Ship monitoring and dispatching operation management system based on Internet of Things

Abstract

The invention belongs to the technical field of ship dispatching management, and discloses a ship monitoring and dispatching operation management system based on the Internet of Things. The system comprises a ship data acquisition module, a channel data monitoring module, a route planning and scheduling module and a risk early warning module. The ship data acquisition module collects ship operation data such as navigational speed, Beidou GIS positioning and immersion depth. The channel data monitoring module collects basic data and meteorological data of a high-risk channel area. The risk early warning module obtains a risk leg change water level and a predicted water depth through a prediction unit according to weather and current water level data and a pre-training model, a risk leg adjustment unit adjusts a risk area according to the risk leg change water level and the predicted water depth, and the risk early warning unit generates a risk area graph and a grade according to ship draft and a new risk area area. Early warning is performed according to low, medium and high risks, and an instruction is given. And the route planning and scheduling module generates a scheduling scheme according to the ship operation data, the risk area map and the grade so as to solve the problem of uncomfortable remote supervision and scheduling of ships in inland rivers, valleys and canyons.

Description

Technical Field

[0001] The present invention relates to the technical field of ship dispatching management, and particularly to a ship monitoring and dispatching operation management system based on the Internet of Things. Background Art

[0002] At present, in provinces with developed inland waterways in China, such as Jiangsu, Zhejiang, and Chongqing, due to the wide river channels, mostly first-class waterways, the channels are wide and the water depth is relatively large, which is suitable for large ships to navigate; and they are navigable all year round, with relatively stable water level changes and less affected by seasons. Most of them have achieved full-scale perception of ship data and remote supervision of ships. At present, there are relatively mature products for sea transportation, while there are few products available for inland water transportation, and they are mainly applied to areas with good waterway conditions such as the Yangtze River and the Beijing-Hangzhou Grand Canal. For example, the current main products are ShipView and ShipX. ShipX is mainly engaged in the shipping industry, and the platform pays more attention to safety monitoring, but there are still deficiencies in ship dispatching. The ship data of the ShipView platform is mainly transmitted relying on AIS signals; however, in the southwestern region, such as Guizhou Province, mainly the Wujiang River, Chishui River, etc., the waterways are often in valleys and canyons, with high mountains standing on both sides, which will block and reflect the AIS signals, causing serious attenuation of the signals during transmission; the waterways are narrow, curved, with shallow water depth, and there are many shoals and rapids; when the AIS signal propagates in a straight line, it is easy to encounter propagation obstacles at bends and cannot effectively cover the area behind the bend, resulting in weak inland river signals of AIS in areas with many mountains, narrow and curved waterways, and easy signal loss, making it impossible to achieve real-time positioning of ships. Moreover, the waterways in such areas are greatly affected by seasons, with the water level rising in the rainy season and falling in the dry season, and the navigation conditions are unstable. Summary of the Invention

[0003] Aiming at the deficiencies of the prior art, the technical problem solved by the present invention is to provide a ship monitoring and dispatching operation management system based on the Internet of Things, which solves the problem that the existing ship remote supervision platform is not applicable to the valleys and canyons of inland rivers; and due to the large seasonal influence on the waterways in such areas, the water level rises in the rainy season and falls in the dry season, and the navigation conditions are unstable, the existing ship dispatching is not applicable.

[0004] To solve the above problems, the technical solution adopted by the present invention is: a ship monitoring and dispatching operation management system based on the Internet of Things, including a ship data collection module, a waterway data monitoring module, a route planning and dispatching module, and a risk warning module;

[0005] The ship data collection module is used to collect the operation data of the ship, and the operation data includes the ship speed, Beidou GIS positioning data, and draft depth;

[0006] The waterway data monitoring module is used to collect data of high-risk waterway areas, and the waterway data includes basic data and meteorological data, and the basic data includes water depth data, waterway width data, and water level data;

[0007] The risk warning module includes a prediction unit, a risk section adjustment unit, and a risk warning unit. The prediction unit is used to obtain the changing water level of the risk section through a pre-trained prediction model based on meteorological data and current water level data, and obtain the predicted water depth data of the risk section according to the changing water level. The risk section adjustment unit is used to adjust the risk area of the risk section according to the predicted water depth data to generate a new risk section area. The risk warning unit is used to generate a risk area map of the current ship based on the draft depth of the obtained ship and the new risk section area, and obtain the risk area level. And issue a warning message and an adjustment instruction according to the risk area level.

