CN115346399A - Bridge ship collision prevention early warning system based on phased array radar, AIS and LSTM network - Google Patents
Bridge ship collision prevention early warning system based on phased array radar, AIS and LSTM network Download PDFInfo
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Abstract
The invention relates to the technical field of ship traffic management, and discloses a bridge ship collision prevention early warning system based on phased array radar, AIS and LSTM networks, which comprises an acquisition system and an analysis system, wherein the analysis system is connected with the acquisition system and is used for predicting information acquired by the acquisition system; the whole system of the invention is reasonable in design, and the continuous wave phased array radar and the AIS system are adopted, so that the information of the target ship in the bridge area can be acquired; the LSTM network data set construction unit can be used for extracting and processing ship track information and establishing an LSTM network data set; the LSTM network construction and training unit is adopted to construct a ship track prediction network model, the ship navigation track of the water area of the bridge area is intelligently predicted, and the ship bridge collision risk early warning unit can accurately early warn the bridge collision risk and give an alarm in time.
Description
Technical Field
The invention relates to the technical field of ship traffic management, and discloses a bridge ship collision prevention early warning system based on a phased array radar, an AIS (automatic identification system) and an LSTM (local positioning system) network.
Background
In recent years, the water transportation industry is rapidly developed, the navigation density is high, particularly, a channel near a bridge area of a bridge is more complex, if a driver operates carelessly to cause a fault, or a ship breaks down, the ship may collide with the bridge to cause a safety accident, the bridge is an obstacle for the sailing ship, when the driver does not drive properly or the ship breaks down, the passing safety of the ship is influenced, the safety guarantee of the bridge is seriously influenced, the probability of bridge collapse caused by ship collision is increased, particularly, when the ship with large volume and weight approaches, the ship cannot be avoided in advance before the bridge collision, and the reason is the important reason that the bridge collapse caused by the ship collision accident.
Therefore, it is necessary to provide a ship collision prevention facility near the bridge area of the bridge, in order to ensure the normal navigation of the ship in the water area of the bridge area and protect the personal safety of the ship and the personnel near the bridge, most of the ship collision prevention facilities adopt a camera monitoring and early warning technology for the water areas at two sides, which consumes manpower and has certain requirements on the terrain environment, the climate, the light and the like, while the mechanical radar ship collision prevention technology can only scan a fixed range in a fixed rotation mode and is not beneficial to being implemented in the water area of the bridge area.
Disclosure of Invention
In order to solve the technical problem, the invention provides a bridge ship collision prevention early warning system based on phased array radar, AIS and LSTM networks.
The technical scheme of the invention is as follows: a bridge ship collision prevention early warning system based on phased array radar, AIS and LSTM networks comprises an acquisition system and an analysis system which is connected with the acquisition system and is used for analyzing and predicting information acquired by the acquisition system; the acquisition system comprises a continuous wave phased array radar system for actively acquiring the target tracking information of the ship in the water area of the bridge area in real time and an AIS system for receiving AIS information of the ship in the water area of the bridge area in real time;
the analysis system comprises a data conversion system and a model construction system, wherein the data conversion system is used for converting the information acquired by the acquisition system into a data set, and the model construction system is used for modeling the data set;
the data conversion system comprises a data storage unit, a data fusion processing unit, a ship track extraction unit and an LSTM network data set construction unit, wherein the data storage unit is used for receiving and storing acquired information in real time;
the model building system comprises an LSTM network building and training unit used for building a ship track prediction network model and carrying out network training test on an LSTM network data set, an LSTM network storage unit used for calling and storing the LSTM neural network, a ship track prediction unit used for carrying out prediction calculation on the LSTM neural network model, and a ship bridge collision risk early warning unit used for carrying out risk pre-control area analysis, risk judgment and alarm processing on the prediction information.
Further, the radar tracking information comprises the distance between the bridge and the target ship, the speed of the target ship and the real-time azimuth information of the target ship; and the information analysis result can be more accurate by adopting a more detailed radar tracking information source.
