FrykenPontoon™ AI Control System

TITLE
Patent Application – Software Component. Is a water Control System and Method for Monitoring, Analyzing, and Visualizing Oxygen Depletion in Aquatic Environments with Targeted Intervention. Learn more about the hardware anchor in the FrykenPontoon™ Greenhouse.

The patent drawings are located at the bottom of the document. Discover how FrykenPontoon™ fits into our broader mission on the About page.

TECHNICAL FIELD
The present invention pertains to the field of environmental monitoring and remediation, particularly addressing the issue of oxygen depletion (hypoxia) in aquatic ecosystems. It offers a comprehensive solution for collecting, processing, and visualizing water quality data, enabling proactive identification and prioritization of areas requiring intervention.

The invention further encompasses the transmission of information about selected oxygen supply sites to a Command Center, which oversees a fleet of platforms equipped with oxygen delivery systems, thus facilitating targeted and efficient ecological restoration efforts.

BACKGROUND OF THE INVENTION
Aquatic ecosystems, encompassing both marine and freshwater environments, are vital for the planet’s overall well-being. However, a growing number of these ecosystems are facing the detrimental effects of oxygen depletion, often referred to as “dead zones.” These zones are characterized by critically low dissolved oxygen levels, which can severely impact aquatic life, biodiversity, and the delicate ecological balance.

The problem of oxygen depletion is exacerbated by various factors, including climate change, nutrient pollution, and human activities, posing a significant threat to the sustainability of aquatic environments globally. And according to a report by the United Nations Environment Program (UNEP), there are already over 700 known dead zones globally, which corresponds to about 245,000 square kilometers.

Current methods for monitoring and addressing oxygen depletion in marine environments are often inadequate. Traditional monitoring techniques, such as satellite imagery, localized sensor deployments and manual sampling, are typically intended for local use and lack standardized sampling systems.

It would be beneficial if all environmental organizations worldwide could use methods and equipment that provide taken samples with coordinates and comparable values for the measurement method and equipment, especially regarding oxygen content in the water, sample depth, water temperature and date, enabling direct comparison of results with samples from different areas of the world and changes on the same coordinates over time.

There is also a lack of a register or computer program where sampling entities from around the world can submit standardized test results. There is no global register of coordinate determined water areas that are particularly worthy of protection, such as coral reefs and important breeding sites. Additionally, there is no global register where areas of high ecological value can be easily located and viewed on a digital map.

Furthermore, there is a lack of a system that aims to solve acute problems with oxygen deficiency in marine environments with directly effective measures. Existing efforts to combat oxygen depletion are often reactive and lack the ability to target specific areas and depths based on real-time data and ecological priorities. There is a pressing need for a proactive, data-driven system that can efficiently identify and prioritize areas in need of oxygenation, guide targeted intervention efforts, and 5 ultimately contribute to the restoration and preservation of aquatic ecosystems.

SUMMARY OF THE INVENTION
The present invention addresses the earlier mentioned challenges by providing a comprehensive system and method for monitoring, analyzing, and visualizing oxygen depletion in aquatic environments, with a particular emphasis on the role of a data program in facilitating these processes.

The system comprises a network of data collection operators, such as environmental organizations in different countries and automatic sensors strategically deployed throughout the aquatic environment. These devices, which may include buoys, underwater sensors, autonomous underwater vehicles (AUVs), and even satellite imagery, gather water quality data including dissolved oxygen levels, coordinates, and depths. In this context, “devices” refer to equipment for collecting water samples, regardless of who or how the samples are taken. The collected data is transmitted to a central database for storage and management.

A sophisticated analysis engine processes the stored data using a combination of algorithms and machine learning models. It identifies oxygen-depleted zones by comparing dissolved oxygen levels to predefined thresholds, assesses the ecological impact of these zones by integrating spatial data on ecologically sensitive areas, and prioritizes intervention efforts based on the severity of oxygen depletion and the ecological value of the affected areas.

Ecologically sensitive areas, such as coral reefs and fish spawning grounds is graded from for example 1 to 5 by monitoring environmental authorities.

If an area has a high number so can that mean that the area may need immediate treatment with oxygen to reduce damage to the ecosystem. The system prioritizes intervention efforts based on the severity of oxygen depletion and the ecological value of the affected areas with available resources and optimal total effect.

A visualization module presents the analyzed data in a user-friendly and interactive format. It displays a digital map highlighting oxygen-depleted zones and prioritized intervention areas, enabling users to visualize the spatial distribution of oxygen levels and understand the ecological implications. The map incorporates interactive features, such as zooming, panning, and data filtering, to facilitate exploration and analysis.

Furthermore, the system can transmit information about the prioritized intervention areas to a command center for coordinating the deployment of oxygenation platforms. These platforms, equipped with oxygen delivery systems, execute targeted oxygenation efforts to restore oxygen levels in the affected zones. The system incorporates a machine learning component that continuously learns and adapts its models based on the collected data and the outcomes of previous interventions. This enables the system to improve its accuracy in predicting oxygen levels, identifying critical zones, and recommending effective intervention strategies.

