A verification method and device for a port fog navigation intelligent decision-making system

Abstract

The present application proposes a verification method and device for an intelligent decision-making system for port fog navigation. The method includes: using a laser radar to obtain port terrain and terminal point cloud data, combining it with an electronic nautical chart to construct a port model; and constructing a ship model based on the target ship drawings and AIS historical data. Simulate different foggy conditions, calculate the collision and grounding safety factors of the target ship, and generate a risk decision text by comparing it with the preset threshold. Statistically calculate the success rate of downshifting for different risks, and calculate the decision rationality evaluation index. Combine the collision and grounding safety factors and the decision rationality index to generate a comprehensive fog navigation evaluation index, compare it with the preset standard value, and generate an optimized decision instruction, which is sent to the management personnel terminal. This method optimizes navigation decisions and improves the safety and efficiency of port fog navigation by highly simulating the real environment and combining risk assessment and decision rationality assessment.

Description

Technical Field

[0001] The present application belongs to the field of fog navigation decision-making, and in particular relates to a verification method and device for a port fog navigation intelligent decision-making system. Background Art

[0002] As the core hub for ship transportation, ports face significantly increased navigation risks in low-visibility weather, especially foggy weather.

[0003] Traditional verification methods for intelligent decision-making systems for port fog navigation rely primarily on sensor-based information collection, data processing, and analysis to generate decision recommendations. However, this approach suffers from oversimplification, misjudgment, and reduced timeliness of historical data. This leads to larger errors in decision-making results and makes it incapable of fully adapting to the complex and changing port fog navigation environment. Summary of the Invention

[0004] The purpose of this application is to overcome the defects in the above-mentioned prior art and provide a method and device for verifying the port fog navigation intelligent decision-making system.

[0005] This application provides a verification method for a port fog navigation intelligent decision-making system, including:

[0006] Obtaining point cloud data of the target port terrain and docks through LiDAR; obtaining channel water depth and obstacle coordinates based on electronic nautical charts; and constructing a port model based on the point cloud data, channel water depth, and obstacle coordinates.

[0007] Obtain ship size parameters based on target ship drawings and build a ship model based on AIS historical data;

[0008] Generate foggy conditions by simulating light fog, moderate fog and dense fog in different levels;

[0009] Calculating a collision safety factor and a grounding safety factor of a target ship based on the port model, the ship model, and foggy weather conditions;

[0010] Comparing the collision safety factor and the grounding safety factor with preset thresholds respectively, and generating decision texts corresponding to no risk, medium risk or high risk respectively according to the comparison results;

[0011] Based on the decision text, statistics are collected on the high-risk downshift success rate, the medium-risk downshift success rate, and the low-risk downshift success rate, and a decision rationality evaluation index is calculated by weighting;

[0012] generating a comprehensive fog navigation assessment index based on the collision safety factor, grounding safety factor, and decision rationality assessment index;

[0013] Based on the comparison result of the comprehensive fog navigation evaluation index and the preset standard value, an optimization decision instruction is generated and sent to the management terminal.

[0014] Optionally, ship size parameters are obtained based on the target ship drawings and a ship model is constructed in combination with AIS historical data, including:

[0015] The ship size parameters are obtained based on the target ship drawings, and the ship motion characteristic model is established by combining the ship turning radius and acceleration extracted from AIS historical data.

[0016] Optionally, in calculating the collision safety factor and the grounding safety factor of the target ship based on the port model, the ship model and the foggy weather condition, the calculation of the collision safety factor includes:

[0017] Obtain the velocity vector and relative position vector of the target ship and other ships, and calculate the closest approach distance and closest approach time between the two ships;

[0018] Comparing the closest point distance with a preset distance safety threshold to obtain a first comparison value, and comparing the closest point time with a preset time safety threshold to obtain a second comparison value;

[0019] A collision safety factor is calculated using a nonlinear logarithmic function according to the first comparison value and the second comparison value.

[0020] Optionally, in calculating the collision safety factor and the grounding safety factor of the target ship based on the port model, the ship model and the foggy weather condition, the calculation of the grounding safety factor includes:

[0021] Monitor the real-time water depth and draft data of the target ship to obtain the excess water depth under the keel;

[0022] Obtain the coordinate deviation between the current position of the target ship and the planned route position, and calculate the course deviation;

[0023] Comparing the excess water depth under the keel with a preset water depth safety threshold to obtain a third comparison value, and comparing the channel deviation with a preset deviation safety threshold to obtain a fourth comparison value;

[0024] A stranding safety factor is calculated using an exponential function according to the third comparison value and the fourth comparison value.

