Carbon sink measurement and calculation method and system of underwater robot for seaweed culture

Abstract

The invention provides a carbon sink measurement and calculation method and system for seaweed culture by an underwater robot, and the method comprises the steps: dividing a culture region in a calibrated water area, culturing seaweeds in the culture region through bamboo rafts, and calibrating the gaps between the seaweeds according to the gaps between the bamboo rafts. The underwater robot is regularly operated to trace at the interval position between the seaweeds in the culture area to perform image acquisition, water quality monitoring is performed on the calibrated water area, the acquired data is collected and summarized to form underwater image data, and the underwater image data is preprocessed; performing segmented transverse cutting on the preprocessed underwater image data according to a growth period interval, marking pictures of a seaweed growth region, recording a marking number, limiting cut seaweed boundary pictures through a neural network, integrating the marked number pictures and the boundary pictures, and performing statistics on an effective culture region area; the underwater robot synchronously obtains effective cultivation area data through laser or sonar.

Description

Technical Field

[0001] The present invention belongs to the field of carbon sink assessment, and in particular relates to a carbon sink measurement and calculation method and system for an underwater robot used in seaweed cultivation. Background Art

[0002] The carbon sink measurement and calculation method and system of underwater robots for seaweed farming is an emerging technology that aims to accurately evaluate the contribution of seaweed farming to carbon capture and storage. As the global climate change problem becomes increasingly serious, finding effective carbon emission reduction and carbon sink methods has become an important topic in current scientific research and industry.

[0003] As a potential blue carbon solution, seaweed farming has attracted widespread attention due to its rapid growth and efficient carbon fixation. Traditional seaweed carbon sink measurement methods mainly rely on manual sampling and laboratory analysis, which has many limitations and disadvantages. First, the frequency and coverage of manual sampling are limited, making it difficult to achieve large-scale, high-frequency data collection, resulting in possible deviations and uncertainties in the measurement results. Manual operations can easily introduce errors, especially in complex marine environments, where the accuracy and consistency of sampling are difficult to guarantee. Furthermore, traditional methods have poor timeliness, and it usually takes a long time from sampling to obtaining the final results, which cannot reflect the dynamic changes in seaweed farming areas in a timely manner.

[0004] The high cost of manual sampling and analysis limits the possibility of large-scale application. Although some existing automated monitoring systems have improved these problems to a certain extent, they still have limitations. For example, although fixed sensor networks can provide continuous data, their spatial resolution is limited and it is difficult to fully capture the spatial heterogeneity of seaweed farming areas. Although satellite remote sensing technology can provide observation data over a large area, its temporal and spatial resolution is often insufficient to meet the needs of refined management and is easily affected by weather conditions.

[0005] Most of the existing automated systems can only measure parameters on the water surface or near the water surface, and their direct measurement capabilities for underwater environments, especially seaweed growth areas, are limited. In terms of data processing and analysis, existing technologies often lack the ability to effectively integrate multi-source and heterogeneous data, and it is difficult to fully consider the impact of multiple dimensions such as seaweed growth, water quality changes, and environmental factors. At the same time, there is still room for improvement in the accuracy and reliability of carbon sink calculations, especially in considering the complexity and dynamics of marine ecosystems. In addition, the capabilities of existing technologies in real-time data transmission, big data processing, and intelligent decision support also need to be strengthened, and it is difficult to meet the real-time monitoring and precise control requirements of seaweed aquaculture carbon sink management. Therefore, it is of great scientific significance and practical application value to develop a new technical system that can overcome these shortcomings and achieve high-precision, high-frequency, large-scale, and multi-dimensional measurements.

[0006] There is an urgent need for carbon sink measurement and calculation methods and systems for seaweed farming using underwater robots. Summary of the invention

[0007] The present invention proposes a carbon sink measurement and calculation method and system for seaweed aquaculture using an underwater robot, which solves the accuracy, automation and real-time problems of seaweed aquaculture carbon sink measurement. The accuracy of area measurement and carbon sink calculation is improved through underwater robots, image processing and multi-source data fusion. The cloud server and dynamic adjustment mechanism are used to achieve long-term data tracking and real-time risk warning, providing reliable technical support for marine carbon sink research and management.