In one embodiment, the system may be integrated with a Command Center responsible for overseeing a fleet of oxygenation platforms. The data program transmits information about the prioritized intervention areas to the Command Center, which then coordinates the deployment of platforms to the designated locations. The platforms, equipped with oxygen delivery systems, execute targeted oxygenation efforts to restore oxygen levels in the affected zones.

The invention also encompasses a computer program product embodied on a non-transitory computerreadable medium, comprising instructions that, when executed by a processor, cause the processor to perform the functions of data collection, analysis, visualization, and intervention guidance.

The system is also capable of receiving data from external sources, such as environmental agencies, research institutions, and citizen science initiatives. These external data sources can provide valuable information on water quality parameters, including dissolved oxygen levels, collected at various locations and depths. The system integrates this external data with data collected from its own data collection devices to provide a comprehensive picture of the marine environment.

A Command Center communicates with the oxygenation platforms to confirm their availability for assignments and to receive updates on the progress and results of their work. This information is then relayed back to the Analysis Engine, which can use it to further refine its models and recommendations for future interventions.

The core innovation of the program lies in:

A system that integrates water quality data (specifically dissolved oxygen levels) with spatial data on ecologically sensitive areas and identifies zones in need of oxygenation.
A method for prioritizing intervention efforts based on the severity of oxygen depletion and the ecological value of the affected areas.
A capability for guiding targeted oxygenation efforts at specific depths based on the collected
data.
The ability to transmit information about selected geographical areas that are to be supplied with oxygen to an organization, referred to herein as the Command Center, which monitors and controls a large number of platforms for oxygen delivery.
The Command Center then chooses which platform to send to each of the missions requested by environmental organizations.
The Command Center’s selection criteria can include each platform’s equipment, seaworthiness (e.g., wave height tolerance), current geographical location, and availability for new assignments. These platforms can also be designed for growing organic matter while supplying oxygen.
The Command Center then coordinates with the owners of each platform, who ensure that the platform is transported to the agreed area and performs the agreed work during the agreed time. When the work is done, the unit is ready for new assignments.
Compensation for contracted work is to be paid by the said environmental organization, which in turn is advantageously financed by the environmental organizations of the countries involved or, for example, the UN.
The type and capacity of the platform’s computer, unit, and software for automatic monitoring of the oxygenation work are agreed upon.
The platform’s electric motors and equipment must be powered by renewable energy, such as electricity from solar cells, wind turbines, or wave power, and stored on batteries for constant operation of air or fluid pumps on the platform.
The air pumps can suck air and push the air down to the desired depth using hoses or pipes, preferably with a diffuser at the outlet to increase the absorption of oxygen into the water. The fluid pumps can work in the same way as described for the air pumps, but the fluid pump sucks oxygen-rich surface water into the pump and pushes it down to the desired depth via hoses or pipes.
For heat-sensitive areas such as coral reefs, it may be necessary to cool the water or air that is pushed down to the bottom, which can be done with already existing electrically driven cooling devices for the respective medium.
The included insurances for work performed and any damage to, for example, the platform is agreed upon.
The requirements for each participating platform are agreed upon, such as seaworthiness, equipment and capacity to carry out the assignment, equipment for positioning (e.g., GPS), wireless communication equipment, sensors for monitoring the work, which functions must be able to be remotely monitored from the command center, and whether the platform is to be equipped with automation to navigate to selected areas automatically with the help of automatically controlled propellers and rudders activated by the platform’s computing unit that affect the electrical motors of said body. Or in one embodiment that the platform is towed to
specific coordinates and anchored to the bottom or to buoys that are already attached to the bottom and that the staff puts the air hose and/or water hose into the water and starts the air pump/water pump.

