Intelligent mooring method for inland anchorages

Abstract

The present invention relates to an intelligent berthing method for inland anchorages, comprising the following steps: S1: a ship sends a berth application and obtains the ship's current information; S2: using the ship information to predict the ship's arrival time at each available anchorage; S3: collecting information on all currently moored ships at the anchorage and allocating berths to incoming ships based on the current berthing situation at the anchorage; S4: after a ship has moored, checking whether it has moored at the designated location as required; S5: after receiving a departure information, collecting the ship's departure time for use in allocating berths to subsequent incoming ships; and S6: upon reaching the scheduled departure time, the ship performs the departure operation. This method can predict ship arrival times and intelligently allocate berths to incoming ships, thereby reducing anchorage management costs, improving anchorage operation efficiency, and enhancing the intelligent construction of anchorages.

Description

Technical Field

[0001] The present invention relates to the technical field of ship scheduling, and more particularly to an intelligent berthing method for an inland river anchorage. Background Art

[0002] Anchorages, as important public facilities for water transport production and the development of the shipping economy, play a vital role in the safe operation of ships, directly impacting the smooth flow of waterways and navigation safety. With the rapid development of waterway transportation, inland waterway vessel traffic is becoming increasingly dense and larger vessels are becoming increasingly larger, necessitating an urgent increase in demand for anchorages. Limited by shoreline resources, the number of inland waterway anchorages has reached saturation. Maximizing anchorage efficiency through the limited availability of anchorage resources is a pressing issue. Furthermore, inland waterway anchorages are geographically dispersed and relatively remote, with limited coverage of information, digitalization, and networking, resulting in a lack of centralized anchorage management. The current anchorage management model, which still relies on on-site personnel, significantly increases the difficulty and cost of anchorage management. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide an intelligent mooring method for inland anchorages, which can realize remote centralized control and management of anchorages, significantly reduce anchorage management costs, improve anchorage operation efficiency, and enhance the intelligent construction of anchorages.

[0004] The technical solution adopted by the present invention to solve the technical problem is to construct an intelligent mooring method for inland anchorages, comprising the following steps:

[0005] S1. The ship sends a berth application and obtains the current information of the ship;

[0006] S2. Predicting the time for the ship to arrive at each available anchorage based on ship information;

[0007] S3. Count all the information of the ships currently moored at the anchorage and allocate berths to the incoming ships based on the current mooring situation at the anchorage;

[0008] S4. After the ship has moored, check whether it has moored at the designated location as required;

[0009] S5. When a ship receives the departure information, the departure time is counted for subsequent berth allocation for incoming ships.

[0010] S6. After the scheduled departure time is reached, the vessel performs departure operations;

[0011] In step S3, a mathematical model is constructed and a genetic algorithm is used to solve the result of ship berth allocation based on the information of all ships currently moored at the anchorage and the current occupancy of the anchorage. The model objective function F minimizes the average impact of ships on berthing and leaving the anchorage, which is expressed as follows:

[0012] (1)

[0013] The constraints of this model include the number of anchorage points and berths, ship berthing conditions, and ship arrival and departure times, which are expressed as follows:

[0014] (2)

[0015] (3)

[0016] (4)

[0017] (5)

[0018] N: represents the anchor point set, , n is the total number of anchor points,

[0019] : Indicates the total number of available berths at the nth anchorage point. , , m represents the mth berth at the nth anchor point,

[0020] I: indicates the number of ships expected to arrive, i indicates the i-th ship,

[0021] V: represents the number of ships assigned berths,

[0022] : represents the length of ship i, ,

[0023] : Indicates the length of the inner ship adjacent to berth i. ,

[0024] : represents the length of the outer ship adjacent to the berth i, ,

[0025] : indicates the estimated arrival time of ship i, ,

[0026] : represents the departure time of ship i, ,

[0027] : Indicates the number of berthing times required for ship i during berthing maneuvers. ,