[0008] The technical solution of the present invention is implemented as follows: a method for measuring and calculating carbon sinks in seaweed cultivation using an underwater robot, the method comprising the following steps:

[0009] Divide the aquaculture area in the calibrated water area, cultivate seaweed in the aquaculture area by bamboo rafts, calibrate the gaps between the seaweeds according to the gaps between the bamboo rafts, regularly operate the underwater robot to track the spacing between the seaweeds in the aquaculture area for image acquisition, and simultaneously monitor the water quality of the calibrated water area, collect and summarize the collected data to form underwater image data, and pre-process the underwater image data;

[0010] The pre-processed underwater image data is segmented and cut horizontally according to the growth cycle interval, the pictures of the seaweed growth area are marked, and the number of marks is recorded. The cut seaweed boundary pictures are limited by neural networks, and the marked number pictures are integrated with the boundary pictures to calculate the effective breeding area.

[0011] The dissolved inorganic carbon data of the water area obtained by the monitoring instrument can be associated with the area data of the aquaculture area obtained by the underwater robot through laser or sonar scanning, and then the relationship between the changes in the aquaculture area and the changes in water quality can be analyzed; the change values ​​of the carbon dioxide content parameters in the water area are recorded as the carbon storage data of the water area, revealing the carbon storage capacity of the water area in a specific area, and the carbon dioxide content values ​​of the water area are saved as carbon fixation data; and through real-time and accurate data recording and analysis, the fluctuations of the carbon dioxide content in the water area are tracked, and combined with the situation of the aquaculture area, the carbon fixation capacity and carbon storage potential of the water area are evaluated;

[0012] Upload the historical carbon storage data and carbon fixation data of the current water area to the cloud server, and during regular water quality monitoring, establish a standard risk threshold interval for the water area in the cloud server, and update the water area composition data regularly. When the data exceeds the standard risk threshold interval, adjust the carbon storage data and carbon fixation data.

[0013] The method adopted in this application document uses precise spatial division and calibration technology, and through bamboo raft farming and gap calibration, it lays an accurate spatial foundation for subsequent data collection and analysis. This refined spatial management method far exceeds the traditional rough estimation and can provide more accurate farming area data. The application of underwater robots is an important technological breakthrough. Traditional methods may rely on manual sampling or fixed sensors, while underwater robots can move flexibly in the gaps between seaweed to perform high-precision image acquisition and water quality monitoring. This not only improves the efficiency and accuracy of data collection, but also can obtain more comprehensive and detailed underwater environmental information. The preprocessing and analysis methods of image data also reflect a high degree of innovation. Through segmented lateral cutting of growth cycle intervals, combined with the definition of seaweed boundaries by neural networks, this method can accurately identify and quantify effective farming areas.

[0014] This image analysis technology combined with machine learning greatly improves the accuracy of area calculations, far exceeding traditional manual estimation or simple image processing methods. Another important innovation is the combination of laser or sonar measurements with image data. This multi-source data fusion method not only improves the accuracy of area measurement, but also verifies and corrects image analysis results, enhancing the reliability of overall measurements. The practice of correlating changes in aquaculture areas with water quality monitoring data is also a significant improvement. This correlation analysis can reveal the complex relationship between seaweed growth and water quality changes, providing an important basis for understanding and optimizing the carbon sink process. In particular, using changes in carbon dioxide content parameters in waters as indicators of carbon storage and carbon fixation directly reflects the carbon sink effect of seaweed aquaculture, which is more accurate and intuitive than traditional indirect estimation methods.

[0015] The practice of uploading data to a cloud server and establishing a standard risk threshold interval for water areas reflects the forward-looking and adaptable nature of the system. This method not only realizes the centralized management and long-term tracking of data, but also dynamically adjusts the calculation of carbon storage and carbon fixation according to real-time water quality data, ensuring the timeliness and accuracy of the measurement results. This dynamic adjustment mechanism is rare in traditional carbon sink measurement methods, and it can better cope with the complex and changing marine environment.

[0016] This method creates a highly accurate, automated and intelligent seaweed aquaculture carbon sink measurement system by combining advanced underwater robot technology, image processing, machine learning, multi-source data fusion and cloud computing. It not only solves the limitations of traditional methods in terms of accuracy, efficiency and adaptability, but also provides a new technical path for marine carbon sink research and management. This method represents an important trend in the development of marine environmental monitoring and carbon sink calculation towards intelligence, precision and systematization, and has the potential to become a new standard for marine carbon sink research and management.

