CN117457096B - Dynamic monitoring and adjusting system for simulating carbon dioxide dissolution in ocean acidification device - Google Patents
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Abstract
The application provides a dynamic monitoring and adjusting system for carbon dioxide dissolution in a simulated marine acidification device. In the system, an acquisition module is used for acquiring historical carbon dioxide concentration data and ocean acidification data; the monitoring module is used for collecting real-time monitoring data of the carbon dioxide concentration from the simulated marine acidizing device; the dynamic prediction module is used for inputting historical carbon dioxide concentration data, ocean acidification data and real-time monitoring data into an ocean acidification dynamic prediction model so as to obtain a dynamic prediction result of the carbon dioxide concentration; the dynamic prediction result is used for indicating the carbon dioxide concentration change trend in a plurality of time periods; the adjusting module is used for adjusting the carbon dioxide concentration in the simulated marine acidification device in real time based on the dynamic prediction result so as to simulate different carbon dioxide concentration conditions. The ocean acidification degree in the device is predicted in real time through cooperation among the modules, so that ocean acidification is analyzed under different carbon dioxide conditions.
Description
Technical Field
The application belongs to the field of data processing, and particularly relates to a dynamic monitoring and adjusting system for carbon dioxide dissolution in a simulated marine acidification device.
Background
The marine environment is a complex system of multi-layer and multi-physical factor interweaving, and the water body is affected by different physical environments such as water temperature, salinity, water flow and the like at different depths and also affected by different biological environments such as illumination, biological activity and the like.
Ocean acidification refers to the process by which the PH in the ocean environment drops, resulting in seawater becoming more acidic. This is because carbon dioxide in the atmosphere is absorbed into the seawater and reacts chemically to form carbonic acid, thereby lowering the PH of the water. The severity of ocean acidification is that as sea water becomes more acidic, organisms in the ocean can be affected, particularly those that require calcareous bones or hulls for growth and maintenance.
Therefore, simulation analysis is required to be carried out on the marine environment so as to better observe the marine acidification degree and the corresponding development and change process, and an environment foundation is provided for the research of the marine acidification problem.
Therefore, simulation analysis is required to be carried out on the marine environment so as to better observe the marine acidification degree and the corresponding development and change process, and an environment foundation is provided for the research of the marine acidification problem.
Disclosure of Invention
The application provides a dynamic monitoring and adjusting system for the dissolution amount of carbon dioxide in a simulated marine acidification device, which is used for combining a dynamic marine acidification prediction model to predict the marine acidification degree in the simulated marine acidification device in real time, and is favorable for providing an environment foundation for the research of the marine acidification problem so as to develop the simulation analysis of the marine acidification problem under different carbon dioxide conditions.
In a first aspect, the application provides a system for dynamically monitoring and adjusting the dissolution amount of carbon dioxide in a simulated marine acidification device, wherein the system comprises an acquisition module, a monitoring module, a dynamic prediction module and an adjustment module; wherein the method comprises the steps of
The acquisition module is used for acquiring historical carbon dioxide concentration data and ocean acidification data; the marine acidification data comprises the following: PH, carbon dioxide dissolution, carbonate ion concentration, dissolved oxygen content;
the monitoring module is used for collecting real-time monitoring data of the carbon dioxide concentration from the simulated marine acidizing device;
the dynamic prediction module is used for inputting the historical carbon dioxide concentration data, the ocean acidification data and the real-time monitoring data into an ocean acidification dynamic prediction model so as to obtain a dynamic prediction result of the carbon dioxide concentration; the dynamic prediction result is used for indicating the carbon dioxide concentration change trend in a plurality of time periods;
and the adjusting module is used for adjusting the carbon dioxide concentration in the simulated marine acidification device in real time based on the dynamic prediction result so as to simulate different carbon dioxide concentration conditions.
In a second aspect, an embodiment of the present application provides a method for dynamically monitoring and adjusting a carbon dioxide dissolution amount in a simulated marine acidification device, including:
Collecting historical carbon dioxide concentration data and ocean acidification data; the marine acidification data comprises the following: PH, carbon dioxide dissolution, carbonate ion concentration, dissolved oxygen content;
collecting real-time monitoring data of carbon dioxide concentration from the simulated marine acidizing device;
inputting the historical carbon dioxide concentration data, the marine acidification data and the real-time monitoring data into a marine acidification dynamic prediction model to obtain a dynamic prediction result of the carbon dioxide concentration; the dynamic prediction result is used for indicating the carbon dioxide concentration change trend in a plurality of time periods;
and based on the dynamic prediction result, adjusting the carbon dioxide concentration in the simulated marine acidification device in real time so as to simulate different carbon dioxide concentration conditions.
In the technical scheme that this application embodiment provided, the dynamic monitoring of simulation ocean acidification device carbon dioxide dissolved amount and adjustment system mainly includes: the system comprises an acquisition module, a monitoring module, a dynamic prediction module and an adjustment module. The collection module is used for collecting historical carbon dioxide concentration data and ocean acidification data; marine acidification data includes the following: PH, amount of carbon dioxide dissolved, carbonate ion concentration, and dissolved oxygen content. The monitoring module is used for collecting real-time monitoring data of the carbon dioxide concentration from the simulated marine acidizing device. The dynamic prediction module is used for inputting historical carbon dioxide concentration data, ocean acidification data and real-time monitoring data into an ocean acidification dynamic prediction model so as to obtain a dynamic prediction result of the carbon dioxide concentration; the dynamic prediction result is used for indicating the carbon dioxide concentration change trend under a plurality of time periods. And the adjusting module is used for adjusting the carbon dioxide concentration in the simulated marine acidification device in real time based on the dynamic prediction result so as to simulate different carbon dioxide concentration conditions. In the embodiment of the application, the marine acidification degree in the marine acidification simulation device is predicted in real time by combining the marine acidification dynamic prediction model, so that an environment foundation is provided for the research of the marine acidification problem, and the simulation analysis of the marine acidification problem can be conveniently developed under different carbon dioxide conditions.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
FIG. 1 is a schematic diagram of a system for dynamically monitoring and adjusting the amount of dissolved carbon dioxide in a simulated marine acidizing device according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a method for dynamically monitoring and adjusting the dissolution of carbon dioxide in a simulated marine acidizing device according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
The marine environment is a complex system of multi-layer and multi-physical factor interweaving, and the water body is affected by different physical environments such as water temperature, salinity, water flow and the like at different depths and also affected by different biological environments such as illumination, biological activity and the like.