[0028] : Indicates the number of times the ship on the inner side of the berth needs to move away after the ship i berths. ,

[0029] : Indicates the number of times the ships outside the berth need to shift berths when the ship i leaves the berth. ,

[0030] : represents the total number of berth shifts caused by ship i approaching and leaving the berth. ,

[0031] The impact of ship i's berthing and leaving berth includes three parts:

[0032] 1) When ship i is berthing, is it necessary for the ships outside the berth to move? Indicates the number of times berthing is required;

[0033] 2) After ship i berths, when the ship on the inner side of the berth leaves, is it necessary for ship i to move berth? Indicates the number of times a berth needs to be moved after berthing;

[0034] 3) When ship i leaves the berth, whether the ship outside the berth needs to move to ship i, Indicates the number of times the berth needs to be moved when leaving the berth;

[0035] The total number of berth shifts caused by ship i berthing and leaving is express.

[0036] According to the above scheme, in step S1, the ship's automatic identification system data is used to obtain the basic information of the ship through decoding, and the current information of the ship is obtained by combining the ship's declared information. The ship's automatic identification system is the Automatic Identification System, abbreviated as AIS.

[0037] According to the above scheme, the ship declaration information includes: ship name, ship type, cargo type, ship length, ship width, ship height, draft and ship size, actual cargo / passenger capacity, actual displacement, declaration time, and navigation scheduling information.

[0038] According to the above scheme, in step S2, a long short-term memory network deep learning model is used to predict the time when the ship arrives at each available anchorage. The long short-term memory network is Long Short-Term Memory, abbreviated as LSTM. Building the LSTM model includes the following steps:

[0039] S201. Define model architecture: Use the deep learning framework TensorFlow to define the LSTM model architecture;

[0040] S202. Select the appropriate number of layers and neurons based on the input data: Use multiple LSTM layers, each containing a number of neurons;

[0041] S203. Add Dense layer: Add multiple Dense layers after the LSTM layer to map the output of the LSTM layer to the target variable.

[0042] S204. Compile the model: use the mean square error to formulate the loss function and use the Adam optimizer to optimize the model.

[0043] According to the above scheme, before the construction and training phase of the LSTM deep learning model, historical ship navigation data, AIS data, weather data, and anchorage data are collected, and ship position data, timestamp data, environmental data, ship information, and ship arrival time are obtained from the collected data;

[0044] After obtaining the collected data, preprocess the data for model training;

[0045] During the model training phase, the training input is time series data with multi-dimensional features, and the training output is the estimated arrival time of the ship;

[0046] When using the LSTM deep learning model to predict the time of a ship's arrival at each available anchorage, the current ship type, cargo type, length, width, height, draft, actual cargo / passenger capacity, actual displacement, current weather data, anchorage data, and AIS data at the current time and several previous time steps are preprocessed and used as input.

[0047] According to the above scheme, the data preprocessing includes time series processing, missing value processing and normalization processing; the time series processing organizes the data into a time series format, the missing value processing processes or fills in missing data, and the normalization processing normalizes or standardizes the feature data.

[0048] According to the above solution, in step S4, the specific steps of checking whether the ship is moored at the designated location as required are as follows:

[0049] S401. Obtain AIS data: The data includes static and dynamic ship data. The data used when checking the berthing position of a ship includes the ship's position, heading, speed, ship name, and MMSI number.

[0050] S402, Identify the target ship: Identify the target ship from the AIS data, and ensure that the selected ship is consistent with the inspected ship by using the MMSI number corresponding to each ship;

[0051] S403. Verify the vessel's position information: Check the position information in the AIS data, including longitude and latitude, to verify whether the information complies with the requirements of the designated berthing area. If the vessel's position information is within the allocated berthing area, the vessel's berthing position is correct.

[0052] S404, AIS data timestamp check: Check the timestamp of the AIS data to ensure that the data is current and real-time, so as to confirm the exact position of the ship in real time;

[0053] S405. Checking the ship's communication status: To ensure that the ship's AIS equipment is in normal working condition, check the communication status in the AIS data.