[0017] As a preferred embodiment, when collecting underwater images, the underwater robot performs horizontal displacement in the same gap, arranges the image data acquired on the same horizontal line in sequence, sets a number of marking points on the edge of each picture, and splices all images according to the marking points to form a whole row of underwater image data on the horizontal line.

[0018] As a preferred implementation, the standard risk threshold of the water area is set as an interval threshold in the cloud server. When the collection parameters in the water area reach the set interval, an alarm is sent to the user end, and the scheduled collection cycle is shortened. After exceeding the setting, the risk level is determined and risk warning information is sent to the user end through the cloud server.

[0019] As a preferred embodiment, when associating the data of changes in the breeding area with the data changes in water quality monitoring, the collected data are correspondingly associated through unique identifiers such as ID and timestamps, and at the same time, the time correlation between the time series analysis data is used to obtain the lag change parameters between the breeding area changes and the water quality changes.

[0020] The carbon sink measurement and calculation system of an underwater robot for seaweed cultivation includes an underwater robot, an image preprocessing module, an association matching database, a cloud server and a user end. The underwater robot regularly tracks the spacing positions between seaweeds in the cultivation area to collect images, and simultaneously monitors the water quality of the calibrated water area, collects and summarizes the collected data to form underwater image data, and sends the collected data to the image preprocessing module. The image preprocessing module denoises the collected image data, cleans each entire line of underwater image data, limits the cropped seaweed boundary image through a neural network, integrates the marked quantity image with the boundary image, and statistically calculates the effective cultivation data. The breeding area is calculated and the calculation result is uploaded to the cloud server; the association matching database associates the data of breeding area changes with the data changes of water quality monitoring, and records the numerical value of the carbon dioxide content parameter in the water area as the water area carbon storage data, and saves the water area carbon dioxide content value as the carbon fixation data; the cloud server stores the historical carbon storage data and carbon fixation data of the water area, and regularly updates the water area composition data. When the data exceeds the standard risk threshold interval, the carbon storage data and carbon fixation data are adjusted, and the user terminal receives the early warning of the cloud server, and views the water area composition data, carbon storage data and carbon fixation data in real time through the user terminal.

[0021] After adopting the above technical solution, the beneficial effects of the present invention are as follows: through the precise positioning and high-resolution image acquisition of the underwater robot, combined with laser or sonar measurement, the measurement accuracy of the aquaculture area and the growth status of seaweed is greatly improved, laying the foundation for accurately evaluating the carbon sink effect; multi-source data fusion improves the reliability and comprehensiveness of the data, and reduces the errors that may be caused by a single data source;

[0022] The application of underwater robots has realized the automation of data collection, improved work efficiency, reduced labor costs and human errors; through regular operation and continuous monitoring, real-time monitoring of seaweed farming areas has been achieved, and environmental changes can be discovered and responded to in a timely manner; the use of neural networks for image analysis has improved the accuracy and efficiency of seaweed boundary recognition and area calculation; the dynamic adjustment mechanism of cloud servers has improved the timeliness and accuracy of measurement results; long-term data storage and analysis are helpful for studying the long-term carbon sink effect of seaweed farming; the establishment of standard risk threshold intervals can detect water quality anomalies in a timely manner and provide early warning for farming management; correlation analysis of changes in farming areas and water quality changes provides a scientific basis for optimizing farming strategies; direct measurement of changes in carbon dioxide content in waters provides a more accurate quantification method for carbon sink effects;

[0023] This method has good scalability and can be applied to aquaculture areas of different sizes; it provides reliable data support for marine carbon sink research and related policy formulation. These effects combined not only improve the scientificity and accuracy of seaweed aquaculture carbon sink measurement, but also provide strong technical support for marine carbon sink management and research, which helps to better understand and utilize the potential of marine carbon sinks, and is of great significance for addressing climate change. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0025] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0026] 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.

[0027] Example:

[0028] like Figure 1 As shown, a method for measuring and calculating carbon sinks using an underwater robot for seaweed farming comprises the following steps:

[0029] Divide the aquaculture area in the calibrated water area, cultivate seaweed in the aquaculture area by bamboo rafts, calibrate the gaps between the seaweeds according to the gaps between the bamboo rafts, regularly operate the underwater robot to track the spacing between the seaweeds in the aquaculture area for image acquisition, and simultaneously monitor the water quality of the calibrated water area, collect and summarize the collected data to form underwater image data, and pre-process the underwater image data;

[0030] The pre-processed underwater image data is segmented and cut horizontally according to the growth cycle interval, the pictures of the seaweed growth area are marked, and the number of marks is recorded. The cut seaweed boundary pictures are limited by neural networks, and the marked number pictures are integrated with the boundary pictures to calculate the effective breeding area.

