... Furthermore, the environmental conditions are continuously monitored. When the environmental conditions stabilize and the measurement window reopens, a measurement recovery signal is generated. The measurement window that reopens at this time is identified as the recovery measurement window, and the time-series carbon flux characteristic corresponding to this window is identified as the recovery flux characteristic.

[0062] If the similarity between the recovered flux feature and the interrupted flux feature is greater than or equal to the flux feature similarity threshold, the recovered measurement window and the interrupted measurement window are associated as a continuous measurement sequence, and the net carbon flux value is recalculated based on the associated sequence.

[0063] The feature vector corresponding to the interrupted flux feature is determined, including the flux change trend, amplitude, and period. When a recovering flux feature appears, it is compared with the interrupted flux features in the recent interrupted measurement queue. If the similarity between the two (here, similarity refers to the morphological similarity between sequences, calculated using the Dynamic Time Warping (DTW) algorithm, i.e., finding the optimal curved path between two sequences, calculating the minimum cumulative distance, and converting this distance into a similarity score) reaches the flux feature similarity threshold, the recovering measurement window and the corresponding interrupted measurement window are associated as a continuous measurement sequence, and the net carbon flux value is recalculated based on the associated complete sequence. Furthermore, each time a recovering flux feature appears and is matched with an interrupted flux feature, if there is an interrupted measurement window in the recent interrupted measurement queue that fails to match the recovering flux feature, the number of unmatched occurrences for that interrupted window is recorded. When the number of unmatched occurrences exceeds a preset unmatched threshold, the interrupted measurement window is removed from the queue, and no further matching is performed. Here, matching refers to determining whether the highest similarity is ≥ the flux feature similarity threshold. If ≥ the threshold, it is considered a match. If < the threshold, it is considered a failure to match.

[0064] In one embodiment, the method of the present invention further includes multiple monitoring points or measurement sequences, and after outputting the net carbon flux value based on time-series carbon flux and environmental factor correction, it further includes: Obtain the data processing time corresponding to the net carbon flux value. If the data processing time is greater than the processing time threshold, identify the non-critical measurement sequences contained in multiple monitoring points or measurement sequences. The non-critical measurement sequences are identified based on the carbon flux value being lower than the flux threshold or the environmental factor stability being lower than the stability threshold. In this embodiment, the time required from the start of processing tidal level data to outputting net carbon flux values ​​can be defined as the data processing duration. If the data processing duration exceeds a preset processing duration threshold, the time-series carbon flux characteristics of each monitoring point or measurement sequence are compared with the baseline carbon flux characteristics. Measurement sequences are sequentially identified as non-critical measurement sequences according to carbon flux values ​​from low to high or environmental factor stability from low to high, until the data processing duration is no greater than the preset processing duration threshold. Specifically, when the data processing duration exceeds the preset processing duration threshold, non-critical measurement sequences are added to a non-critical sequence queue. The set of sequence IDs in the non-critical sequence queue is denoted as... When the data processing time is not greater than the preset processing time threshold, non-critical measurement sequences are popped out in order of carbon flux value from high to low or environmental factor stability from high to low, and their measurements are gradually restored.

[0065] The sequence other than the non-critical measurement sequence in the current monitoring point or measurement sequence is identified as the current priority measurement sequence, and the subsequent tidal level driven carbon flux measurement steps are only performed on the priority measurement sequence.

[0066] The sequences other than the non-critical measurement sequences in the current monitoring points or measurement sequences are identified as the current priority measurement sequences. In this way, during subsequent tidal-driven carbon flux measurement, the system only performs the complete measurement steps for the priority measurement sequences, while the measurement frequency of non-critical measurement sequences is paused or reduced. This frequency reduction process can reduce the system's data processing time and improve resource utilization efficiency.