[0054] The intelligent mooring method for inland river anchorages according to the present invention has the following beneficial effects:

[0055] 1. This invention comprehensively utilizes modern information technology to carry out research on key technologies for intelligent operation of unmanned anchorages, realize remote centralized control and management of anchorages, and provide an effective and reliable solution for unmanned and intelligent operation of anchorages;

[0056] 2. The present invention can streamline the number of anchorage personnel, reduce anchorage management costs, improve anchorage management efficiency, enhance anchorage business management, tap into inland anchorage resources, innovate anchorage management models, promote intelligent and digital construction of anchorages, and improve the intelligence level of shipping supporting facilities and equipment;

[0057] 3. The present invention can realize remote centralized control and management of anchorages, which can help anchorage management departments streamline anchorage staff, significantly reduce anchorage management costs, improve anchorage operation efficiency, and enhance the intelligent construction of anchorages;

[0058] 4. The present invention uses a long short-term memory network deep learning model to predict the arrival time of ships, which can estimate the arrival of multiple ships in advance, realize the comprehensive management of multiple ships, improve the practicality and rationality of ship management, and improve the accuracy of ship berthing;

[0059] 5. The present invention establishes a mathematical model based on the requirements for berthing of ships to increase the safety of ships during berthing, and uses a genetic algorithm to seek the optimal solution of the model, which can improve the comprehensive utilization rate of anchorages and reduce the impact of ships on other ships during berthing. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0061] Figure 1 This is a flow chart of the intelligent mooring method for inland river anchorages of the present invention;

[0062] Figure 2It is a flow chart of checking the berthing position of a ship according to the present invention. DETAILED DESCRIPTION

[0063] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, specific embodiments of the present invention are now described in detail with reference to the accompanying drawings.

[0064] like Figure 1 As shown, the inland anchorage intelligent mooring method of the present invention comprises the following steps:

[0065] S1. Predict the arrival time of each available anchorage using vessel information. Automatic Identification System (AIS) data is decoded to obtain basic vessel information. This information, combined with the vessel's declared information, provides the vessel's current status. Declared information includes at least: vessel name, type, cargo type, length, breadth, height, draft and other dimensions, actual displacement, actual cargo / passenger capacity, declared time, and other information required for navigation scheduling.

[0066] S2: After entering the lock's designated waters, a vessel submits a berth application and vessel information to the cloud server. A Long Short-Term Memory (LSTM) deep learning model is used to predict the vessel's arrival time at each available anchorage.

[0067] Before model building and training, historical ship navigation data, AIS data, weather data, and anchorage data are collected. From this data, vessel position data, timestamp data, environmental data, vessel information, and arrival time are obtained. Vessel position data includes longitude, latitude, speed, and heading. Timestamp data records the time of the vessel's position data. Environmental data includes weather conditions and anchorage conditions. Vessel information includes vessel type, cargo type, length, width, height, draft, actual displacement, and actual cargo / passenger capacity. Arrival time refers to the vessel's actual arrival time.

[0068] After acquiring the above data, preprocess it for model training. Data preprocessing includes time series processing (organizing the data into a time series format); missing value processing (addressing or filling missing data); and normalization (normalizing or standardizing feature data to improve model training results). The time series data must be converted to a format acceptable to the model. For LSTM, the data must be converted to a 3D array format: [number of samples, time steps, number of features].

[0069] The method for building an LSTM deep learning model includes the following steps:

[0070] 1) Define the model architecture: Use the deep learning framework TensorFlow to define the LSTM model architecture.

[0071] 2) Choose the appropriate number of layers and neurons based on the input data: Use multiple LSTM layers, each containing several neurons.

[0072] 3) Add Dense layers: Add multiple Dense layers after the LSTM layer to map the output of the LSTM layer to the target variable.

[0073] 4) Compile the model: use mean square error to formulate the loss function and use Adam optimizer to optimize the model.