[0025] Optionally, the collision safety factor and the grounding safety factor are compared with preset thresholds respectively, and decision texts corresponding to no risk, medium risk or high risk are generated according to the comparison results, including:

[0026] If the collision safety factor and the grounding safety factor are both greater than or equal to a preset threshold, a risk-free decision is generated and navigation is maintained;

[0027] If the collision safety factor is less than a preset threshold and the grounding safety factor is greater than or equal to a preset threshold, a medium-risk decision is generated and a detour route is generated;

[0028] If the collision safety factor is greater than or equal to a preset threshold and the grounding safety factor is less than a preset threshold, a medium-risk decision is generated and the course is corrected;

[0029] If the collision safety factor and the grounding safety factor are both less than a preset threshold, a high-risk decision is generated and emergency anchoring is performed.

[0030] The present application also provides a verification device for a port fog navigation intelligent decision-making system, comprising:

[0031] The port module uses LiDAR to obtain point cloud data of the target port terrain and docks; obtains the channel water depth and obstacle coordinates based on the electronic nautical chart; and constructs a port model based on the point cloud data, channel water depth, and obstacle coordinates.

[0032] The ship module obtains ship size parameters based on the target ship drawings and builds a ship model based on AIS historical data;

[0033] The weather module generates foggy conditions by simulating light fog, moderate fog, and dense fog in different levels;

[0034] A coefficient module, which calculates the collision safety factor and the grounding safety factor of the target ship based on the port model, the ship model and the foggy weather condition;

[0035] A decision module compares the collision safety factor and the grounding safety factor with preset thresholds, and generates decision texts corresponding to no risk, medium risk, or high risk, respectively, based on the comparison results;

[0036] an index module, which calculates a high-risk downshift success rate, a medium-risk downshift success rate, and a low-risk downshift success rate based on the decision text, and calculates a decision rationality evaluation index by weighting;

[0037] A comprehensive module generates a comprehensive fog navigation evaluation index based on the collision safety factor, the grounding safety factor and the decision rationality evaluation index;

[0038] The management module generates an optimization decision instruction based on the comparison result of the comprehensive fog navigation evaluation index and the preset standard value and sends it to the management terminal.

[0039] Optionally, the ship module obtains ship size parameters based on the target ship model drawing and constructs a ship model in combination with AIS historical data, including:

[0040] The ship size parameters are obtained based on the target ship drawings, and the ship motion characteristic model is established by combining the ship turning radius and acceleration extracted from AIS historical data.

[0041] Optionally, the coefficient module calculates the collision safety factor and the grounding safety factor of the target ship based on the port model, the ship model and the foggy weather condition, and the calculation of the collision safety factor includes:

[0042] Obtain the velocity vector and relative position vector of the target ship and other ships, and calculate the closest approach distance and closest approach time between the two ships;

[0043] Comparing the closest point distance with a preset distance safety threshold to obtain a first comparison value, and comparing the closest point time with a preset time safety threshold to obtain a second comparison value;

[0044] A collision safety factor is calculated using a nonlinear logarithmic function according to the first comparison value and the second comparison value.

[0045] Optionally, the coefficient module calculates the collision safety factor and the grounding safety factor of the target ship based on the port model, the ship model and the foggy weather condition, and the calculation of the grounding safety factor includes:

[0046] Monitor the real-time water depth and draft data of the target ship to obtain the excess water depth under the keel;

[0047] Obtain the coordinate deviation between the current position of the target ship and the planned route position, and calculate the course deviation;

[0048] Comparing the excess water depth under the keel with a preset water depth safety threshold to obtain a third comparison value, and comparing the channel deviation with a preset deviation safety threshold to obtain a fourth comparison value;

[0049] A stranding safety factor is calculated using an exponential function according to the third comparison value and the fourth comparison value.

[0050] Optionally, the decision module compares the collision safety factor and the grounding safety factor with preset thresholds respectively, and generates decision texts corresponding to no risk, medium risk or high risk respectively according to the comparison results, including:

[0051] If the collision safety factor and the grounding safety factor are both greater than or equal to a preset threshold, a risk-free decision is generated and navigation is maintained;

[0052] If the collision safety factor is less than a preset threshold and the grounding safety factor is greater than or equal to a preset threshold, a medium-risk decision is generated and a detour route is generated;

[0053] If the collision safety factor is greater than or equal to a preset threshold and the grounding safety factor is less than a preset threshold, a medium-risk decision is generated and the course is corrected;

[0054] If the collision safety factor and the grounding safety factor are both less than a preset threshold, a high-risk decision is generated and emergency anchoring is performed.