[0031] The dissolved inorganic carbon data of the water area obtained by the monitoring instrument can be associated with the area data of the aquaculture area obtained by the underwater robot through laser or sonar scanning, and then the relationship between the changes in the aquaculture area and the changes in water quality can be analyzed; the change values ​​of the carbon dioxide content parameters in the water area are recorded as the carbon storage data of the water area, revealing the carbon storage capacity of the water area in a specific area, and the carbon dioxide content values ​​of the water area are saved as carbon fixation data; and through real-time and accurate data recording and analysis, the fluctuations of the carbon dioxide content in the water area are tracked, and combined with the situation of the aquaculture area, the carbon fixation capacity and carbon storage potential of the water area are evaluated;

[0032] Upload the historical carbon storage data and carbon fixation data of the current water area to the cloud server, and during regular water quality monitoring, establish a standard risk threshold interval for the water area in the cloud server, and update the water area composition data regularly. When the data exceeds the standard risk threshold interval, adjust the carbon storage data and carbon fixation data.

[0033] The working principle and operation steps of the carbon sink measurement and calculation method of this underwater robot for seaweed farming are as follows:

[0034] The aquaculture area is divided in the calibrated waters, and seaweed is cultivated on bamboo rafts. This step provides a clear spatial range and reference points for subsequent measurements and calculations. Then, the underwater robot regularly collects images and monitors water quality in the gaps between the seaweed. This method can comprehensively and accurately capture the growth of seaweed and changes in water quality, providing rich raw data for subsequent analysis.

[0035] Next, the collected underwater image data is preprocessed and cut horizontally in sections, and the seaweed growth area is marked. This step converts the raw data into analyzable structured information through image processing technology. The use of neural networks to limit the cropped seaweed boundary images further improves the accuracy of recognition. By integrating the marked quantity images and boundary images, the effective aquaculture area can be accurately counted, which provides key basic data for carbon sink calculations.

[0036] The online monitoring system for dissolved inorganic carbon in water is the world's first instrument that can detect inorganic carbon in the entire liquid path. It successfully solves the problem of inconvenient gas preparation and carrying in field monitoring of dissolved inorganic carbon in water. The online monitoring system for dissolved inorganic carbon in water has many advantages. First, it has excellent accuracy and can perform high-precision dissolved inorganic carbon detection in a variety of complex water quality environments; second, it is small in size, easy to carry and operate, and is especially suitable for field monitoring applications; in addition, the amount of sample required for detection is small and the time resolution is high, which makes real-time monitoring possible. The low reagent consumption and high degree of automation not only greatly reduce the operating cost, but also improve the monitoring efficiency, further promoting the system's wide application in research fields such as ocean acidification, carbon source and sink pattern in water bodies, and carbon cycle processes. Through this system, real-time and accurate inorganic carbon data can be provided for marine and water carbon cycle research, promoting the scientific evaluation and research of carbon storage, carbon fixation, and carbon emissions in water bodies. Combining this inorganic carbon monitoring system with underwater robot technology, the area data of the effective aquaculture area can be obtained synchronously through laser or sonar technology, and accurately matched through image data. Underwater robots are able to capture aquaculture area data in water bodies in real time and combine it with water quality monitoring data.

[0037] Specifically, the dissolved inorganic carbon data of the water area obtained by the monitoring instrument can be associated with the area data of the aquaculture area obtained by the underwater robot through laser or sonar scanning, and then the relationship between the changes in the aquaculture area and the changes in water quality can be analyzed. This process can further record the change values ​​of the carbon dioxide content parameters of the water area as the carbon storage data of the water area, reveal the carbon storage capacity of the water area in a specific area, and save the carbon dioxide content values ​​of the water area as carbon fixation data. The integration of this series of data not only improves the accuracy of water quality monitoring and carbon storage analysis, but also provides more abundant scientific data support for marine environmental monitoring and carbon cycle research. Through real-time and accurate data recording and analysis, the system can effectively track the fluctuations of carbon dioxide content in the water area, and combine the actual situation of the aquaculture area to evaluate the carbon fixation capacity and carbon storage potential of the water area, providing a scientific basis for responding to global climate change and formulating carbon reduction policies. Through this technical solution combining inorganic carbon monitoring instruments with underwater robots, dynamic data of the carbon cycle process can be obtained comprehensively and in real time under different water environments, and the interaction between carbon sources and carbon sinks in water bodies can be deeply studied. This innovative system integration not only improves the effectiveness of traditional water quality monitoring methods, but also brings new opportunities and ideas for the study of aquatic carbon storage and carbon fixation processes.

