Safety dynamic management system for belt sand ship

Abstract

The invention discloses a belt sand ship safety dynamic management system, and the system comprises a ship information collection module which is used for collecting ship information, including position, speed and course information; the tide change trend prediction module is used for collecting tide information of the navigation area and obtaining the tide height and the tide change trend of the area where the ship is located in combination with the ship information; and the shallow water area depth change prediction module is used for collecting the sea chart data of the navigation area, and judging the depth change of the shallow water area during low tide by combining overlay analysis of the tide height data and the current sea chart water depth data. By integrating the tide change trend prediction module, the shallow water depth analysis module and the channel safety planning module, the system can monitor tide fluctuation and shallow water depth dynamic change in real time, and provide real-time data-based safety channel planning in combination with the ship draft.

Description

Technical Field

[0001] The invention relates to the technical field of water transport engineering, in particular to a safety dynamic management system for belt sand ships. Background Art

[0002] With the rapid development of the modern shipping industry, the demand for the application of multi-ship cooperative navigation in tidal complex areas and shallow waterways has gradually increased. However, existing technologies still have many shortcomings in dealing with dynamic environmental changes and cooperative navigation in complex waters. Traditional navigation management systems usually rely on static channel planning or path adjustment based on simple rules, and have limited real-time prediction capabilities for tidal changes and dynamic changes in water depth in shallow waters. Especially in shallow water areas with significant tidal fluctuations, the existing system lacks effective dynamic analysis tools and cannot fully consider the combined effects of ship draft, tidal changes and chart data, which may lead to reduced navigation safety. In addition, in multi-ship cooperative navigation, traditional methods make it difficult to accurately calculate the dynamic avoidance priority between ships, and cannot adjust the path in real time according to the relative position, speed and heading of the ships, thereby increasing the risk of collision and the possibility of path conflict.

[0003] On the other hand, existing navigation scheduling technologies have difficulty balancing the relationship between fuel efficiency and navigation stability. In complex water environments, traditional fixed adjustment frequencies or adjustments based on simple threshold triggers often lead to frequent ship operations or slow responses, which in turn reduces fuel efficiency or causes unnecessary navigation risks. Existing methods also fail to fully combine comprehensive information on dynamic tidal changes, shallow water depth distribution, and ship operating status, and lack support for multi-factor dynamic optimization. Therefore, the development of an intelligent management system that can perceive the dynamic environment in real time, accurately predict tidal and shallow water changes, and optimize multi-ship collaborative paths has become a key requirement for improving shipping efficiency and safety. Summary of the invention

[0004] In view of the problems existing in the above-mentioned prior art, the present invention provides a belt sand carrier safety dynamic management system.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0006] A belt sand carrier safety dynamic management system, the system comprising:

[0007] The ship information collection module is used to collect ship information, including location, speed, and heading information;

[0008] The tidal change trend prediction module collects tidal information of the navigation area and combines the ship information to obtain the tidal height and the tidal change trend of the area where the ship is located;

[0009] Shallow water depth change prediction module collects nautical chart data of the navigation area, combines tidal height data with current chart water depth data for superposition analysis, and determines the depth change of shallow water areas at low tide;

[0010] The channel safety depth prediction module uses real-time tidal change trends and shallow water depth analysis results to determine the safe depth channel suitable for each ship to pass through;

[0011] The ship passage priority calculation module combines the channel safety prediction results, obtains the avoidance priority between ships according to the relative distance and speed between ships, and determines the dynamic navigation path of multi-ship coordination through the avoidance priority and shallow water channel information;

[0012] The ship navigation scheduling module is used to analyze the dynamic navigation path and obtain the speed and heading adjustment instructions of each ship through the dynamic navigation path, and then plan a reasonable navigation plan based on the speed and heading adjustment instructions of each ship.

[0013] Furthermore, the ship information collection module is used to collect ship information: including the Beidou positioning system to obtain the current longitude and latitude position of each ship in real time, the ship positioning and directional compass to obtain the ship's azimuth and heading data in real time, the speed sensor to collect the ship's speed information in real time, and calculate the speed of the ship relative to the water flow. The collected ship information is aggregated by the data collection unit and then transmitted to the cloud database.