[0067] In one embodiment, after outputting the net carbon flux value based on time-series carbon flux and adjusted for environmental factors, the method further includes: Obtain key carbon flux features from the target database. If the similarity between the time-series carbon flux features and the key carbon flux features is greater than the feature similarity threshold, determine the current measurement sequence as a suspected key measurement sequence. Specifically, key carbon flux features are obtained from the target database (referring to typical carbon flux pattern features pre-stored in the target database that have significant scientific or environmental indicative value, which may originate from historical high-risk event records, templates of key monitoring areas, or specific ecological process models). Measurement sequences whose similarity (referring to spatial similarity between features, i.e., distance in feature space or similarity measure such as cosine similarity) between time-series carbon flux features and key carbon flux features is greater than a feature similarity threshold are identified as suspected key measurement sequences, and their sequence IDs are added to the suspected key sequence set, denoted as... When the similarity between the time-series carbon flux features of a suspected key measurement sequence and the key carbon flux features subsequently falls below the feature similarity threshold, it is removed from the set.

[0068] Add the tidal driving features and measurement timing sequences corresponding to suspected key measurement sequences to the historical tidal driving feature queue and measurement timing sequence queue to update the current tidal driving features and measurement timing sequences.

[0069] In this embodiment, the tidal level driving features and measurement timing sequences corresponding to suspected key measurement sequences are added to the historical tidal level driving feature queue and measurement timing sequence queue, respectively, to update the current queues. These updated queues will be used for subsequent rounds of measurement timing planning and feature generation. Specifically, an overlapping window monitoring method (i.e., the monitoring periods of adjacent measurement windows partially overlap) can be used for sequences in the suspected key measurement sequence set. That is, when the similarity between the output time-series carbon flux features and the key carbon flux features is higher than a threshold, the tidal level and flux data features of the latter half of the monitoring period already executed for the suspected key measurement sequence are extracted and added again to its corresponding historical feature queue to improve the monitoring frequency or data resolution of the sequence.

[0070] Furthermore, in this embodiment, the similarity between multiple suspected key measurement sequences and the target database can be sorted, and detailed information of the top m (m can be preset) sequences with the highest similarity can be continuously maintained. When a measurement sequence is continuously identified as a suspected key measurement sequence more than a preset threshold number of times, a high-priority alarm or flag can be triggered to prompt operators to pay close attention.

[0071] Through the above steps, when a sequence highly similar to the key carbon flux feature library exists in the monitoring data, the historical queue is dynamically updated to improve its monitoring priority, thereby optimizing the allocation of monitoring resources. In complex tidal dynamic environments, potential high-flux emission events or important ecological processes are captured and confirmed first, improving the timeliness and effectiveness of monitoring.

[0072] This embodiment also discloses a tide-driven carbon flux measuring device, which includes a tide sensing module, a sequence planning module, a dynamic control module, a steady-state establishment module, a flux calculation and output module, and a water exchange mechanism: the tide sensing module is used to monitor real-time tide data of the external environment and collect tide change signals. Based on the real-time tide data, it identifies the tide phase state, including high tide, low tide, or slack tide stages, and generates tide-driven characteristics based on the tide phase state and a pre-set tide reference model. The sequence planning module is used to construct the measurement timing sequence based on the tidal level driving characteristics and the first control strategy; the dynamic control module is used to acquire the water exchange trigger signal within the current tidal level cycle, determine the opening and closing status of the current measurement window based on the water exchange trigger signal, and extract the current flux characteristics based on the opening and closing status of the current measurement window and the initial flux reading. The tidal level driving characteristics and the current flux characteristics correspond to the same monitoring point and tidal cycle. A water exchange mechanism (such as an electrically controlled valve) is used to control the connection between the bottom or sidewall of the gas chamber and the external water body. A steady-state establishment module adjusts the operating parameters of the water exchange mechanism based on the measurement timing sequence, current flux characteristics, and a second control strategy to establish steady-state measurement conditions. A flux calculation and output module monitors greenhouse gas concentration changes using gas sensors under steady-state measurement conditions and calculates time-series carbon flux based on the concentration time series. Based on the time-series carbon flux and environmental factor corrections, it outputs the net carbon flux value. Furthermore, the device is powered by solar panels and batteries, supporting long-term field operations.