[0008] The risk area levels include low risk, medium risk, and high risk. If the risk level is low risk, no warning is given. If the risk level is medium risk, a speed reduction adjustment instruction is generated and a warning is issued. If the risk level is high risk, a detour adjustment instruction is generated and a warning is issued.

[0009] The route planning and scheduling module is used to generate a scheduling plan based on the operation data of the current ship, the risk area map, and the risk area level.

[0010] The beneficial effects of this solution are as follows: By means of meteorological data, this solution uses the prediction unit to accurately predict the water level change of the risk section, and updates the risk area of the risk section in real time according to the prediction result to generate new risk area data. It is convenient to avoid the risk areas that do not meet the navigation conditions in advance during subsequent route planning, thus providing a strong guarantee for the safety of ship navigation. At the same time, the system provides different navigation guidelines for ships according to the different risk levels presented in the risk area map.

[0011] Since the ships sailing in the waterway are of different sizes and have different draft depths, the risk areas that do not meet the navigation water depth requirements for large ships may not be applicable to medium or small ships. Therefore, by predicting the water level change and customizing different risk area maps in combination with the draft depths of different ships, it is used to guide the navigation of different types of ships, so as to realize the route planning for different ships according to the customized risk area maps. And according to the dynamically updated risk area map, the route is flexibly and dynamically adjusted. Whether in the complex waterway environment where sudden heavy rain causes a sudden change in water level or the tidal rise and fall affects the water depth of the waterway, it can ensure that the ship can always sail safely and efficiently, significantly improving the safety and efficiency of ship navigation.

[0012] Further, the prediction unit is further configured to obtain the change time point of the changing water level, and calculate the time difference between the time point when the ship travels to the risk section and the change time point according to the current ship speed and positioning. The risk warning unit is further configured to, when the time difference is greater than a set time threshold, not adjust the risk area map of the current ship according to the area of the new risk section.

[0013] When the time difference is greater than the set time threshold, it indicates that the ship has reached or passed through the risk area before the water level changes, and the time of arriving in advance is greater than the set time threshold, indicating that when the ship passes through, the risk area is mainly distributed according to the original risk area map, the water level change is small, and the subsequent water level change has little impact on the ship. Therefore, the ship sails according to the original risk area map, and the safety can also be guaranteed, and there is no need to update the risk area map frequently.

[0014] Further, the risk warning unit dynamically adjusts the time threshold T according to the areas of the risk areas with different risk levels in the risk area map;

[0015]

[0016] Among them, K r represents the risk level coefficient, where the high risk K r = 1.5, the medium risk K r = 1, the low risk K r = 0.5; ΔA r represents the risk area change rate; ΔH represents the water level change rate, positive for rising and negative for falling; V S represents the current ship speed.

[0017] The update time of the risk area map is controlled by using a dynamically changing time threshold, so as to adapt to the water level change response of different risk levels; taking the high-risk area as an example; when the high-risk area increases, the time when the ship arrives at the high-risk area in advance is closer to the water level change time, that is, the time difference is smaller, the more dangerous it is; the greater the time difference of passing through the high-risk area in advance, the safer it is; therefore, when the high-risk area increases, the time threshold for not updating the risk area map is large; when the time difference between the ship arriving at the high-risk area in advance and the water level change is greater than the time threshold, it indicates that the ship passed through the high-risk area long before the water level change, that is, before the high-risk area expanded, and the change of the high-risk area has little impact on the ship. Therefore, there is no need to update the risk area map for this ship. If it is less than the time threshold, the risk area map needs to be updated immediately according to the water level change; while the change of the medium-risk area has little impact on the ship's travel, that is, when the ship arrives at the risk area in advance for a short time, the risk area map may not be updated either.

[0018] Further, the risk area level D is judged by the following formula;

[0019]

[0020] Among them, d1 represents low risk, d2 represents medium risk, d3 represents high risk, H represents water depth, C represents the draft of the ship, S represents the safety threshold, and m represents the grade difference.

[0021] According to the difference between the current water depth and the draft of the current ship and the safety threshold, different risk levels are divided, and a customized risk area map is generated for ships with different drafts.

[0022] Furthermore, the safety threshold S is dynamically adjusted according to the water level change rate, and the adjustment formula is as follows;

[0023] S = S base + (-k·ΔH)

[0024] Among them, S base represents the basic safety margin, ΔH represents the water level change rate, which is positive for rising and negative for falling; k is the adjustment coefficient.