[0014] Furthermore, the tidal change trend prediction module collects tidal information of the navigation area, and combines the ship information to obtain the tidal height and the tidal change trend of the area where the ship is located: obtain tidal data from multiple channels, and integrate the obtained tidal data, remove duplicate data and abnormal data, smooth tidal fluctuations through methods such as difference, reduce the impact of trend fluctuations on data analysis, fuse the integrated tidal data and the obtained ship information, align the data by timestamp, ensure that the two can be correctly matched, avoid errors caused by time, and use random forest, XGBoost, and regression analysis algorithms to analyze the fused data. By analyzing the tidal change trend, the height value of the current tide level and the expected height of future tidal fluctuations are obtained.

[0015] Furthermore, the shallow water depth change prediction module collects nautical chart data of the navigation area, and combines the tidal height data with the current nautical chart water depth data for superposition analysis to determine the depth change of the shallow water area at low tide: obtains nautical chart data of the navigation area through multiple channels, integrates the obtained data to ensure the accuracy of the nautical chart data, converts the nautical chart data into a standard format, aligns the nautical chart data with the ship data and tidal data, extracts water depth information, channel information, shoals, reefs and other obstacle information from the nautical chart data, constructs a topographic map of the navigation area, rasterizes or vectorizes the nautical chart data, merges the tidal data with the nautical chart data and ship information, and uses a machine learning algorithm to build a prediction model to predict the depth change of the shallow water area at low tide.

[0016] Furthermore, the waterway safety depth prediction module adopts the real-time tidal change trend and shallow water depth analysis results to determine the safe depth waterway suitable for each ship to pass through: the water depth of the shallow water area is dynamically calculated according to the nautical chart data and tidal changes, and the time series of tidal data is obtained. The distribution of shallow water areas is obtained in real time through the time series of tidal data. The draft of the ship is dynamically evaluated according to factors such as the type of ship, load conditions, and cargo loaded in the cabin. Combined with the distribution of shallow water areas and the draft of each ship, the depth path that meets the safety of ship navigation is determined, and the time period distribution information of safe waterways in shallow water areas under tidal changes is obtained.

[0017] Furthermore, the ship passage priority calculation module, in combination with the channel safety prediction results, obtains the avoidance priority between ships according to the relative distance and speed between each ship, and determines the dynamic navigation path of multiple ships in coordination through the avoidance priority and shallow water channel information: the relative positions of multiple ships in multiple sea areas are analyzed through Beidou positioning, and the speed and heading of each ship are calculated in real time, and the distance ranking is generated by calculating the relative distance between each ship and other ships, and the collision risk is analyzed by calculating the relative speed and relative heading between the ships, and the risk level is divided according to the level of collision risk, the avoidance priority is determined according to the risk level, and the relative navigation status and avoidance priority list between the current ships are obtained according to the avoidance priority.

[0018] Furthermore, the distance Di,j between the ships is calculated based on the real-time position data, the distance matrix [Di,j] of each ship relative to other ships is obtained, and the closest ship j is determined for each ship; the real-time speed Vi of each ship is obtained, and the influence of the relative speed is expressed according to the square value of the speed difference (Vi-Vj); the tidal level change rate is calculated through the tidal monitoring data, H is the current tidal level height, is the rate of change of tidal height; using the current tidal height H and the rate of change of tidal level, use the formula Calculate and determine the tidal fluctuation influence coefficient f(Ti) of each ship. f(Ti) is the tidal fluctuation function, which represents the tidal fluctuation influence coefficient of the waters where the i-th ship is currently located. Based on the tidal fluctuation influence coefficient of each ship, use the priority coefficient evaluation formula Calculate the dynamic priority coefficient Pi of each ship. VI and Vj are the current speeds of the i-th and j-th ships respectively. Pi represents the dynamic priority coefficient of the i-th ship. The smaller the value, the higher the priority. Sort the priority coefficients of all ships, generate the avoidance priority order, and output the dynamic avoidance instruction according to the priority list.

[0019] Furthermore, the ship navigation scheduling module determines the dynamic navigation path of multi-ship collaboration through avoidance priority and shallow water channel information, and obtains the speed and heading adjustment instructions of each ship according to the dynamic path planning scheme: by accurately obtaining the real-time speed, heading and path adjustment parameters of each ship, and combining the constantly changing tide data under the influence of tidal fluctuations, the navigation path is dynamically evaluated and optimized, and based on the results of path planning, the speed and heading of each ship are calculated and dynamically adjusted to ensure safe and stable navigation in the complex environment of tidal changes and multi-ship collaborative navigation. The real-time dynamic data of multi-ship collaborative navigation is used to comprehensively analyze the relative positions, speeds and heading change trends between ships, flexibly adjust the speed and heading adjustment frequency, fully consider the balance between navigation stability and fuel efficiency, and reduce unnecessary frequent adjustments by formulating reasonable adjustment strategies to maximize fuel saving effects while ensuring safe distances and navigation coordination between ships.

