CN115346399B - 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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- G—PHYSICS
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- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
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- G01S13/584—Velocity or trajectory determination systems; Sense-of-movement determination systems using transmission of continuous unmodulated waves, amplitude-, frequency-, or phase-modulated waves and based upon the Doppler effect resulting from movement of targets adapted for simultaneous range and velocity measurements
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- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
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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 a phased array radar, an AIS (automatic identification system) and an LSTM (least squares) network, which comprises an acquisition system and an analysis system, wherein the analysis system is connected with the acquisition system and used for predicting information acquired by the acquisition system; the system has reasonable design, adopts a continuous wave phased array radar and an AIS system, and can realize acquisition of bridge area target ship information; 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 in the bridge area water area is intelligently predicted, the ship bridge collision risk early warning unit can accurately early warn the bridge collision risk and timely warn, and the whole system is convenient to implement and is suitable for the bridge area water area.
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 (least squares) network.
Background
In recent years, the water transportation industry rapidly develops, the navigation density is high, particularly, the navigation channel near a bridge area of a bridge is more complex, if a driver carelessly operates the bridge to cause errors, or if a ship breaks down, the ship can strike the bridge to cause a safety accident, and the bridge is an obstacle for the ship to navigate, when the driving is improper or unexpected, the safety of the ship is influenced, the safety assurance of the bridge is seriously influenced, the probability of collapse of the bridge can be increased due to the ship strike, particularly, when the ship with larger volume and weight approaches, the bridge cannot be avoided in advance before the bridge is struck, and the bridge collapse is caused by the ship strike accident.
Therefore, the ship collision prevention facility is very necessary to ensure the normal navigation of the ship in the bridge area water area, so that the personal safety of people nearby the ship and the bridge is protected, most of the ship collision prevention facilities adopt the camera shooting monitoring and early warning technology for the water areas at two sides, however, the manpower is consumed, certain requirements are met for the land environment, the climate, the light and the like, and the mechanical radar ship collision prevention technology can only scan a fixed range in a fixed rotation mode, so that the implementation in the bridge area water area is not facilitated.
Disclosure of Invention
In order to solve the technical problems, the invention provides a bridge ship collision prevention early warning system based on a phased array radar, an AIS (automatic identification system) and an LSTM (least squares) network.
The technical scheme of the invention is as follows: a bridge ship collision prevention early warning system based on a phased array radar, an AIS and an LSTM network comprises an acquisition system and an analysis system which is connected with the acquisition system and 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 ship target tracking information in the bridge area in real time and an AIS system for receiving AIS information of the ship in the bridge area in real time;
the analysis system comprises a data conversion system for converting the information acquired by the acquisition system into a data set and a model construction system for modeling the data set;
the data conversion system comprises a data storage unit for receiving and storing acquired information in real time, a data fusion processing unit for carrying out multi-source data fusion processing on the information in the data storage unit, a ship track extraction unit for extracting multi-source data fusion information, and an LSTM network data set construction unit for extracting the information in the storage unit and establishing an LSTM network data set with equal time intervals;
the model construction system comprises an LSTM network construction and training unit for constructing a ship track prediction network model and carrying out network training test on the LSTM network data set, an LSTM network storage unit for calling and storing the LSTM neural network, a ship track prediction unit for carrying out prediction calculation on the LSTM neural network model, and a ship collision bridge risk early warning unit 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; the adoption of a more detailed radar tracking information source can enable the information analysis result to be more accurate.
Further, the AIS information comprises 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 perfect, and the analysis accuracy of the ship track is improved.
Further, the ship static data information comprises ship name, call sign, MMSI number, ship type, ship length and ship width information; the ship dynamic data information comprises ship position longitude, ship position latitude, ship bow direction, track direction and navigational 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 which is generated by collecting the target track information is further ensured.
Further, the data storage unit is further 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 target fusion information and the ship target track information output by the data fusion processing unit and the ship track extraction unit are received and stored, so that the received information can be effectively reserved, data loss is prevented, and the realization of a subsequent flow is ensured.