Further, the AIS information includes ship static data information, ship dynamic data information, and ship voyage data information; the more detailed AIS information can enable the data set to be more complete and improve the analysis accuracy of the ship track.
Further, the ship static data information comprises a ship name, a call sign, an MMSI number, a ship type, a ship length and ship width information; the dynamic data information of the ship comprises ship position longitude, ship position latitude, ship bow direction, track direction and speed information; the ship voyage data information comprises ship state, draft, destination and ETA information; by receiving and storing the target track information, the integrity of the data set generated by acquiring the target track information is further ensured.
Furthermore, the data storage unit is also used for receiving and storing the target fusion information and the ship target track information output by the data fusion processing unit and the ship track extraction unit; by receiving and storing the target fusion information and the ship target track information output by the data fusion processing unit and the ship track extraction unit, the received information can be effectively retained, so that data loss is prevented, and the realization of a subsequent flow is ensured.
Furthermore, the LSTM network data set construction unit adopts an interpolation method to establish an LSTM network data set with an equal time interval; by adopting an interpolation method, the cyclic neural network data set with the equal time interval can be quickly established, and the calculation efficiency is effectively improved.
Further, the ship bridge collision risk early warning unit comprises a risk pre-control area analysis system for setting a bridge area water area risk pre-control area, a risk judgment system for judging the ship bridge collision risk and an alarm system for generating ship bridge collision alarm information; the risk pre-control area analysis system can divide the pre-control area, so that the risk judgment result is more accurate.
Furthermore, the risk pre-control area analysis system comprises an extraction module for referring to the information of the bridge area water area risk pre-control area and a setting module for setting the bridge area water area risk pre-control area; the extraction module can accurately extract effective information, and the setting module can analyze the effective information and effectively set a reasonable risk pre-control area range.
Further, the condition of the multi-source data fusion processing is that a radar target point trace and an AIS target point trace meet similar conditions; and the accuracy of the target ship track data is further enhanced.
Further, the radar target point trace and the AIS target point trace motion model are similar under the following conditions: the difference between the movement positions of the radar target point track and the AIS target point track is less than 0.1 nautical mile, the difference between the navigational speed is less than 0.5 section/second, the difference between the acceleration is less than 3 sections/second, the difference between the course direction is less than 20 degrees, the difference between the steering speed is less than 10 degrees/second, and the time difference is less than 3 seconds; and realizing data fusion by adopting a judgment condition that the radar target point trace is similar to the AIS target point trace motion model.
Compared with the prior art, the invention has the beneficial effects that: the system is reasonable in design, realizes real-time monitoring of the water area of the bridge area through the acquisition system, and can realize remote acquisition of target ship information by adopting the continuous wave phased array radar and the AIS system; the LSTM network data set construction unit can be used for extracting and processing information and establishing an LSTM network data set with an equal long interval; the LSTM network construction and training unit is adopted to construct a ship track prediction network model, network training tests can be effectively carried out on the LSTM network data set, and the LSTM network storage unit can effectively realize calling modeling; the ship track prediction unit can perform accurate analysis and calculation on the model; the ship bridge collision risk early warning unit can accurately analyze the risk and perform alarm processing in time, and the integral system is convenient to implement and is suitable for bridge area water areas.
Drawings
FIG. 1 is a system block diagram of the present invention;
FIG. 2 is a system process flow diagram of the present invention.
The system comprises an acquisition system 1, an analysis system 2, a data conversion system 21 and a model construction system 22.
Detailed Description
The present invention will be described in further detail with reference to specific embodiments thereof for better understanding the advantages of the invention.