Detailed Description of at least one Embodiments

Refers to a system to alleviate the effects of the enormous problem of extensive oxygen deficiency in the world’s rivers, lakes, and seas.
The system includes a computer program for compiling test results of especially the oxygen level taken from different locations and depths around the world’s watercourses.
Performed tests are taken with synchronized methods and equipment to provide comparable values, with registration for the depth the sample was taken from and the geographical location expressed as coordinates.
It is also advantageous to include the temperature of the water where the sample was taken, as the water’s oxygen-retaining ability decreases with increased temperature. Different species are differently adapted to tolerate low oxygen levels and there are partly different perceptions of at which values oxygen levels should be classified as, for example, critically low, so without 10 entering that discussion, the values here are only examples, and can of course also be adapted for the protection of a specific species in a specific area.
The computer program stores the collected data with a timestamp and categorizes test results into classes for oxygen levels, for example:
o Normal: oxygen levels above 3 ml/L
o Danger: values below 3 ml/L
o Critical: below 2 ml/L
o Lifeless: below 1 ml/L (usually defined as bottom death if at the bottom layer)
The program calculates the position of each test result and displays it on a digital map.
Stored coordinates of known areas considered particularly worthy of protection can also be classified into different groups depending on the protection value the area is considered to have.
The computer program places areas with specific levels of protection value, for example graded from 1 to 5 on the map with a color or number adapted to the level for easy identification.
Each test is given a specific large area with an estimated similar oxygen level for example, a circle with a diameter of 1000 meters around the test site, but can of course be adapted to depth variations, currents and proximity to land etc. In case of low oxygen levels and if the area is considered extra worthy of protection, this area can be chosen for the placement of one or more platforms to supply oxygen to the water.
The program calculates the coordinates of test results with stored coordinates for sensitive areas and preferably also considers ocean currents in those areas. Test values coinciding with extra areas worthy of protection are given a higher priority, indicated by letters, numbers, and different colors for severity.
Based on continuously collected information on how oxygen content improves with a known amount of oxygen added during a certain time interval at different water layers and depth conditions, currents, water temperature and used equipment etc., the computer program can use AI to optimize the system as more data comes in.
The program is advantageously given at least a rough idea of available financial resources and platforms and their capabilities at the start, and what results can be expected from each platform.
With this data, the application can optimize where actions that provide the best impact with available resources should be deployed, and the program learns and improves over time.
The program calculates, based on collected data, calculations, and known limitations, which areas with the greatest need for oxygenation can be oxygenated with the desired result within the given limitations.
These limitations may include the number of available platforms or their transport distance to the areas needing oxygenation.
When the computer program has chosen the area, the Command Center is contacted, which has updated information about available platforms, their capacity, geographical location, and availability for assignments.
The Command Center chooses the appropriate platform based on factors such as the location and timing of the oxygenation, the platform’s capacity, and its distance to the coordinates.
Once in place, the platform drops anchor or moors to buoys and begins oxygenating the water. An electric motor activates, unwinding a hose connected to an air pump. The hose or pipe is placed at the desired depth, and the air pump pushes air down to a diffuser for optimal oxygen absorption. Alternatively, a fluid pump can suck oxygen-rich surface water and push it down to the desired depth.
Each platform is equipped with GPS or equivalent equipment for positioning, and the platform’s coordinates are transmitted wirelessly to monitoring bodies and can be displayed as a symbol on a map.
After completing the assignment, the platform can be transported to new coordinates or brought in for service and maintenance.
Water samples are continuously taken to measure the effect of the oxygen supply and sent to the Command Center. The information includes oxygen level, coordinates, depth, amount of air pumped, depth of air release, equipment used, and current conditions.
The Command Center forwards this information to the supervising environmental organization, which manages the computer program.
The computer program stores the incoming data and calculates the effect of the work performed. With this data, it evaluates which methods, equipment, and oxygen supply amounts provide the best value for invested capital.
The program is connected to a powerful data unit with advanced AI programs that continuously process incoming data and provide output that shows how the system can be improved.

The goal of the invention is to create a system for maximally efficient oxygenation of the world’s water areas with the greatest need for oxygen supply. This is done by:

Collecting test results from all over the world.
Processing the collected data in a computer program to determine the position of each test result and display the result on a digital map, with each sample shown with a specific color for each test value below normal values.
Using collected coordinates for areas considered particularly worthy of protection and compiling results in a computer program where areas with graded deficiencies can be shown with selected colors.
Using this data on areas most worthy of protection to calculate where available resources are best utilized.
Transferring the information to the Command Center, which can contribute to carrying out the 10 desired work by choosing the suitable platform.
The Command Center transferring information to the monitoring computer program where each oxygen delivery platforms are located and how many are in use, where each platform is given a symbol on a map of the water area where they are located.
The invention is intended to be fully automated and handled by advanced computer programs together with AI, but various tasks can also be handled by staff if it can be done within set cost.

Map Visualization comprise in at least one embodiment a visualization module of the invention and employs a digital map interface to present the analyzed water quality data in a user-friendly and informative manner. The map serves as a central tool for understanding the spatial distribution of oxygen levels and identifying areas in need of intervention.

Map Structure

Base Layer: The map utilizes a geographical base layer, such as a world map or a regional map, providing spatial context for the displayed data. The base layer can include coastlines, major water bodies, and other relevant geographical features.
Oxygen Level Representation: The map visually depicts the varying levels of dissolved oxygen across the monitored aquatic environment. This can be achieved through a color gradient or heatmap, where different colors or color intensities correspond to specific oxygen concentration ranges. Alternatively, the map may utilize contour lines to connect areas with similar oxygenlevels, akin to elevation contours on a topographic map.
Sampling Location Markers: Distinct markers or symbols are plotted on the map to indicate the precise locations where water quality data has been collected. These markers can be differentiated based on the type of data collection device or source, and they may include labels or tooltips providing additional information, such as the measured oxygen level, sampling depth, and date.
Platform Location Markers: Distinct markers or symbols are plotted on the map to indicate the precise locations where each oxygen supply platform is located, and they may include labels or tooltips that provide additional information, such as the amount of oxygen being delivered, oxygenation depth, and the amount of time it has been operational at the site.