Ocean acidification refers to the process by which the PH in the ocean environment drops, resulting in seawater becoming more acidic. This is because carbon dioxide in the atmosphere is absorbed into the seawater and reacts chemically to form carbonic acid, thereby lowering the PH of the water. The severity of ocean acidification is that as sea water becomes more acidic, organisms in the ocean can be affected, particularly those that require calcareous bones or hulls for growth and maintenance.
Therefore, simulation analysis is required to be carried out on the marine environment so as to better observe the marine acidification degree and the corresponding development and change process, and an environment foundation is provided for the research of the marine acidification problem. In summary, a technical solution is needed to simulate the ocean acidification condition in the ocean environment.
In order to solve at least one technical problem, an embodiment of the application provides a dynamic monitoring and adjusting scheme for simulating the carbon dioxide dissolution in a marine acidification device.
Specifically, in the scheme for dynamically monitoring and adjusting the carbon dioxide dissolution in the simulated marine acidification device, the system for dynamically monitoring and adjusting the carbon dioxide dissolution in the simulated marine acidification device mainly comprises: the system comprises an acquisition module, a monitoring module, a dynamic prediction module and an adjustment module. The collection module is used for collecting historical carbon dioxide concentration data and ocean acidification data; marine acidification data includes the following: PH, amount of carbon dioxide dissolved, carbonate ion concentration, and dissolved oxygen content. Comprehensive analysis of these data indicators can more fully analyze the impact and impact range of marine acidizing. The monitoring module is used for collecting real-time monitoring data of the carbon dioxide concentration from the simulated marine acidizing device. The dynamic change of the ocean acidification can be detected by collecting and simulating the carbon dioxide concentration in the ocean acidification device in real time, so that the device can effectively monitor and manage the ocean acidification simulation condition in time. The dynamic prediction module is used for inputting historical carbon dioxide concentration data, ocean acidification data and real-time monitoring data into an ocean acidification dynamic prediction model so as to obtain a dynamic prediction result of the carbon dioxide concentration; the dynamic prediction result is used for indicating the carbon dioxide concentration change trend under a plurality of time periods. And the adjusting module is used for adjusting the carbon dioxide concentration in the simulated marine acidification device in real time based on the dynamic prediction result so as to simulate different carbon dioxide concentration conditions. The dynamic prediction module can realize the dynamic prediction of the carbon dioxide concentration, and improves the prediction accuracy of the carbon dioxide concentration, so that the adjustment module can more accurately adjust the carbon dioxide concentration in the simulated ocean acidification device, and the aim of simulating different carbon dioxide concentration conditions is fulfilled.
In the dynamic monitoring and adjusting scheme of the carbon dioxide dissolution in the simulated ocean acidification device, the ocean acidification degree in the simulated ocean acidification device is predicted in real time by combining the ocean acidification dynamic prediction model through the mutual coordination among the modules, so that an environment foundation is provided for the research of the ocean acidification problem, and the simulation analysis of the ocean acidification problem is conveniently developed under different carbon dioxide conditions.
The scheme for dynamically monitoring and adjusting the carbon dioxide dissolution in the simulated marine acidification device provided by the embodiment of the application can be executed by an electronic device, and the electronic device can be a server, a server cluster and a cloud server. The electronic device may also be a terminal device such as a mobile phone, a computer, a tablet computer, a wearable device, or a dedicated device (e.g., a dedicated terminal device configured to simulate a marine acidizing device, etc.). In an alternative embodiment, the electronic device may be provided with a service program for performing a dynamic monitoring and adjustment scheme for the amount of carbon dioxide dissolved in the simulated marine acidizing device.
Fig. 1 is a schematic diagram of a system for dynamically monitoring and adjusting the dissolution amount of carbon dioxide in a simulated marine acidification device according to an embodiment of the present application, as shown in fig. 1, the system includes: the system comprises an acquisition module 101, a monitoring module 102, a dynamic prediction module 103 and an adjustment module 104.
The specific functions of the four modules are described below.
The collection module 101 is used for collecting historical carbon dioxide concentration data and ocean acidification data.
In an embodiment of the present application, the marine acidification data comprises the following: PH, amount of carbon dioxide dissolved, carbonate ion concentration, and dissolved oxygen content. Wherein the marine acidification data includes, but is not limited to, the following:
the PH value is an index for measuring the acidity and alkalinity of seawater. The acquisition module 101 can measure the PH in the simulated marine acidizing device and record the changes. This is of great importance for understanding the degree of acid-base in seawater and the degree of acidification in the ocean.
The dissolved oxygen content refers to the concentration of dissolved oxygen molecules in seawater. The acquisition module 101 may use techniques such as oxygen sensors to measure the dissolved oxygen content in the simulated marine acidizing device. This helps to understand the supply of oxygen in a simulated marine acidizing plant and the historical relationship between carbon dioxide and dissolved oxygen.
The carbon dioxide dissolution amount refers to the amount of carbon dioxide dissolved in a liquid at a certain temperature and pressure. The dissolution process is a dynamic equilibrium process involving the transfer of carbon dioxide molecules between a gas phase and a solution phase. The amount of carbon dioxide dissolved has an important effect on ocean acidification, and is one of the main causes of ocean acidification. When carbon dioxide dissolves into seawater, it reacts with water molecules to form carbonic acid (H2 CO 3), thereby generating hydrogen ions (h+). This process results in a change in the acid-base of the seawater, which increases the acidity of the seawater. Further alternatively, the acquisition module 101 may monitor the amount of carbon dioxide dissolved in the simulated marine acidizing device by chemical analysis and/or sensing techniques. Specifically, in chemical analysis, the concentration of carbon dioxide in a simulated marine acidizing device is measured by taking a water sample and analyzing it with a chemical reagent. The sensor technology is to use a sensor or a probe to monitor the carbon dioxide concentration in the simulated marine acidification device in real time so as to realize automatic monitoring of the carbon dioxide concentration in the simulated marine acidification device. These monitoring data can be used to evaluate the performance of the marine acidizing device in order to timely monitor and adjust the carbon dioxide concentration in the device.
