Multifunctional gas sensor teaching experiment platform

Abstract

The invention discloses a multifunctional gas sensor teaching experiment platform, particularly relates to the field of plant respiration gas measurement, and is used for solving the problem that the experiment accuracy is influenced due to the fact that the volume of a container cannot be dynamically adjusted in plant respiration measurement. And extracting a gas concentration transient asynchronism rate and a concentration fluctuation accumulation deviation index, generating a comprehensive decision value to judge the environmental stability, and adjusting the volume of the container as required. The preliminary container volume is calculated by combining a polynomial fitting technology with a plant respiration rate, and is adjusted for the first time. And then performing nonlinear correction in combination with environmental parameters, and further finely adjusting the volume of the container through a finite difference method to ensure that the gas concentration is stable. And finally, performing feedback optimization on gas concentration data by adopting a Kalman filtering technology, approaching an ideal container volume, ensuring that the gas concentration is kept in a reasonable range, and improving data precision and experimental reliability.

Description

Technical Field

[0001] The present invention relates to the field of plant respiratory gas determination, and more specifically, to a multifunctional gas sensor teaching experiment platform. Background Art

[0002] In the experiment of measuring plant respiratory gases, plants are usually placed in a closed container, and the sensor calculates the respiration rate of plants by detecting the changes in gas concentration in the container. The size of the container plays a key role in experimental design. It not only affects the speed of change of gas concentration during the experiment, but also directly determines the stability and natural state of the environment in which the plants are located. When plants breathe, they release carbon dioxide and consume oxygen. The concentration changes of these gases are nonlinearly related to the volume of air in the container. If the container is too large, the gas concentration may not change significantly, affecting the detection sensitivity of the sensor; if the container is too small, the gas concentration will accumulate or be exhausted quickly, causing the plant to enter an abnormal respiration state. In addition, environmental parameters such as temperature and humidity inside the container are also affected by the size of the container, and temperature and humidity fluctuations will also interfere with the physiological processes of plants. Therefore, it is crucial to reasonably set the size of the container based on different plant types and experimental purposes. How to monitor the changes in plant respiratory gases in real time and make intelligent decisions on the adjustment of the container is the key to ensuring the accuracy of the experimental results.

[0003] Most of the existing plant respiration measurement technologies rely on preset container sizes, but fail to dynamically adapt to changes in plant respiratory gases, resulting in too fast or too slow changes in the living environment of plants in the experiment. In traditional methods, once the volume of the container is set, the gas environment in which the plant is located cannot be automatically adjusted according to the respiration of the plant during the measurement process. For example, when the container volume is small, the carbon dioxide released during the plant's respiration will accumulate rapidly, and the oxygen consumption rate is also rapid, causing the gas environment inside the container to change rapidly. Due to the limitation of the container volume, the carbon dioxide concentration will quickly rise to a level higher than the normal physiological tolerance range of the plant, thereby inhibiting its respiration. This problem is particularly evident in long-term experiments. Plants enter a state of stress due to lack of oxygen or excessive carbon dioxide concentration during the experiment, resulting in the experimental results being unable to truly reflect the natural respiration rate of the plant. The existing technology cannot adjust the container volume in real time, making the experimental conditions too rigid and unable to flexibly respond to interference caused by environmental changes during plant respiration, affecting the accuracy and repeatability of the experiment.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiment of the present invention provides a multifunctional gas sensor teaching experiment platform, which adjusts the container volume through multi-step linkage to ensure the dynamic balance of the plant respiratory environment and the accuracy of the experimental data. First, the gas sensor monitors the concentration changes of carbon dioxide and oxygen in the container in real time, calculates the concentration change rate in combination with the differential algorithm, extracts the gas concentration transient asynchronous rate and the concentration fluctuation cumulative offset index, and is used to evaluate short-term asynchronous fluctuations and long-term cumulative offsets. The generated comprehensive decision value judges the stability of the gas environment in the container and triggers the container volume adjustment signal when necessary. The preliminary container volume is calculated by polynomial fitting technology, combined with the gas concentration change rate and the plant respiration rate, and the first adjustment is made. Subsequently, nonlinear correction is performed in combination with environmental parameters to approximate the optimal growth environment of the plant. On this basis, the finite difference method further fine-tunes the container volume to ensure that the gas concentration tends to be stable, while monitoring the environmental parameters in real time to avoid adverse effects on the plants. Finally, the Kalman filtering technology is used to feedback optimize the gas concentration data, so that the container volume gradually approaches the ideal state and ensures that the gas concentration remains within a reasonable measurement range. The overall method ensures the long-term stability of the plant respiratory environment through multiple precise adjustments and feedbacks, and greatly improves the accuracy and reliability of experimental data to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: The multifunctional gas sensor teaching experiment platform includes: concentration monitoring module, volume estimation module, fine-tuning correction module, differential correction module and feedback optimization module.

[0007] Concentration monitoring module: The real-time monitoring module is responsible for detecting the concentrations of carbon dioxide and oxygen in the container, calculating the concentration gradient change rate, and extracting asynchrony and cumulative offset information to determine whether the container size needs to be adjusted. If necessary, an adjustment signal is generated and the relevant data is passed to the volume estimation module.

[0008] Volume estimation module: Based on the gas concentration change rate and plant respiration rate transmitted by the concentration monitoring module, the ideal container volume is preliminarily estimated through polynomial fitting technology and the first adjustment is performed. The volume estimation result is transmitted to the fine-tuning correction module.

[0009] Fine-tuning correction module: Combined with environmental parameters, weighted regression analysis is used to correct the initial adjustment results of the volume estimation module, and the corrected volume parameters are passed to the differential correction module.

[0010] Differential correction module: Using the finite difference method, the container volume is gradually adjusted according to the corrected volume data transmitted from the fine-tuning correction module until the gas concentration change gradually tends to be within the measurement range. After the differential correction, the data is transmitted to the feedback optimization module.

[0011] Feedback optimization module: The Kalman filtering technology is used to feedback optimize the adjusted gas concentration data, gradually approaching the final ideal container size and verifying that the concentration returns to the normal range.