[0055] The beneficial effects of this application are:

[0056] The present application provides a verification method for a port fog navigation intelligent decision-making system, comprising: obtaining point cloud data of a target port terrain and a wharf through a laser radar; obtaining channel water depths and obstacle coordinates based on an electronic nautical chart; constructing a port model based on the point cloud data, channel water depths, and obstacle coordinates; obtaining ship size parameters based on a target ship model drawing, and constructing a ship model in combination with AIS historical data; generating fog conditions by simulating light fog, moderate fog, and dense fog in a graded manner; calculating a collision safety factor and a grounding safety factor of a target ship based on the port model, the ship model, and the fog conditions; comparing the collision safety factor and the grounding safety factor with preset thresholds, respectively, and generating decision texts corresponding to no risk, moderate risk, or high risk, respectively, based on the comparison results; calculating a high-risk downshift success rate, a medium-risk downshift success rate, and a low-risk downshift success rate based on the decision texts, and calculating a decision rationality evaluation index by weighting; generating a comprehensive fog navigation evaluation index based on the collision safety factor, the grounding safety factor, and the decision rationality evaluation index; and generating an optimization decision instruction based on the comparison result of the comprehensive fog navigation evaluation index with a preset standard value and sending it to a management personnel terminal. This application constructs a highly realistic port environment, ship model and foggy conditions, combines collision and grounding risk assessment, and decision rationality assessment to generate a comprehensive fog navigation assessment index, thereby optimizing navigation decisions and improving the safety and efficiency of port fog navigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 This is a schematic diagram of the verification process of the port fog navigation intelligent decision-making system in this application;

[0058] Figure 2 This is a schematic diagram of the verification device of the port fog navigation intelligent decision-making system in this application. DETAILED DESCRIPTION

[0059] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that various forms of implementation of the present disclosure are not limited to the embodiments set forth herein. Rather, the embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0060] Please refer to Figure 1 As shown, the present application provides a verification method for a port fog navigation intelligent decision-making system, comprising:

[0061] S101. Obtaining point cloud data of the target port terrain and docks using a laser radar; obtaining channel water depths and obstacle coordinates based on an electronic nautical chart; and constructing a port model based on the point cloud data, channel water depths, and obstacle coordinates.

[0062] Obtain point cloud data of target port terrain and docks through LiDAR;

[0063] Obtain channel depth and obstacle coordinates based on electronic nautical charts;

[0064] A port model is constructed based on the point cloud data, channel water depth and obstacle coordinates, thereby establishing a real-scene three-dimensional grid model.

[0065] S102, obtaining ship size parameters based on the target ship model drawing, and building a ship model in combination with AIS historical data;

[0066] Through comprehensive analysis of target ship drawings and AIS historical data, a ship model is constructed and typical ship motion characteristics including turning radius and acceleration are extracted.

[0067] S103, generating foggy weather conditions by simulating light fog, moderate fog, and dense fog in different levels;

[0068] Different levels of fog conditions include light fog, moderate fog and dense fog. At the same time, the diffusion speed of the fog is dynamically adjusted, and AIS noise interference is generated at this time to simulate foggy days.

[0069] For example, the visibility range for light fog is 500 to 1000 meters, with blurred outlines of distant buildings. The visibility range for moderate fog is 200 to 500 meters, with reduced visibility of channel markers and diffused lights. The visibility range for dense fog is less than 200 meters, with blurred outlines of nearby ships and increased radar clutter. The diffusion speed of fog is simulated based on the Navier-Stokes equation to simulate the flow of fog with wind speed.

[0070] During meteorological simulation, different levels of fog conditions are simulated instead of a single fog condition. This is because a single level of fog condition cannot fully test the system's performance under different visibility and fog conditions. At the same time, due to the complex and changeable fog navigation conditions in ports, the concentration, range and duration of fog are also different. By simulating different levels of fog conditions, the reliability and accuracy of system decision-making in different environments can be improved, and the occurrence of collision and grounding safety incidents can be effectively avoided.

[0071] Generate AIS noise interference to interfere with fog navigation. This is because AIS is an automatic ship identification system that can automatically exchange information between ships, including key data such as the ship's position, heading, and speed. The density of fog will interfere with AIS. The greater the fog density, the greater the degree of interference to AIS. Simulating noise interference can improve the accuracy of system verification.

[0072] S104, calculating a collision safety factor and a grounding safety factor of the target ship based on the port model, the ship model, and foggy weather conditions;

[0073] The specific process of collision risk assessment is as follows:

[0074] The port uses the AIS system to collect dynamic information of ships at different time points, including location, heading and speed, and then determine the speed vector and relative position vector of the target ship and other ships.