[0038] Upload historical carbon storage data and carbon fixation data to the cloud server, and establish a standard risk threshold interval for the water area. This design allows the system to accumulate and analyze data over a long period of time, which helps to discover the long-term trends and patterns of carbon sink changes. When the water component data exceeds the standard risk threshold, the system will automatically adjust the carbon storage and carbon fixation data. This dynamic adjustment mechanism can ensure the accuracy and reliability of carbon sink calculations, and can also promptly reflect changes in the water ecosystem.

[0039] This method is set up and operated in this way mainly for the following reasons: Accuracy and comprehensiveness: Regular and systematic data collection by underwater robots can obtain high-precision and high-coverage seaweed growth and water quality data, avoiding the limitations and uncertainties of manual sampling. Automation and intelligence: The use of image processing and neural network technology realizes the automatic identification and area calculation of seaweed growth areas, greatly improving work efficiency and accuracy. Multi-source data fusion: Combining image data, laser / sonar measurement data and water quality monitoring data can comprehensively evaluate the carbon sink effect of seaweed farming and improve the reliability of calculation results. Dynamic monitoring and adjustment: By storing and analyzing long-term data on cloud servers and establishing risk threshold intervals, dynamic monitoring and timely adjustment of carbon sink effects can be achieved to ensure the timeliness and accuracy of calculation results. Overall consideration of the ecosystem: Combining the analysis of seaweed growth, water quality changes and carbon cycle processes reflects the consideration of the overall function of the marine ecosystem and helps to more comprehensively evaluate the ecological benefits of seaweed farming. Long-term monitoring capability: This method designs a sustainable and repeatable monitoring and calculation system, which is conducive to long-term tracking of the carbon sequestration effect of seaweed aquaculture and provides a scientific basis for related research and policy making.

[0040] When the underwater robot is collecting underwater images, it performs horizontal displacement in the same gap, arranges the image data obtained on the same horizontal line in order, sets several marking points on the edge of each picture, and splices all the images according to the marking points to form a whole row of underwater image data on the horizontal line.

[0041] The horizontal displacement acquisition method ensures the continuity and integrity of the image data, which is fundamentally different from the traditional random sampling or fixed point sampling methods. By moving horizontally in the same gap, the underwater robot can capture continuous images of the entire aquaculture area, avoiding blind spots and omissions in data collection. Arranging the image data on the same horizontal line in sequence is an important innovation.

[0042] This method not only maintains the spatial relationship of the images, but also provides accurate sequence information for subsequent image stitching, greatly improving the accuracy and efficiency of stitching. Traditional methods may require complex image matching algorithms to determine the relative positions between images, and this pre-sorting method simplifies the process. Setting markers on the edges of the pictures is another key technical innovation. These markers serve as reference points for image stitching, greatly improving the accuracy and reliability of stitching. Compared with traditional methods that rely on image content for matching, this marker method can better handle interference factors such as changes in illumination and turbidity that may occur in underwater environments, ensuring the accuracy of stitching.

[0043] The image stitching method based on marker points also significantly improves processing efficiency. Traditional image stitching may require complex feature matching and image registration algorithms, but through preset marker points, the system can directly align and stitch images, greatly reducing computational complexity and time consumption. This method is particularly suitable for processing large amounts of underwater image data and can quickly generate high-quality panoramic images.

[0044] The whole row of underwater image data generated by this method provides more comprehensive and coherent information for subsequent analysis. Compared with single or discrete images, the whole row of images can better reflect the growth status, distribution density and overall aquaculture environment of seaweed, providing a more reliable data basis for accurately assessing the area of ​​aquaculture and the growth status of seaweed.

[0045] The cloud server sets the water area standard risk threshold as an interval threshold. When the collection parameter in the water area reaches the set interval, an alarm is sent to the user end, and the scheduled collection cycle is shortened. After exceeding the setting, the risk level is determined and risk warning information is sent to the user end through the cloud server.