[0012] In a preferred embodiment, the processing of the concentration monitoring module includes the following: Gas concentration values ​​are collected by sensors at fixed time intervals and the data are stored on a monitoring platform. The collected gas concentration data are processed using a differential algorithm to calculate the rate of change of carbon dioxide and oxygen concentrations per unit time. Asynchrony information and cumulative offset information are extracted based on the concentration change rate data, wherein the asynchronous information includes the transient asynchronous rate of gas concentration; the cumulative offset information includes the cumulative offset index of concentration fluctuations.

[0013] In a preferred embodiment, the process of obtaining the gas concentration transient asynchronous rate is as follows: First, choose a short time window , calculate the time rate of change of carbon dioxide and oxygen concentrations at each time t, and use the difference form to capture the instantaneous change of gas concentration: ; ; and Respectively represent the concentrations of carbon dioxide and oxygen at time t; Represents a time interval, reflecting the difference between two consecutive time points.

[0014] After calculating the gas concentration change rate at each time t, analyze whether there is asynchrony in the changes of carbon dioxide and oxygen concentrations, that is, whether the change rates of the two are consistent; asynchrony is expressed by the following formula: ; is the asynchrony difference value at time t.

[0015] The asynchronous difference value at each time point is summed up in time series in the form of cumulative piecewise integration: .

[0016] Finally, the asynchronous fluctuation results after accumulation processing are normalized to generate the final gas concentration transient asynchronous rate value to capture the asynchronous fluctuation trend of gas concentration: ; is the square of the time window length; It is an adjustment factor for the cumulative value, used to smooth out fluctuations.

[0017] In a preferred embodiment, the concentration fluctuation cumulative deviation index is obtained as follows: First, select a time window , and at each time t, calculate the cumulative offset of carbon dioxide and oxygen relative to the initial concentration, capture the long-term fluctuation trend, and record the gradual offset of the concentration in cumulative form: ; ; and are the carbon dioxide and oxygen concentrations at time i, respectively; and are the carbon dioxide and oxygen concentrations at the initial moment.

[0018] To evaluate the long-term cumulative difference between CO2 and O2 concentrations, we calculate the absolute difference between the cumulative offsets of the two gases and capture the magnitude of the cumulative offset as a time-series sum: ; Represents the cumulative offset difference of carbon dioxide and oxygen at time t.

[0019] The accumulated offset is converted into a decision basis, and the accumulated offset difference is normalized to generate a concentration fluctuation accumulated offset index. , used to measure whether the gas concentration in the container deviates from the normal value for a long time; the formula is as follows: ; is the length of the long time window; Smooth the accumulated deviation using an exponential form.

[0020] In a preferred embodiment, the processing of the concentration monitoring module further includes the following: The transient asynchronous rate of gas concentration and the cumulative deviation index of concentration fluctuation are comprehensively calculated through a nonlinear combination function to generate a decision value, which is used to analyze whether the container size needs to be adjusted; if the decision value is greater than or equal to the decision threshold, it means that the gas environment in the container has shown a significant imbalance, and the short-term asynchronous fluctuations and long-term cumulative deviations of carbon dioxide and oxygen are more prominent, and the container size needs to be adjusted immediately to restore the gas balance; on the contrary, if the decision value is less than the decision threshold, it means that the fluctuation of gas concentration is still within a reasonable range, the container size is appropriate, and the respiratory state of the plant is stable, and there is no need to adjust the container size.

[0021] In a preferred embodiment, the processing of the volume estimation module includes the following: Based on the real-time gas concentration data obtained by the concentration monitoring module, the concentration change rates of carbon dioxide and oxygen are extracted; using the gas concentration change rate and plant respiration rate data, a polynomial fitting model is constructed to relate the container volume V to the gas concentration change rate and the respiration rate; based on the results of the polynomial fitting, a preliminary estimate of the container volume is obtained, which reflects the ideal container size under current conditions; according to the container volume estimation result obtained from the fitting model, the first container adjustment is performed.

[0022] In a preferred embodiment, the processing of the fine-tuning correction module includes the following: Get the temperature, humidity and light intensity inside the container in real time.

[0023] Set the temperature for the ideal growing environment for your plants ,humidity , light intensity , while defining the acceptable deviation range for each parameter That is, the allowable range of environmental fluctuations.

[0024] The nonlinear deviation formula is used to calculate the deviation of the current temperature, humidity and light intensity relative to the ideal parameters. First, the deviation values ​​of temperature, humidity and light intensity are calculated using the following formula: ; ; ; , , are the nonlinear deviations of temperature, humidity and light, respectively.

[0025] The deviations of various environmental parameters are combined to generate a unified container volume correction, using the following nonlinear deviation synthesis formula: ; Indicates the correction amount for the container volume.

[0026] Combined with the correction amount calculated by multi-dimensional deviation, the nonlinear correction of the container volume is performed, and the final formula is as follows: ; is the volume of the container after fine-tuning.

[0027] In a preferred embodiment, the processing of the differential correction module includes the following: Through real-time monitoring, the concentration data of carbon dioxide and oxygen in the container are obtained, and the rate of change of gas concentration at multiple time points is recorded.

[0028] The finite difference method is used to calculate the collected gas concentration change data step by step.

[0029] After calculating the finite difference of the gas concentration, the decision value in the current situation is retrieved. If the decision value is greater than or equal to the decision threshold, it means that the container volume needs to be adjusted to restore the gas concentration to a reasonable stable range.

[0030] Through the finite difference results of gas concentration, the container volume is gradually adjusted to make the gas concentration approach the measurement range.

[0031] Based on the results of finite differences, the container volume is continuously adjusted; after each correction, the gas concentration change is monitored in real time, and the correction amount of the container volume is recalculated based on the new difference results until the gas concentration change rate is stabilized within the measurement range.

[0032] In the process of gradually adjusting the container volume, the temperature, humidity and light intensity are monitored in real time to ensure that the deviations of these parameters from the ideal parameters during the container adjustment are within an acceptable deviation range. If not, the adjustment is stopped and the container volume is returned to the volume before this adjustment.