[0025] By predicting water level data, when the water level continues to rise, the actual water depth of the waterway increases at this time, and the safety threshold can be appropriately reduced to narrow the risk area. When the water level continues to fall, the actual water depth of the waterway decreases, and the safety threshold needs to be increased to expand the risk area. By predicting water level data and the water level change rate, the safety margin can be dynamically adjusted to make the risk area division more accurate, thus ensuring the safe navigation of ships.

[0026] Furthermore, the route planning and scheduling module includes a waterway network diagram construction unit, a filtering and dynamic update unit, and a path generation unit;

[0027] The waterway network diagram construction unit is used to construct a waterway network diagram according to waterway nodes and edges. The nodes include waterway intersections, ports, the starting and ending points of waterway sections; each edge includes the sailing time T ij , fuel consumption F ij , risk value R ij and constraint condition marks. The constraint condition marks include whether the draft, waterway width, and turning radius requirements are met;

[0028] The filtering and dynamic update unit is used to remove infeasible edges that do not meet the requirements according to the constraint conditions; and recalculate the feasibility of the edges when the risk area is updated, and remove the infeasible edges;

[0029] The path generation unit uses an improved Dijkstra algorithm to calculate the optimal route. The improved Dijkstra algorithm calculates the optimal route with the lowest comprehensive cost as the optimization goal; the comprehensive cost formula Cost is as follows;

[0030] Cost = ω1·T ij + ω2·F ij + ω3·R ij

[0031] Wherein, ω1, ω2, and ω3 are weight coefficients and satisfy ω1 + ω2 + ω3 = 1; the sailing time T ij , the fuel consumption F ij , the risk value R ij , are calculated through the following formulas;

[0032] T ij = d ij / v

[0033] F ij = d ij ·v 3 ·τ

[0034] R ij = L ij / Q

[0035] Wherein, d ij represents the voyage, v represents the sailing speed, τ represents the ship energy efficiency coefficient, L ij represents the length of the risk section, and Q represents the total length of the voyage section.

[0036] The traditional Dijkstra algorithm uses a single cost as the weight, such as distance, to find the shortest path. This solution transforms multiple objectives into a single-objective weight sum through a comprehensive cost function; thus, realizing the embedding of the multi-objective optimization function into the Dijkstra algorithm. Combining dynamic waterway map update and constraint handling, an optimal path that takes into account time, fuel, and risk can be generated. Therefore, the sailing safety and economy of ships in complex waterways can be effectively improved. Brief Description of the Drawings

[0037] Figure 1 It is a schematic diagram of the system module of the present invention. Detailed Description of the Embodiments

[0038] The following is a further detailed description through specific embodiments:

[0039] Basically as shown in the appendix Figure 1 : A ship monitoring and dispatching operation management system based on the Internet of Things, including a ship data acquisition module, a waterway data monitoring module, a route planning and dispatching module, and a risk warning module;

[0040] The ship data acquisition module is used to collect the operation data of the ship, and the operation data includes the sailing speed, Beidou GIS positioning data, and draft depth;

[0041] The waterway data monitoring module is used to collect data in high-risk waterway areas. The waterway data includes basic data and meteorological data. The basic data includes water depth data, waterway width data, and water level data;

[0042] The risk warning module includes a prediction unit, a risk section adjustment unit, and a risk warning unit. The prediction unit is used to obtain the changing water level of the risk section through a pre-trained prediction model based on meteorological data and the current water level data, and obtain the predicted water depth data of the risk section according to the changing water level. The risk section adjustment unit is used to adjust the risk area of the risk section according to the predicted water depth data to generate a new risk area of the risk section. The risk warning unit is used to generate a risk area map of the current ship based on the draft depth of the ship and the new risk section area, and obtain the risk area level; and issue a warning message and an adjustment instruction according to the risk area level;

[0043] The prediction unit is also used to obtain the change time point of the changing water level, and calculate the time difference between the travel time point of the ship to the risk section and the change time point according to the current ship speed and positioning. The risk warning unit is also used to not adjust the risk area map of the current ship according to the new risk section area when the time difference is greater than the set time threshold. Dynamically adjust the time threshold T according to the risk area of different risk levels in the risk area map;

[0044]

[0045] Among them, K r represents the risk level coefficient, where the high risk K r = 1.5, the medium risk K r = 1, the low risk K r = 0.5; ΔA r represents the risk area change rate; ΔH represents the water level change rate, positive for rising and negative for falling; V S represents the current ship speed.