Examples
The bridge ship-collision prevention early warning system based on phased array radar, AIS and LSTM networks as shown in FIG. 1 comprises a bridge ship-collision prevention early warning system based on phased array radar, AIS and LSTM networks, and comprises an acquisition system 1 and an analysis system 2 which is connected with the acquisition system 1 and is used for analyzing and predicting information acquired by the acquisition system 1; the acquisition system 1 comprises a continuous wave phased array radar system for actively acquiring the target tracking information of the ship in the water area of the bridge area in real time and an AIS system for receiving AIS information of the ship in the water area of the bridge area in real time;
the analysis system 2 comprises a data conversion system 21 for converting the information acquired by the acquisition system 1 into a data set, and a model construction system 22 for modeling the data set;
the data conversion system 21 comprises a data storage unit for receiving and storing the acquired information in real time, a data fusion processing unit for performing multi-source data fusion processing on the information in the data storage unit, a ship track extraction unit for extracting the multi-source data fusion information, and a data conversion unit for extracting the information in the storage unit and establishing equal-duration intervalsLSTM network data setThe LSTM network data set construction unit;
the model construction system 22 comprises an LSTM network construction and training unit for constructing a ship track prediction network model and performing network training test on an LSTM network data set, an LSTM network storage unit for calling and storing an LSTM neural network, a ship track prediction unit for performing prediction calculation on the LSTM neural network model, and a ship bridge collision risk early warning unit for performing risk pre-control area analysis, risk judgment and alarm processing on prediction information;
the radar tracking information comprises the distance between the bridge and the target ship, the speed of the target ship and the real-time azimuth information of the target ship; the AIS information comprises ship static data information, ship dynamic data information and ship range data information; the ship static data information comprises a ship name, a call sign, an MMSI number, a ship type, a ship length and ship width information; the dynamic data information of the ship comprises ship position longitude, ship position latitude, ship bow direction, track direction and speed information; the ship voyage data information comprises ship state, draught, destination and ETA information;
the data storage unit is also used for receiving and storing the target fusion information and the ship target track information output by the data fusion processing unit and the ship track extraction unit; the LSTM network data set construction unit adopts an interpolation method to establish an LSTM network data set with an equal time interval;
the ship bridge collision risk early warning unit comprises a risk pre-control area analysis system for setting a bridge area water area risk pre-control area, a risk judgment system for judging the ship bridge collision risk and an alarm system for generating ship bridge collision alarm information; the risk pre-control area analysis system comprises an extraction module for referencing information of a bridge area water area risk pre-control area and a setting module for setting the bridge area water area risk pre-control area;
the condition of the multi-source data fusion processing is that the radar target trace and the AIS target trace meet similar conditions; the radar target point trace and the AIS target point trace motion model are similar under the following conditions: the difference between the moving positions of the radar target point track and the AIS target point track is less than 0.1 nautical mile, the difference between the navigational speed is less than 0.5 section/second, the difference between the acceleration is less than 3 sections/second, the difference between the course direction is less than 20 degrees, the difference between the steering speed is less than 10 degrees/second, and the time difference is less than 3 seconds.
The working principle of the embodiment is as follows: as shown in fig. 2, the system performs real-time monitoring through an acquisition system 1, the continuous wave phased array radar and the AIS system acquire dynamic and static information of a target ship such as direction, distance and destination in real time, the acquired information is received and stored in real time through data storage, the information in the data storage unit is subjected to multi-source data fusion processing through a data fusion processing unit, the ship track extraction unit extracts the multi-source data fusion information, the information in the storage unit is extracted and processed through an LSTM network data set construction unit and an equal-duration LSTM network data set is established, a ship track prediction network model can be constructed through the LSTM network construction and training unit, then a network training test is performed on the LSTM network data set, the LSTM network storage unit can perform retrieval modeling and storage on an LSTM neural network model, the LSTM neural network model is subjected to prediction calculation through the ship track prediction unit, the prediction information is subjected to risk precontrol area analysis through a risk judgment system, and performs risk judgment on the target, and when the data meets the risk judgment requirement, the alarm processing is performed.