[0033] In a preferred embodiment, the processing of the feedback optimization module includes the following: After adjusting the volume of the container, the real-time data of the carbon dioxide and oxygen concentrations corrected by the differential correction module are obtained; first, the initial conditions of the Kalman filter are set, including the initial state estimation and the covariance matrix.

[0034] The prediction step of Kalman filtering is used to estimate the gas concentration at the next moment based on the current gas concentration change trend.

[0035] While predicting the gas concentration, the covariance matrix at the next moment is calculated to evaluate the uncertainty of the predicted value.

[0036] After the actual gas concentration data is collected at the next moment, the predicted value is corrected using the Kalman filter update step; first, the prediction error is calculated, that is, the difference between the actual observed value and the predicted value.

[0037] Based on the prediction covariance and observation noise, the Kalman gain is calculated to determine the strength of the correction of the prediction value.

[0038] The predicted gas concentration is corrected using the Kalman gain.

[0039] The technical effects and advantages of the multifunctional gas sensor teaching experiment platform of the present invention are as follows: The present invention adjusts the volume of the container by multi-step linkage to ensure the dynamic balance of the plant respiratory environment and the accuracy of the experimental data. First, the gas sensor monitors the concentration changes of carbon dioxide and oxygen in the container in real time, calculates the concentration change rate in combination with the differential algorithm, extracts the gas concentration transient asynchronous rate and the concentration fluctuation cumulative offset index, and is used to evaluate the short-term asynchronous fluctuation and long-term cumulative offset. The generated comprehensive decision value judges the stability of the gas environment in the container and triggers the container volume adjustment signal when necessary. The preliminary container volume is calculated by combining the gas concentration change rate and the plant respiration rate through polynomial fitting technology, and the first adjustment is made. Subsequently, nonlinear correction is performed in combination with environmental parameters to approximate the optimal growth environment of the plant. On this basis, the finite difference method further fine-tunes the container volume to ensure that the gas concentration tends to be stable, while monitoring the environmental parameters in real time to avoid adverse effects on the plant. Finally, the Kalman filtering technology is used to feedback and optimize the gas concentration data, so that the container volume gradually approaches the ideal state and ensures that the gas concentration remains within a reasonable measurement range. The overall method ensures the long-term stability of the plant respiratory environment through multiple precise adjustments and feedbacks, and greatly improves the accuracy and reliability of the experimental data. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a structural schematic diagram of the multifunctional gas sensor teaching experiment platform of the present invention.

[0041] Figure 2 This is a schematic diagram of the processing steps of the feedback optimization module of the multifunctional gas sensor teaching experiment platform of the present invention. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] Embodiment 1: Figure 1 A multifunctional gas sensor teaching experiment platform of the present invention is provided, comprising: a concentration monitoring module, a volume estimation module, a fine-tuning correction module, a differential correction module and a feedback optimization module.

[0044] Concentration monitoring module: The real-time monitoring module is responsible for detecting the concentrations of carbon dioxide and oxygen in the container, calculating the concentration gradient change rate, and extracting asynchrony and cumulative offset information to determine whether the container size needs to be adjusted. If necessary, an adjustment signal is generated and the relevant data is passed to the volume estimation module.

[0045] Volume estimation module: Based on the gas concentration change rate and plant respiration rate transmitted by the concentration monitoring module, the ideal container volume is preliminarily estimated through polynomial fitting technology and the first adjustment is performed. The volume estimation result is transmitted to the fine-tuning correction module.

[0046] Fine-tuning correction module: Combined with environmental parameters, weighted regression analysis is used to correct the initial adjustment results of the volume estimation module, and the corrected volume parameters are passed to the differential correction module.

[0047] Differential correction module: Using the finite difference method, the container volume is gradually adjusted according to the corrected volume data transmitted from the fine-tuning correction module until the gas concentration change gradually tends to be within the measurement range. After the differential correction, the data is transmitted to the feedback optimization module.

[0048] Feedback optimization module: The Kalman filtering technology is used to feedback optimize the adjusted gas concentration data, gradually approaching the final ideal container size and verifying that the concentration returns to the normal range.

[0049] The processing of the concentration monitoring module includes the following: Gas sensors are installed to monitor the concentration of carbon dioxide and oxygen in the container. The sensors collect gas concentration values ​​at fixed time intervals and store the data on the monitoring platform for subsequent calculation and analysis.

[0050] The collected gas concentration data are processed using a differential algorithm to calculate the rate of change of carbon dioxide and oxygen concentrations per unit time. This rate reflects the gas generation and consumption during plant respiration.

[0051] According to the concentration change rate data, asynchrony information and cumulative offset information are extracted, wherein the asynchrony information includes the transient asynchronous rate of gas concentration; the cumulative offset information includes the concentration fluctuation cumulative offset index.

[0052] The gas concentration transient asynchronous rate (GCTAR) evaluates the asynchronous fluctuations of gas concentrations by calculating the instantaneous rate of change of carbon dioxide and oxygen concentrations respectively within a short time window and then calculating the difference between the two.

[0053] The cumulative concentration fluctuation shift index (CFCDI) uses the cumulative shift of gas concentrations relative to the initial moment over a longer time window to calculate the cumulative difference in carbon dioxide and oxygen concentrations to quantify their fluctuation shifts over time.

[0054] The process of obtaining the transient asynchronous rate of gas concentration is as follows: First, choose a short time window , calculate the time change rate of carbon dioxide and oxygen concentration at each time t. In order to improve the complexity and accuracy of the calculation, the differential form is used to capture the instantaneous change of gas concentration: ; ; and represent the carbon dioxide and oxygen concentrations at time t respectively. Represents a time interval, reflecting the difference between two consecutive time points.

[0055] After calculating the gas concentration change rate at each time t, we analyze whether there is asynchrony in the changes of carbon dioxide and oxygen concentrations, that is, whether the change rates of the two are consistent. Asynchrony is expressed by the following formula: ; is the asynchrony difference value at time t.