[0074] During the model training phase, the training input is time series data with multi-dimensional features, namely, ship position data, timestamp data, environmental data, ship information, etc. at several past time steps. The training output is the estimated arrival time of the ship.

[0075] When using the LSTM model to predict the arrival time of a ship at each available anchorage, the current ship type, cargo type, length, ship width, ship height, draft, actual cargo / passenger capacity, actual displacement, current weather data, anchorage data, and AIS data at the current time and several previous time steps are preprocessed and used as input, that is, time series data in the same format as during training. The output is the predicted value, that is, the ship's arrival time at each available anchorage.

[0076] S3. Count all the information of the ships currently moored at the anchorage, and allocate berths to the incoming ships based on the current mooring situation at the anchorage.

[0077] Ships are divided into five categories based on their type and cargo: Class I flammable and explosive dangerous goods ships, Class II flammable and explosive dangerous goods ships, non-flammable and explosive dangerous goods ships, bulk carriers, and container ships. Berth allocation is based on safety considerations and is assigned to different anchorages: Class I flammable and explosive dangerous goods ships and Class II flammable and explosive dangerous goods ships are assigned to different dangerous goods anchorages, non-flammable and explosive dangerous goods ships and bulk carriers are assigned to bulk carrier anchorages, and container ships are assigned to container anchorages. Multiple anchorages of the same type are often available, and ships generally have multiple anchorages available for berthing.

[0078] When ships are berthing together, in order to provide more contact area between the ships, the contact area between the inner ship and the anchor point is increased, thereby improving the stability of the ship when berthing. When berthing together, the length of the inner berth is required to be greater than the length of the outer berth, which helps to reduce the shaking and swinging of the ship during the berthing process. Therefore, the requirements for ships berthing together are: (1) the outer berth can only be docked after the inner berth is docked; (2) the length of the inner berth must be greater than the length of the outer berth.

[0079] Since other ships at the same anchorage need to perform berthing operations when a ship berths or leaves a berth. In order to reduce the impact of a ship berthing or leaving a berth on other ships, a mathematical model is constructed and a genetic algorithm is used to solve the result of ship berth allocation based on the information of all anchored ships and the current occupancy status of the anchorage.

[0080] The model objective function F minimizes the average impact of ships on berthing and leaving berthing as follows:

[0081] (1)

[0082] The constraints of this model include the number of anchorage points and berths, ship berthing conditions, and ship arrival and departure times, which are expressed as follows:

[0083] (2)

[0084] (3)

[0085] (4)

[0086] (5)

[0087] N: represents the anchor point set, , n is the total number of anchorage points.

[0088] : Indicates the total number of available berths at the nth anchorage point. , , m represents the mth berth at the nth anchor point.

[0089] I: represents the number of ships expected to arrive, and i represents the i-th ship.

[0090] V: Indicates the number of ships allocated berths.

[0091] : represents the length of ship i, .

[0092] : Indicates the length of the inner ship adjacent to berth i. .

[0093] : represents the length of the outer ship adjacent to the berth i, .

[0094] : indicates the estimated arrival time of ship i, .

[0095] : represents the departure time of ship i, .

[0096] : Indicates the number of berthing times required for ship i during berthing maneuvers. .

[0097] : Indicates the number of times the ship on the inner side of the berth needs to move away after the ship i berths. .

[0098] : Indicates the number of times the ships outside the berth need to shift berths when the ship i leaves the berth. .

[0099] : represents the total number of berth shifts caused by ship i approaching and leaving the berth. .

[0100] The impact of ship i's berthing and leaving berth mainly includes three parts:

[0101] 1) When ship i is berthing, is it necessary for the ships outside the berth to move berths? Indicates the number of times berthing is required;

[0102] 2) After ship i berths, when the ship on the inner side of the berth leaves, is it necessary for ship i to move berth? Indicates the number of times a berth needs to be moved after berthing;

[0103] 3) When ship i leaves the berth, whether the ship outside the berth needs to move to ship i, Indicates the number of times the vessel needs to shift berths when leaving a berth.