[0020] Furthermore, the balance between navigation stability and fuel efficiency is fully considered, and unnecessary frequent adjustments are reduced by formulating reasonable adjustment strategies to maximize fuel saving effects, while ensuring safe distances and navigation coordination between ships, including: obtaining real-time tide data through a tidal monitoring system, and calculating the tide change rate ΔH per unit time; using Beidou positioning data to calculate the offset of each ship relative to its target path, and obtaining the average deviation distance ΔDi of each ship; calculating the deviation angle based on the angle between the current heading of the ship and the target path, and obtaining the deviation angle Δθi value of each ship; setting the environmental complexity coefficient k based on real-time tidal intensity, wind speed and other data to reflect the impact of the current navigation environment on the adjustment frequency; using the optimal adjustment frequency coefficient c is the smoothing factor, T last is the last adjustment interval, T ref The optimal frequency of speed and course adjustment under current tidal conditions is calculated for the reference time interval, F i It represents the optimal adjustment frequency coefficient of the i-th ship, and its unit is adjustment times / hour.

[0021] Beneficial effects of the present invention:

[0022] The belt sand ship safety dynamic management system proposed in the present invention solves the problem of lack of real-time perception of dynamic environment and optimization of multi-ship coordinated driving in traditional navigation management, and significantly improves navigation safety. By integrating tidal trend prediction, shallow water depth analysis and waterway safety planning modules, the system can monitor tidal fluctuations and dynamic changes in shallow water depth in real time, and provide safe waterway planning based on real-time data in combination with the ship's draft depth. This dynamic prediction capability effectively reduces the navigation risks of ships in shallow waters and areas with significant tidal fluctuations, ensuring the safe passage of ships in complex waters.

[0023] In addition, this system solves the real-time and accuracy issues in multi-ship coordinated avoidance through the ship passage priority calculation module and dynamic path planning function. The system uses the relative distance between ships, speed differences and heading change trends to dynamically calculate the avoidance priority, and combines the shallow water safety channel information to generate an optimized path for multi-ship coordinated travel. This function can effectively reduce the possibility of multi-ship encounters or path conflicts in dense waters, improve the efficiency of coordinated avoidance, and provide safety guarantees for multi-ship coordinated navigation.

[0024] At the same time, this solution achieves a balanced optimization between fuel efficiency and navigation stability. By introducing the optimal adjustment frequency formula, the speed and heading adjustment frequency of each ship is dynamically evaluated to reduce unnecessary frequent adjustment operations and maximize fuel saving effects. The system comprehensively considers the complexity of the environment, the historical adjustment time, and the influence of tidal fluctuations to formulate a reasonable adjustment strategy to optimize operating costs while ensuring navigation safety. Overall, the system can significantly improve the efficiency, safety, and economy of multi-ship collaborative navigation, and provide an efficient solution for intelligent ship scheduling in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a principle block diagram of the belt sand carrier safety dynamic management system of the present invention. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0027] Please refer to Figure 1 , a belt sand ship safety dynamic management system provided by the present invention, the system includes: a ship information collection module, used to collect ship information, including position, speed, and heading information.

[0028] Specifically, the ship information collection module includes the Beidou positioning system to obtain the current longitude and latitude of each ship in real time, the ship positioning compass to obtain the azimuth and heading data of the ship in real time, and the speed sensor to collect the speed information of the ship in real time and calculate the speed of the ship relative to the water flow. The Beidou positioning system, the positioning compass, and the speed sensor are all set on the hull, follow the movement of the hull, and obtain the hull information in real time. The speed sensor uses a Doppler radar or sonar speed meter. The data acquisition unit summarizes the data obtained by the Beidou positioning system, the positioning compass, and the speed sensor to ensure data synchronization and perform simple data verification on the data, such as checking the validity and integrity of the data. The data summarized by the data acquisition unit is transmitted to the cloud database for storage through the communication module. The data transmission can be based on the communication conditions of the area where the ship is located, using satellite communication Inmarsat / Iridium or 4G / 5G network for data upload, and the data is uploaded to the cloud database in real time using standard protocols such as MQTT / TCP / IP. All historical data are stored in chronological order to provide support for subsequent data query, analysis, and trajectory playback.

[0029] The tidal change trend prediction module collects tidal information of the navigation area and combines the ship information to obtain the tidal height and the tidal change trend of the area where the ship is located.