[0075] The speed vectors of the ship and other ships are subtracted to perform cross multiplication on the relative position vectors, and then the ratio is calculated to obtain the closest point distance. The specific calculation formula is:

[0076]

[0077] Among them, DC represents the closest point distance, represents the relative position vector, represents the velocity vector of the target ship, represents the velocity vector of other ships.

[0078] From the above formula, we can see that Represents the relative velocity vector of the two ships. The smaller sinθ is, the closer the headings of the two ships are to parallel, and the higher the collision risk is. The larger sinθ is, the closer the headings of the two ships are to perpendicular, and the lower the collision risk is.

[0079] The time of closest approach is obtained by calculating the difference between the relative position vector and the speed vector of the ship and other ships. The specific calculation formula is:

[0080]

[0081] Where TC represents the time of closest approach, and θ represents the heading angle between the two ships. As can be seen from the above formula, the smaller cosθ is, the direction of relative velocity is opposite to the direction of relative position, and the collision risk is lower. The larger cosθ is, the closer the two ships are, and the higher the collision risk is.

[0082] The collision safety factors of different foggy conditions are calculated based on the closest approach distance and the closest approach time combined with the collision safety threshold. The collision safety threshold includes the distance safety threshold and the time safety threshold. The specific operation is to add 1 to the closest approach distance and the closest approach time respectively and take the logarithm to obtain the collision safety factor, which is specifically expressed as:

[0083]

[0084] Among them, Cr represents the collision safety factor, Indicates the distance safety threshold, Indicates the time safety threshold.

[0085] It can be seen from the above formula that the closer the closest point distance and time are to their respective safety thresholds, the larger the logarithm of the ratio plus 1, that is, the larger the collision safety factor, which means the collision risk of the target ship is smaller.

[0086] The closest point of approach distance and closest point of approach time are obtained separately because the closest point of approach distance represents the minimum distance at which two ships will meet without changing their current course and speed, and the closest point of approach time represents the time required to reach the closest point of approach distance. A smaller closest point of approach distance means a higher potential risk of collision between the two objects, while a smaller closest point of approach time indicates that a collision event is about to occur and immediate action is required to avoid collision.

[0087] The specific process of stranding risk assessment is as follows:

[0088] Apply heave amplitude to the target ship model, monitor the real-time water depth and ship draft at different time stamps, and then use the formula for calculating the excess water depth under the keel. The calculation formula for the excess water depth under the keel is:

[0089] UD = real-time water depth - ship draft - dynamic settlement

[0090] Dynamic settlement is the correction for heave caused by waves;

[0091] The current position of the target ship and the planned route position are obtained through the electronic chart. The square root of the square sum of the coordinates is calculated to obtain the channel deviation. The specific calculation formula is:

[0092]

[0093] Where DD represents the course deviation, ( , ) indicates the current position of the target ship, ( , ) indicates the planned route position.

[0094] Based on the excess water depth under the keel and the channel deviation, the stranding safety factors for different levels of fog conditions are calculated in combination with the stranding safety threshold. The stranding safety threshold includes the water depth safety threshold and the deviation safety threshold.

[0095] The stranding safety factor is obtained, and the specific calculation formula is:

[0096]

[0097] Where Ar represents the stranding safety factor, represents the water depth safety threshold, Indicates a deviation from the safety threshold.

[0098] It can be seen from the above formula that the closer the excess water depth under the keel is to the water depth safety threshold, the smaller the water depth deviation value is. The closer the channel deviation is to the deviation safety threshold, the smaller the channel deviation is. At this time, the grounding safety factor is greater and the grounding risk is lower.

[0099] Calculating excess water depth under the keel and channel deviation is crucial, as excess water depth provides a safety margin for the vessel. During foggy navigation, visibility is low, making it difficult for crew members to detect shallow waters in advance. Therefore, sufficient excess water depth significantly reduces the risk of grounding. Furthermore, limited visibility forces crew members to rely on navigational equipment for course determination, and increasing channel deviation increases navigational difficulties.

[0100] S105: Compare the collision safety factor and the grounding safety factor with preset thresholds respectively, and generate decision texts corresponding to no risk, medium risk, or high risk respectively according to the comparison results;

[0101] The decision text is obtained as follows:

[0102] Set a preset collision safety factor and compare the collision safety factor with the preset collision safety factor. If the collision safety factor is greater than or equal to the preset collision safety factor, it will be displayed as no risk; otherwise, it will be displayed as risky;

[0103] Set a preset grounding safety factor and compare the grounding safety factor with the preset grounding safety factor. If the grounding safety factor is greater than or equal to the preset grounding safety factor, it will be displayed as no risk; otherwise, it will be displayed as risky.