Carbonate ion concentration, a measure of the content of soluble carbonates in seawater, helps us to understand the process of ocean acidification and carbon recycling. When carbon dioxide enters sea water, it reacts with water molecules to form carbonic acid (H2 CO 3), which then breaks down into carbonate ions and hydrogen ions (h+). As the carbon dioxide increases, the concentration of carbonate ions increases, while the pH of the seawater decreases and the seawater becomes more acidic. The concentration of carbonate ions in the seawater is reduced by ocean acidification, so that the growth and survival of ocean benthos and plankton are affected. Optionally, the acquisition module 101 may employ one or more of titration, ion selective electrode, optical methods to monitor carbonate ion concentration in the simulated marine acidizing device. The titration method is to dropwise add acid or alkali to a sample in a simulated marine acidification device, and determine the color change phenomenon of a titration end point by using an indicator (such as a phenolphthalein indicator or a methyl orange indicator), so as to calculate the concentration of carbonate ions in the sample. The ion selective electrode method is to directly measure the concentration of carbonate ions in seawater using a specific ion selective electrode (e.g., carbonate ion selective electrode). The optical method is to measure the carbonate ion concentration in the simulated ocean acidification device by an optical sensor. These optical sensors infer carbonate ion concentration by measuring fluorescence intensity based on the principle that a specific chemical agent (e.g., a fluorescent probe) undergoes a fluorescence change upon binding to carbonate ions. In practical application, according to the specific experimental requirements and equipment availability of the simulated marine acidizing device, a proper measuring method is selected to measure the concentration of carbonate ions.
It will be appreciated that in order to further increase the accuracy and sensitivity of the monitoring process, in addition to the index data described above, the following auxiliary indices may be introduced to provide a data basis for assessing the degree of ocean acidification, including but not limited to:
salinity refers to the content of dissolved salt species in seawater. The acquisition module 101 may measure and record salinity data of the seawater in the simulated marine acidizing device by means of a sensor or sampling module. This helps to monitor salinity changes in the simulated marine acidizing device and correlate with historical carbon dioxide concentrations.
Climate indicators, including but not limited to temperature, humidity, pressure. The acquisition module 101 can collect and record climate index data of the ocean area, which has an important role in analyzing the influence of climate change on the simulated ocean acidification device.
Microbial indicators play an important role in the marine ecosystem. The acquisition module 101 may acquire and record relevant index data simulating microorganisms in a marine acidizing device, including but not limited to bacterial concentration, algae concentration, and the like. This helps to understand the changing conditions of the marine ecosystem and simulate the effect of the marine acidizing device on the microbial ecology.
The above data collected by the collection module 101 can provide a basis for historical carbon dioxide concentration data and ocean acidification data, and provide necessary reference data for subsequent monitoring, prediction and adjustment modules. Thus, the simulated marine environment conditions in the simulated marine acidification device can be better understood, and support is provided for research and management of marine acidification problems.
The monitoring module 102 is used for collecting real-time monitoring data of the carbon dioxide concentration from the simulated marine acidizing device.
The real-time carbon dioxide concentration data provided by the monitoring module 102 may be used to study the marine acidizing process. By monitoring and recording the carbon dioxide concentration at different time points, the acidification speed, the acidification trend and the influencing factors can be analyzed, and the influence of ocean acidification on the ocean ecosystem can be further explored. The carbon dioxide concentration data obtained by the monitoring can be used for monitoring and evaluating the performance effect of the simulated marine acidizing device, so that the simulated marine acidizing device can reach the expected experimental target.
Further alternatively, the working state of the device can be mastered in time by the carbon dioxide concentration data, and whether the device operates normally or not can be judged. If the carbon dioxide concentration obtained by the monitoring module 102 deviates from the expected range, the adjustment can be performed in time to ensure that the simulation experiment is under the required carbon dioxide concentration condition.
Further alternatively, the real-time monitored carbon dioxide concentration data may be used to verify the accuracy and reliability of the marine acidification model. By comparing with the prediction result obtained by the dynamic prediction module 103, the accuracy of the ocean acidification dynamic prediction model can be evaluated and optimized.
In addition, the real-time monitoring data can also be used to calibrate sensors and instrumentation used in analog marine acidizing devices. By comparing with the standard concentration, the accuracy and reliability of the readings of the sensor and the instrument can be ensured, and the accuracy and reliability of experimental data are improved.
Real-time carbon dioxide concentration data can be provided through the monitoring module 102, and important monitoring means and decision support are provided for research and experiments of simulating the ocean acidification device.
The dynamic prediction module 103 is configured to input historical carbon dioxide concentration data, ocean acidification data and real-time monitoring data into an ocean acidification dynamic prediction model, so as to obtain a dynamic prediction result of the carbon dioxide concentration.
In the embodiment of the application, the dynamic prediction result is used for indicating the carbon dioxide concentration change trend in a plurality of time periods. In connection with the examples below, it is described how dynamic predictions are used to indicate the trend of change in carbon dioxide concentration over a plurality of time periods:
It is assumed that historical carbon dioxide concentration data indicates an upward trend in carbon dioxide concentration in recent years, and that marine acidification data indicates that the degree of marine acidification is exacerbated as the carbon dioxide concentration increases. Based on these data, the dynamic prediction module 103 may use historical carbon dioxide concentration data, ocean acidification data, and real-time monitoring data as inputs to an ocean acidification dynamic prediction model to predict the trend of carbon dioxide concentration in the future over multiple time periods. Then, the trend of the carbon dioxide concentration change under the following periods may be obtained from the dynamic prediction result:
1) Short-term prediction (days to weeks in the future): predictions show that carbon dioxide concentration may continue to rise over a short period of time. This means that the degree of acidification of the ocean will be further exacerbated, with a greater impact on the marine ecosystem.
2) Mid-term prediction (months to years in the future): predictions show that carbon dioxide concentrations may remain relatively stable, but still remain at higher levels. The degree of marine acidification may continue to be exacerbated and may have a long-term impact on the stability of existing biocenosis and marine ecosystems.
3) Long-term prediction (years to decades in the future): predictions show that there is a possibility that the carbon dioxide concentration will continue to rise and reach higher levels. This may result in further aggravation of the degree of ocean acidification, which has a wide and profound impact on the integrity and diversity of the marine ecosystem.
The future change trend of the carbon dioxide concentration can be known through the dynamic prediction result, and the development condition of the ocean acidification degree can be predicted.
In the embodiment of the application, the ocean acidification dynamic prediction model is a model for predicting the future carbon dioxide concentration change trend according to historical carbon dioxide concentration data, ocean acidification data and real-time monitoring data.