[0030] Specifically, tidal data is obtained through multiple channels, which are generally provided by national or regional marine meteorological bureaus, such as NOAA, the State Oceanic Administration, etc.; APIs that provide tidal data, such as NOAA TidePredictions API, Tide-forecast, XTide, etc., are used; tidal observation equipment is deployed in relevant sea areas to collect real-time tidal data in specific areas; or third-party service providers, such as MarineTraffic, provide more accurate tidal predictions and data sets. Integrate the data collected by multiple parties, clean up duplicate and abnormal data, ensure that there are no duplicates in the data, remove possible outliers, smooth tidal fluctuations through methods such as difference, remove abnormal instantaneous fluctuations, and reduce the impact of instantaneous fluctuations on data analysis. The integrated tidal data and ship information are merged, and the ship information and tidal data are aligned according to the timestamp according to the corresponding area to ensure that the two can be correctly matched to avoid errors caused by time. A tide prediction model was constructed, and the fused data was analyzed using random forest, XGBoost, and regression analysis algorithms. By analyzing the tide change trend, the height value of the current tide level and the expected height of future tidal fluctuations were obtained.

[0031] The shallow water depth change prediction module collects the nautical chart data of the navigation area, and combines the tidal height data with the current nautical chart water depth data for superposition analysis to determine the depth change of the shallow water area at low tide.

[0032] Specifically, obtain nautical chart data of the navigation area through multiple channels, integrate the obtained data, and ensure the accuracy of the nautical chart data. The nautical chart data comes from the International Nautical Chart Organization IHO: IHO provides global nautical chart data standards, and standardized formats such as S-57 and S-63 can be used to obtain nautical chart data of the navigation area; Electronic nautical chart ENC: Electronic nautical chart ENC issued by the maritime authorities of various countries is a standardized data set containing information such as water depth, waterway, shoal, obstacles, etc.; Commercial suppliers: Companies such as Navionics and Jeppesen provide nautical chart data, usually on a subscription basis; Open data sources: For example, some open nautical chart data provided by OpenStreetMap and MarineTraffic are suitable for obtaining information about some areas. Acquisition method: Through the API interface or downloading nautical chart data sets; Purchase commercial nautical chart data and obtain standardized files, such as the S-57 format; Obtain real-time sea conditions data through satellite remote sensing and other methods. Integrate the chart data obtained from multiple channels, compare the chart data at the same time and the same area, retain the correct data, remove duplicate data, correct the wrong data, and convert the chart data into a standard format. Align the chart data with the ship data and tide data, extract the water depth information, channel information, shoals, reefs and other obstacle information from the chart data, build a topographic map of the navigation area, rasterize or vectorize the chart data, map the extracted information with the tide data, dynamically evaluate the potential risks of obstacles according to the tidal changes, and obtain the possibility of ship passage at the lowest tide. Fuse the fused data with the ship data to analyze the impact of tides on ship navigation. Use random forests, SVR, etc. to build a prediction model to predict the impact of tidal changes on navigation in real time and predict the depth changes in shallow water areas at low tide.

[0033] The waterway safety depth prediction module uses real-time tidal change trends and shallow water depth analysis results to determine the safe depth of the waterway for each ship to pass through.

[0034] Specifically, shallow water refers to an area where the water depth is not enough for ships to pass through. Under different tidal conditions, the distribution and depth of shallow water will change. The water depth of shallow water is dynamically calculated based on chart data and tidal changes. Assume that at high tide, the water depth is deep; at low tide, the shoal may be exposed, resulting in insufficient water depth. Based on tidal changes, the water depth of shallow water should be updated in real time. For example, the water depth of a channel may be 5 meters at high tide, but it may drop to 2 meters at low tide. At this time, ships with a draft greater than 2 meters cannot pass. The water depth of shallow water is dynamically calculated based on chart data and tidal changes, and the time series of tidal data is obtained. The distribution of shallow water is obtained in real time through the time series of tidal data. The draft of each ship will change under different circumstances. The draft may change according to factors such as the type of ship, load, and cargo loaded in the cabin. The draft of a tanker is 10 meters. During the voyage, the draft of the ship will not change, but if it is a cargo ship, the draft may be adjusted with changes in the load. By obtaining the AIS data of the ship, the draft of each ship is known. Each ship usually has a minimum and maximum draft, and the system updates the draft according to the real-time situation of the ship, such as the cargo capacity. The draft of the ship is dynamically evaluated based on factors such as the type of ship, load, cargo loaded in the cabin, etc., and the depth path that meets the safety of ship navigation is determined by combining the distribution of shallow water areas and the draft of each ship, and the time distribution information of safe waterways in shallow water areas under tidal changes is obtained.