[0046] The use of interval thresholds instead of a single fixed threshold is an important advancement. Traditional systems usually use fixed critical values ​​to trigger alarms, while interval thresholds provide a more flexible and sophisticated risk assessment mechanism. This approach can better adapt to complex water environments, reduce false alarms and improve the accuracy of alarms.

[0047] The practice of shortening the scheduled collection cycle after reaching the set interval reflects the intelligence and adaptability of the system. This method of dynamically adjusting the sampling frequency can provide more intensive monitoring data when potential risks arise, while maintaining the regular sampling frequency under normal circumstances, which not only ensures the adequacy of data during critical periods, but also optimizes the use of system resources. This is more efficient and targeted than the traditional fixed sampling frequency.

[0048] Another innovation is the determination of risk levels and differentiated alerts. Unlike simple binary alerts (risky / no risk), this system can assess the risk level based on the degree of parameter deviation and send warning information of the corresponding level. This detailed risk grading and targeted alerts can help users assess the situation more accurately and take appropriate countermeasures. The method of sending warning information to the user end through the cloud server also significantly improves the real-time and accessibility of the system.

[0049] Compared with traditional local alarm systems, cloud services allow users to receive alarms anytime and anywhere, greatly improving response speed and management efficiency. This design is particularly suitable for large-scale seaweed farming scenarios that require remote monitoring. The design of this system also reflects support for long-term data analysis and trend prediction. By accumulating and analyzing long-term data in the cloud, the system can not only provide instant alarms, but also predict potential risk trends and provide more forward-looking guidance for farming management. This predictive analysis is rare in traditional monitoring systems.

[0050] This cloud-based water risk monitoring system creates a highly intelligent, flexible and efficient water management tool through innovative designs such as interval threshold setting, dynamic sampling adjustment, risk level determination and cloud alarm. It not only solves the limitations of traditional monitoring systems in terms of accuracy, real-time and accessibility, but also provides strong technical support for the refined management and risk control of seaweed farming.

[0051] When associating the data of changes in aquaculture areas with the data changes in water quality monitoring, the collected data are correspondingly associated through unique identifiers such as ID and timestamps. At the same time, the time correlation between time series analysis data is used to obtain the lag change parameters between the changes in aquaculture areas and water quality changes.

[0052] The use of unique identifiers and timestamps for data association is a key technological advancement. Traditional methods may rely solely on time information for data matching, while the introduction of unique identifiers such as IDs greatly improves the accuracy and reliability of data association. This dual indexing mechanism not only ensures the correct pairing of data, but also provides a solid foundation for subsequent data processing and analysis.

[0053] The application of time series analysis reflects the system's in-depth consideration of the temporal dynamics of data. Unlike simple static data comparisons, time series analysis can capture trends and patterns in data over time. This method is particularly suitable for analyzing complex systems such as seaweed farming that are affected by multiple time-varying factors, and can reveal temporal correlations that are difficult to discover using traditional methods. Obtaining the lag change parameters between changes in the farming area and changes in water quality is another important innovation.

[0054] This analysis not only focuses on immediate correlations, but also takes into account the time delay effects between changes. In marine ecosystems, the impact of environmental changes on organisms usually has a time lag, and the acquisition of such lag parameters provides key information for understanding and predicting system dynamics. This is more in-depth and comprehensive than traditional synchronous correlation analysis.

[0055] This method lays the foundation for multi-dimensional analysis of data. Through precise data association and time series analysis, researchers can simultaneously consider the impact of both spatial (farming area) and temporal (water quality changes) dimensions to build a more comprehensive and accurate ecosystem model. This multi-dimensional analysis capability is difficult to achieve with traditional methods. This method also significantly improves the traceability and reproducibility of data. Through unique identifiers and timestamps, the source and collection time of each set of data can be accurately tracked, which is crucial for the reliability and subsequent verification of scientific research.

[0056] This data organization method also provides an ideal data structure for the application of big data analysis and machine learning algorithms, laying the foundation for more in-depth data mining and pattern recognition in the future. This data association and analysis method creates a highly accurate, dynamic and comprehensive data analysis system through precise data matching, time series analysis and hysteresis effect research. It not only solves the limitations of traditional methods in data association and time dynamic analysis, but also provides a powerful tool for the study of the complex relationship between seaweed cultivation and water quality changes.