[0056] Denominator It is used to introduce nonlinear relationships, avoid the simple linear difference amplification effect, and ensure that the asynchronous differences are more obvious during drastic fluctuations.

[0057] The amplification of instantaneous differences is balanced through nonlinear processing, ensuring that abnormal fluctuations are effectively captured.

[0058] In order to reflect the gas concentration in the entire short time window The accumulated fluctuation of asynchrony within the system is summed up in time series by using the form of cumulative piecewise integration: ; It is a time normalization factor, which is used to assign different weights to asynchronous fluctuations in different time periods. The closer to the current time point, the greater the weight.

[0059] This cumulative summation approach emphasizes the temporal variation trend of instantaneous asynchronous fluctuations, making early and late fluctuations have different effects on asynchrony.

[0060] Finally, the asynchronous fluctuation results after accumulation processing are normalized to generate the final gas concentration transient asynchronous rate value to capture the asynchronous fluctuation trend of gas concentration: ; It is the square of the time window length to reduce the impact of cumulative value expansion when the time window is long. It is an adjustment factor for the cumulative value, which is used to smooth the fluctuation range and avoid excessive impact of extreme changes on the indicator.

[0061] The transient asynchronous rate of gas concentration is used to indicate the asynchrony of the concentration changes of carbon dioxide and oxygen in a short time window, reflecting the degree of difference in the fluctuation of the two gases during plant respiration. The larger the index is, the more obvious the difference in the rate of change of gas concentration is, indicating that there is a mismatch between the plant respiration process and the environment in the container, which may lead to an imbalance in gas exchange in the container, and it is necessary to consider adjusting the container size to optimize the respiratory environment; if the index is small, it means that the concentration changes of the two gases are relatively synchronized, and the plant is in a relatively stable respiration state. At this time, the need for adjusting the container size is low and the environment is relatively suitable.

[0062] The process of obtaining the concentration fluctuation cumulative deviation index is as follows: The concentration fluctuation cumulative deviation index is used to quantify the concentration fluctuation trend and deviation of carbon dioxide and oxygen over a long period of time, reflecting the deviation of gas concentration in the container accumulated over time. Unlike the gas concentration transient asynchrony rate, which focuses on the instantaneous asynchrony in a short period of time, the concentration fluctuation cumulative deviation index mainly examines the accumulation of concentration changes in a longer time window and measures the long-term stability of carbon dioxide and oxygen. This indicator captures potential gas imbalance trends through the accumulation of fluctuations over a large time span, providing a basis for container adjustments.

[0063] First, choose a longer time window , and at each time t, calculate the cumulative deviation of carbon dioxide and oxygen relative to the initial concentration. In order to capture the long-term fluctuation trend, the gradual deviation of the concentration is recorded in cumulative form: ; ; and are the carbon dioxide and oxygen concentrations at time i, respectively. and are the carbon dioxide and oxygen concentrations at the initial moment.

[0064] By accumulating the offset at each moment, the cumulative change of concentration over time is captured, reflecting the long-term offset trend.

[0065] To assess the long-term cumulative difference between CO2 and O2 concentrations, the absolute difference between the cumulative offsets of the two gases is calculated and the magnitude of the cumulative offset is captured as a time-series sum: ; Represents the cumulative offset difference of carbon dioxide and oxygen at time t.

[0066] By summing over all time periods, the cumulative deviation of gas concentration fluctuations over long periods of time is quantified, reflecting whether there is a persistent imbalance in gas exchange inside the container.

[0067] In order to convert the accumulated offset into a decision basis, the accumulated offset difference is normalized to generate the concentration fluctuation accumulated offset index , used to measure whether the gas concentration in the container deviates from the normal value for a long time. The formula is as follows: ; is the length of the long time window. Smooth the accumulated offset in exponential form to prevent the index from being unbalanced when the offset is too large or too small.

[0068] The concentration fluctuation cumulative deviation index is used to indicate the degree of cumulative deviation of carbon dioxide and oxygen concentrations over a long period of time, reflecting whether the gas environment in the container is continuously unbalanced over time. The larger the index value, the more significant the fluctuation deviation of gas concentration over a long period of time, indicating that the gas exchange in the container is unstable, which may affect the normal respiration of the plant, and the container size needs to be adjusted to restore the balance; the smaller the index value, the smaller the cumulative fluctuation of gas concentration, the more stable the gas environment, the plant is in a suitable respiration state, and the container size does not need to be adjusted.

[0069] The gas concentration transient asynchronous rate and the concentration fluctuation cumulative deviation index are comprehensively calculated through a nonlinear combination function to generate a decision value for analyzing whether the container size needs to be adjusted. For example, the specific comprehensive calculation steps are as follows: ; It is the final decision value, which is used to analyze whether the container size needs to be adjusted.

[0070] Index part It is used to amplify the weight of the concentration fluctuation cumulative offset index in the decision value. As the gas concentration transient asynchronous rate and the concentration fluctuation cumulative offset index increase, the exponential effect increases, indicating that the possibility of gas imbalance increases.

[0071] Nonlinear terms Used to increase the sensitivity of transient asynchrony rates so that they have a more direct impact on container sizing decisions. The exponential square treatment emphasizes the amplification effect of the transient asynchrony of the gas on environmental fluctuations.

[0072] Logarithmic Correction Ensure that the range of comprehensive decision values ​​remains balanced, prevent excessive amplification of decision values ​​during extreme fluctuations, and ensure that container adjustments are performed within a reasonable range.

[0073] The decision value is used to represent the combined situation of asynchronous fluctuations of carbon dioxide and oxygen in a short period of time and long-term cumulative deviations, reflecting the overall stability of the gas environment in the container. The larger the value, the more significant the short-term asynchrony and long-term deviation of the gas concentration are, indicating that the gas environment in the container is gradually unbalanced, the respiratory process of the plant is disturbed, and the size of the container needs to be adjusted to restore the balance; if the decision value is small, it means that the fluctuation of the gas concentration is small and stable, the container size is appropriate, and no further adjustment is required.