Ecologically Sensitive Areas Overlay: If data on ecologically valuable areas is available, it is overlaid on the map using distinct visual elements, such as polygons or outlines. These overlays highlight the spatial relationship between sensitive areas and oxygen-depleted zones, aiding in the assessment of potential ecological impacts and the prioritization of intervention efforts. Said ecologically valuable areas are selected by, for example, the international environmental organization United Nations Environment Program (UNEP). And which gives the location of the affected areas coordinates and grades each area according to its assessed importance for the area’s maritime life and the people who live in its vicinity on a suggested 5-point scale. The assessment then forms the basis for where and in what order available resources for oxygenation should be deployed. The calculations also include the amount of resources required to achieve a positive impact for each ecologically valuable area, based on the available economic and material resources to achieve the greatest possible impact overall.

Map Functionality

Zooming and Panning: The map allows users to zoom in and out to explore the data at different scales, from a broad overview to a detailed view of specific regions. Users can also pan across the map to focus on areas of interest.
Data Filtering: The map provides filtering capabilities, enabling users to display data based on specific criteria, such as time period, oxygen level thresholds, or data source. This allows for focused analysis and visualization of relevant information.
Marker Interaction: Users can interact with the markers on the map to access detailed information about each sampling location. Clicking or hovering over a marker may reveal a tooltip or pop-up window displaying the measured oxygen level, depth, date, and other relevant parameters.
Historical Trends: The map can display historical trends and changes in oxygen levels over time, allowing users to track the progression of oxygen depletion and evaluate the effectiveness of intervention efforts. This can be achieved through time-series animations or overlays of historical data on the map.

Display Options
Color Scales and Legends: The map utilizes intuitive color scales or other visual cues to represent the range of oxygen levels using distinct symbols or patterns. A clear and concise legend is included within the map to explain the meaning of the colors or symbols used.
Data Labels and Annotations: The map may include labels or annotations to highlight specific areas, features, or trends. These can provide additional context and insights into the displayed data.
Customization: The map interface allows for user customization, enabling users to adjust the display settings, such as color schemes, marker styles, and data filters, to suit their preferences and analysis needs.

Data Program for Registration and Visualization of Low Oxygen Levels in Water Core Functionalities in at least one embodiment:

Data Input and Storage:
Data Collection: The program needs a mechanism to receive data on water quality, including:
▪ Sampling Location: Coordinates (latitude, longitude)
▪ Sampling Depth: Depth at which the sample was taken
▪ Oxygen Level: Measured oxygen level in the water sample (e.g., in ml/liter) o Data Storage: A database (e.g., PostgreSQL, MySQL, SQLite) to store the collected data securely and efficiently.
Data Processing and Analysis:
o Threshold Comparisons: The program should compare the recorded oxygen levels against predefined thresholds: (These are only examples of set levels).
▪ Critical Level (e.g., < 3 ml/liter): Indicates the water is nearing a state where many species are at risk.
▪ Danger Level (e.g., < 2 ml/liter): Indicates many species are already fleeing the area and biodiversity is threatened.
▪ Near-Lifeless Level (e.g., < 1 ml/liter): Indicates the water is almost devoid of life.
Data Visualization on a Digital Map:
o Map Integration: Utilize a mapping library or API (e.g., Leaflet, Google Maps API, Mapbox) to display a digital map.
o Marker Placement: Plot markers on the map at the coordinates of each sampling location.
o. Color-Coded Markers: Different colors represent the severity of the low oxygen levels based on the threshold comparisons.
▪ Example:
▪ Green: Oxygen levels are within acceptable limits.
▪ Yellow: Critical level.
▪ Orange: Danger level.
▪ Red: Near-lifeless level.
o Marker Information: When a marker is clicked, display details such as sampling depth
and the exact oxygen level.
Integration with Valuable Area Data:
o Data Overlay: If data on valuable areas (e.g., coral reefs, fish spawning grounds) is available, overlay this information on the map.
o Visual Distinction: Distinct symbols or colors can be used to differentiate valuable areas from sampling locations.
o Spatial Analysis: Implement spatial analysis capabilities to identify overlaps between
low oxygen zones and valuable areas.

By incorporating these elements and functionalities, the map visualization effectively communicates complex water quality data, empowering users to identify critical areas, assess ecological impacts, and make informed decisions regarding intervention strategies for the restoration of aquatic ecosystems. In at least one embodiment of the invention the collected data on oxygen levels and the of researchers graded areas worthy of extra protection are intended to be used to select optimal sites for the supply of oxygen from one or more platforms. And a computer program calculates the most efficient and economical way based on the financial framework and available equipment and other resources. The platforms can be designed to be fully self-propelled and transport themselves from quay to site to start oxygenation of the water. Or they can be designed to be towed by vessels to the desired location, and that personnel anchor the platform and insert hoses or pipes into the water to the desired depth. And activate the platform’s compressor or water pump and transport air or oxygen-rich water to the desired depth. Even this simple variant platform is powered exclusively by electricity generated by solar cells, wind or wave power and stored in batteries. The platform is simple in design and can operate fully automatically after it has been activated.