In an alternative embodiment, the marine acidification dynamic prediction model comprises an autoregressive unit, an integrating unit and a moving average unit. Specifically, when the dynamic prediction module 103 inputs the historical carbon dioxide concentration data, the marine acidification data and the real-time monitoring data into the marine acidification dynamic prediction model to obtain a dynamic prediction result of the carbon dioxide concentration, the dynamic prediction module is specifically configured to:
performing correlation analysis on the stable time sequence in the historical carbon dioxide concentration data through an autoregressive unit to obtain autocorrelation information of the historical carbon dioxide concentration data in different time states;
the method comprises the steps that through an integration unit, historical carbon dioxide concentration data and ocean acidification data with non-stationary time series characteristics are converted into a historical carbon dioxide concentration data sequence and ocean acidification data sequence with stationary time series characteristics through differential processing;
Extracting random noise from the historical carbon dioxide concentration data through a moving average unit, and predicting based on the random noise to obtain an error term in a future time state;
and predicting the historical carbon dioxide concentration data sequence and the ocean acidification data sequence based on the autocorrelation information and the error item through an autoregressive unit to obtain a dynamic prediction result.
Specifically, by means of autoregressive analysis of a stable time sequence of historical carbon dioxide concentration data, autocorrelation information of the historical data in different time states can be known, long-term dependence and correlation of the carbon dioxide concentration data can be explored and understood, and a basis is provided for predicting future trends. The historical carbon dioxide concentration data and the ocean acidification data with the non-stationary time series characteristics are subjected to differential processing through the integration unit and are converted into the historical carbon dioxide concentration data sequence and the ocean acidification data sequence with the stationary time series characteristics, so that trends and seasonal changes in the original data can be eliminated, and the data is more suitable for model modeling and prediction. Random noise is extracted from the historical carbon dioxide concentration data by a moving average unit, and error term predictions in future time states are made based on the random noise. By modeling and predicting the prediction error, the model can more accurately estimate the change trend of the carbon dioxide concentration in the future period.
In summary, the dynamic prediction module 103 inputs the historical carbon dioxide concentration data, the ocean acidification data and the real-time monitoring data into the ocean acidification dynamic prediction model, and the dynamic prediction result of the carbon dioxide concentration can be obtained more accurately through the processing of the autoregressive unit, the integrating unit and the moving average unit. These results will provide important information about future trends in carbon dioxide concentration, helping users to understand and address challenges of environmental changes such as ocean acidification.
Further optionally, the dynamic prediction module 103 performs correlation analysis on the stationary time sequence in the historical carbon dioxide concentration data through an autoregressive unit, so as to obtain autocorrelation information of the historical carbon dioxide concentration data in different time states, which is specifically configured to:
acquiring first correlation information of historical carbon dioxide concentration data on different hysteresis orders based on a first autocorrelation function; wherein the first autocorrelation function is expressed as the following formula:
，
where ACF (k) represents first correlation information of the historical carbon dioxide concentration data on the hysteresis order k, x (t) is an observed value of the historical carbon dioxide concentration data on the hysteresis order k on the time state t, N is the total number of observed values, and μ is the average value of the historical carbon dioxide concentration data.
In the above mathematical expression, the correlation between the time-series observed value and the observed value at the hysteresis order k is compared. The ACF (k) has a value ranging from-1 to 1 and represents the correlation strength at the hysteresis order k. If ACF (k) is close to 1, then it indicates that there is a positive correlation at lag k; if ACF (k) is close to-1, then this indicates that there is a negative correlation at lag k; if ACF (k) is close to 0, it indicates that there is no correlation at lag k. By calculating the first autocorrelation function and observing the ACF (k) values of the hysteresis orders, first correlation information for historical carbon dioxide concentration data at different hysteresis orders can be obtained.
In other embodiments, the dynamic prediction module 103 performs correlation analysis on the stationary time series in the historical carbon dioxide concentration data by using an autoregressive unit to obtain autocorrelation information of the historical carbon dioxide concentration data in different time states, which is specifically configured to:
acquiring second correlation information of the historical carbon dioxide concentration data on a hysteresis order based on a second partial autocorrelation function; wherein the second partial autocorrelation function is expressed as the following formula:
，
wherein PACF (k) represents second correlation information of historical carbon dioxide concentration data on a hysteresis order, ρ (k) is first correlation information on the hysteresis order k, ρ (k-1) is first correlation information on the hysteresis order k-1, and ρ (1) is first correlation information on the hysteresis order 1.
It will be appreciated that PACF (k) has a range of values between-1 and 1, representing the strength of partial autocorrelation over the hysteresis order k. By observing the value of PACF (k), second correlation information for historical carbon dioxide concentration data over the hysteresis order can be obtained.
In either of the foregoing embodiments, further optionally, the dynamic prediction module 103 is specifically configured to, when predicting, by the autoregressive unit, the historical carbon dioxide concentration data sequence and the marine acidification data sequence based on the autocorrelation information and the error term, to obtain a dynamic prediction result:
based on the first correlation information and/or the second correlation information, a hysteresis coefficient for adapting to each historical carbon dioxide concentration data in the dynamic autoregressive model is estimated.
Specifically, the coefficients of the lag term (i.e., the lag coefficients) in the dynamic autoregressive model can be estimated using a maximum likelihood estimation method. For example, minimizing the sum of squares of residuals allows the hysteresis coefficient to be best adapted to each historical carbon dioxide concentration data, thereby helping subsequent steps to better predict future trends in carbon dioxide concentration.
Further, a dynamic prediction result is obtained through dynamic autoregressive model prediction; wherein the dynamic autoregressive model is expressed as the following formula:
，
Where y' (t) represents the dynamic prediction result over time state t, y (t-1) to y (t-p) represent the historical carbon dioxide concentration data sequences over time states t-1 to t-p, respectively, c is a constant term, p is a hysteresis order,represents the hysteresis coefficient corresponding to each of the hysteresis order 1 to the hysteresis order p, +.>Is an error term.
For a time t, the existing history data may be substituted into the dynamic autoregressive model to calculate a predicted value y' (t) corresponding to y (t). In this process, y (t) is predicted using autoregressive means, i.e., the predicted value of y (t) is correlated with y (t-1) and y (t-2) for the past period.