[0104] Under different levels of fog conditions, if neither collision nor grounding risk exists, the decision is made as no risk and the voyage continues; if only collision risk exists but no grounding risk exists, the decision is medium risk and collision needs to be avoided and a detour route needs to be planned; if only grounding risk exists but no collision risk exists, the decision is also medium risk and the course needs to be adjusted; if both risks exist, the decision is high risk and the ship needs to stop immediately and anchor in safe waters.

[0105] Furthermore, the decision texts are integrated, and the decision texts include no risk, medium risk and high risk.

[0106] S106: Calculating a high-risk downshift success rate, a medium-risk downshift success rate, and a low-risk downshift success rate based on the decision text, and calculating a decision rationality evaluation index by weighting;

[0107] Adjust the target ship model according to the decision text;

[0108] The total number of scenarios where the decision text is high-risk is counted. At the same time, the number of scenarios where the downshift is from high-risk to medium-risk is counted, and the success rate of high-risk downshift is calculated. The specific calculation formula is:

[0109] P1=Nd1 / Nt1×100%

[0110] Where P1 represents the success rate of high-risk downshifting, Nd1 represents the number of scenarios where high-risk downshifting is medium-risk, and Nt1 represents the total number of scenarios where the decision text is high-risk.

[0111] The statistical decision text is the total scenario of medium risk. At the same time, the scenarios of downshifting from medium risk to low risk are counted. Similarly, the success rate of medium risk downshifting is calculated. The specific calculation formula is:

[0112] P2=Nd2 / Nt2×100%

[0113] Among them, P2 represents the success rate of medium-risk downshifting, Nd2 represents the number of scenarios where medium-risk downshifting is low-risk, and Nt2 represents the total number of scenarios where the decision text is medium-risk.

[0114] The statistical decision text is the total low-risk scenario, and the scenarios of downshifting from low risk to no risk are also counted. Similarly, the low-risk downshift success rate is calculated. The specific calculation formula is:

[0115] P3=Nd3 / Nt3×100%

[0116] Among them, P3 represents the success rate of low-risk downshifting, Nd3 represents the number of scenarios where low-risk downshifting is risk-free, and Nt3 represents the total number of scenarios where the decision text is low-risk.

[0117] The decision rationality evaluation index is obtained by weighted summing the high-risk downshift success rate, the medium-risk downshift success rate, and the low-risk downshift success rate, which is specifically expressed as:

[0118] P=λ1×P1+λ2×P2+λ3×P3

[0119] Where P represents the decision rationality evaluation index, and λ1, λ2, and λ3 represent the weight coefficients of the high-risk downshift success rate, the medium-risk downshift success rate, and the low-risk downshift success rate, respectively.

[0120] For example, the weight coefficients of the high-risk downshift success rate, the medium-risk downshift success rate, and the low-risk downshift success rate are 0.5, 0.3, and 0.2, respectively.

[0121] Calculating the success rate of downshifting for high-risk, medium-risk and low-risk conditions can accurately assess the risk level of the navigation status and predict the trend of risk changes. This can not only help the system make more reasonable decisions and reduce the risk of misjudgment, but also in foggy navigation conditions, ships face the challenges of low visibility and high collision risk. The downshifting success rate reflects the effectiveness of different navigation strategies in reducing risks. The fog navigation decision-making system selects the best navigation strategy based on this, such as adjusting speed, changing course or emergency braking, to minimize navigation risks. At the same time, it can evaluate the practicality and applicability of the fog navigation decision-making system under different navigation conditions and ship types, thereby improving fog navigation safety.

[0122] S107. Generate a comprehensive fog navigation evaluation index based on the collision safety factor, the grounding safety factor, and the decision rationality evaluation index;

[0123] The comprehensive fog flight assessment process is as follows:

[0124] Based on the weighted summation of the risk assessment results, the driving safety assessment index is obtained, which is specifically expressed as:

[0125]

[0126] Among them, R represents the driving safety assessment index, Cr represents the collision safety factor, Ar represents the stranding safety factor, η1 and η2 represent the weights of the stranding safety factor and the collision safety factor, respectively.

[0127] For example, the weighting factors for the collision safety factor and the grounding safety factor are 0.7 and 0.3 respectively.

[0128] A comprehensive fog flight evaluation model is established, and the driving safety evaluation index and decision rationality evaluation index are introduced into the comprehensive fog flight evaluation model, which is specifically expressed as follows:

[0129]

[0130] Among them, Z represents the comprehensive fog navigation evaluation index, P represents the decision rationality evaluation index, and R represents the driving safety evaluation index. represents the communication attenuation assessment index, 、 、 is the proportional coefficient corresponding to the evaluation index, >0, >0, >0, To select the fog concentration corresponding to different levels of fog conditions.