[0073] This embodiment also provides a computer device applicable to a tide-driven carbon flux measurement method, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the tide-driven carbon flux measurement method proposed in the above embodiment.

[0074] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0075] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements a tide-driven carbon flux measuring device and its measuring method as described in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0076] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for measuring carbon flux driven by tidal level, characterized by, The method comprises the following steps: monitoring real-time tidal level data of an external environment and collecting tidal level change signals, identifying a tidal level phase state according to the real-time tidal level data, generating a tidal level driving feature based on the tidal level phase state and a pre-set tidal level reference model; constructing a measurement opportunity sequence according to the tidal level driving feature and a first regulation strategy; obtaining a water body exchange trigger signal in a current tidal level cycle, determining an opening and closing state of a current measurement window according to the water body exchange trigger signal, extracting a current flux feature based on the opening and closing state of the current measurement window and an initial flux reading, the tidal level driving feature and the current flux feature corresponding to the same monitoring point and tidal cycle; adjusting operation parameters of the water body exchange mechanism according to the measurement opportunity sequence, the current flux feature and a second regulation strategy, so as to establish a steady-state measurement condition; under the steady-state measurement condition, monitoring greenhouse gas concentration changes through a gas sensor and calculating a time-series carbon flux based on a concentration time sequence; outputting a net carbon flux value according to the time-series carbon flux in combination with environmental factor correction.

2. The method according to claim 1, wherein: The first regulation strategy comprises a tidal level phase analysis unit and a sequence generation unit, and the construction of the measurement opportunity sequence according to the tidal level driving feature and the first regulation strategy comprises the following steps: inputting the tidal level driving feature into the tidal level phase analysis unit to determine a tidal level key phase feature, wherein the tidal level key phase feature comprises a tidal level turning point, a phase duration and a phase change rate; inputting the tidal level driving feature into the sequence generation unit to determine a reference measurement sequence, wherein the reference measurement sequence is generated based on a historical tidal level data model and defines an initial distribution of a measurement window; determining the measurement opportunity sequence by optimizing the time sequence and interval of the measurement window through a dynamic adjustment algorithm according to the tidal level key phase feature and the reference measurement sequence.

3. The method of claim 2, wherein: The second regulation strategy comprises an operation parameter optimization unit and a steady-state determination unit, and the adjustment of the operation parameters of the water body exchange mechanism according to the measurement opportunity sequence, the current flux feature and the second regulation strategy to establish the steady-state measurement condition comprises the following steps: inputting the current flux feature into the operation parameter optimization unit to determine an operation parameter adjustment amount, wherein the operation parameters comprise an exchange duration, an exchange frequency or an exchange flow rate; inputting the measurement opportunity sequence and the current flux feature into the steady-state determination unit to determine a system steady-state index, wherein the system steady-state index comprises a concentration change rate, a temperature stability or a pressure balance value; dynamically adjusting the operation parameters of the water body exchange mechanism through a feedback control algorithm according to the operation parameter adjustment amount and the system steady-state index until the system steady-state index meets a pre-set threshold, so as to establish the steady-state measurement condition.

4. The method of claim 3, wherein: The dynamically adjusting of the operation parameters of the water body exchange mechanism through the feedback control algorithm according to the operation parameter adjustment amount and the system steady-state index until the system steady-state index meets the pre-set threshold, so as to establish the steady-state measurement condition comprises the following steps: According to the operation parameter adjustment amount and the system steady-state index, a control error value is calculated, wherein the control error value is based on a deviation of the system steady-state index from a preset threshold value; According to the control error value, an operation parameter correction amount is generated through a proportional-integral-derivative control algorithm; According to the operation parameter correction amount, the operation parameter of the water body exchange mechanism is adjusted, and the system steady-state index is monitored in real time, and the above steps are iteratively executed until the control error value tends to zero, thereby establishing the steady-state measurement condition.