For example, assume a 10 year monthly carbon dioxide concentration and corresponding marine acidification data sequence. The hysteresis coefficient is estimated to be { phi 1=0.5, phi 2=0.3 }, and the change trend of the carbon dioxide concentration in a future period of time can be predicted by a dynamic autoregressive model. For example, it may be predicted that the carbon dioxide concentration for the next two months will be 450ppm and 460ppm, respectively.
To summarize, predicting the trend of carbon dioxide concentration change by the dynamic autoregressive model in the dynamic prediction module 103 requires estimating the hysteresis coefficient and the coefficient based on the historical data and the ocean acidification data, and calculating the predicted value of carbon dioxide concentration in the future period by using autoregressive mode.
Further alternatively, the dynamic prediction module 103 is specifically configured to, when extracting random noise from the historical carbon dioxide concentration data by the moving average unit and predicting an error term in a future time state based on the random noise:
firstly, taking historical carbon dioxide concentration data as input of a random noise extraction model, acquiring average values of q observation values corresponding to the historical carbon dioxide concentration data in time states (t-1) to (t-q) through the random noise extraction model, and taking the average values as moving average sequence values MA (t); wherein the random noise extraction model is expressed as the following formula:
，
wherein x (t-i) represents an observed value at a time state t-i, the moving average unit order of the moving average sequence value MA (t) is q, and i is a positive integer;
further, the moving average sequence value MA (t) is subtracted from the original observations of the historical carbon dioxide concentration data to obtain an error term at time state t.
For example, assume that there is a time series containing 10 year monthly carbon dioxide concentration data. A moving average unit order of q=3, that is, an average of carbon dioxide concentration data of the past 3 months is used as a moving average sequence value MA (t) in each time state. Further, when calculating the moving average sequence value MA (t), the average value of the past 3 months is calculated as MA (t) for the data of the 4 th month, and then MA (t) is subtracted from the original observed value to obtain the error term of the 4 th month. The above process is repeated to obtain error terms for each time state, which represent random noise in the historical data. Finally, these error terms can be used to predict errors in future time states, resulting in more accurate dynamic predictions.
To summarize, the dynamic prediction module 103 extracts random noise of the historical carbon dioxide concentration data by a moving average unit, calculates a moving average sequence value to obtain a mean value of the random noise, and then subtracts the mean value from an original observed value of the historical data to obtain an error term. These error terms can be used to predict errors at future time states, thereby improving the accuracy of the dynamic prediction results.
The adjustment module 104 is configured to adjust the carbon dioxide concentration in the simulated marine acidification device in real time based on the dynamic prediction result so as to simulate different carbon dioxide concentration conditions.
Further optionally, the adjustment module 104 is specifically configured to, when adjusting the carbon dioxide concentration in the simulated marine acidification device in real time based on the dynamic prediction result, to simulate different carbon dioxide concentration conditions: measuring a carbon dioxide concentration measurement value in the simulated marine acidizing device in real time; carrying out adjustment quantity prediction treatment on the dynamic prediction result and the carbon dioxide concentration measurement value to obtain the carbon dioxide concentration adjustment quantity of the simulated marine acidification device; detecting whether the carbon dioxide concentration adjustment quantity meets adjustment conditions; if the carbon dioxide concentration adjustment amount meets the adjustment condition, adding or reducing the carbon dioxide concentration adjustment amount in the current carbon dioxide concentration value so as to simulate different carbon dioxide concentration conditions.
In the embodiment of the present application, the adjustment amount prediction processing is expressed as the following formula:
，
wherein,represents the carbon dioxide concentration adjustment amount, < > in the time state t>Represents the prediction of the carbon dioxide concentration value in the time state t+1 based on the dynamic prediction model, C t Representing real time in time state tThe carbon dioxide concentration measurement value obtained by the measurement is referred to as beta, which represents the scale factor of the carbon dioxide concentration adjustment amount.
For example, assume that the dynamic prediction result indicates that the carbon dioxide concentration in the future may rise, and that the predicted adjustment amount is 10 ppm. The current carbon dioxide concentration measurement is 400 ppm. The adjustment module 104 may detect whether the predicted adjustment meets an adjustment condition, such as setting a maximum adjustment of 30 ppm. Since the predicted adjustment amount (10 ppm) meets the limit of the maximum adjustment amount (30 ppm), the adjustment module will increase the current carbon dioxide concentration value by 10 ppm to 410 ppm.
In summary, the adjustment module 104 is configured to adjust the carbon dioxide concentration in the simulated marine acidification device in real time according to the dynamic prediction result so as to simulate different carbon dioxide concentration conditions. The adjustment module 104 measures the carbon dioxide concentration, predicts the adjustment amount, detects the adjustment condition, and adjusts the current carbon dioxide concentration according to the requirement to simulate different carbon dioxide concentration conditions.
The system for dynamically monitoring and adjusting the carbon dioxide dissolution in the simulated marine acidification device further comprises a display module, wherein the display module is used for visually displaying dynamic prediction results and real-time monitoring data so as to monitor the real-time state of the simulated marine acidification device. In some alternative examples, the display module may perform several specific functions:
displaying icons for triggering different display instructions in a visual interface; the first icon is used for triggering a first display instruction of the real-time monitoring data, the second icon is used for triggering a second display instruction of the dynamic prediction result, and the third icon is used for triggering a third display instruction of the biological model;
responding to the first display instruction, and generating ocean acidification degree corresponding to the carbon dioxide concentration in the current period based on the real-time monitoring data; wherein the ocean acidification degree corresponds to the appearance of the liquid in the visual interface; the liquid appearance includes liquid color and/or liquid texture;
responding to the second display instruction, and generating a marine acidification degree change trend corresponding to the carbon dioxide concentration change trend in a plurality of time periods based on the dynamic prediction result;
responding to the third display instruction, and determining the distribution situation of marine organisms in the simulated marine environment based on the dynamic prediction result and the real-time monitoring data; the distribution condition comprises at least one of the growth characteristics, the quantity and the movement track characteristics of marine organisms; generating a corresponding biological model and a virtual three-dimensional image of the biological model based on the distribution condition; a virtual three-dimensional image of the biological model is presented in a visual interface.
For example, when the user clicks the first icon to trigger the first display instruction, the display module generates the ocean acidification degree corresponding to the carbon dioxide concentration in the current period according to the real-time monitoring data. To more intuitively demonstrate the degree of marine acidification, the appearance of the liquid (including color and texture) in the visual interface will change accordingly to reflect the different levels of marine acidification.