Claims (10)
1. A bridge ship collision prevention early warning system based on phased array radar, AIS and LSTM networks comprises an acquisition system (1) and an analysis system (2) which is connected with the acquisition system (1) and used for intelligently predicting information acquired by the acquisition system (1); the system is characterized in that the acquisition system (1) comprises a continuous wave phased array radar system for actively acquiring tracking information of a ship target in a water area of a bridge area in real time and an AIS system for receiving AIS information of the ship in the water area of the bridge area in real time;
the analysis system (2) comprises a data conversion system (21) for converting information acquired by the acquisition system (1) into a data set, and a model building system (22) for modeling the data set;
the data conversion system (21) comprises a data storage unit, a data fusion processing unit, a ship track extraction unit and an LSTM network data set construction unit, wherein the data storage unit is used for receiving and storing the acquired information in real time;
the model building system (22) comprises an LSTM network building and training unit, an LSTM network storage unit, a ship track prediction unit and a ship bridge collision risk early warning unit, wherein the LSTM network building and training unit is used for building a ship track prediction network model and carrying out network training test on an LSTM network data set, the LSTM network storage unit is used for calling and storing the LSTM neural network, the ship track prediction unit is used for carrying out prediction calculation on the LSTM neural network model, and the ship bridge collision risk early warning unit is used for carrying out risk pre-control area analysis, risk judgment and alarm processing on the prediction information.
2. The bridge ship-collision prevention early warning system based on phased array radar, AIS and LSTM networks according to claim 1, wherein the radar tracking information comprises the distance between the bridge and the target ship, the speed of the target ship and the real-time azimuth information of the target ship.
3. The bridge ship-collision prevention early warning system based on phased array radar, AIS and LSTM networks according to claim 2, wherein the AIS information comprises ship static data information, ship dynamic data information and ship range data information.
4. The bridge ship-collision prevention early warning system based on phased array radar, AIS and LSTM networks according to claim 3, wherein the ship static data information comprises ship name, call number, MMSI number, ship type, ship length and ship width information; the dynamic data information of the ship comprises ship position longitude, ship position latitude, ship bow direction, track direction and speed information; the vessel voyage data information includes vessel state, draft, destination, and ETA information.
5. The bridge ship-collision prevention early warning system based on phased array radar, AIS and LSTM networks according to claim 1, wherein the data storage unit is further configured to receive and store the target fusion information and the ship target trajectory information output by the data fusion processing unit and the ship trajectory extraction unit.
6. The bridge ship-collision prevention early warning system based on phased array radar, AIS and LSTM networks as claimed in claim 1, wherein said LSTM network data set construction unit adopts interpolation method to build LSTM network data set with equal time interval.
7. The bridge ship-collision prevention early warning system based on the phased array radar, the AIS and the LSTM network as claimed in claim 1, wherein the ship-bridge-collision risk early warning unit comprises a risk pre-control area analysis system for setting a bridge area water area risk pre-control area, a risk judgment system for judging the ship-bridge-collision risk, and an alarm system for generating ship-bridge-collision alarm information.
8. The phased array radar, AIS and LSTM network based bridge ship collision prevention early warning system as claimed in claim 7, wherein said risk pre-control area analysis system comprises an extraction module for referencing information of a bridge area water risk pre-control area, and a setting module for setting the bridge area water risk pre-control area.
9. The bridge ship-collision prevention early warning system based on phased array radar, AIS and LSTM networks according to claim 1, wherein the condition of the multi-source data fusion processing is that a radar target point trace and an AIS target point trace meet a similar condition.
10. The bridge ship-collision-prevention early warning system based on the phased array radar, the AIS and the LSTM network as claimed in claim 9, wherein the radar target point track and the AIS target point track motion model are similar under the following conditions: the difference between the moving positions of the radar target point track and the AIS target point track is less than 0.1 nautical mile, the difference between the navigational speed is less than 0.5 section/second, the difference between the acceleration is less than 3 sections/second, the difference between the course direction is less than 20 degrees, the difference between the steering speed is less than 10 degrees/second, and the time difference is less than 3 seconds.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117351781A (en) * | 2023-10-11 | 2024-01-05 | 宁波朗达工程科技有限公司 | Active anti-collision early warning system and method for cross-sea bridge |
| CN117576951A (en) * | 2024-01-16 | 2024-02-20 | 宁波朗达工程科技有限公司 | Cross-sea bridge active early warning method based on ship collision risk probability identification |
| CN117351781B (en) * | 2023-10-11 | 2024-06-04 | 宁波朗达科技有限公司 | Active anti-collision early warning system and method for cross-sea bridge |
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