According to the present invention, a route controller generates a route that makes a ship dock at a berthing facility. The route includes a first location and a second location (P2). The first location is a via point that is offset a prescribed distance in a direction that is orthogonal to the direction in which the berthing facility is oriented from a docking point for the ship at the berthing facility. The second location is the docking point for the ship at the berthing facility. The first location is immediately before the second location in the sequence to be realized by the ship. The route is generated such that the ship remains oriented in the direction in which the berthing facility is oriented as the ship moves from the first location to the second location.

Embodiments of the present disclosure disclose a typhoon trajectory prediction method, device, electronic equipment and computer readable medium. A detailed implementation of the method includes acquiring typhoon status information that includes typhoon wind speed information and typhoon location information, acquiring information of target distance from typhoon to a target position, obtaining typhoon time difference information based on the information of target distance and the typhoon wind speed information, performing slicing processing on the typhoon time difference information to generate time difference ratio information, performing data processing on the time difference ratio information and the information of target distance to generate time ratio distance information, and performing data processing on the time ratio distance information and the typhoon position information to generate typhoon position prediction information. The implementation realizes targeted prediction typhoon position information, thus enhancing the accuracy of broadcasting typhoon position.

Embodiments of the present disclosure disclose a typhoon trajectory prediction method, device, electronic equipment and computer readable medium. A detailed implementation of the method includes acquiring typhoon status information that includes typhoon wind speed information and typhoon location information, acquiring information of target distance from typhoon to a target position, obtaining typhoon time difference information based on the information of target distance and the typhoon wind speed information, performing slicing processing on the typhoon time difference information to generate time difference ratio information, performing data processing on the time difference ratio information and the information of target distance to generate time ratio distance information, and performing data processing on the time ratio distance information and the typhoon position information to generate typhoon position prediction information. The implementation realizes targeted prediction typhoon position information, thus enhancing the accuracy of broadcasting typhoon position.

A vessel includes a body that floats in water. One or more thrusters and sensors are provided on the body. A controller is configured to selectively activate the thrusters to cause the vessel to move along a path through the water, receive sensor data from the one or more sensors while the vessel is moving along the path, determine, based on the sensor data, whether an obstacle or a dangerous condition is present in the water; control the thrusters to avoid the obstacle, and output a warning when the dangerous condition is present in the water. The collected sensor data may relate to locations and directions of currents in the water, the dangerous condition may relate to a rip current, and the warning may identify at least one attribute of the rip current. A map identifying a location of the dangerous condition may be generated and forwarded to other devices.

A vessel includes a body that floats in water. One or more thrusters and sensors are provided on the body. A controller is configured to selectively activate the thrusters to cause the vessel to move along a path through the water, receive sensor data from the one or more sensors while the vessel is moving along the path, determine, based on the sensor data, whether an obstacle or a dangerous condition is present in the water; control the thrusters to avoid the obstacle, and output a warning when the dangerous condition is present in the water. The collected sensor data may relate to locations and directions of currents in the water, the dangerous condition may relate to a rip current, and the warning may identify at least one attribute of the rip current. A map identifying a location of the dangerous condition may be generated and forwarded to other devices.

A watercraft control system is configured to track and follow a lead watercraft cruising ahead of a host watercraft. The watercraft control system basically includes a detector and a digital controller. The watercraft control system can be integrated with a main watercraft control system of the host watercraft, or can be an add-on watercraft control system that supplements the main watercraft control system of the host watercraft. The detector is configured to detect the lead watercraft in front of the host watercraft. The digital controller is configured to communicate with the detector's processor to receive a detection signal from the detector. The digital controller is configured to output at least one control command related to a propulsion direction of the host watercraft and a propulsion force of the host watercraft to at least a propulsion unit of the host watercraft to track and follow the lead watercraft.

A watercraft control system is configured to track and follow a lead watercraft cruising ahead of a host watercraft. The watercraft control system basically includes a detector and a digital controller. The watercraft control system can be integrated with a main watercraft control system of the host watercraft, or can be an add-on watercraft control system that supplements the main watercraft control system of the host watercraft. The detector is configured to detect the lead watercraft in front of the host watercraft. The digital controller is configured to communicate with the detector's processor to receive a detection signal from the detector. The digital controller is configured to output at least one control command related to a propulsion direction of the host watercraft and a propulsion force of the host watercraft to at least a propulsion unit of the host watercraft to track and follow the lead watercraft.

The present invention discloses a simulation system and testing method of AIS signals for airborne receiver. The simulation system comprises: multiple AIS receivers for receiving AIS signals of vessels, each AIS receiver being respectively connected to a timing unit, and the multiple AIS receivers being connected to a centralized processor by means of a communication network; the centralized processor for combining data of the multiple AIS receivers, deleting redundant data, and forming an AIS database; a flight simulator for manipulating an altitude and direction of an aircraft, and simulating a flight path of a real aircraft; and an AIS signal generator for incorporating the AIS database according to an aircraft position and time point sent from the flight simulator, calculating all AIS signals received by the aircraft at the time point, and sending the AIS signals to an RF signal generator to generate VHF RF signals. The present invention resolves the issue of large differences between existing airborne AIS simulation tests and actual application scenarios, and is applicable to the development of a airborne AIS signal simulation and a testing apparatus.

An approach for analyzing and predicting a pattern of debris accumulation in area based on a deviation in mobility pattern. The approach includes receiving data from one or more sensors from an area and identifying objects based on the received data. The approach determines whether the received data is a first instance and stores the data. Based on the stored data, the approach analyzes, via machine learning, a mobility pattern associated with the retrieved data. Furthermore, the approach generates an actionable task list based on the analyzed data and instructs one or more computing devices based on the actionable task list.

An approach for analyzing and predicting a pattern of debris accumulation in area based on a deviation in mobility pattern. The approach includes receiving data from one or more sensors from an area and identifying objects based on the received data. The approach determines whether the received data is a first instance and stores the data. Based on the stored data, the approach analyzes, via machine learning, a mobility pattern associated with the retrieved data. Furthermore, the approach generates an actionable task list based on the analyzed data and instructs one or more computing devices based on the actionable task list.