After the user clicks the second icon to trigger the second display instruction, the display module generates a change trend of the ocean acidification degree corresponding to the change trend of the carbon dioxide concentration in a plurality of time periods according to the dynamic prediction result. Thus, the change trend of the ocean acidification degree in different periods can be intuitively known.
After the user clicks the third icon to trigger the third display instruction, the display module determines the distribution situation of marine organisms in the simulated marine environment according to the dynamic prediction result and the real-time monitoring data. The distribution may include growth characteristics, quantity, movement trajectory, etc. of the marine organisms. Based on the distribution conditions, the system can generate a corresponding biological model, and a virtual three-dimensional image is displayed in a visual interface, so that a user can intuitively observe the distribution conditions of marine organisms in the simulated marine environment. For example, the marine organisms may be the microorganisms described above or various fish species, corals, etc.
Through the functions in the optional examples, a user can know the ocean acidification degree corresponding to the carbon dioxide concentration in real time through a visual interface, observe the change trend of the ocean acidification degree, and visually display the distribution situation of the marine organisms in the simulated ocean environment. These functions provide an intuitive, visual way to understand the effects of ocean acidification and changes in the ocean ecosystem.
In the embodiment of the application, through the mutual coordination among the acquisition module 101, the monitoring module 102, the dynamic prediction module 103 and the adjustment module 104, the ocean acidification degree in the ocean acidification simulation device is predicted in real time by combining the ocean acidification dynamic prediction model, so that an environment foundation is provided for the research of ocean acidification problems, and the simulation analysis of the ocean acidification problems is conveniently developed under different carbon dioxide conditions.
In still another embodiment of the present application, there is further provided a method for dynamically monitoring and adjusting the amount of dissolved carbon dioxide in a simulated marine acidizing device, as shown in fig. 2, the method including:
201. collecting historical carbon dioxide concentration data and ocean acidification data; marine acidification data includes the following: PH, carbon dioxide dissolution, carbonate ion concentration, dissolved oxygen content;
202. Collecting real-time monitoring data of carbon dioxide concentration from a simulated marine acidizing device;
203. inputting historical carbon dioxide concentration data, ocean acidification data and real-time monitoring data into an ocean acidification dynamic prediction model to obtain a dynamic prediction result of carbon dioxide concentration; the dynamic prediction result is used for indicating the carbon dioxide concentration change trend in a plurality of time periods;
204. based on the dynamic prediction result, the carbon dioxide concentration in the simulated marine acidification device is adjusted in real time so as to simulate different carbon dioxide concentration conditions.
Further optionally, the ocean acidification dynamic prediction model comprises an autoregressive unit, an integrating unit and a moving average unit;
inputting historical carbon dioxide concentration data, ocean acidification data and real-time monitoring data into an ocean acidification dynamic prediction model to obtain a dynamic prediction result of the carbon dioxide concentration, wherein the method comprises the following steps of:
performing correlation analysis on the stable time sequence in the historical carbon dioxide concentration data through an autoregressive unit to obtain autocorrelation information of the historical carbon dioxide concentration data in different time states;
the method comprises the steps that through an integration unit, historical carbon dioxide concentration data and ocean acidification data with non-stationary time series characteristics are converted into a historical carbon dioxide concentration data sequence and ocean acidification data sequence with stationary time series characteristics through differential processing;
Extracting random noise from the historical carbon dioxide concentration data through a moving average unit, and predicting based on the random noise to obtain an error term in a future time state;
and predicting the historical carbon dioxide concentration data sequence and the ocean acidification data sequence based on the autocorrelation information and the error item through an autoregressive unit to obtain a dynamic prediction result.
Further optionally, the autoregressive unit performs correlation analysis on the stationary time sequence in the historical carbon dioxide concentration data to obtain autocorrelation information of the historical carbon dioxide concentration data in different time states, and specifically includes:
acquiring first correlation information of historical carbon dioxide concentration data on different hysteresis orders based on a first autocorrelation function; wherein the first autocorrelation function is expressed as the following formula:
，
wherein ACF (k) represents first correlation information of the historical carbon dioxide concentration data on a hysteresis order k, x (t) is an observed value of the historical carbon dioxide concentration data on the hysteresis order k on a time state t, N is the total number of observed values, and μ is a mean value of the historical carbon dioxide concentration data.
Further optionally, performing correlation analysis on the stationary time sequence in the historical carbon dioxide concentration data through an autoregressive unit to obtain autocorrelation information of the historical carbon dioxide concentration data in different time states, which specifically includes:
Acquiring second correlation information of the historical carbon dioxide concentration data on a hysteresis order based on a second partial autocorrelation function; wherein the second partial autocorrelation function is expressed as the following formula:
，
wherein PACF (k) represents second correlation information of the historical carbon dioxide concentration data on the hysteresis order, ρ (k) is first correlation information about the hysteresis order k, ρ (k-1) is first correlation information about the hysteresis order k-1, ρ (1) is first correlation information about the hysteresis order 1.
Further optionally, the autoregressive unit predicts the historical carbon dioxide concentration data sequence and the ocean acidification data sequence based on the autocorrelation information and the error term to obtain a dynamic prediction result, which specifically includes:
estimating a hysteresis coefficient for adapting to each historical carbon dioxide concentration data in the dynamic autoregressive model based on the first correlation information and/or the second correlation information;
obtaining a dynamic prediction result through dynamic autoregressive model prediction; wherein the dynamic autoregressive model is expressed as the following formula:
，
where y' (t) represents the dynamic prediction result over time state t, y (t-1) to y (t-p) represent the historical carbon dioxide concentration data sequences over time states t-1 to t-p, respectively, c is a constant term, p is a hysteresis order, Represents the hysteresis coefficient corresponding to each of the hysteresis order 1 to the hysteresis order p, +.>Is the error term.
Further alternatively, the method further comprises extracting random noise from the historical carbon dioxide concentration data by a moving average unit, and predicting an error term in a future time state based on the random noise, specifically including:
taking historical carbon dioxide concentration data as input of a random noise extraction model, acquiring average values of q observation values corresponding to the historical carbon dioxide concentration data in time states (t-1) to (t-q) through the random noise extraction model, and taking the average values as moving average sequence values MA (t); wherein the random noise extraction model is expressed as the following formula:
，
wherein x (t-i) represents an observed value at a time state t-i, the moving average unit order of the moving average sequence value MA (t) is q, and i is a positive integer;
the raw observations of the historical carbon dioxide concentration data are subtracted by the moving average sequence value MA (t) to obtain an error term at time state t.