The present invention discloses a system and method for monitoring, analyzing, and visualizing oxygen depletion in aquatic environments. The system comprises:

Data Collection Network: A network of data collection devices, including buoys, sensors, and potentially other sources like Autonomous Underwater Vehicles (AUVs) or satellite imagery, to gather water quality data at various locations and depths.
Centralized Database: A secure repository for storing and managing the collected data, ensuring data integrity and accessibility for analysis.
Analysis Engine: Employs algorithms to process the stored data, identify zones with low oxygen levels, assess ecological impact by integrating spatial data on sensitive areas, and prioritize intervention areas based on severity and ecological value.
Visualization Module: Presents the analyzed data on a digital map, highlighting oxygendepleted zones and prioritized areas using color-coded markers and detailed information on sampling locations.
Machine Learning Module: Continuously learns and adapts its models based on collected data and intervention outcomes, improving prediction accuracy and optimization recommendations.
Command Center: An operational hub that receives recommendations from the analysis engine and coordinates the deployment of intervention measures (e.g., oxygenation platforms) if integrated with such capabilities.

The invention offers several advantages, including:

Proactive Identification: Enables early detection of oxygen depletion, allowing for timely intervention.
Prioritization: Guides resource allocation towards the most critical areas based on ecological impact.
Targeted Interventions: Facilitates precise and efficient oxygenation efforts at specific depths.
Data-Driven Decision Making: Empowers stakeholders with real-time insights for informed actions.
Adaptability: Leverages machine learning to continuously improve its models and recommendations.
Collaboration: Fosters collaboration between various entities involved in environmental monitoring and conservation.

The invention offers several advantages over existing technologies. It enables proactive identification and prioritization of intervention areas, optimizes resource allocation, and facilitates data-driven decision-making for effective ecological restoration efforts. By addressing the limitations of current approaches, the invention has the potential to significantly contribute to the preservation and restoration of aquatic ecosystems.

BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1: Illustrates the overall system flowchart, depicting the data flow and key processes involved in data collection, analysis, visualization, and intervention guidance.
Figure 2: Provides a schematic overview of the system architecture, showcasing the various components and their interactions.
Figure 3: Presents a flowchart detailing the AI optimization process, highlighting how the system leverages machine learning to continuously improve its models and recommendations.
Figure 4: Depicts the operational flow of an individual platform, emphasizing its autonomous decision-making capabilities based on local data and instructions from the Command Center.
Figure 5: Showcases a conceptual representation of the digital map interface, illustrating how it displays oxygen-depleted zones, prioritized intervention areas, sampling locations, and ecologically valuable areas.
Figure 6: Provides a schematic overview of an autonomous floating platform equipped with cultivation and oxygenation systems.
Figure 7: Illustrates the communication and data flow between the Command Center, platforms, and external entities.
Figure 8: Presents a system-level overview showcasing the interaction between multiple platforms, the Command Center, Transshipment Centers, and GPS satellites.
Figure 9: Shows a flowchart of an example of the operational flow of an individual platform.
Figure 10: Depicts a schematic view of a liquid pump oxygenation system.
Figure 11: Provides a block diagram outlining the tasks performed by the Command Center.
Figure 12: Shows a flowchart of the overall system flow, highlighting the central role of the data program in managing and processing information crucial for identifying and addressing oxygen-depleted zones.

DETAILED DESCRIPTION OF A PREFERED EMBODIMENT

Data Collection
The system employs a network of data collection devices strategically placed in the aquatic environment. These devices may include:

Buoys: Equipped with sensors to measure dissolved oxygen (DO), temperature, salinity, pH, and other relevant parameters. Buoys can be anchored or drifting, providing continuous or periodic monitoring.
Underwater Sensors: Deployed at various depths to capture real-time data on DO levels, temperature, and other parameters throughout the water column.
Autonomous Underwater Vehicles (AUVs): Equipped with sensors and programmed to traverse specific areas, collecting data at various depths and locations.
Satellite Imagery: Can provide a broader perspective on surface water conditions, including chlorophyll levels and sea surface temperature, which can be indirectly correlated with oxygen
levels.
External Data Sources: The system can integrate data from various external sources, such as governmental agencies, research institutions, and citizen science initiatives, to enhance its understanding of the aquatic environment.

Data Storage and Management
The collected data is transmitted to a central database, which serves as a secure and organized repository for all water quality information. The database is designed to handle large volumes of data and support efficient data retrieval and analysis. Robust data validation and cleaning procedures are implemented to ensure data integrity and accuracy.

Data Analysis and Prioritization
The analysis engine, the core of the system, processes the stored data using a combination of algorithms and machine learning models. Key functionalities include:

Threshold Comparisons: The system compares the recorded oxygen levels against predefined thresholds to classify areas into different severity levels (e.g., normal, critical, danger, near-lifeless).
Ecological Impact Assessment: The system integrates spatial data on ecologically sensitive areas (e.g., coral reefs, fish spawning grounds) to assess the potential impact of low oxygen levels on marine life and biodiversity.
Spatial and Temporal Analysis: The system analyzes the spatial and temporal patterns of oxygen depletion, identifying trends, hotspots, and potential contributing factors.
Prioritization: The system ranks the identified oxygen-depleted zones based on the severity of oxygen depletion and the ecological value of the affected areas. This prioritization helps guide intervention efforts towards the most critical areas.
Machine Learning: The system employs machine learning algorithms to continuously learn and adapt its models based on the collected data and the outcomes of previous interventions. This
enables the system to improve its accuracy in predicting oxygen levels, identifying critical zones, and recommending effective intervention strategies.