[0131] For example: When the fog condition is selected as light fog, = ,at this time = , when the fog condition is selected as medium fog, = ,at this time = , when the fog condition is selected as dense fog, = ,at this time = ,and > > . Indicates the concentration of light mist. Indicates medium fog concentration, Indicates the concentration of fog. is the communication attenuation assessment index, Represents the communication attenuation evaluation index under light fog conditions, represents the communication attenuation evaluation index under moderate fog conditions, Indicates the communication attenuation assessment index under dense fog conditions.

[0132] The communication attenuation assessment index is fitted by simulating AIS noise interference under different levels of fog. Combined with visibility adjustment parameters for corresponding fog conditions, it can more realistically reproduce the actual navigation environment. This makes the testing and evaluation of the port fog navigation intelligent decision-making system closer to reality, ensuring that the system can operate effectively in various fog conditions. When assessing the safety of ship navigation, the port fog navigation intelligent decision-making system improves the reliability of safe navigation decision-making by identifying collision and grounding risks in the navigation environment. When the decision rationality assessment index is high, the system has a higher decision success rate in different levels of fog, effectively reducing navigation risks and improving fog navigation efficiency. Improving the reliability of fog navigation safety decision-making, and when the decision rationality assessment index is high, it means that the port fog navigation intelligent decision-making system has a higher decision success rate in different levels of fog. By making appropriate decisions for different situations, it minimizes navigation risks and improves fog navigation efficiency.

[0133] S108: Generate an optimization decision instruction based on the comparison result of the comprehensive fog navigation evaluation index and the preset standard value and send it to the management terminal;

[0134] The navigation decision optimization process is as follows:

[0135] Based on the comprehensive fog navigation evaluation index, the comprehensive fog navigation standard value Zc is set, and the comprehensive fog navigation evaluation index is compared with the comprehensive fog navigation standard value. If Zc is greater than Z, it means that the comprehensive fog navigation evaluation index is lower than the standard value. At this time, the fog navigation decision is unreasonable and needs to be optimized and adjusted. If Zc is equal to Z, the comprehensive fog navigation evaluation index is the same as the standard value. At this time, the fog navigation decision is relatively balanced, but still needs further verification. If Zc is less than Z, the comprehensive fog navigation evaluation index is higher than the standard value. At this time, the fog navigation decision is reasonable and the generated navigation decision can continue to be executed.

[0136] Decision feedback is automatically provided through the manager terminal after the navigation decision is optimized, including the fog conditions at that time, the decision text and the optimized navigation decision, thereby generating a fog navigation decision verification report. The manager can view the navigation decision through the fog navigation decision verification report. The preset summary method includes any one of the report summary, picture summary and chart summary.

[0137] like Figure 2 As shown, the present application also provides a verification device for a port fog navigation intelligent decision-making system, comprising:

[0138] The port module 201 acquires point cloud data of the target port terrain and docks through a laser radar; obtains channel water depth and obstacle coordinates based on an electronic nautical chart; and constructs a port model based on the point cloud data, channel water depth, and obstacle coordinates.

[0139] The ship module 202 obtains ship size parameters based on the target ship model drawing and builds a ship model in combination with AIS historical data;

[0140] The weather module 203 generates foggy weather conditions by simulating light fog, moderate fog, and dense fog in different levels;

[0141] A coefficient module 204 calculates a collision safety factor and a grounding safety factor of the target ship based on the port model, the ship model and the foggy weather condition;

[0142] The decision module 205 compares the collision safety factor and the grounding safety factor with preset thresholds, and generates decision texts corresponding to no risk, medium risk, or high risk, respectively, based on the comparison results;

[0143] An index module 206 calculates a high-risk downshift success rate, a medium-risk downshift success rate, and a low-risk downshift success rate based on the decision text, and calculates a decision rationality evaluation index by weighting;

[0144] A comprehensive module 207 generates a comprehensive fog navigation evaluation index based on the collision safety factor, the grounding safety factor, and the decision rationality evaluation index;

[0145] The management module 208 generates an optimization decision instruction based on the comparison result of the comprehensive fog navigation evaluation index and the preset standard value and sends it to the management terminal.

[0146] Optionally, the ship module obtains ship size parameters based on the target ship model drawing and constructs a ship model in combination with AIS historical data, including:

[0147] The ship size parameters are obtained based on the target ship drawings, and the ship motion characteristic model is established by combining the ship turning radius and acceleration extracted from AIS historical data.