[0035] The ship passage priority calculation module, combined with the channel safety prediction results, obtains the avoidance priority between ships according to the relative distance and speed between each ship, and determines the dynamic navigation path of multi-ship coordination through the avoidance priority and shallow water channel information.

[0036] Specifically, each ship is equipped with a Beidou positioning system, which obtains the latitude and longitude, speed, heading and other information of the ship in real time, providing data support for subsequent priority calculation and path planning. By analyzing the data collected by the Beidou positioning system, the relative positions of multiple ships in the sea area are obtained, and the speed and heading of each ship are calculated in real time through the relative positions. By calculating the relative distance between each ship and other ships, a distance ranking is generated. The closer the relative distance, the higher the risk of collision. If there is the following ship position information, the unit is nautical miles, the position of ship A is (30.0, 120.0), the position of ship B is (30.2, 120.1), the position of ship C is (30.4, 120.3), and the position of ship D is (30.6, 120.5). The Euclidean distance formula is used to calculate the distance Di,j between the ships, obtain the distance matrix [Di,j] of each ship relative to other ships, and determine the closest ship j for each ship. The Euclidean distance formula is Where (x i,y i ) and (x j ,y j ) are the positions of ship i and ship j respectively. Therefore, the distance between ship A and ship B is The distance between ship A and ship C is The distance between ship A and ship D is The distance between ship B and ship C is The distance between ship B and ship D is The distance between ship C and ship D is Calculate the distance matrix Get the real-time speed Vi of each ship, and calculate the speed difference (V i -V j ) 2 Represents the influence of relative speed. If the speed of the ships is, the speed of ship A is 15 knots, the speed of ship B is 12 knots, the speed of ship C is 18 knots, and the speed of ship D is 10 knots. Therefore, the speed difference between ship A and ship B is calculated by square (V A -V B ) 2 =(15-12) 2 =9, the speed difference between ship A and ship C is (V A -V C ) 2 =(15-18) 2 =9, the speed difference between ship A and ship D is (V A -V D ) 2 =(15-10) 2 =25, the speed difference between ship B and ship C is (V B -V C ) 2 =(12-18) 2 =36, the speed difference between ship B and ship D is (V B -V D ) 2 =(12-10) 2 =4, the speed difference between ship C and ship D is (V C -V D ) 2 =(18-10) 2 =64, calculate the speed difference matrix The tidal level change rate is calculated through tidal monitoring data, where H is the current tidal level height. is the rate of change of tidal height. Using the current tidal height H and the tidal change rate, use the formula Calculate and determine the tidal fluctuation influence coefficient f(Ti) of each ship. f(Ti) is the tidal fluctuation function, which represents the tidal fluctuation influence coefficient of the waters where the i-th ship is currently located. Calculate the tidal fluctuation influence coefficient f(Ti ), if the tidal data is, the current tidal height H = 2.5 meters tidal change rate m / h, according to the tidal fluctuation function f(T i ) The tidal fluctuation influence coefficient of ship A is obtained as f(T A )=2.5×(1+0.1)=2.75, the tidal fluctuation coefficient of ship B is f(T B )=2.5×(1+0.1)=2.75, the tidal fluctuation influence coefficient of ship C is f(T C )=2.5×(1+0.1)=2.75, the tidal fluctuation influence coefficient of ship D is f(T D )=2.5×(1+0.1)=2.75. Based on the tidal fluctuation influence coefficient of each ship, the priority coefficient evaluation formula is used Calculate the dynamic priority coefficient Pi of each ship. VI and Vj are the current speeds of the i-th and j-th ships respectively. Pi represents the dynamic priority coefficient of the i-th ship. The smaller the value, the higher the priority. According to the priority coefficient evaluation formula, the priority coefficients of ship A and ship B are The priority coefficients of ship A and ship C are The priority coefficients of ship A and ship D are The priority coefficients of ship B and ship C are The priority coefficients of ship B and ship D are The priority coefficients of ship C and ship D are Sort the priority coefficients of all ships to generate the avoidance priority order, and output dynamic avoidance instructions according to the priority list. The smaller the priority value, the higher the priority. The priority order is:

[0037] 1. Ship A and Ship C: P = 23.5P = 23.5P = 23.5

[0038] 2. Ship A and Ship D: P = 35.5P = 35.5P = 35.5

[0039] 3. Ship A and Ship B: P = 52.5P = 52.5P = 52.5

[0040] 4. Ship B and Ship D: P = 11.9P = 11.9P = 11.9

[0041] 5. Ship B and Ship C: P = 137.0P = 137.0P = 137.0

[0042] 6. Ship C and ship D: P=236.0P=236.0P=236.0.