[0074] If the decision value is greater than or equal to the decision threshold, it means that the gas environment in the container has been significantly unbalanced, and the short-term asynchronous fluctuations and long-term cumulative deviations of carbon dioxide and oxygen are more prominent. The container size needs to be adjusted immediately to restore the gas balance, ensure the normal breathing process of the plant, and generate an adjustment signal. On the contrary, if the decision value is less than the decision threshold, it means that the fluctuation of gas concentration is still within a reasonable range, the container size is appropriate, and the respiratory state of the plant is stable, so there is no need to adjust the container size.

[0075] By real-time monitoring of the changes in carbon dioxide and oxygen concentrations in the container, the dynamic fluctuations in gas concentration during plant respiration can be accurately captured. Combined with the calculation of the concentration gradient change rate and the extraction of asynchrony and cumulative offset, the degree of imbalance in the gas environment can be promptly determined. This step ensures that when the gas concentration in the container approaches or exceeds the set stable range, potential problems can be quickly identified, triggering the container adjustment signal, and effectively avoiding plant respiratory disorders caused by abnormal gas concentrations. Through this processing process, it can be ensured that the respiratory environment of the plant is always in a suitable range, avoiding the interference of short-term and long-term concentration fluctuations on the experimental data, thereby improving the accuracy and reliability of the experiment, and providing a strong basis for subsequent environmental control and adjustment.

[0076] The processing of the volume estimation module includes the following: Based on the real-time gas concentration data obtained by the concentration monitoring module, the concentration change rate of carbon dioxide and oxygen is further extracted. This data reflects the dynamic rate of gas generation and consumption during plant respiration and is used for subsequent container volume calculations.

[0077] Determine the respiration rate of plants under specific environmental conditions based on experimental conditions and plant species, either through experiments or reference data in the literature. Respiration rate is defined as the rate at which plants produce carbon dioxide or consume oxygen per unit time, usually expressed as the change in gas concentration per unit volume.

[0078] Using the gas concentration change rate and plant respiration rate data, a polynomial fitting model is constructed to relate the container volume V to the gas concentration change rate and respiration rate. The goal of the fitting model is to find a container volume that can maintain the gas concentration balance in the container under the current respiration rate conditions. The model form is: ;in, , is the fitting coefficient, and is the polynomial order, is a constant term used to adjust the basic value of the container volume. Through fitting, a preliminary estimate of the container volume that meets the current gas concentration change rate is obtained.

[0079] Based on the results of the polynomial fit, a preliminary estimate of the container volume is obtained, which reflects the ideal container size under the current conditions. The preliminary container volume estimate is combined with the plant respiration rate to ensure that the container size can match the gas release and consumption rate of the plant so that the gas concentration remains balanced within a controllable range.

[0080] The first container adjustment is performed based on the container volume estimation results obtained from the fitting model. The container adjustment optimizes the gas concentration environment by modifying its volume, so that the fluctuation amplitude of carbon dioxide and oxygen concentrations is reduced and tends to be stable. This adjustment ensures that the container volume adapts to the dynamic changes of plant respiration in a short period of time and initially restores the stability of gas concentration.

[0081] After making the first container volume adjustment, continue to monitor the gas concentration change to ensure that the adjusted gas concentration change rate matches the breathing rate. If the gas concentration change rate still does not reach equilibrium, more detailed adjustments will be required in subsequent steps.

[0082] The processing of the fine-tuning correction module includes the following: Real-time acquisition of temperature, humidity and light intensity inside the container. These parameters directly affect the respiration rate and gas exchange process of the plant, and accurate collection of environmental parameters must be ensured.

[0083] Set the temperature for the ideal growing environment for your plants ,humidity , light intensity , while defining the acceptable deviation range for each parameter That is, the allowable range of environmental fluctuations.

[0084] The nonlinear deviation formula is used to calculate the deviation of the current temperature, humidity and light intensity from the ideal parameters. First, the deviation values ​​of temperature, humidity and light intensity are calculated using the following formula: ; ; ; , , are the nonlinear deviations of temperature, humidity and light, respectively.

[0085] Index term The function is used to smooth the deviation of parameters, avoid sudden changes in parameters in extreme cases, and make the adjustment process smoother.

[0086] In order to combine the deviations of various environmental parameters and generate a unified container volume correction, the following nonlinear deviation synthesis formula is used: ; Indicates the correction amount for the container volume.

[0087] The formula uses square and cube root operations to amplify the contribution of environmental parameters with large deviations to the container volume correction, and balances the impact of various parameters on the correction amount to ensure accurate fine-tuning of the container volume.

[0088] Combined with the correction amount calculated by multi-dimensional deviation, the nonlinear correction of the container volume is performed, and the final formula is as follows: ; is the volume of the container after fine-tuning. It is used to make nonlinear corrections to the container volume and dynamically adjust the container volume to approach the ideal growth environment according to the deviation of the current environmental parameters.

[0089] The present invention further accurately corrects the container volume by introducing dynamic adjustment of environmental parameters (such as temperature, humidity, light, etc.), so that the respiratory environment of the plant can better meet its physiological needs. This process not only takes into account the changes in gas concentration in the adjustment of the container volume, but also incorporates the external environmental factors on which plant growth depends into the comprehensive analysis, ensuring that the adjusted container can maintain suitable gas exchange and stable environmental conditions at the same time. The deviation of environmental parameters is analyzed by nonlinear calculation methods, and the optimal growth environment is gradually approached, thereby avoiding potential interference with plant respiration caused by environmental fluctuations and improving the accuracy and stability of the experiment. Ultimately, this step ensures that the plant breathes in a near-ideal environment, the adjustment of the container volume is more reasonable, and the reliability of the experimental data is greatly improved.