[0148] Furthermore, the coefficient module calculates the collision safety factor and the grounding safety factor of the target ship based on the port model, the ship model and the foggy weather condition. The calculation of the collision safety factor includes:

[0149] Obtain the velocity vector and relative position vector of the target ship and other ships, and calculate the closest approach distance and closest approach time between the two ships;

[0150] Comparing the closest point distance with a preset distance safety threshold to obtain a first comparison value, and comparing the closest point time with a preset time safety threshold to obtain a second comparison value;

[0151] A collision safety factor is calculated using a nonlinear logarithmic function according to the first comparison value and the second comparison value.

[0152] Furthermore, the coefficient module calculates the collision safety factor and the grounding safety factor of the target ship based on the port model, the ship model and the foggy weather condition. The calculation of the grounding safety factor includes:

[0153] Monitor the real-time water depth and draft data of the target ship to obtain the excess water depth under the keel;

[0154] Obtain the coordinate deviation between the current position of the target ship and the planned route position, and calculate the course deviation;

[0155] Comparing the excess water depth under the keel with a preset water depth safety threshold to obtain a third comparison value, and comparing the channel deviation with a preset deviation safety threshold to obtain a fourth comparison value;

[0156] A stranding safety factor is calculated using an exponential function according to the third comparison value and the fourth comparison value.

[0157] Furthermore, the decision module compares the collision safety factor and the grounding safety factor with preset thresholds, and generates decision texts corresponding to no risk, medium risk, or high risk, respectively, based on the comparison results, including:

[0158] If the collision safety factor and the grounding safety factor are both greater than or equal to a preset threshold, a risk-free decision is generated and navigation is maintained;

[0159] If the collision safety factor is less than a preset threshold and the grounding safety factor is greater than or equal to a preset threshold, a medium-risk decision is generated and a detour route is generated;

[0160] If the collision safety factor is greater than or equal to a preset threshold and the grounding safety factor is less than a preset threshold, a medium-risk decision is generated and the course is corrected;

[0161] If the collision safety factor and the grounding safety factor are both less than a preset threshold, a high-risk decision is generated and emergency anchoring is performed.

[0162] The above description of the embodiments is intended to facilitate understanding and application of the present invention by those skilled in the art. It will be readily apparent to those skilled in the art that various modifications to the above embodiments can be made, and the general principles described herein can be applied to other embodiments without requiring inventive effort. Therefore, the present invention is not limited to the above embodiments, and improvements and modifications made by those skilled in the art based on the present disclosure are intended to fall within the scope of protection of the present invention.

Claims

1. A verification method for a port fog navigation intelligent decision-making system, characterized in that: include: Obtain point cloud data of the target port terrain and docks through LiDAR; obtain channel water depth and obstacle coordinates based on electronic nautical charts; Constructing a port model based on the point cloud data, channel water depth and obstacle coordinates; Obtain ship size parameters based on target ship drawings and build a ship model based on AIS historical data; Generate foggy conditions by simulating light fog, moderate fog and dense fog in different levels; Based on the port model, the ship model and the foggy weather conditions, the collision safety factor and the grounding safety factor of the target ship are calculated, wherein the calculation of the collision safety factor includes: obtaining the velocity vector and the relative position vector of the target ship and the other ship, calculating the closest point distance and the closest point time of the two ships; comparing the closest point distance with a preset distance safety threshold to obtain a first comparison value, and comparing the closest point time with a preset time safety threshold to obtain a second comparison value; calculating the collision safety factor through a nonlinear logarithmic function based on the first comparison value and the second comparison value; wherein the calculation of the grounding safety factor includes: monitoring the real-time water depth and draft data of the target ship to obtain the excess water depth under the keel; obtaining the coordinate deviation between the current position of the target ship and the planned route position, and calculating the channel deviation; comparing the excess water depth under the keel with the preset water depth safety threshold to obtain a third comparison value, and comparing the channel deviation with the preset deviation safety threshold to obtain a fourth comparison value; and calculating the grounding safety factor through an exponential function based on the third comparison value and the fourth comparison value; Comparing the collision safety factor and the grounding safety factor with preset thresholds respectively, and generating decision texts corresponding to no risk, medium risk or high risk respectively according to the comparison results; Based on the decision text, statistics are collected on the high-risk downshift success rate, the medium-risk downshift success rate, and the low-risk downshift success rate, and a decision rationality evaluation index is calculated by weighting; generating a comprehensive fog navigation assessment index based on the collision safety factor, grounding safety factor, and decision rationality assessment index; Based on the comparison result of the comprehensive fog navigation evaluation index and the preset standard value, an optimization decision instruction is generated and sent to the management terminal.