[0043] By calculating the relative speed and relative heading between the ships, the collision risk is analyzed. The greater the speed difference, the higher the collision risk, and the closer the relative heading, the greater the collision risk. The risk level is divided according to the level of collision risk. The risk level can be divided into: Low risk: the relative distance between the two ships is far, the speed difference is small, and the heading difference is large. Medium risk: the distance between the two ships is moderate, the headings are close, and the speed difference is large. High risk: the distance between the two ships is close, the headings are close, and the relative speed is large. The avoidance priority is determined according to the risk level, and the relative navigation status and avoidance priority list between the current ships are obtained according to the avoidance priority. Through risk analysis, the risk P between ship A and ship C is 23.5, and the risk level is medium risk. The risk P between ship A and ship D is 35.5, and the risk level is medium risk. The risk P between ship A and ship B is 52.5, and the risk level is low risk. The risk P between ship B and ship D is 11.9, and the risk level is high risk. The risk P between ship B and ship C is 137.0, and the risk level is low risk. The risk P between ship C and ship D is 236.0, and the risk level is low risk. Therefore, the avoidance priority list is: ship B and ship D have priority, ship A and ship C, ship A and ship D have second priority, and ship A and ship B, ship B and ship C, ship C and ship D do not need to actively avoid each other.

[0044] The ship navigation scheduling module is used to analyze the dynamic navigation path and obtain the speed and heading adjustment instructions of each ship through the dynamic navigation path, and then plan a reasonable navigation plan based on the speed and heading adjustment instructions of each ship.

[0045] Specifically, by accurately obtaining the real-time speed, heading and path adjustment parameters of each ship, and combining the constantly changing tide data under the influence of tidal fluctuations, the navigation path is dynamically evaluated and optimized. Based on the results of path planning, the speed and heading of each ship are calculated and dynamically adjusted to ensure safe and stable navigation in the complex environment of tidal changes and multi-ship collaborative navigation. By utilizing the real-time dynamic data of multi-ship collaborative navigation, the relative position, speed and heading change trends between ships are comprehensively analyzed, and the speed and heading adjustment frequency are flexibly adjusted. The balance between navigation stability and fuel efficiency is fully considered, and unnecessary frequent adjustments are reduced by formulating reasonable adjustment strategies to maximize fuel savings, while ensuring safe distances and navigation coordination between ships.

[0046] The method of fully considering the balance between navigation stability and fuel efficiency, reducing unnecessary frequent adjustments by formulating reasonable adjustment strategies, maximizing fuel saving effects, and ensuring safe distances and navigation coordination between ships includes: obtaining real-time tide data through a tide monitoring system, and calculating the tide change rate ΔH per unit time; using Beidou positioning data to calculate the offset of each ship relative to its target path, and obtaining the average deviation distance ΔDi of each ship; calculating the deviation angle according to the angle between the current heading of the ship and the target path, and obtaining the deviation angle Δθi value of each ship; setting the environmental complexity coefficient k according to real-time tidal intensity, wind speed and other data to reflect the impact of the current navigation environment on the adjustment frequency; using the optimal adjustment frequency coefficient c is the smoothing factor, T last is the last adjustment interval. If the last adjustment was relatively recent, the current adjustment frequency will be reduced. If the adjustment interval is relatively long, the frequency will be increased. ref It is the reference time interval and the reference adjustment time interval, which can be set to 0.5 to 2 hours and is used to control the frequency responsiveness. Exponential smoothing factor, which reduces the frequency of sharp changes as the time interval increases, F i represents the optimal adjustment frequency coefficient of the i-th ship, in units of adjustment times / hour. Specifically, the data collected from the target ship include: the average deviation distance ΔDi of ship A is 20, the heading deviation angle Δθi is 8, the transverse position change rate ΔHi is 0.2, the environmental complexity coefficient is 2.0, the smoothing factor C is 0.2, and the last adjustment interval T last is 0.5, the reference time interval T ref is 1.0. The average deviation distance ΔDi of ship B is 15, the heading deviation angle Δθi is 5, the transverse position change rate ΔHi is 0.15, the environmental complexity coefficient is 1.8, the smoothing factor C is 0.2, and the last adjustment interval T last is 1.0, the reference time interval T ref is 1.0. The average deviation distance ΔDi of ship C is 25, the heading deviation angle Δθi is 10, the transverse position change rate ΔHi is 0.1, the environmental complexity coefficient is 2.2, the smoothing factor C is 0.2, and the last adjustment interval T last is 1.5, reference time interval T ref is 1.0. Calculate the exponential smoothing factor e of ship A -0.5 / 1.0 =e -0.5 ≈0.606, exponential smoothing factor of ship B, exponential smoothing factor of ship C e -1.5 / 1.0 =e -1.5 ≈0.223, and the adjustment frequency of ship A is Ship B adjusts its frequency to The adjustment frequency of ship C is e -1.0 / 1.0 =e-1.0 ≈0.368.

