A risk assessment method for smart shipping waters based on remote sensing satellite data

Abstract

The invention discloses a method for risk assessment of smart shipping waters based on remote sensing satellite data, and relates to the field of remote sensing satellite technology; the method comprises the following steps: S10, establishing a rain and fog influence model; S20, establishing a hydrodynamic influence model; S30, establishing a model of average intensity in a Poisson process; S40, obtaining a waters risk assessment coefficient; the invention has the beneficial effect that: based on the method proposed by the invention, a risk coefficient can be defined for each waters, thereby indicating the risk faced by the current ship when sailing in the waters.

Description

Technical Field

[0001] The present invention relates to the field of remote sensing satellite technology, and more specifically, to a smart shipping waters risk assessment method based on remote sensing satellite data. Background Art

[0002] Smart shipping is to completely digitize information such as ship navigation, equipment, and energy consumption, and analyze and learn the ship's operating rules through artificial intelligence and big data technologies, so as to realize the intelligence of ship operation management in terms of smart navigation, smart engine room management, and smart energy efficiency management, and improve the safety of ship navigation and energy efficiency and economy.

[0003] The existing ship navigation assistance products or results mainly help ships plan routes and design speeds, but there is no quantitative assessment method for the risk of the sea areas where the ship's route passes. Due to the lack of assessment of the risk of the sea area, the route planning cannot help ship operators avoid dangerous sea areas, or give timely reminders when the ship enters a high-risk sea area. Summary of the invention

[0004] In order to overcome the deficiencies of the prior art, the present invention provides a smart shipping waters risk assessment method based on remote sensing satellite data.

[0005] The technical solution adopted by the present invention to solve the technical problem is: a smart shipping waters risk assessment method based on remote sensing satellite data, the improvement of which is that the method includes the following steps:

[0006] S10. Establish a rain and fog impact model, characterize the rain and fog impact by the overall motion intensity of rain and fog molecules, and obtain the following model:

[0007] F fog =lg(f+1)+Δ;

[0008] Among them, f is the rain and fog concentration coefficient, Δ is the offset coefficient, and both f and Δ are obtained through field measurements;

[0009] S20. Establish a hydrodynamic influence model, and use the wave coefficient to characterize the current operating environment of the ship, which is obtained from the following model:

[0010] F w = w cos α;

[0011] Among them, w is the wave coefficient, α is the relationship between the water flow direction and the ship's navigation direction, and both w and α are obtained through field measurements;

[0012] S30. Establish a model for the average intensity λ in the Poisson process:

[0013] λ=ρ·F fog -F w=ρ·[lg(f+1)+Δ]-w cosα;

[0014] Among them, ρ is the ship density, which is the number of ships per unit area;

[0015] S40. Obtain the water area risk assessment coefficient. According to the Poisson process model, the probability of an event occurring k times is:

[0016]

[0017] For the probability of a collision, 1-P(x=0), substituting into λ, we get:

[0018]

[0019] Where P is the water area risk assessment coefficient.

[0020] Furthermore, in step S10, the thicker the rain and fog, the higher the molecular concentration, the greater the number of molecules per unit volume, and the greater the overall motion intensity.

[0021] Furthermore, in step S10, the rain and fog concentration coefficient f is an indicator of the visibility level, which is represented by a value ranging from 0 to infinity. A larger value indicates a higher concentration and worse visibility.

[0022] Furthermore, in step S20, the wave coefficient w is an indicator of the impact of flow velocity and wave height on the ship, and is represented by a value ranging from 0 to infinity. A larger value indicates a more intense hydrodynamic condition.

[0023] Furthermore, in step S20, the larger the wave coefficient w is, the more difficult it is to control the ship.

[0024] Furthermore, in step S30, the ship density ρ is obtained by a remote sensing satellite through an optical or infrared method, or by using an AIS / VDES method.