5. The method according to any one of claims 1-4, wherein: The method further comprises: obtaining a measurement interruption signal, in the case that the measurement interruption signal indicates that a measurement window is abnormally closed due to environmental interference, determining a time-series carbon flux feature corresponding to the abnormally closed measurement window as an interrupted flux feature; obtaining a measurement recovery signal, in the case that the measurement recovery signal indicates that the measurement window is reopened, determining the current time-series carbon flux feature as a recovered flux feature; In the case that the similarity between the recovered flux feature and the interrupted flux feature is greater than or equal to a flux feature similarity threshold value, the recovered measurement window and the interrupted measurement window are associated as a continuous measurement sequence, and the net carbon flux value is recalculated based on the associated sequence.

6. The method of claim 5, wherein: The method further comprises a plurality of monitoring points or measurement sequences, and after the step of outputting the net carbon flux value based on the time-series carbon flux and the environmental factor correction, the method further comprises: obtaining a data processing time length corresponding to the determination of the net carbon flux value, in the case that the data processing time length is greater than a processing time length threshold value, determining a non-key measurement sequence included in a plurality of the monitoring points or measurement sequences, wherein the non-key measurement sequence is identified based on a carbon flux value lower than a flux threshold value or an environmental factor stability lower than a stability threshold value; determining a sequence other than the non-key measurement sequence in the current monitoring point or measurement sequence as a current priority measurement sequence, and only performing subsequent steps of determining the tide-driven carbon flux on the priority measurement sequence.

7. The method of claim 6, wherein: After the step of outputting the net carbon flux value based on the time-series carbon flux and the environmental factor correction, the method further comprises: obtaining a key carbon flux feature in a target database, in the case that the similarity between the time-series carbon flux feature and the key carbon flux feature is greater than a feature similarity threshold value, determining a current measurement sequence as a suspected key measurement sequence; adding a tide-driven feature corresponding to the suspected key measurement sequence and a measurement time sequence to a historical tide-driven feature queue and a measurement time sequence queue to update the current tide-driven feature and the measurement time sequence.

8. A measuring device for use in a method of measuring carbon flux driven by tidal level according to claim 7, characterised in that: The device comprises: a tide sensing module, a sequence planning module, a dynamic control module, a steady-state establishing module, a flux calculation and output module, and a water body exchange mechanism: The tide sensing module is configured to monitor real-time tide data of an external environment, collect a tide change signal, identify a tide phase state based on the real-time tide data, including a rising tide, a falling tide, or a flat tide stage, and generate a tide-driven feature based on the tide phase state and a pre-set tide reference model. The sequence planning module is configured to construct a measurement time sequence based on the tide-driven feature and a first regulation strategy. The dynamic control module is configured to acquire a water body exchange trigger signal in a current tidal level period, determine an opening and closing state of a current measurement window according to the water body exchange trigger signal, and extract a current flux feature based on the opening and closing state of the current measurement window and an initial flux reading, wherein the tidal level driving feature and the current flux feature correspond to a same monitoring point and a tidal period; The water body exchange mechanism is configured to control the bottom or sidewall of the air chamber to be in communication with the external water body; The steady state establishment module is configured to adjust operation parameters of the water body exchange mechanism according to the measurement time sequence, the current flux feature and a second regulation strategy, so as to establish a steady state measurement condition. The flux calculation output module is configured to monitor a change in concentration of the greenhouse gas by using the gas sensor under the steady state measurement condition, calculate a time-series carbon flux based on a time sequence of the concentration, and output a net carbon flux value according to the time-series carbon flux in combination with environmental factor correction. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: The processor executes the computer program to implement the steps of the tidal level driving carbon flux measurement method according to any one of claims 6 or 7.

10. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to implement the steps of the tidal level driving carbon flux measurement method according to any one of claims 6 or 7.

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