[0057] The carbon sink measurement and calculation system of an underwater robot for seaweed cultivation includes an underwater robot, an image preprocessing module, an association matching database, a cloud server and a user end. The underwater robot regularly tracks the spacing positions between seaweeds in the cultivation area to collect images, and simultaneously monitors the water quality of the calibrated water area, collects and summarizes the collected data to form underwater image data, and sends the collected data to the image preprocessing module. The image preprocessing module denoises the collected image data, cleans each entire line of underwater image data, limits the cropped seaweed boundary image through a neural network, integrates the marked quantity image with the boundary image, and statistically calculates the effective cultivation data. The breeding area is calculated and the calculation result is uploaded to the cloud server; the association matching database associates the data of breeding area changes with the data changes of water quality monitoring, and records the numerical value of the carbon dioxide content parameter in the water area as the water area carbon storage data, and saves the water area carbon dioxide content value as the carbon fixation data; the cloud server stores the historical carbon storage data and carbon fixation data of the water area, and regularly updates the water area composition data. When the data exceeds the standard risk threshold interval, the carbon storage data and carbon fixation data are adjusted, and the user terminal receives the early warning of the cloud server, and views the water area composition data, carbon storage data and carbon fixation data in real time through the user terminal.

[0058] The working principle and operation steps of this underwater robot carbon sink measurement and calculation system for seaweed farming involve multiple key links, each of which has been carefully designed to achieve efficient and accurate carbon sink measurement.

[0059] The underwater robot regularly collects images and monitors water quality in the aquaculture area. This automated data collection method not only improves efficiency, but also ensures data consistency and continuity. The underwater robot can move flexibly in the small space between the seaweeds to obtain high-quality images and water quality data, which is difficult to achieve with manual collection. The collected data is then sent to the image preprocessing module, which is a crucial step. The image preprocessing module performs denoising and cleaning operations, which helps to improve the accuracy of subsequent analysis. The underwater environment is complex, and image quality is often affected by factors such as turbidity and changes in light, so this preprocessing step is crucial to obtaining reliable analysis results. In particular, the use of neural networks to define the boundaries of seaweed, this advanced image processing technology can accurately identify the growth range of seaweed, greatly improving the accuracy of area statistics.

[0060] The integration of the number of markers and boundary images further improves the accuracy of the statistics of the effective aquaculture area. These processed data are uploaded to the cloud server, which enables centralized management and remote access of data. The design of the association matching database is another key innovation of the system. It associates changes in aquaculture areas with water quality monitoring data. This association analysis can reveal the complex relationship between seaweed growth and water quality changes. In particular, recording changes in carbon dioxide content parameters in the water area as carbon storage and carbon fixation data directly reflects the carbon sink effect of seaweed aquaculture. This direct measurement method is more accurate and reliable than traditional estimation methods. The cloud server plays a core role in the entire system. It not only stores historical data, but also updates and analyzes it in real time.

[0061] Setting standard risk threshold intervals and making dynamic adjustments reflects the intelligence and adaptability of the system. When the data exceeds the threshold, the system can automatically adjust the carbon storage and carbon fixation data to ensure the accuracy and timeliness of the measurement results. This dynamic adjustment mechanism can cope with the complex changes in the marine environment, which is an advantage that traditional static measurement methods do not have. Finally, the design of the user end enables the monitoring results of the entire system to be presented to managers in real time and intuitively. Users can view water composition data, carbon storage and carbon fixation data at any time through terminal devices, and receive early warning information. This real-time monitoring and early warning mechanism greatly improves the efficiency and response speed of management, allowing managers to make decisions and adjustments in a timely manner.

[0062] The reasons for this setup and operation are as follows: It realizes the automation of the entire process from data collection to analysis and decision support, greatly improving the efficiency and accuracy of the entire carbon sink measurement process. Through advanced image processing and data analysis technology, the system can handle complex underwater environmental data and provide more accurate carbon sink measurement results. The application of cloud servers realizes centralized data management and remote access, which facilitates long-term data tracking and large-scale data analysis.

[0063] The dynamic adjustment mechanism and real-time early warning function enhance the adaptability and practicality of the system, and can better cope with changes in the marine environment. The user-friendly interface design enables complex data analysis results to be presented to managers intuitively, supporting scientific decision-making. This system integrates advanced underwater robot technology, image processing, data analysis and cloud computing technologies to create a highly automated, accurate and intelligent seaweed aquaculture carbon sink measurement system, providing strong technical support for marine carbon sink research and management.