[0090] Although the fine-tuning correction module has made preliminary optimization adjustments to the container volume by correcting the environmental parameters, this step is mainly based on the influence of environmental factors such as temperature and humidity, which can only indirectly reflect the respiratory state of the plant and the changes in gas concentration. Due to the complexity of the plant's respiratory process and the dynamic changes in gas concentration, it is impossible to fully ensure the most accurate match between the container volume and gas exchange by adjusting the environmental parameters alone. Although the adjustment of environmental parameters can improve the respiratory environment, it may not be able to accurately capture the instantaneous fluctuations and long-term cumulative deviations of gas concentration. Therefore, the adjustment of the fine-tuning correction module only provides a rough correction of the container volume, and has not fully considered the microscopic dynamic changes of gas concentration.

[0091] The purpose of executing the differential correction module and the feedback optimization module is to further refine the container volume on this basis. The differential correction module gradually adjusts the changes in gas concentration through the finite difference method to ensure that the gas concentration gradually approaches the target measurement range, especially in the process of finely capturing the changes in gas concentration, gradually reducing the deviation and avoiding the impact of environmental fluctuations on plants. The feedback optimization module uses Kalman filtering technology to perform feedback optimization on the gas concentration data to further approach the ideal container size and ensure that the gas concentration is stable within the measurement range. In this way, through continuous feedback and correction, the gas concentration can be accurately controlled to achieve the optimal respiratory environment in the container, ensuring that the plant's respiratory process is stable and accurately measured. Therefore, the differential correction module and the feedback optimization module are necessary supplements and optimizations to the fine-tuning correction module.

[0092] The processing of the differential correction module includes the following: Through real-time monitoring, the concentration data of carbon dioxide and oxygen in the container are obtained, and the gas concentration change rate at multiple time points is recorded. These data are used to analyze the gas production and consumption during plant respiration and provide a basis for the gradual correction of the container volume.

[0093] The finite difference method is used to gradually calculate the collected gas concentration change data. For each time t, the finite difference of the gas concentration is expressed as: ; represents the gas concentration at time t, For the time interval.

[0094] After calculating the finite difference of the gas concentration, the decision value in the current situation is retrieved. If the decision value is greater than or equal to the decision threshold, it means that the container volume needs to be adjusted to restore the gas concentration to a reasonable stable range.

[0095] Through the finite difference results of gas concentration, the container volume is gradually adjusted to make the gas concentration approach the measurement range. The correction formula is: ; represents the corrected container volume, is the current container volume, is the step size of each adjustment. is the gas concentration change rate at the current moment, It is the rate of change of target gas concentration, which is used to guide the direction of gradual adjustment.

[0096] Among them, the target gas concentration change rate It is a key parameter calculated by combining the plant's respiration rate, experimental design requirements, and container volume. First, determine the ideal gas concentration range based on the plant type and experimental purpose, such as the percentage increase or decrease in carbon dioxide and oxygen per unit time. Then, use the plant's respiration rate and the current volume of the container to calculate the ideal gas concentration range. , combined with the initial gas concentration, through the model formula ; Calculate the ideal rate of change of gas concentration, where and Respectively represent the amount of carbon dioxide released and the amount of oxygen consumed by the plant. This calculation result is used to guide the adjustment of the container volume to ensure that the plant's respiratory environment is maintained in the best state.

[0097] Based on the results of finite differences, the container volume is adjusted continuously. After each correction, the gas concentration change is monitored in real time, and the correction amount of the container volume is recalculated based on the new difference results until the gas concentration change rate is stable within the measurement range. This gradual correction method ensures a smooth adjustment process of the container volume and avoids sudden volume changes from interfering with the plant environment.

[0098] In the process of gradually adjusting the container volume, the environmental parameters such as temperature and humidity are monitored in real time to ensure that the deviations of these parameters from the ideal parameters during the container adjustment are within the acceptable deviation range. If not, the adjustment is stopped and the container volume is returned to the previous adjustment. By simultaneously monitoring the changes in gas concentration and environmental parameters, the negative impact of temperature and humidity fluctuations caused by the container volume adjustment on the plants can be avoided.

[0099] By gradually correcting the change in gas concentration using the finite difference method, the container volume can be adjusted more accurately, so that the concentrations of carbon dioxide and oxygen gradually approach a reasonable measurement range. This method uses continuous differential calculations to avoid large and sudden adjustments to the container volume, ensuring that the rate of change of gas concentration gradually approaches the target value, thereby reducing interference with the plant's respiratory environment. At the same time, by real-time monitoring of key environmental parameters such as temperature and humidity, environmental fluctuations caused during the container adjustment process can be effectively avoided, thereby ensuring stable plant growth conditions. This refined adjustment method ensures the stability and precision of the experiment, and maximizes the accuracy and reliability of plant respiration measurements.

[0100] The processing of the feedback optimization module includes the following: After adjusting the container volume, obtain the real-time data of carbon dioxide and oxygen concentration corrected by the differential correction module. First, set the initial conditions of the Kalman filter, including the initial state estimation and the covariance matrix , these values ​​reflect the initial estimate of the current gas concentration and its uncertainty. The initial state can be set based on the latest monitored gas concentration data.

[0101] The prediction step of Kalman filtering is used to estimate the gas concentration at the next moment based on the current gas concentration change trend. The state prediction formula is as follows: ; It's the next moment The predicted concentration. is the state transfer matrix, which is used to describe how the gas concentration at the current moment affects the estimated value at the next moment. For control input, including the current container volume adjustment and other influencing factors, is the input matrix.

[0102] While predicting the gas concentration, the covariance matrix at the next moment is calculated to evaluate the uncertainty of the predicted value. The formula for updating the covariance is: ; is the prediction covariance matrix, which represents the error range of the predicted value at the next moment. is the process noise covariance matrix, which is used to describe the impact of uncertain factors in the system on gas concentration.

[0103] After the actual gas concentration data is collected at the next moment, the predicted value is corrected using the Kalman filter update step. First, the prediction error is calculated, that is, the difference between the actual observed value and the predicted value: ; is the prediction error (residual), is the actual measured gas concentration data, is the observation matrix.