Visualization
The visualization module presents the analyzed data in a user-friendly and interactive format. Key features include:

Digital Map: A digital map displays the spatial distribution of oxygen levels, highlighting areas of concern using color-coded gradients or other visual cues.
Markers and Information: Markers on the map indicate sampling locations and provide detailed information about specific locations, including oxygen levels, depth, and historical trends.
Valuable Area Overlay: Data on ecologically sensitive areas is overlaid on the map to visualize their relationship with oxygen-depleted zones.
Interactive Features: Users can zoom in and out, pan across the map, and click on markers to access detailed information.
Time-Series Visualization: The system can display historical trends and changes in oxygen levels over time, allowing users to track the effectiveness of intervention efforts.

If the system is integrated with a Command Center and a fleet of oxygenation platforms, it can provide guidance for targeted intervention efforts. Key features include:

Optimal Depth Recommendations: Based on the collected data and analysis, the system can recommend the optimal depths for oxygenation in the identified zones.
Platform Deployment Recommendations: The system can suggest the most suitable platforms for deployment based on their capabilities, location, and the specific needs of the intervention area.
Real-time Monitoring and Adjustment: The system can continuously monitor the progress of oxygenation efforts and provide feedback to the Command Center, allowing for adjustments to platform placement or oxygen delivery rates.
FIGURE DESCRIPTIONS

Figure 1 illustrates the overall system flow, highlighting the central role of the data program in managing and processing information crucial for identifying and addressing oxygen-depleted zones.
Process description:
Start: The process initiates with the collection of water quality data (402), including dissolved oxygen levels, from various sources such as buoys, sensors, and external databases. Validate Data (403): The
collected data is validated to ensure its accuracy and integrity before further processing. Store Data in Database (404): Validated data is stored in a central database for future reference and analysis. Retrieve Data from Database (405): Relevant data is retrieved from the database for comparison and analysis. Compare Oxygen Levels to Thresholds (406): The retrieved oxygen levels are compared to predefined thresholds to assess the severity of oxygen depletion. Decision Points:

The flowchart includes a series of decision points based on the comparison of oxygen levels to thresholds:

o < 1 ml/L: If the oxygen level is below 1 ml/L, the area is classified as “Near-Lifeless.”

o < 2 ml/L: If the oxygen level is below 2 ml/L, the area is classified as “Danger.”

o < 3 ml/L: If the oxygen level is below 3 ml/L, the area is classified as “Critical.”

o > 3 ml/L: If the oxygen level is above 3 ml/L, the area is classified as “Normal.”

Initialize Digital Map (410): A digital map is initialized to display the collected data and analysis results. Retrieve Valuable Area Data (407): Data on ecologically valuable areas, such as coral reefs or fish spawning grounds, is retrieved from the database. Plot Markers on Map (411): Markers are plotted on the digital map at the sampling locations (412), indicating the oxygen levels and severity at each location. Overlay Valuable Areas on Map (408): The valuable areas are overlaid on the map to visualize their spatial relationship with the oxygen-depleted zones. Color-Code Sampling Depth and Marker Severity (413): The markers on the map are color-coded based on the severity of oxygen depletion and the depth at which the samples were taken. Perform Spatial Analysis (409): Spatial analysis techniques are applied to the map data to identify patterns, clusters, and correlations between oxygen levels, valuable areas, and other relevant factors. Display on-Coded Level on Marker Click (414): When a user clicks on a marker, detailed information about the location, including the oxygen level and depth, is displayed. End: The process concludes after the spatial analysis and visualization are complete.

Summary:

Figure 1 illustrates the system’s workflow, from data collection and validation to analysis, visualization, and decision support for targeted oxygenation efforts. The flowchart highlights the key decision points based on oxygen level thresholds and the integration of spatial data on valuable areas to prioritize interventions.

Figure 2: System Architecture and AI Optimization Process and provides a schematic overview of the system architecture, highlighting the key components and their interactions to identify, prioritize, and address oxygen-depleted marine zones. The process begins with data collection from various devices like buoys (91) and all other collected water information, here shown as sensors (92). This data is then stored in a central database (94). The analysis engine (95) processes the data, identifying critical zones, assessing ecological impact, and prioritizing interventions. A user interface (96) visualizes the results and provides AI-generated recommendations. The Command Center (1) receives recommendations and coordinates the deployment of oxygenation platforms (5). Communication links ensure data flow and control between components, enabling efficient monitoring and action.