Further optionally, based on the dynamic prediction result, the carbon dioxide concentration in the simulated marine acidification device is adjusted in real time to simulate different carbon dioxide concentration conditions, and specifically includes:
Measuring a carbon dioxide concentration measurement value in the simulated marine acidizing device in real time;
carrying out adjustment quantity prediction treatment on the dynamic prediction result and the carbon dioxide concentration measurement value to obtain the carbon dioxide concentration adjustment quantity of the simulated marine acidification device;
detecting whether the carbon dioxide concentration adjustment quantity meets adjustment conditions;
if the carbon dioxide concentration adjustment amount meets the adjustment condition, adding or reducing the carbon dioxide concentration adjustment amount in the current carbon dioxide concentration value so as to simulate different carbon dioxide concentration conditions.
Further alternatively, the adjustment amount prediction process is expressed as the following formula:
，
wherein,represents the carbon dioxide concentration adjustment amount, < > in the time state t>Represents the prediction of the carbon dioxide concentration value in the time state t+1 based on the dynamic prediction model, C t Represents a carbon dioxide concentration measurement value obtained by real-time measurement in a time state t, and β represents a scale factor of the carbon dioxide concentration adjustment amount.
Further optionally, the method further comprises: and visually presenting the dynamic prediction result and the real-time monitoring data for monitoring the real-time state of the simulated marine acidizing device.
Further optionally, the dynamic prediction result and the real-time monitoring data are visually presented for real-time state monitoring of the simulated marine acidizing device, which specifically comprises:
Displaying icons for triggering different display instructions in a visual interface; the first icon is used for triggering a first display instruction of the real-time monitoring data, the second icon is used for triggering a second display instruction of the dynamic prediction result, and the third icon is used for triggering a third display instruction of the biological model;
responding to the first display instruction, and generating ocean acidification degree corresponding to the carbon dioxide concentration in the current period based on the real-time monitoring data; wherein the ocean acidification degree corresponds to the appearance of the liquid in the visual interface; the liquid appearance includes liquid color and/or liquid texture;
responding to the second display instruction, and generating a marine acidification degree change trend corresponding to the carbon dioxide concentration change trend in a plurality of time periods based on the dynamic prediction result;
responding to the third display instruction, and determining the distribution situation of marine organisms in the simulated marine environment based on the dynamic prediction result and the real-time monitoring data; the distribution condition comprises at least one of the growth characteristics, the quantity and the movement track characteristics of marine organisms; generating a corresponding biological model and a virtual three-dimensional image of the biological model based on the distribution condition; a virtual three-dimensional image of the biological model is presented in a visual interface.
According to the method for dynamically monitoring and adjusting the carbon dioxide dissolution in the simulated marine acidification device, the marine acidification degree in the simulated marine acidification device is predicted in real time by combining the marine acidification dynamic prediction model, so that an environment foundation is provided for the research of the marine acidification problem, and the simulated analysis of the marine acidification problem can be conveniently developed under different carbon dioxide conditions.
In yet another embodiment of the present application, there is also provided an electronic device including: the device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory are communicated with each other through the communication bus;
a memory for storing a computer program;
and the processor is used for realizing the dynamic monitoring and adjusting system for the carbon dioxide dissolution in the simulated marine acidification device according to the embodiment of the method when executing the program stored in the memory.
The communication bus 1140 mentioned above for the electronic devices may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, etc. The communication bus 1140 may be divided into an address bus, a data bus, a control bus, and the like.
For ease of illustration, only one thick line is shown in fig. 3, but not only one bus or one type of bus.
The communication interface 1120 is used for communication between the electronic device and other devices described above.
The memory 1130 may include random access memory (Random Access Memory, RAM) or may include non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. Optionally, the memory may also be at least one memory device located remotely from the aforementioned processor.
The processor 1110 may be a general-purpose processor including a Central Processing Unit (CPU)
Processing Unit, CPU), network processor (Network Processor, NP), etc.; but also digital signal processors (Digital Signal Processing, DSP), application specific integrated circuits (Application SpecificIntegrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
Accordingly, the present application further provides a computer readable storage medium storing a computer program, where the computer program is executed to implement the steps executable by the electronic device in the above method embodiments.
Claims (8)
1. The system is characterized by comprising an acquisition module, a monitoring module, a dynamic prediction module and an adjustment module; wherein the method comprises the steps of
The acquisition module is used for acquiring historical carbon dioxide concentration data and ocean acidification data; the marine acidification data comprises the following: PH, carbon dioxide dissolution, carbonate ion concentration, dissolved oxygen content;
the monitoring module is used for collecting real-time monitoring data of the carbon dioxide concentration from the simulated marine acidizing device;
the dynamic prediction module is used for inputting the historical carbon dioxide concentration data, the ocean acidification data and the real-time monitoring data into an ocean acidification dynamic prediction model so as to obtain a dynamic prediction result of the carbon dioxide concentration; the dynamic prediction result is used for indicating the carbon dioxide concentration change trend in a plurality of time periods;
the adjusting module is used for adjusting the carbon dioxide concentration in the simulated marine acidification device in real time based on the dynamic prediction result so as to simulate different carbon dioxide concentration conditions;
the adjusting module is specifically used for:
measuring a carbon dioxide concentration measurement value in the simulated marine acidizing device in real time;
Performing adjustment quantity prediction processing on the dynamic prediction result and the carbon dioxide concentration measurement value to obtain the carbon dioxide concentration adjustment quantity of the simulated marine acidizing device;
detecting whether the carbon dioxide concentration adjustment amount meets an adjustment condition;
if the carbon dioxide concentration adjustment quantity meets the adjustment condition, adding or reducing the carbon dioxide concentration adjustment quantity in the current carbon dioxide concentration value so as to simulate different carbon dioxide concentration conditions;
the adjustment amount prediction process is expressed as the following formula:
，
wherein,represents the carbon dioxide concentration adjustment amount, < > in the time state t>Represents predicting the carbon dioxide concentration value in the time state t+1 based on the dynamic prediction model, C t Represents a carbon dioxide concentration measurement value obtained by real-time measurement in a time state t, and beta represents a scale factor of the carbon dioxide concentration adjustment amount.