Further, the LSTM network data set construction unit adopts an interpolation method to establish an LSTM network data set with equal time intervals; the interpolation method can be used for quickly establishing the cyclic neural network data set with equal time intervals, 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 a risk judgment result is more accurate.
Further, the risk pre-control area analysis system comprises an extraction module for referencing the information of the risk pre-control area of the bridge area water area and a setting module for setting the risk pre-control area of the bridge area water area; the effective information can be accurately extracted by the extraction module, the effective information can be analyzed by the setting module, and a reasonable risk pre-control area range can be effectively set.
Further, the condition of the multi-source data fusion processing is that the radar target point trace and the AIS target point trace meet similar conditions; 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 to each other in terms of: the difference of the movement positions of the radar target point trace and the AIS target point trace is smaller than 0.1 sea, the navigation speed difference is smaller than 0.5 section/second, the acceleration difference is smaller than 3 section/second, the heading difference is smaller than 20 degrees, the steering speed difference is smaller than 10 degrees/second, and the time difference is smaller than 3 seconds; and realizing data fusion by adopting a judging 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 has reasonable design, realizes real-time monitoring of the bridge area water area through the acquisition system, and can realize long-distance acquisition of the target ship information by adopting the continuous wave phased array radar and the AIS system; the information can be extracted and processed by utilizing the LSTM network data set construction unit, and the LSTM network data set with equal time intervals is established; the LSTM network construction and training unit is adopted to construct a ship track prediction network model, network training test is 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 accurately analyze and calculate the model; the ship bridge collision risk early warning unit can accurately analyze the risk and timely perform alarm processing, and the whole system is convenient to implement and is suitable for a bridge area water area.
Drawings
FIG. 1 is a system module frame diagram of the present invention;
fig. 2 is a flow chart of the system process of the present invention.
Wherein, 1-acquisition system, 2-analysis system, 21-data conversion system, 22-model construction system.
Detailed Description
The invention will be described in further detail with reference to the following embodiments to better embody the advantages of the invention.
Examples
The bridge ship collision prevention early warning system based on the phased array radar, the AIS and the LSTM comprises a bridge ship collision prevention early warning system based on the phased array radar, the AIS and the LSTM, and comprises an acquisition system 1 and an analysis system 2 which is connected with the acquisition system 1 and 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 ship target tracking information in a bridge area water area in real time and an AIS system for receiving AIS information of the bridge area water area ship 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 dataset, and a model building system 22 for modeling the dataset;
the data conversion system 21 comprises a data storage unit for receiving and storing the acquired information in real time, and a multi-source data fusion place for the information in the data storage unitThe system comprises a data fusion processing unit, a ship track extraction unit and a storage unit, wherein the data fusion processing unit is used for extracting multisource data fusion information, and the ship track extraction unit is used for extracting information in the storage unit and establishing equal-duration intervalsLSTM network data setAn 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 voyage data information; the ship static data information comprises ship name, call sign, MMSI number, ship type, ship length and ship width information; the ship dynamic data information 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;
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 equal-time-interval LSTM network data set;
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 the information of the risk pre-control area of the bridge area water area and a setting module for setting the risk pre-control area of the bridge area water area;
the condition of the multi-source data fusion processing is that the radar target point trace and the AIS target point trace meet similar conditions; the radar target point trace and AIS target point trace motion model are similar to each other: the difference of the movement positions of the radar target point trace and the AIS target point trace is smaller than 0.1 sea, the navigation speed difference is smaller than 0.5 section/second, the acceleration difference is smaller than 3 section/second, the heading difference is smaller than 20 degrees, the steering speed difference is smaller than 10 degrees/second, and the time difference is smaller than 3 seconds.