[0104] The total number of berth shifts caused by ship i berthing and leaving is express.

[0105] The model is solved using a genetic algorithm. Depending on the specific problem, the algorithm first encodes each feasible solution into an individual using a genetic algorithm encoding method. This population of individuals then forms a population. Next, by simulating the evolutionary laws of "natural selection" and "survival of the fittest," the algorithm calculates the fitness function of each individual to determine whether to retain it. If an individual passes the crossover probability test, the selected individual undergoes crossover to produce a new individual, which is then added to the population. If an individual passes the mutation probability test, the selected individual undergoes mutation to produce a new individual, increasing the diversity of the population, and is then added to the population. This process continues generation by generation until the algorithm's termination criteria are met. Finally, the individual with the highest fitness value in the current population is selected as the approximate optimal solution to the problem.

[0106] S4. Check whether the vessel is moored at the designated location as required, such as Figure 2 As shown, the following steps are included:

[0107] S401. Obtain ship AIS data: This data includes ship static data and ship dynamic data. The data mainly used when checking the ship's berthing position is the ship's position, heading, speed, ship name, MMSI number and other data.

[0108] S402, identifying the target ship: identifying the target ship from the AIS data, and ensuring that the selected ship is consistent with the ship to be inspected by using the MMSI number corresponding to each ship.

[0109] S403. Verify the vessel's position information: Check the position information in the AIS data, including longitude and latitude, and verify whether the longitude and latitude are consistent with the requirements of the designated berthing area. If the vessel's position information is within the allocated berthing area, the vessel's berthing position is correct.

[0110] S404. AIS data timestamp check: Check the timestamp of the AIS data to ensure that the data is current and real-time, so as to confirm the exact position of the ship in real time.

[0111] S405. Ship communication status check: Check the communication status in the AIS data to ensure that the ship's AIS equipment is in normal working condition, which helps maintain real-time position information and smooth communication in abnormal situations.

[0112] S5. In the constructed mathematical model for ship berth allocation, information such as the ship's departure time is required when solving the ship berth. When the ship receives the departure information, the ship's departure time is counted and used for berth allocation for subsequent ships that come to berth.

[0113] S6. After arriving at the scheduled unberthing time, the vessel performs unberthing operations.

[0114] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are protected by the present invention.

Claims

1. An intelligent mooring method for inland anchorages, characterized in that: The following steps are involved: S1. The ship sends a berth application and obtains the current information of the ship; S2. Predicting the time for the ship to arrive at each available anchorage based on ship information; S3. Count all the information of the ships currently moored at the anchorage and allocate berths to the incoming ships based on the current mooring situation at the anchorage; S4. After the ship has moored, check whether it has moored at the designated location as required; S5. When a ship receives the departure information, the departure time is counted for subsequent berth allocation for incoming ships. S6. After the scheduled departure time is reached, the vessel performs departure operations; In step S3, a mathematical model is constructed and a genetic algorithm is used to solve the result of ship berth allocation based on the information of all ships currently moored at the anchorage and the current occupancy of the anchorage. The model objective function F minimizes the average impact of ships on berthing and leaving the anchorage, which is expressed as follows: （1） The constraints of this model include the number of anchorage points and berths, ship berthing conditions, and ship arrival and departure times, which are expressed as follows: （2） （3） （4） （5） N: represents the anchor point set, , n is the total number of anchor points, : Indicates the total number of available berths at the nth anchorage point. , , m represents the mth berth at the nth anchor point, I: indicates the number of ships expected to arrive, i indicates the i-th ship, V: represents the number of ships assigned berths, : represents the length of ship i, , : Indicates the length of the inner ship adjacent to berth i. , : represents the length of the outer ship adjacent to the berth i, , : indicates the estimated arrival time of ship i, , : represents the departure time of ship i, , : Indicates the number of berthing times required for ship i during berthing maneuvers. , : Indicates the number of times the ship on the inner side of the berth needs to move away after the ship i berths. , : Indicates the number of times the ships outside the berth need to shift berths when the ship i leaves the berth. , : represents the total number of berth shifts caused by ship i approaching and leaving the berth. , The impact of ship i's berthing and leaving berth includes three parts: 1) When ship i is berthing, is it necessary for the ships outside the berth to move berths? Indicates the number of times berthing is required; 2) After ship i berths, when the ship on the inner side of the berth leaves, is it necessary for ship i to move berth? Indicates the number of times a berth needs to be moved after berthing; 3) When ship i leaves the berth, whether the ship outside the berth needs to move to ship i, Indicates the number of times the berth needs to be moved when leaving the berth; The total number of berth shifts caused by ship i berthing and leaving is express.