2. The system for dynamically monitoring and adjusting the dissolution amount of carbon dioxide in a simulated marine acidizing device according to claim 1, wherein the marine acidizing dynamic prediction model comprises an autoregressive unit, an integrating unit and a moving average unit;
the dynamic prediction module inputs the historical carbon dioxide concentration data, the ocean acidification data and the real-time monitoring data into an ocean acidification dynamic prediction model so as to obtain a dynamic prediction result of the carbon dioxide concentration, and the dynamic prediction module is specifically used for:
Performing correlation analysis on the stable time sequence in the historical carbon dioxide concentration data through an autoregressive unit to obtain autocorrelation information of the historical carbon dioxide concentration data in different time states;
the historical carbon dioxide concentration data and the ocean acidification data with non-stationary time series characteristics are converted into a historical carbon dioxide concentration data sequence and an ocean acidification data sequence with stationary time series characteristics through differential processing by an integration unit;
extracting random noise from the historical carbon dioxide concentration data through a moving average unit, and predicting an error term in a future time state based on the random noise;
and predicting the historical carbon dioxide concentration data sequence and the ocean acidification data sequence based on the autocorrelation information and the error term through an autoregressive unit to obtain the dynamic prediction result.
3. The system for dynamically monitoring and adjusting the amount of dissolved carbon dioxide in a simulated marine acidizing device according to claim 2, wherein the dynamic prediction module is configured to perform a correlation analysis on a stationary time series in the historical carbon dioxide concentration data by an autoregressive unit, so as to obtain autocorrelation information of the historical carbon dioxide concentration data in different time states, when the autocorrelation information is:
Acquiring first correlation information of the historical carbon dioxide concentration data on different hysteresis orders based on a first autocorrelation function; wherein the first autocorrelation function is expressed as the following formula:
，
wherein ACF (k) represents first correlation information of the historical carbon dioxide concentration data on a hysteresis order k, x (t) is an observed value of the historical carbon dioxide concentration data on the hysteresis order k on a time state t, N is the total number of observed values, and μ is a mean value of the historical carbon dioxide concentration data.
4. The system for dynamically monitoring and adjusting the amount of dissolved carbon dioxide in a simulated marine acidizing device according to claim 3, wherein the dynamic prediction module is configured to perform a correlation analysis on a stationary time series in the historical carbon dioxide concentration data by an autoregressive unit, so as to obtain autocorrelation information of the historical carbon dioxide concentration data in different time states, when the autocorrelation information is:
acquiring second correlation information of the historical carbon dioxide concentration data on a hysteresis order based on a second partial autocorrelation function; wherein the second partial autocorrelation function is expressed as the following formula:
，
wherein PACF (k) represents second correlation information of the historical carbon dioxide concentration data on the hysteresis order, ρ (k) is first correlation information about the hysteresis order k, ρ (k-1) is first correlation information about the hysteresis order k-1, ρ (1) is first correlation information about the hysteresis order 1.
5. The system for dynamically monitoring and adjusting the dissolution rate of carbon dioxide in a simulated marine acidizing device according to claim 3 or 4, wherein the dynamic prediction module predicts the historical carbon dioxide concentration data sequence and the marine acidizing data sequence based on the autocorrelation information and the error term by an autoregressive unit, and is specifically configured to:
estimating a hysteresis coefficient for adapting to each historical carbon dioxide concentration data in the dynamic autoregressive model based on the first correlation information and/or the second correlation information;
predicting by the dynamic autoregressive model to obtain the dynamic prediction result; wherein the dynamic autoregressive model is expressed as the following formula:
，
where y' (t) represents the dynamic prediction result over time state t, y (t-1) to y (t-p) represent the historical carbon dioxide concentration data sequences over time states t-1 to t-p, respectively, c is a constant term, p is a hysteresis order,represents the hysteresis coefficient corresponding to each of the hysteresis order 1 to the hysteresis order p, +.>Is the error term.
6. The system for dynamically monitoring and adjusting the dissolution amount of carbon dioxide in a simulated marine acidizing device according to claim 2, wherein the dynamic prediction module is specifically configured to, when a random noise is extracted from the historical carbon dioxide concentration data by a moving average unit and an error term in a future time state is predicted based on the random noise:
Taking the historical carbon dioxide concentration data as input of a random noise extraction model, acquiring average values of q observation values corresponding to the historical carbon dioxide concentration data in time states (t-1) to (t-q) through the random noise extraction model, and taking the average values as moving average sequence values MA (t); wherein the random noise extraction model is expressed as the following formula:
，
wherein x (t-i) represents an observed value at a time state t-i, the moving average unit order of the moving average sequence value MA (t) is q, and i is a positive integer;
subtracting the moving average sequence value MA (t) from the original observed value of the historical carbon dioxide concentration data to obtain an error term in a time state t.
7. The system for dynamically monitoring and adjusting the dissolution rate of carbon dioxide in a simulated marine acidizing device as recited in claim 1, further comprising a display module for
And visually presenting the dynamic prediction result and the real-time monitoring data for monitoring the real-time state of the simulated marine acidizing device.
8. The system for dynamically monitoring and adjusting the amount of dissolved carbon dioxide in a simulated marine acidizing device of claim 7, wherein said display module is specifically configured to:
Displaying icons for triggering different display instructions in a visual interface; the first icon is used for triggering a first display instruction of the real-time monitoring data, the second icon is used for triggering a second display instruction of the dynamic prediction result, and the third icon is used for triggering a third display instruction of the biological model;
responding to the first display instruction, and generating ocean acidification degree corresponding to the carbon dioxide concentration in the current period based on the real-time monitoring data; wherein the degree of ocean acidification corresponds to the appearance of the liquid in the visual interface; the liquid appearance includes liquid color and/or liquid texture;
responding to the second display instruction, and generating a marine acidification degree change trend corresponding to the carbon dioxide concentration change trend in a plurality of time periods based on the dynamic prediction result;
determining a distribution situation of marine organisms in the simulated marine environment based on the dynamic prediction result and the real-time monitoring data in response to the third display instruction; the distribution condition comprises at least one of growth characteristics, quantity and movement track characteristics of marine organisms; generating a corresponding biological model and a virtual three-dimensional image of the biological model based on the distribution condition; and displaying the virtual three-dimensional image of the biological model in the visual interface.
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