The working principle of the embodiment is as follows: as shown in fig. 2, the real-time monitoring is performed by the acquisition system 1, the continuous wave phased array radar and the AIS system acquire the dynamic and static information such as the azimuth, the distance and the destination of the target ship in real time, the acquired information is received and stored in real time by utilizing the data storage, the multi-source data fusion processing is performed on the information in the data storage unit by utilizing the data fusion processing unit, the multi-source data fusion information is extracted by the ship track extraction unit, the information in the storage unit is extracted and processed by utilizing the LSTM network data set construction unit, the equal-time interval LSTM network data set is established, the LSTM network construction and training unit can construct a ship track prediction network model, then the LSTM network data set is subjected to network training test, the LSTM network storage unit can perform retrieval modeling and storage on the LSTM neural network, the LSTM neural network model is predicted and calculated by utilizing the ship track prediction unit, the risk pre-control area analysis is performed on the predicted information by utilizing the ship collision risk pre-control area analysis unit, and the risk judgment system judges the target ship risk when the data meets the risk judgment requirement.
Claims (7)
1. The bridge ship collision prevention early warning system based on the phased array radar, the AIS and the LSTM 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 system is characterized in that the acquisition system (1) comprises a continuous wave phased array radar system for actively acquiring ship target tracking information of the bridge area water area in real time and an AIS system for receiving AIS information of the bridge area water area ship in real time;
the analysis system (2) comprises a data conversion system (21) for converting information acquired by the acquisition system (1) into a dataset, and a model construction system (22) for modeling the dataset;
the data conversion system (21) comprises a data storage unit for receiving and storing acquired information in real time, a data fusion processing unit for carrying out multi-source data fusion processing on the information in the data storage unit, a ship track extraction unit for extracting multi-source data fusion information, and an LSTM network data set construction unit for extracting the information in the storage unit and establishing an LSTM network data set with equal time intervals; 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 condition of the multi-source data fusion processing is that the radar target point trace and the AIS target point trace meet similar conditions; the radar target point trace and the AIS target point trace motion model have similar conditions that: the difference of the movement positions of the radar target point trace and the AIS target point trace is smaller than 0.1 sea, the navigation speed difference is smaller than 0.5 section/second, the acceleration difference is smaller than 3 section/second, the heading difference is smaller than 20 degrees, the steering speed difference is smaller than 10 degrees/second, and the time difference is smaller than 3 seconds;
the model construction system (22) comprises an LSTM network construction and training unit for constructing an LSTM neural network and performing network training test on the LSTM network data set, an LSTM network storage unit for calling, modeling and storing the 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.
2. The bridge anti-ship collision early warning system based on the phased array radar, the AIS and the LSTM network, according to claim 1, wherein the bridge area water ship target tracking information comprises the distance between the bridge and the target ship, the target ship speed and the target ship real-time azimuth information.
3. The bridge anti-collision warning system based on phased array radar, AIS and LSTM networks of claim 1, wherein the AIS information comprises vessel static data information, vessel dynamic data information and vessel voyage data information.
4. A bridge anti-collision 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 sign, MMSI number, ship type, captain and width information; the ship dynamic data information comprises ship position longitude, ship position latitude, ship bow direction, track direction and navigational speed information; the vessel voyage data information includes vessel status, draft, destination and ETA information.
5. The bridge anti-ship collision early warning system based on phased array radar, AIS and LSTM networks according to claim 1, wherein the LSTM network data set construction unit establishes the equal-time-interval LSTM network data set by interpolation.
6. The bridge anti-ship collision early warning system based on the phased array radar, the AIS and the LSTM network according to claim 1, wherein the ship 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 judging system for judging the ship collision risk, and an alarm system for generating ship collision alarm information.
7. The bridge anti-ship collision early warning system based on the phased array radar, the AIS and the LSTM network according to claim 6, wherein the risk pre-control area analysis system comprises an extraction module for referencing bridge area water risk pre-control area information and a setting module for setting the bridge area water risk pre-control area.
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| CN117576951B (en) * | 2024-01-16 | 2024-04-16 | 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 |
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