2. The verification method of a port fog navigation intelligent decision-making system according to claim 1 is characterized in that: Obtain ship size parameters based on target ship drawings and build a ship model based on AIS historical data, including: The ship size parameters are obtained based on the target ship drawings, and the ship motion characteristic model is established by combining the ship turning radius and acceleration extracted from AIS historical data.

3. The verification method of a port fog navigation intelligent decision system according to claim 1 is characterized in that: The collision safety factor and the grounding safety factor are compared with preset thresholds respectively, and decision texts corresponding to no risk, medium risk or high risk are generated according to the comparison results, including: If the collision safety factor and the grounding safety factor are both greater than or equal to a preset threshold, a risk-free decision is generated and navigation is maintained; If the collision safety factor is less than a preset threshold and the grounding safety factor is greater than or equal to a preset threshold, a medium-risk decision is generated and a detour route is generated; If the collision safety factor is greater than or equal to a preset threshold and the grounding safety factor is less than a preset threshold, a medium-risk decision is generated and the course is corrected; If the collision safety factor and the grounding safety factor are both less than a preset threshold, a high-risk decision is generated and emergency anchoring is performed.

4. A verification device for a port fog navigation intelligent decision-making system, characterized in that: include: The port module uses LiDAR to obtain point cloud data of the target port terrain and docks; Obtaining channel water depth and obstacle coordinates based on the electronic nautical chart; constructing a port model based on the point cloud data, channel water depth and obstacle coordinates; The ship module obtains ship size parameters based on the target ship drawings and builds a ship model based on AIS historical data; The weather module generates foggy conditions by simulating light fog, moderate fog, and dense fog in different levels; A coefficient module calculates a collision safety factor and a grounding safety factor of a target ship based on the port model, the ship model, and foggy weather conditions, wherein the calculation of the collision safety factor comprises: obtaining a velocity vector and a relative position vector of the target ship and the other ship, and calculating the closest point distance and the closest point time of the two ships; comparing the closest point distance with a preset distance safety threshold to obtain a first comparison value, and comparing the closest point time with a preset time safety threshold to obtain a second comparison value; calculating the collision safety factor through a nonlinear logarithmic function based on the first comparison value and the second comparison value; wherein the calculation of the grounding safety factor comprises: monitoring the real-time water depth and draft data of the target ship to obtain a surplus water depth under the keel; obtaining a coordinate deviation between the current position of the target ship and the planned route position, and calculating a channel deviation; comparing the surplus water depth under the keel with a preset water depth safety threshold to obtain a third comparison value, and comparing the channel deviation with a preset deviation safety threshold to obtain a fourth comparison value; and calculating the grounding safety factor through an exponential function based on the third comparison value and the fourth comparison value; A decision module compares the collision safety factor and the grounding safety factor with preset thresholds, and generates decision texts corresponding to no risk, medium risk, or high risk, respectively, based on the comparison results; an index module, which calculates a high-risk downshift success rate, a medium-risk downshift success rate, and a low-risk downshift success rate based on the decision text, and calculates a decision rationality evaluation index by weighting; A comprehensive module generates a comprehensive fog navigation evaluation index based on the collision safety factor, the grounding safety factor and the decision rationality evaluation index; The management module generates an optimization decision instruction based on the comparison result of the comprehensive fog navigation evaluation index and the preset standard value and sends it to the management terminal.

5. A verification device for a port fog navigation intelligent decision-making system according to claim 4, characterized in that: The ship module obtains ship size parameters based on the target ship drawings and builds a ship model based on AIS historical data, including: The ship size parameters are obtained based on the target ship drawings, and the ship motion characteristic model is established by combining the ship turning radius and acceleration extracted from AIS historical data.

6. A verification device for a port fog navigation intelligent decision-making system according to claim 4, characterized in that: The decision module compares the collision safety factor and the grounding safety factor with preset thresholds respectively, and generates decision texts corresponding to no risk, medium risk or high risk respectively according to the comparison results, including: If the collision safety factor and the grounding safety factor are both greater than or equal to a preset threshold, a risk-free decision is generated and navigation is maintained; If the collision safety factor is less than a preset threshold and the grounding safety factor is greater than or equal to a preset threshold, a medium-risk decision is generated and a detour route is generated; If the collision safety factor is greater than or equal to a preset threshold and the grounding safety factor is less than a preset threshold, a medium-risk decision is generated and the course is corrected; If the collision safety factor and the grounding safety factor are both less than a preset threshold, a high-risk decision is generated and emergency anchoring is performed.

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