Figure 3: Flowchart of the AI Training Process and depicts the iterative AI optimization flow that enables continuous improvement of oxygenation strategies based on data and outcomes. The process begins with data collection (801) followed by preprocessing (802) to ensure data quality. AI models are then trained using this data (803) to predict oxygen levels and identify intervention areas. The model’s performance is evaluated and validated (804), and if it doesn’t meet the standards, the model is further refined. If the performance is satisfactory (805), optimization recommendations are generated (806) and then implemented (807). The system continuously gathers data on the outcomes, providing feedback to the data collection for further refinement of the AI models and improved recommendations in the future.

Figure 4: Flowchart of Platform Operation and illustrates the workflow of an individual platform (101), highlighting its ability to make autonomous decisions based on local data and instructions from the Command Center (107). The platform collects data from its sensors (102), processes it locally (103), and receives instructions from the Command Center (107). Based on this information, the platform makes decisions and performs tasks such as cultivation (104), oxygenation (105), or relocation (106). Throughout the process, the platform sends feedback to the Command Center (107), enabling continuous monitoring and adjustment. The circular arrow indicates that these steps are repeated continuously, showcasing the platform’s autonomous operation and dynamic interaction with the Command Center.

Figure 5 provides a conceptual representation of the digital map interface, illustrating how the system visually conveys critical information about the aquatic environment. The map serves as a powerful tool for understanding the spatial distribution of oxygen levels and identifying areas that require intervention. The map employs a color gradient or heatmap to represent varying levels of dissolved oxygen across the monitored area. For example, deeper shades of blue could indicate higher oxygen concentrations, while red or orange hues could signify areas with critically low oxygen levels. The map also displays distinct markers (71) at sampling locations, providing detailed information about measured oxygen levels, depth, and date of sampling. The map also includes interactive features, such as zooming and panning (73, 75), that enable users to explore the data at different scales and focus on specific regions of interest, and in this case bounded by land (79). To further aid in ecological assessment, the map overlays data on ecologically sensitive areas (77), such as coral reefs or fish spawning grounds.

These areas might be visually distinguished using polygons or outlines, perhaps in contrasting colors like green or yellow. By visualizing the proximity of oxygen-depleted zones to these sensitive areas, users can quickly assess potential ecological impacts and prioritize intervention efforts. The map also allows users to filter the displayed data based on specific criteria, facilitating focused analysis and visualization of the most relevant information. By integrating these visual and interactive elements, the map visualization effectively communicates complex water quality data, empowering users to identify critical areas, assess ecological impacts, and make informed decisions regarding intervention strategies for the restoration of aquatic ecosystems.

Figure 6 presents a schematic overview of an autonomous floating platform (5) designed for both water oxygenation and cultivation of organic material. The platform features cultivation containers (27) for growing organic material and is equipped with two oxygenation systems. The first system utilizes an air compressor (19), a hose reel (17), and a diffuser (15) to supply oxygen to the water. The second system
employs a pipe (21) mounted on a motorized swivel (23) to pump oxygen to deeper water layers. Solar panels (29) provide renewable energy, and the platform is autonomously controlled using GPS (39), radar (33), and an anchor (25). A communication unit (35) enables data exchange with the Command Center, and an onboard computer (31) manages the platform’s functions. An identification plate (37) displays the platform’s unique identifier.

Figure 7: Schematic View of Data Streams within the System and illustrates the dynamic interplay between the Command Center (1), the platforms (5), and external entities like customers (11) and environmental monitoring organizations (3). The Command Center serves as the central hub, receiving data from platforms (5) and external sources (10), and sending commands and instructions back to the
platforms (5). It also interacts with customers (11) to provide information and receive requests, and with the environmental monitoring entity (3) to exchange data and coordinate actions. This figure emphasizes the bidirectional communication channels that enable the Command Center to effectively manage and control the system, ensuring efficient monitoring and targeted interventions.

Figure 8: Provides a system-level perspective of the invention, showcasing the interaction between multiple autonomous floating platforms (5), a land-based Command Center (1), floating Transshipment Centers (7), and GPS satellites (9). The platforms (5), strategically deployed across a water surface (47), utilize GPS (39) for precise positioning and are equipped with unique identification codes for seamless communication with the Command Center (1). The Command Center (1) acts as the central control hub, equipped with a computer unit (43) and specialized software to coordinate operations, process orders from customers (11), and interact with an environmental organization (3) responsible for monitoring water quality. The environmental organization (3) plays a crucial role by collecting and analyzing water quality data, including oxygen levels, and communicating areas in need of oxygenation to the Command Center (1). Customers (11) can place orders for cultivated crops through the Command Center (1), which leverages weather forecasts and real-time platform operational data to identify suitable platforms (5) for fulfilling these orders or oxygenation requests. The Command Center ensures that the selected platforms (5) are adequately equipped and provisioned for their assigned tasks. Upon task completion, harvested
crops are efficiently transported to the strategically located Transshipment Centers (7) or directly to the customer, highlighting the system’s coordinated operation and logistical capabilities, as emphasized in the patent claims.