2. The intelligent mooring method for inland river anchorage according to claim 1, characterized in that: In step S1, the basic information of the ship is obtained by decoding the AIS data, and the current information of the ship is obtained by combining the ship's declared information.

3. The intelligent mooring method for inland river anchorage according to claim 2, characterized in that: The ship declaration information includes: ship name, ship type, cargo type, ship length, ship width, ship height, draft and ship dimensions, actual cargo / passenger capacity, actual displacement, declaration time, and navigation scheduling information.

4. The intelligent mooring method for inland river anchorage according to claim 1, characterized in that: In step S2, a long short-term memory network (LSTM) deep learning model is used to predict the time when the ship arrives at each available anchorage. The method for constructing the LSTM deep learning model includes the following steps: S201. Define model architecture: Use the deep learning framework TensorFlow to define the LSTM model architecture; S202. Select the appropriate number of layers and neurons based on the input data: Use multiple LSTM layers, each containing a number of neurons; S203. Add Dense layer: Add multiple Dense layers after the LSTM layer to map the output of the LSTM layer to the target variable. S204. Compile the model: use the mean square error to formulate the loss function and use the Adam optimizer to optimize the model.

5. The intelligent mooring method for inland river anchorage according to claim 4, characterized in that: Before the LSTM deep learning model is built and trained, historical ship navigation data, AIS data, weather data, and anchorage data are collected, and ship position data, timestamp data, environmental data, ship information, and ship arrival time are obtained from the collected data; After obtaining the collected data, preprocess the data for model training; During the model training phase, the training input is time series data with multi-dimensional features, and the training output is the estimated arrival time of the ship; When using the LSTM deep learning model to predict the time of a ship's arrival at each available anchorage, the current ship type, cargo type, length, width, height, draft, actual cargo / passenger capacity, actual displacement, current weather data, anchorage data, and AIS data at the current time and several previous time steps are preprocessed and used as input.

6. The intelligent mooring method for inland river anchorage according to claim 5, characterized in that: The data preprocessing includes time series processing, missing value processing and normalization processing; the time series processing organizes the data into a time series format, the missing value processing processes or fills in missing data, and the normalization processing normalizes or standardizes the feature data.

7. The intelligent mooring method for inland river anchorage according to claim 1, characterized in that: In step S4, the specific steps of checking whether the ship is moored at the designated location as required are as follows: S401. Obtain AIS data: The data includes static and dynamic ship data. The data used when checking the berthing position of a ship includes the ship's position, heading, speed, ship name, and MMSI number. S402, Identify the target ship: Identify the target ship from the AIS data, and ensure that the selected ship is consistent with the inspected ship by using the MMSI number corresponding to each ship; S403. Verify the vessel's position information: Check the position information in the AIS data, including longitude and latitude, to verify whether the information complies with the requirements of the designated berthing area. If the vessel's position information is within the allocated berthing area, the vessel's berthing position is correct. S404, AIS data timestamp check: Check the timestamp of the AIS data to ensure that the data is current and real-time, so as to confirm the exact position of the ship in real time; S405. Checking the ship's communication status: To ensure that the ship's AIS equipment is in normal working condition, check the communication status in the AIS data.

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