[0046] The risk area level includes low risk, medium risk, and high risk; when the risk level is low risk, no warning is issued. When the risk level is medium risk, a speed reduction adjustment instruction is generated and a warning is issued. When the risk level is high risk, a detour adjustment instruction is generated and a warning is issued; the risk area level D is judged by the following formula;

[0047]

[0048] Among them, d1 represents low risk, d2 represents medium risk, d3 represents high risk, H represents water depth, C represents the draft depth of the ship, S represents the safety threshold, and m represents the level difference. The safety threshold S is dynamically adjusted according to the water level change rate, and the adjustment formula is as follows;

[0049] S = Sbase +(-k·ΔH)

[0050] Wherein, S base represents the basic safety margin, ΔH represents the water level change rate, with the unit of m / h, rising is positive and falling is negative; k is an adjustment coefficient. For example, when the water level drops by 0.1 m per hour, S base is taken as 0.5 m and k is taken as 0.2, then S = 0.5 + (-0.2)·(-0.1) = 0.52 m, and the safety threshold increases.

[0051] By predicting the water level data, when the water level is rising continuously, the actual water depth of the waterway increases at this time, and the safety threshold can be appropriately reduced to narrow the risk area. When the water level is falling continuously, the actual water depth of the waterway decreases, and the safety threshold needs to be increased to expand the risk area. By predicting the water level data and the water level change rate, the safety margin can be dynamically adjusted to make the division of the risk area more accurate, thus ensuring the safe navigation of ships.

[0052] The route planning and scheduling module is used to generate a scheduling plan according to the operation data of the current ship, the risk area map, and the risk area level. The route planning and scheduling module includes a waterway network diagram construction unit, a filtering and dynamic update unit, and a path generation unit;

[0053] The waterway network diagram construction unit is used to construct a waterway network diagram according to the waterway nodes and edges. The nodes include waterway intersections, ports, the starting and ending points of waterway sections; each edge includes the navigation time T ij 、fuel consumption F ij 、risk value R ij and a constraint condition mark. The constraint condition mark includes whether the waterway section of this edge meets the requirements of the ship's draft, waterway width, and turning radius;

[0054] The filtering and dynamic update unit is used to remove infeasible edges that do not meet the requirements according to the constraint conditions; and recalculate the feasibility of the edges when the risk area is updated and remove the infeasible edges;

[0055] The path generation unit uses an improved Dijkstra algorithm to calculate the optimal route. The improved Dijkstra algorithm calculates the optimal route with the lowest comprehensive cost as the optimization goal; the comprehensive cost formula Cost is as follows;

[0056] Cost = ω1·T ij +ω2·F ij +ω3·R ij

[0057] Wherein, ω1, ω2, and ω3 are weight coefficients, and ω1 + ω2 + ω3 = 1 is satisfied. The weight coefficients can be dynamically adjusted according to the priority, and the value of ω3 is dynamically adjusted according to the change of the real-time risk level. For example, when the risk level increases, the value of ω3 increases.

[0058] Sailing time T ij , Fuel consumption F ij , Risk value R ij , are calculated by the following formulas;

[0059] T ij = d ij / v

[0060] F ij = d ij · v 3 · τ

[0061] R ij = L ij / Q

[0062] Wherein, d ij represents the voyage, v represents the speed, τ represents the ship energy efficiency coefficient, L ij represents the length of the risk section, and Q represents the total length of the voyage.

[0063] The traditional Dijkstra algorithm uses a single cost as the weight, such as distance, to find the shortest path. This solution transforms multiple objectives into a single objective weight sum through a comprehensive cost function; thus, embedding the multi-objective optimization function into the Dijkstra algorithm, combined with dynamic waterway map update and constraint handling, can generate an optimal path that takes into account time, fuel, and risk. Therefore, it can effectively improve the navigation safety and economy of ships in complex waterways.

[0064] The above are only embodiments of the present invention, and common knowledge such as specific structures and characteristics known in the solution is not described in detail here. It should be noted that for those skilled in the art, without departing from the structure of the present invention, several modifications and improvements can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the implementation effect of the present invention and the practicality of the patent. The protection scope required by this application should be subject to the content of its claims, and the specific implementation manners described in the specification can be used to interpret the content of the claims.