Figure 9 provides a detailed flowchart illustrating the operational sequence of an individual platform (5), emphasizing its autonomous decision-making capabilities and its interaction with the Command Center (1). The platform initiates its operation by collecting data from its onboard sensors (32). This data is then processed by the local computing device (31), which also receives instructions from the Command Center (1). Based on this combined information, the platform makes autonomous decisions and executes tasks related to cultivation (34), oxygenation (36), or relocation (38). Throughout the
process, the platform maintains communication with the Command Center (1), providing feedback data that enables continuous monitoring and adjustment of operations as needed. The cyclical nature of the flowchart underscores the platform’s (5) continuous and adaptive operation, showcasing its ability to respond dynamically to changing environmental conditions and instructions from the Command Center.

Figure 10 presents a schematic view of an alternative oxygenation system utilizing a liquid pump mechanism (51). This system, situated on the platform, comprises a fluid pump that draws in oxygenrich surface water through an intake hose or pipe. The oxygenated water is then propelled downwards to deeper layers with lower oxygen levels through a separate hose or pipe. The system may also incorporate a cooling mechanism to adjust the water temperature before delivery, particularly beneficial for temperature-sensitive ecosystems like coral reefs. The operation of this liquid pump oxygenation system is managed by the platform’s onboard computer unit, ensuring precise control and efficient oxygen delivery. This embodiment demonstrates the system’s flexibility in adapting to diverse aquatic environments and oxygenation requirements.

Figure 11 presents a block diagram outlining the key tasks and responsibilities of the Command Center (1) within the system. It starts with receiving orders (301) from customers and collecting essential data, including water quality information and platform capabilities (302). The Command Center then assesses the suitability (303) of each platform for specific tasks based on their stored capabilities and geographical location. Once a platform is selected, a contract is established with the customer (305) of crops and or the authorities for oxygenation of water bodies (304), and platform owner (306) who prepares and deploys the platform to the designated location. Throughout the operation, the Command Center actively monitors the platform’s progress and provides instructions for optimization if necessary (307). Upon completion of the task, (308), (309) including the delivery of any cultivated crops, the platform becomes available for new assignments (310). The Command Center continuously sends information to the authorities for oxygenation of water bodies for insertion into the analysis engine about available units and ongoing work on each platform and measured changes in the water around the platforms. This block diagram effectively illustrates the Command Center’s pivotal role in coordinating and managing the system’s operations, ensuring efficient resource allocation and successful execution of both oxygenation and cultivation tasks.

Figure 12 schematically illustrates the Analysis Engine (201), which serves as the central hub of the entire system. This computer program is designed to process acquired information and compare it against predefined conditions. Its primary function is to compile data on performed tests of oxygen levels in various water bodies, along with coordinates of specific areas identified by environmental authorities as being of particular conservation value and classified on a proposed 5-point scale. The Analysis Engine also plays the crucial role of calculating how and where oxygenation efforts can be most effectively deployed given the available resources.

Data Collection and Processing: Data Collection Module (204): Gathers data from diverse sources, including buoys, vessels equipped with oxygen testing instruments, and manual test results. External Databases (203): Retrieves supplementary data from external sources, such as aforementioned organizations involved in collecting and analyzing similar data. Command Center (205): Provides continuous updates on available platforms equipped for oxygenation, ongoing oxygenation activities, and changes in oxygen levels. Additional Data: Collects information on financial frameworks, material costs, personnel, and their tasks. Database (202): Stores and preprocesses the collected data before it is sent to the Analysis Engine for analysis. Analysis and Actions: Analysis Engine (201): Processes data and categorizes it based on the current status, including test results, platform locations, and the need to initiate or terminate oxygenation efforts. Visualization Module (206): Receives calculated visualization data and generates informative maps and text for clear presentation of the results. Communication
Module (207): Transmits action-related information to relevant units within the system, such as the Command Center. Command Center: Monitors and controls the fleet of platforms based on the recommendations from the Analysis Engine.

Summary Figure 12 clearly demonstrates how the Analysis Engine acts as the core of the system, handling and processing data from various sources to enable efficient and targeted oxygenation of aquatic environments. By combining data on oxygen levels, protected areas, available resources, and platform statuses, the Analysis Engine can make intelligent decisions about where and how oxygenation can be best implemented. This capability to optimize resource utilization and prioritize interventions is critical in achieving the overarching goal of effectively restoring oxygen levels in our waters and safeguarding marine life.

Although the figures may show a specific order of method steps, the order of the steps may differ from what is depicted. Also, two or more steps may be performed concurrently or with partial concurrence. Such variation will depend on the software and hardware systems chosen and on designer choice. All such variations are within the scope of the disclosure. Likewise, software implementations could be accomplished with standard programming techniques with rule-based logic and other logic to accomplish the various connection steps, processing steps, comparison steps, and decision steps.

The FrykenPontoon™ Greenhouse Solution is part of a broader innovation ecosystem. For hardware integration details, see the FrykenPontoon™ Greenhouse Anchor. This invention also connects to our ongoing Climate Resilience Research, where planetary resilience strategies are explored. To discover related technical advances, visit the FrykenFrost™ Machine Patent.

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