[0047] This solution effectively takes into account the complexity of the environment, the degree of navigation deviation and the historical adjustment time, and realizes the intelligent optimization of the adjustment frequency. When the deviation distance is large or the environment is complex, the formula tends to increase the adjustment frequency to ensure safety; when the adjustment time interval is short, the frequency is gradually reduced to ensure operational stability. This dynamic and adaptive adjustment strategy fully demonstrates the practical value of the formula in complex navigation scenarios.

[0048] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. A belt sand ship safety dynamic management system, characterized in that: The system comprises: The ship information collection module is used to collect ship information, including location, speed, and heading information; The tidal change trend prediction module collects tidal information of the navigation area and combines the ship information to obtain the tidal height and the tidal change trend of the area where the ship is located; Shallow water depth change prediction module collects nautical chart data of the navigation area, combines tidal height data with current chart water depth data for superposition analysis, and determines the depth change of shallow water areas at low tide; The channel safety depth prediction module uses real-time tidal change trends and shallow water depth analysis results to determine the safe depth channel suitable for each ship to pass through; The ship passage priority calculation module combines the channel safety prediction results, obtains the avoidance priority between ships according to the relative distance and speed between ships, and determines the dynamic navigation path of multi-ship coordination through the avoidance priority and shallow water channel information; The ship navigation scheduling module determines the dynamic navigation path of multiple ships through avoidance priority and shallow water channel information, and obtains the speed and heading adjustment instructions of each ship according to the dynamic path planning plan.

2. The belt sand ship safety dynamic management system according to claim 1 is characterized in that: The ship information collection module is used to collect ship information: including the Beidou positioning system to obtain the current longitude and latitude position of each ship in real time, the ship positioning and directional compass to obtain the ship's azimuth and heading data in real time, the speed sensor to collect the ship's speed information in real time, and calculate the speed of the ship relative to the water flow. The collected ship information is aggregated by the data collection unit and then transmitted to the cloud database.

3. The belt sand ship safety dynamic management system according to claim 1 is characterized in that: The tidal change trend prediction module collects tidal information of the navigation area, and combines the ship information to obtain the tidal height and the tidal change trend of the area where the ship is located: obtain tidal data from multiple channels, and integrate the obtained tidal data, remove duplicate data and abnormal data, smooth tidal fluctuations through methods such as difference, reduce the impact of trend fluctuations on data analysis, fuse the integrated tidal data and the obtained ship information, align the data by timestamp, ensure that the two can be matched correctly, avoid errors caused by time, and use random forest, XGBoost, and regression analysis algorithms to analyze the fused data. By analyzing the tidal change trend, the height value of the current tide level and the expected height of future tidal fluctuations are obtained.

4. The belt sand ship safety dynamic management system according to claim 1 is characterized in that: The shallow water depth change prediction module collects the nautical chart data of the navigation area, and combines the tidal height data with the current nautical chart water depth data for superposition analysis to determine the depth change of the shallow water area at low tide: obtains the nautical chart data of the navigation area through multiple channels, integrates the obtained data to ensure the accuracy of the nautical chart data, converts the nautical chart data into a standard format, aligns the nautical chart data with the ship data and tidal data, extracts water depth information, channel information, shoals, reefs and other obstacle information from the nautical chart data, constructs a topographic map of the navigation area, rasterizes or vectorizes the nautical chart data, merges the tidal data with the nautical chart data and ship information, and uses a machine learning algorithm to build a prediction model to predict the depth change of the shallow water area at low tide.