[0104] Based on the prediction covariance and observation noise, the Kalman gain is calculated to determine the strength of the correction prediction value. The calculation formula of the Kalman gain is: ; is the Kalman gain, which indicates how to adjust the predicted value according to the actual observed value. is the observation noise covariance matrix, which represents the noise level when measuring gas concentration.

[0105] The Kalman gain is used to correct the predicted gas concentration to make it close to the actual observed value. The correction formula is as follows: ; Corrected represents the updated gas concentration estimate, which combines the predicted value and the actual observed value.

[0106] The revised covariance matrix reflects the accuracy of the new state estimate. The formula for updating the covariance is: ; This step corrects the uncertainty in the forecasting process, making the next forecast more accurate.

[0107] Through the feedback optimization process of Kalman filtering, the gas concentration data is continuously corrected to gradually approach the ideal gas concentration range. If the observed gas concentration and the predicted value deviation are within the standard range, and the residual is less than the corresponding threshold, it means that the container size is close to the ideal value and no further adjustment is required. Otherwise, continue to make subtle adjustments to the container volume until the gas concentration returns to the normal range.

[0108] After multiple Kalman filter corrections, it is finally verified whether the gas concentration is stable within the set measurement range. If the fluctuation of the gas concentration has been controlled within the target range, and the container volume has been optimized and adjusted to no longer exceed the deviation range, it can be considered that the container volume has reached the ideal state, the plant respiration process is in a stable stage, and the experimental environment is in the optimal state.

[0109] By optimizing the feedback of gas concentration data through Kalman filtering technology, the container volume can be accurately corrected so that the gas concentration gradually stabilizes and remains within a reasonable measurement range. It can not only effectively reduce measurement noise and data fluctuations, but also dynamically adjust the prediction results to make the adjustment of the container volume more accurate and continuous, thereby avoiding excessive or insufficient adjustments that interfere with the plant growth environment. In addition, Kalman filtering gradually corrects the error of concentration data to ensure that the difference between the gas concentration in the container and the ideal value gradually decreases, thereby improving the accuracy and consistency of experimental data, and ultimately optimizing the measurement effect of the plant respiration process and ensuring the long-term stability of the experimental environment.

[0110] The present invention adjusts the volume of the container by multi-step linkage to ensure the dynamic balance of the plant respiratory environment and the accuracy of the experimental data. First, the gas sensor monitors the concentration changes of carbon dioxide and oxygen in the container in real time, calculates the concentration change rate in combination with the differential algorithm, extracts the gas concentration transient asynchronous rate and the concentration fluctuation cumulative offset index, and is used to evaluate the short-term asynchronous fluctuation and long-term cumulative offset. The generated comprehensive decision value judges the stability of the gas environment in the container and triggers the container volume adjustment signal when necessary. The preliminary container volume is calculated by combining the gas concentration change rate and the plant respiration rate through polynomial fitting technology, and the first adjustment is made. Subsequently, nonlinear correction is performed in combination with environmental parameters to approximate the optimal growth environment of the plant. On this basis, the finite difference method further fine-tunes the container volume to ensure that the gas concentration tends to be stable, while monitoring the environmental parameters in real time to avoid adverse effects on the plant. Finally, the Kalman filtering technology is used to feedback and optimize the gas concentration data, so that the container volume gradually approaches the ideal state and ensures that the gas concentration remains within a reasonable measurement range. The overall method ensures the long-term stability of the plant respiratory environment through multiple precise adjustments and feedbacks, and greatly improves the accuracy and reliability of the experimental data.

[0111] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0112] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0113] It should be noted that, in this article, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including a..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0114] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. Multifunctional gas sensor teaching experiment platform, characterized by: include: Concentration monitoring module, volume estimation module, fine-tuning correction module, differential correction module and feedback optimization module; Concentration monitoring module: The real-time monitoring module is responsible for detecting the concentration of carbon dioxide and oxygen in the container, calculating the concentration gradient change rate, and extracting asynchrony and cumulative offset information to determine whether the container size needs to be adjusted. If necessary, it generates an adjustment signal and transmits the relevant data to the volume estimation module; Volume estimation module: Based on the gas concentration change rate and plant respiration rate transmitted by the concentration monitoring module, the ideal container volume is preliminarily estimated through polynomial fitting technology, and the first adjustment is performed; the volume estimation result is transmitted to the fine-tuning correction module; Fine-tuning correction module: Combined with environmental parameters, weighted regression analysis is used to correct the initial adjustment results of the volume estimation module, and the corrected volume parameters are passed to the differential correction module; Differential correction module: using the finite difference method, according to the corrected volume data transmitted from the fine-tuning correction module, the container volume is gradually adjusted until the gas concentration change gradually tends to be within the measurement range. After the differential correction, the data is transmitted to the feedback optimization module; Feedback optimization module: The Kalman filtering technology is used to feedback optimize the adjusted gas concentration data, gradually approaching the final ideal container size and verifying that the concentration returns to the normal range.

2. The multifunctional gas sensor teaching experiment platform according to claim 1 is characterized in that: The processing of the concentration monitoring module includes the following: Gas concentration values ​​are collected by sensors at fixed time intervals and the data are stored on a monitoring platform. The collected gas concentration data are processed using a differential algorithm to calculate the rate of change of carbon dioxide and oxygen concentrations per unit time. Asynchrony information and cumulative offset information are extracted based on the concentration change rate data, wherein the asynchronous information includes the transient asynchronous rate of gas concentration; the cumulative offset information includes the cumulative offset index of concentration fluctuations.

3. The multifunctional gas sensor teaching experiment platform according to claim 2 is characterized in that: The process of obtaining the transient asynchronous rate of gas concentration is as follows: First, choose a short time window , calculate the time rate of change of carbon dioxide and oxygen concentrations at each time t, and use the difference form to capture the instantaneous change of gas concentration: ; ; and Respectively represent the concentrations of carbon dioxide and oxygen at time t; Indicates a time interval, reflecting the difference between two consecutive time points; After calculating the gas concentration change rate at each time t, analyze whether there is asynchrony in the changes of carbon dioxide and oxygen concentrations, that is, whether the change rates of the two are consistent; asynchrony is expressed by the following formula: ; is the asynchrony difference value at time t; The asynchronous difference value at each time point is summed up in time series in the form of cumulative piecewise integration: ； Finally, the asynchronous fluctuation results after accumulation processing are normalized to generate the final gas concentration transient asynchronous rate value to capture the asynchronous fluctuation trend of gas concentration: ; is the square of the time window length; It is an adjustment factor for the cumulative value, used to smooth the fluctuation range.