[0025] The beneficial effects of the present invention are: different from the existing technical achievements that help ships plan routes and design speeds, this invention is the first to derive a risk assessment method for smart shipping waters that takes into account factors such as rain and fog, wave factors, and ship density based on satellite remote sensing detection. Based on the method proposed in the present invention, a risk coefficient can be determined for each water area, thereby indicating the risk currently faced by the ship when sailing in the water area, and providing navigation references and appropriate warnings for ship drivers and managers. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 The present invention is a flowchart of a method for risk assessment of smart shipping waters based on remote sensing satellite data. DETAILED DESCRIPTION

[0027] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0028] The following will clearly and completely describe the concept, specific structure and technical effects of the present invention in combination with the embodiments and drawings, so as to fully understand the purpose, characteristics and effects of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, other embodiments obtained by technicians in this field without creative work are all within the scope of protection of the present invention. In addition, all the connection / connection relationships involved in the patent do not refer to the direct connection of components, but refer to the formation of a better connection structure by adding or reducing connection accessories according to the specific implementation situation. The various technical features in the invention can be combined interchangeably without conflicting with each other.

[0029] Example 1

[0030] The present invention discloses a smart shipping waters risk assessment method based on remote sensing satellite data. Different from the existing technical achievements that help ships plan routes and design speeds, this invention is the first to derive a smart shipping waters risk assessment method that takes into account factors such as rain and fog, waves, and ship density detected based on satellite remote sensing. Based on the method proposed in the present invention, a risk coefficient can be assigned to each water area, thereby indicating the risk currently faced by the ship when sailing in the water area, and providing navigation references and appropriate warnings for ship drivers and managers.

[0031] Reference Figure 1 As shown, the present invention provides a specific embodiment. In this embodiment, the method includes the following steps:

[0032] S10. Establish a rain and fog impact model, characterize the rain and fog impact by the overall motion intensity of rain and fog molecules, and obtain the following model:

[0033] F fog =lg(f+1)+Δ:

[0034] Among them, f is the rain and fog concentration coefficient, Δ is the offset coefficient, and both f and Δ are obtained through field measurements;

[0035] In step S10, the thicker the rain and fog, the higher the molecular concentration, the greater the number of molecules per unit volume, and the greater the overall movement intensity; more specifically, the rain and fog concentration coefficient f is an indicator of the visibility level, which is represented by 0 to infinity. The larger the value, the higher the concentration and the worse the visibility.

[0036] S20. Establish a hydrodynamic influence model, and use the wave coefficient to characterize the current operating environment of the ship, which is obtained from the following model:

[0037] F w =w cos α:

[0038] Among them, w is the wave coefficient, α is the relationship between the water flow direction and the ship's navigation direction, and both w and α are obtained through field measurements;

[0039] In step S20, the wave coefficient w is an indicator of the impact of flow velocity and wave height on the ship, which is expressed from 0 to infinity. The larger the value, the more intense the hydrodynamic conditions. The larger the wave coefficient w, the more difficult it is to control the ship.

[0040] S30. Establish a model for the average intensity λ in the Poisson process:

[0041] λ=p·F fog -F w =ρ·[lg(f+1)+Δ]-w cosα;

[0042] Among them, ρ is the ship density, which is the number of ships per unit area;

[0043] In step S30, the ship density ρ is obtained by a remote sensing satellite through an optical or infrared method, or by using an AIS / VDES method.

[0044] S40. Obtain the water area risk assessment coefficient. According to the Poisson process model, the probability of an event occurring k times is:

[0045]

[0046] For the probability of a collision, 1-P(x=0), substituting into λ, we get:

[0047]

[0048] Where P is the water area risk assessment coefficient.

[0049] Example 2

[0050] The present invention provides a method for risk assessment of smart shipping waters based on remote sensing satellite data, the method comprising the following steps:

[0051] S10, using a remote sensing satellite to obtain an image of the sea area by optical means, performing image recognition to obtain the number of ships, and calculating the ship density ρ;

[0052] S20, on-site measurement of rain and fog concentration coefficient f and deviation coefficient Δ in various places; the deviation coefficient Δ in various parts of the world can be measured in advance to form a database for future calculations;

[0053] S30, measuring the wave coefficient w and the angle α between the water flow direction and the ship's navigation direction in the navigation area; the coefficient can be measured by remote sensing satellites;

[0054] S40, substitute into the formula:

[0055]

[0056] Calculation is performed to obtain the risk assessment coefficient of each sea area.