5. The belt sand ship safety dynamic management system according to claim 1 is characterized in that: The waterway safety depth prediction module adopts the real-time tidal change trend and shallow water depth analysis results to determine the safe depth waterway suitable for each ship to pass through: the water depth of the shallow water area is dynamically calculated according to the nautical chart data and tidal changes, and the time series of tidal data is obtained. The distribution of shallow water areas is obtained in real time through the time series of tidal data. The draft of the ship is dynamically evaluated according to factors such as the type of ship, load conditions, and cargo loaded in the cabin. Combined with the distribution of shallow water areas and the draft of each ship, the depth path that meets the safety of ship navigation is determined, and the time period distribution information of the safe waterway in the shallow water area under tidal changes is obtained.

6. The belt sand ship safety dynamic management system according to claim 1 is characterized in that: The ship passage priority calculation module, combined with the channel safety prediction results, obtains the avoidance priority between ships according to the relative distance and speed between each ship, and determines the dynamic navigation path of multiple ships in coordination through the avoidance priority and shallow water channel information: the relative positions of multiple ships in multiple sea areas are analyzed through Beidou positioning, and the speed and heading of each ship are calculated in real time. The distance ranking is generated by calculating the relative distance between each ship and other ships. The collision risk is analyzed by calculating the relative speed and relative heading between the ships, and the risk level is divided according to the level of collision risk. The avoidance priority is determined according to the risk level, and the relative navigation status and avoidance priority list between the current ships are obtained according to the avoidance priority.

7. The belt sand ship safety dynamic management system according to claim 6 is characterized by: The distance between ships is calculated based on real-time position data. Di,j , get the distance matrix of each ship relative to other ships [Di,j] And for each ship, determine the closest ship j ; Get the real-time speed of each ship Vi , and according to the speed difference (Vi-Vj) The square value of represents the influence of relative speed; the tidal level change rate is calculated through tidal monitoring data. H is the current tide height, is the rate of change of tide height; using the current tide height H and the rate of change of tide level, using the formula Calculate and determine the tidal fluctuation influence coefficient for each ship f(Ti) , f(Ti) is the tidal fluctuation function, which represents the tidal fluctuation influence coefficient of the waters where the i-th ship is currently located; based on the tidal fluctuation influence coefficient of each ship, the priority coefficient evaluation formula is used Calculate the dynamic priority coefficient for each ship Pi , Vi and Vj are the current speeds of the ith and jth ships respectively, Pi represents the dynamic priority coefficient of the ith ship, the smaller the value, the higher the priority; the priority coefficients of all ships are sorted, the avoidance priority order is generated, and the dynamic avoidance instruction is output according to the priority list.

8. The belt sand ship safety dynamic management system according to claim 1 is characterized in that: The ship navigation scheduling module determines the dynamic navigation path of multi-ship coordination through avoidance priority and shallow water channel information, and obtains the speed and heading adjustment instructions of each ship according to the dynamic path planning scheme: by accurately obtaining the real-time speed, heading and path adjustment parameters of each ship, and combining the constantly changing tide data under the influence of tidal fluctuations, the navigation path is dynamically evaluated and optimized, and based on the results of path planning, the speed and heading of each ship are calculated and dynamically adjusted to ensure safe and stable navigation in the complex environment of tidal changes and multi-ship coordinated navigation. The real-time dynamic data of multi-ship coordinated navigation is used to comprehensively analyze the relative positions, speeds and heading change trends between ships, flexibly adjust the speed and heading adjustment frequency, fully consider the balance between navigation stability and fuel efficiency, and reduce unnecessary frequent adjustments by formulating reasonable adjustment strategies to maximize fuel saving effects, while ensuring safe distances and navigation coordination between ships.

9. The belt sand ship safety dynamic management system according to claim 8, characterized in that: The method of fully considering the balance between navigation stability and fuel efficiency, reducing unnecessary frequent adjustments by formulating reasonable adjustment strategies, maximizing fuel saving effects, and ensuring safe distances and navigation coordination between ships includes: obtaining real-time tide data through a tide monitoring system, and calculating the tide change rate ΔH per unit time; using Beidou positioning data to calculate the offset of each ship relative to its target path, and obtaining the average deviation distance ΔDi of each ship; calculating the deviation angle according to the angle between the current heading of the ship and the target path, and obtaining the deviation angle Δθi value of each ship; setting the environmental complexity coefficient k according to real-time tidal intensity, wind speed and other data to reflect the impact of the current navigation environment on the adjustment frequency; using the optimal adjustment frequency coefficient c is the smoothing factor, T last is the last adjustment interval, T ref The optimal frequency of speed and course adjustment under current tidal conditions is calculated for the reference time interval, F i It represents the optimal adjustment frequency coefficient of the i-th ship, and its unit is adjustment times / hour.

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