[0064] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for measuring and calculating carbon sinks in seaweed cultivation using an underwater robot, characterized in that: The method comprises the following steps: Divide the aquaculture area in the calibrated water area, cultivate seaweed in the aquaculture area by bamboo rafts, calibrate the gaps between the seaweeds according to the gaps between the bamboo rafts, regularly operate the underwater robot to track the spacing between the seaweeds in the aquaculture area for image acquisition, and simultaneously monitor the water quality of the calibrated water area, collect and summarize the collected data to form underwater image data, and pre-process the underwater image data; The pre-processed underwater image data is segmented and cut horizontally according to the growth cycle interval, the pictures of the seaweed growth area are marked, and the number of marks is recorded. The cut seaweed boundary pictures are limited by neural networks, and the marked number pictures are integrated with the boundary pictures to calculate the effective breeding area. The dissolved inorganic carbon data of the water area obtained by the monitoring instrument can be associated with the area data of the aquaculture area obtained by the underwater robot through laser or sonar scanning, and then the relationship between the changes in the aquaculture area and the changes in water quality can be analyzed; the change values ​​of the carbon dioxide content parameters in the water area are recorded as the carbon storage data of the water area, revealing the carbon storage capacity of the water area in a specific area, and the carbon dioxide content values ​​of the water area are saved as carbon fixation data; and through real-time and accurate data recording and analysis, the fluctuations of the carbon dioxide content in the water area are tracked, and combined with the situation of the aquaculture area, the carbon fixation capacity and carbon storage potential of the water area are evaluated; Upload the historical carbon storage data and carbon fixation data of the current water area to the cloud server, and during regular water quality monitoring, establish a standard risk threshold interval for the water area in the cloud server, and update the water area composition data regularly. When the data exceeds the standard risk threshold interval, adjust the carbon storage data and carbon fixation data.

2. The method for measuring and calculating carbon sinks in seaweed cultivation using an underwater robot as claimed in claim 1, characterized in that: When the underwater robot is collecting underwater images, it performs horizontal displacement in the same gap, arranges the image data obtained on the same horizontal line in order, sets several marking points on the edge of each picture, and splices all the images according to the marking points to form a whole row of underwater image data on the horizontal line.

3. The method for measuring and calculating carbon sinks in seaweed cultivation using an underwater robot as claimed in claim 1, characterized in that: The cloud server sets the water area standard risk threshold as an interval threshold. When the collection parameter in the water area reaches the set interval, an alarm is sent to the user end, and the scheduled collection cycle is shortened. After exceeding the setting, the risk level is determined and risk warning information is sent to the user end through the cloud server.

4. The method for measuring and calculating carbon sinks in seaweed cultivation using an underwater robot as claimed in claim 1, characterized in that: When associating the data of changes in aquaculture areas with the data changes in water quality monitoring, the collected data are correspondingly associated through unique identifiers such as ID and timestamps. At the same time, the time correlation between time series analysis data is used to obtain the lag change parameters between the changes in aquaculture areas and water quality changes.

5. The carbon sink measurement and calculation system of underwater robot for seaweed cultivation is characterized by: It includes an underwater robot, an image preprocessing module, an association matching database, a cloud server and a user end. The underwater robot regularly tracks the spacing between seaweeds in the breeding area to collect images, and simultaneously monitors the water quality of the calibrated water area, collects and summarizes the collected data to form underwater image data, and sends the collected data to the image preprocessing module. The image preprocessing module denoises the collected image data, cleans each entire line of underwater image data, limits the cropped seaweed boundary image through a neural network, integrates the marked quantity image with the boundary image, counts the effective breeding area, and uploads the calculation result to the cloud server; The association matching database associates the data of changes in the breeding area with the data changes in water quality monitoring, and records the numerical changes in the water area carbon dioxide content parameters as water area carbon storage data, and saves the water area carbon dioxide content value as carbon fixation data; the cloud server stores the historical carbon storage data and carbon fixation data of the water area, and regularly updates the water area composition data. When the data exceeds the standard risk threshold range, the carbon storage data and carbon fixation data are adjusted. The user terminal receives the early warning of the cloud server, and views the water area composition data, carbon storage data and carbon fixation data in real time through the user terminal.

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