[0057] According to the risk assessment coefficient of each sea area, the coefficient is loaded into the smart shipping system, and the ship management unit can set the risk assessment coefficient as a high-risk threshold based on its own tolerance for ship navigation risks. The smart shipping system compares the ship's planned route with the ship's positioning data in real time. When it is determined that the ship's planned route passes through a high-risk area or the ship's actual position is close to or enters a high-risk area, the smart shipping platform issues an early warning and prompt, and the visualization platform displays it simultaneously.

[0058] Therefore, the present invention provides a smart shipping waters risk assessment method based on remote sensing satellite data, adopts the risk assessment coefficient of the sea area described in the present invention, and establishes a risk level assessment system for global sea areas, so that smart navigation can predictively reflect the navigation risks that ships will face, or display the risks faced by ships in the current waters, and provide navigation references and appropriate warnings for ship drivers and managers. According to statistics, due to early warning, ship drivers take preventive measures in advance, and the response time in dangerous situations during ship navigation is shortened by more than 70%, and the on-duty monitoring personnel at the shore command center have a 100% grasp rate of accidents.

[0059] The above is a specific description of the preferred implementation of the present invention, but the invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A smart shipping waters risk assessment method based on remote sensing satellite data, characterized in that: The method comprises the following steps: S10. Establish a rain and fog impact model, characterize the rain and fog impact by the overall motion intensity of rain and fog molecules, and obtain the following model: F fog =lg(f+1)+Δ; Among them, f is the rain and fog concentration coefficient, Δ is the offset coefficient, and both f and Δ are obtained through field measurements; S20. Establish a hydrodynamic influence model, and use the wave coefficient to characterize the current operating environment of the ship, which is obtained from the following model: F w =wcosα; Among them, w is the wave coefficient, α is the angle between the water flow direction and the ship's navigation direction, and both w and α are obtained through field measurements; S30. Establish a model for the average intensity λ in the Poisson process: λ=ρ·F fog -F w =ρ·[lg(f+1)+Δ]-wcosα; Among them, ρ is the ship density, which is the number of ships per unit area; S40. Obtain the water area risk assessment coefficient. According to the Poisson process model, the probability of an event occurring k times is: For the probability of a collision, 1-P(x=0), substituting into λ, we get: Where P is the water area risk assessment coefficient.

2. According to claim 1, a smart shipping waters risk assessment method based on remote sensing satellite data is characterized in that: In step S10, the thicker the rain and fog, the higher the molecular concentration, the greater the number of molecules per unit volume, and the greater the overall motion intensity.

3. The method for risk assessment of smart shipping waters based on remote sensing satellite data according to claim 1 is characterized in that: In step S10, the rain and fog concentration coefficient f is an indicator of the visibility level, which is represented by a value ranging from 0 to infinity. A larger value indicates a higher concentration and worse visibility.

4. The method for risk assessment of smart shipping waters based on remote sensing satellite data according to claim 1 is characterized in that: In step S20, the wave coefficient w is an indicator of the impact of flow velocity and wave height on the ship, and is represented by a value ranging from 0 to infinity. The larger the value, the more intense the hydrodynamic conditions.

5. The method for risk assessment of smart shipping waters based on remote sensing satellite data according to claim 1 is characterized in that: In step S20, the greater the wave coefficient w is, the greater the difficulty in maneuvering the ship is.

6. The method for risk assessment of smart shipping waters based on remote sensing satellite data according to claim 1 is characterized in that: In step S30, the ship density ρ is obtained by a remote sensing satellite through an optical or infrared method, or by using an AIS / VDES method.

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