A behavior prediction system includes an information obtainer, a probability distribution model generator, a map generator, and a display. The information obtainer obtains information on a traveling direction of a moving object whose behavior is to be predicted. The probability distribution model generator generates a probability distribution model regarding a traveling direction of the moving object based on the information on an obtained traveling direction of the moving object. The map generator uses the probability distribution model to calculate probabilities that the moving object passes through respective areas into which a movable region of the moving object is divided and that generates a probability map in which the probabilities are assigned to the respective areas. The display displays the probability map.

A behavior prediction system includes an information obtainer, a probability distribution model generator, a map generator, and a display. The information obtainer obtains information on a traveling direction of a moving object whose behavior is to be predicted. The probability distribution model generator generates a probability distribution model regarding a traveling direction of the moving object based on the information on an obtained traveling direction of the moving object. The map generator uses the probability distribution model to calculate probabilities that the moving object passes through respective areas into which a movable region of the moving object is divided and that generates a probability map in which the probabilities are assigned to the respective areas. The display displays the probability map.

In a conventional flocculation and magnetic separation device, it was not possible to make the device downsized because the flocs are easily broken. In addition, there was no system for the ballast water treatment that is capable of simultaneous removal of plastics and microplastics drifting in the ocean. Furthermore, there were no ships and their navigation method capable of solving the pollution problem caused by plastics and microplastics floating in the ocean. By arranging a magnetic drum that rotates in a direction opposite to the flow of a fluid containing flocs and by changing the flow path by about 180 degrees or so immediately before contacting the magnetic drum, the flocs can be removed without breaking. This method can downsize the size of the magnetic drum with the required area reduced. By combining small-sized flocculation and magnetic separation device and a device that breaks and recovers floating plastics, it is possible to remove plastics and microplastics floating in the ocean at the same time. By taking into account the status of marine plastics in the ship's planned route information, it becomes possible to remove plastics and microplastics floating on the ocean by the ship.

In a conventional flocculation and magnetic separation device, it was not possible to make the device downsized because the flocs are easily broken. In addition, there was no system for the ballast water treatment that is capable of simultaneous removal of plastics and microplastics drifting in the ocean. Furthermore, there were no ships and their navigation method capable of solving the pollution problem caused by plastics and microplastics floating in the ocean. By arranging a magnetic drum that rotates in a direction opposite to the flow of a fluid containing flocs and by changing the flow path by about 180 degrees or so immediately before contacting the magnetic drum, the flocs can be removed without breaking. This method can downsize the size of the magnetic drum with the required area reduced. By combining small-sized flocculation and magnetic separation device and a device that breaks and recovers floating plastics, it is possible to remove plastics and microplastics floating in the ocean at the same time. By taking into account the status of marine plastics in the ship's planned route information, it becomes possible to remove plastics and microplastics floating on the ocean by the ship.

A control method and system for collaborative interception by multiple unmanned surface vessels are provided. The method includes obtaining task environment information of each unmanned surface vessel in an unmanned surface vessel group at a current moment, estimating interception point information of the intruding target at the current moment by using a Kalman filter according to the task environment information of the unmanned surface vessels at the current moment, determining process state information of each unmanned surface vessel at the current moment, inputting the process state information of each unmanned surface vessel at the current moment into a corresponding intruding target interception policy output model respectively to obtain an execution action of each unmanned surface vessel at a next moment to intercept the intruding target. The application can intercept the intruding target accurately.

A control method and system for collaborative interception by multiple unmanned surface vessels are provided. The method includes obtaining task environment information of each unmanned surface vessel in an unmanned surface vessel group at a current moment, estimating interception point information of the intruding target at the current moment by using a Kalman filter according to the task environment information of the unmanned surface vessels at the current moment, determining process state information of each unmanned surface vessel at the current moment, inputting the process state information of each unmanned surface vessel at the current moment into a corresponding intruding target interception policy output model respectively to obtain an execution action of each unmanned surface vessel at a next moment to intercept the intruding target. The application can intercept the intruding target accurately.

Artificial-intelligence-based river information system. In an embodiment, a first training dataset is used to train a travel time prediction model to predict a travel time along the waterway for a given trip. In addition, a second training dataset is used to train a river level prediction model to predict a river level along the waterway for a given time. For each of a plurality of trips, a request is received that specifies the trip and a time of the trip, and, in response to the request, the travel time prediction model is used to predict a travel time for the trip, and the river level prediction model is used to predict a river level of the waterway at one or more points along the trip. Then, a voyage plan is generated based on one or both of the predicted travel time and the predicted river level.

Artificial-intelligence-based river information system. In an embodiment, a first training dataset is used to train a travel time prediction model to predict a travel time along the waterway for a given trip. In addition, a second training dataset is used to train a river level prediction model to predict a river level along the waterway for a given time. For each of a plurality of trips, a request is received that specifies the trip and a time of the trip, and, in response to the request, the travel time prediction model is used to predict a travel time for the trip, and the river level prediction model is used to predict a river level of the waterway at one or more points along the trip. Then, a voyage plan is generated based on one or both of the predicted travel time and the predicted river level.

A solar-powered post light preferably designed to fit snugly within an outer end of a tubular member, such as a pipe. By being lit at nighttime hours, the light provides a guide to boaters at a boatlift while centering their boat, as well as providing light for various other nighttime conditions related to the water, as well as outside of a water environment. The light preferably provides for a “glow” effect, such that at whatever visible distance, the boater will know where the boat lift is located. An upper angled member of the guide light can be provided with a plurality of facet cuts which provide for prismatic reflection of sun rays for solar collection 180 degrees across a horizon.

This disclosure provides a data-driven approach to departure detection. In particular, the disclosed approach uses location data, such as GPS, which is readily available for many vessels globally and historically through the automatic identification system (AIS). The approach clusters this data and then constructs a convex hull around these clusters for each port to define a port area. A vessel is then determined as departed when it leaves that port area. Clustering is computationally efficient as a number of optimised algorithms exist, which means even a very large dataset like historical locations of thousands of vessels can be processed relatively quickly. Further, the departure detection enables accurate estimation of arrival times. Further, actions can be triggered by the departure determination, such as automatic control of cranes, trucks, trains and other port equipment.