Claims

1. A ship monitoring and dispatching operation management system based on the Internet of Things, characterized in that: It includes a ship data acquisition module, a waterway data monitoring module, a route planning and scheduling module, and a risk warning module; The ship data acquisition module is used to collect the operation data of the ship, and the operation data includes the ship speed, Beidou GIS positioning data, and draft depth; The waterway data monitoring module is used to collect the data of high-risk waterway areas. The waterway data includes basic data and meteorological data. The basic data includes water depth data, waterway width data, and water level data; The risk warning module includes a prediction unit, a risk section adjustment unit, and a risk warning unit. The prediction unit is used to obtain the changing water level of the risk section through a pre-trained prediction model based on the meteorological data and the current water level data, and obtain the predicted water depth data of the risk section according to the changing water level. The risk section adjustment unit is used to adjust the risk area area of the risk section according to the predicted water depth data to generate a new risk section area. The risk warning unit is used to generate a risk area map of the current ship according to the draft depth of the ship and the new risk section area obtained, and obtain the risk area level; and issue a warning message and an adjustment instruction according to the risk area level; The risk area levels include low risk, medium risk, and high risk; if the risk level is low risk, no warning is given. If the risk level is medium risk, a speed reduction adjustment instruction is generated and a warning is issued. If the risk level is high risk, a detour adjustment instruction is generated and a warning is issued; The route planning and scheduling module is used to generate a scheduling plan according to the operation data, risk area map, and risk area level of the current ship.

2. The ship monitoring and dispatching operation management system based on the Internet of Things according to claim 1, wherein: The prediction unit is also used to obtain the change time point of the changing water level, and calculate the time difference between the time point when the ship travels to the risk section and the change time point according to the current ship speed and positioning. The risk warning unit is also used to not adjust the risk area map of the current ship according to the new risk section area when the time difference is greater than the set time threshold.

3. The ship monitoring and dispatching operation management system based on the Internet of Things according to claim 2, characterized in that: The risk warning unit dynamically adjusts the time threshold T according to the risk area areas of different risk levels in the risk area map; Among them, K r represents the risk level coefficient, where the high risk K r = 1.5, the medium risk K r = 1, the low risk K r = 0.5; ΔA r represents the risk area change rate; ΔH represents the water level change rate, rising is positive and falling is negative; V S represents the current ship speed.

4. The ship monitoring and dispatching operation management system based on the Internet of Things according to claim 1, characterized in that: The risk area level D is judged by the following formula; Among them, d1 represents low risk, d2 represents medium risk, d3 represents high risk, H represents water depth, C represents the draft depth of the ship, S represents the safety threshold, and m represents the grade difference.

5. The ship monitoring and dispatching operation management system based on the Internet of Things according to claim 4, characterized in that: The safety threshold S is dynamically adjusted according to the water level change rate, and the adjustment formula is as follows; S = S base + (-k·ΔH) Among them, S base represents the basic safety margin, ΔH represents the water level change rate, which is positive for rising and negative for falling; k is the adjustment coefficient.

6. The ship monitoring and dispatching operation management system based on the Internet of Things according to claim 1, wherein: The route planning and scheduling module includes a waterway network diagram construction unit, a filtering and dynamic update unit, and a path generation unit; The shipping lane network diagram construction unit is used to construct a shipping lane network diagram based on shipping lane nodes and edges. The nodes include shipping lane intersections, ports, the starting points and ending points of shipping segments; each edge includes the sailing time T ij , the fuel consumption F ij , the risk value R ij and a constraint condition flag, and the constraint condition flag includes whether the draft depth, the shipping lane width, and the turning radius requirements are met; The filtering and dynamic update unit is used to remove infeasible edges that do not meet the requirements according to the constraint conditions; and recalculate the feasibility of the edges when the risk area is updated, and remove the infeasible edges; The path generation unit uses an improved Dijkstra algorithm to calculate the optimal route. The improved Dijkstra algorithm calculates the optimal route with the lowest comprehensive cost as the optimization goal. The comprehensive cost formula Cost is as follows; Cost = ω1·T ij + ω2·F ij + ω3·R ij Among them, ω1, ω2, and ω3 are weight coefficients and satisfy ω1 + ω2 + ω3 = 1; the sailing time T ij , the fuel consumption F ij , the risk value R ij , is calculated through the following formula; T ij = d ij / v F ij = d ij · v 3 · τ R ij = L ij / Q Among them, d ij represents the voyage, v represents the speed, τ represents the ship energy efficiency coefficient, L ij represents the length of the risky voyage section, and Q represents the total voyage length.

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