4. The multifunctional gas sensor teaching experiment platform according to claim 3 is characterized in that: The process of obtaining the concentration fluctuation cumulative deviation index is as follows: First, select a time window , and at each time t, calculate the cumulative offset of carbon dioxide and oxygen relative to the initial concentration, capture the long-term fluctuation trend, and record the gradual offset of the concentration in cumulative form: ; ; and are the carbon dioxide and oxygen concentrations at time i, respectively; and are the carbon dioxide and oxygen concentrations at the initial moment; To evaluate the long-term cumulative difference between CO2 and O2 concentrations, we calculate the absolute difference between the cumulative offsets of the two gases and capture the magnitude of the cumulative offset as a time-series sum: ; represents the cumulative deviation difference of carbon dioxide and oxygen at time t; The accumulated offset is converted into a decision basis, and the accumulated offset difference is normalized to generate a concentration fluctuation accumulated offset index. , used to measure whether the gas concentration in the container deviates from the normal value for a long time; the formula is as follows: ; is the length of the long time window; Smooth the accumulated deviation using an exponential form.

5. The multifunctional gas sensor teaching experiment platform according to claim 4 is characterized in that: The processing of the concentration monitoring module also includes the following: The transient asynchronous rate of gas concentration and the cumulative deviation index of concentration fluctuation are comprehensively calculated through a nonlinear combination function to generate a decision value, which is used to analyze whether the container size needs to be adjusted; if the decision value is greater than or equal to the decision threshold, it means that the gas environment in the container has shown a significant imbalance, and the short-term asynchronous fluctuations and long-term cumulative deviations of carbon dioxide and oxygen are more prominent, and the container size needs to be adjusted immediately to restore the gas balance; on the contrary, if the decision value is less than the decision threshold, it means that the fluctuation of gas concentration is still within a reasonable range, the container size is appropriate, and the respiratory state of the plant is stable, and there is no need to adjust the container size.

6. The multifunctional gas sensor teaching experiment platform according to claim 5 is characterized by: The processing of the volume estimation module includes the following: Based on the real-time gas concentration data obtained by the concentration monitoring module, the concentration change rates of carbon dioxide and oxygen are extracted; using the gas concentration change rate and plant respiration rate data, a polynomial fitting model is constructed to relate the container volume V to the gas concentration change rate and the respiration rate; based on the results of the polynomial fitting, a preliminary estimate of the container volume is obtained, which reflects the ideal container size under current conditions; according to the container volume estimation result obtained from the fitting model, the first container adjustment is performed.

7. The multifunctional gas sensor teaching experiment platform according to claim 6 is characterized in that: The processing of the fine-tuning correction module includes the following: Obtain the temperature, humidity and light intensity inside the container in real time; Set the temperature for the ideal growing environment for your plants ,humidity , light intensity , while defining the acceptable deviation range for each parameter That is, the permissible range of environmental fluctuations; The nonlinear deviation formula is used to calculate the deviation of the current temperature, humidity and light intensity relative to the ideal parameters. First, the deviation values ​​of temperature, humidity and light intensity are calculated using the following formula: ; ; ; , , are the nonlinear deviations of temperature, humidity, and light, respectively; The deviations of various environmental parameters are combined to generate a unified container volume correction, using the following nonlinear deviation synthesis formula: ; Indicates the correction amount of the container volume; Combined with the correction amount calculated by multi-dimensional deviation, the nonlinear correction of the container volume is performed, and the final formula is as follows: ; is the volume of the container after fine-tuning.

8. The multifunctional gas sensor teaching experiment platform according to claim 7 is characterized in that: The processing of the differential correction module includes the following: Through real-time monitoring, the concentration data of carbon dioxide and oxygen in the container are obtained, and the rate of change of gas concentration at multiple time points is recorded; The finite difference method is used to gradually calculate the collected gas concentration change data; After calculating the finite difference of the gas concentration, the decision value of the current situation is obtained again. If the decision value is greater than or equal to the decision threshold, it means that the container volume needs to be adjusted to restore the gas concentration to a reasonable stable range; Through the finite difference results of gas concentration, the container volume is gradually adjusted to make the gas concentration approach the measurement range; Based on the results of finite difference, the container volume is continuously adjusted; after each correction, the gas concentration change is monitored in real time, and the correction amount of the container volume is recalculated based on the new difference result until the gas concentration change rate is stable within the measurement range; In the process of gradually adjusting the container volume, the temperature, humidity and light intensity are monitored in real time to ensure that the deviations of these parameters from the ideal parameters during the container adjustment are within an acceptable deviation range. If not, the adjustment is stopped and the container volume is returned to the volume before this adjustment.

9. The multifunctional gas sensor teaching experiment platform according to claim 8 is characterized in that: The processing of the feedback optimization module includes the following: After adjusting the volume of the container, the real-time data of the carbon dioxide and oxygen concentrations corrected by the differential correction module are obtained; first, the initial conditions of the Kalman filter are set, including the initial state estimation and the covariance matrix; The prediction step of using Kalman filtering is to estimate the gas concentration estimate at the next moment based on the current gas concentration change trend; While predicting the gas concentration, the covariance matrix at the next moment is calculated to evaluate the uncertainty of the predicted value; After the actual gas concentration data is collected at the next moment, the predicted value is corrected using the Kalman filter update step; First, the prediction error is calculated, which is the difference between the actual observed value and the predicted value; Calculate the Kalman gain based on the prediction covariance and observation noise to determine the strength of the correction prediction value; The predicted gas concentration is corrected using the Kalman gain.

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