A method and system for intelligent route planning based on sea ice density

Abstract

The present invention relates to the field of route planning, and more particularly to a sea ice density-based intelligent route planning method and system. The method comprises: acquiring navigational environment data in real time, identifying a sea ice density parameter in the navigational environment data; acquiring currently preset channel position data, and marking target sea ice data within a preset range of the channel position data based on the channel position data and the sea ice density parameter; determining whether the target sea ice data meets the conditions for replacing the channel position data; determining that the target sea ice data meets the conditions for replacing the channel position data when the sea ice density parameter constitutes a passing obstacle on the channel position data; determining route avoidance data based on the target sea ice data based on the determination result; and modifying the channel position data based on the route avoidance data. The method determines route avoidance data based on the target sea ice data and modifies the channel position data based on the data, thereby ensuring that the ship avoids areas with dense sea ice during navigation and reducing navigation risks.

Description

Technical Field

[0001] The present application relates to the technical field of route planning, and in particular to a method and system for intelligent route planning based on sea ice density. Background Art

[0002] Sea ice density refers to the distribution density of icebergs or sea ice within a specific sea area. It is a critical marine environmental parameter that has a direct impact on ship navigation safety. Areas with high sea ice density indicate denser distribution of icebergs or sea ice, posing a greater navigation risk. Route planning involves developing a reasonable route based on factors such as the marine environment, vessel performance, and navigation requirements. The purpose of route planning is to ensure that ships avoid hazardous areas during navigation, thereby improving navigation safety and efficiency.

[0003] Existing navigation systems may rely solely on static sea ice data, while sea ice density may change over time, leading to inaccurate identification results. Furthermore, because a ship's steering and avoidance maneuvers can produce large-scale overall movement, failure to provide a suitable avoidance strategy can easily lead to overly conservative avoidance strategies or excessive avoidance within safe areas. Furthermore, the movement and distribution of sea ice are, in most cases, irregular. Data collection using traditional single-ship methods can easily result in inaccuracies. For example, signal delay is a common problem in maritime communications, which can result in ships acquiring delayed data rather than real-time data. Route analysis based on this data can be misleading.

[0004] Therefore, the prior art has defects and needs to be improved. Summary of the Invention

[0005] In order to solve one or several problems in the prior art, the main purpose of this application is to provide a method and system for intelligent route planning based on sea ice density.

[0006] To achieve the above-mentioned purpose of the invention, the present application proposes a method for intelligent route planning based on sea ice density, the method comprising:

[0007] acquiring marine environment data in real time and identifying sea ice concentration parameters in the marine environment data;

[0008] Obtaining currently preset channel position data, and marking target sea ice data within a preset range of the channel position data based on the channel position data and a sea ice density parameter;

[0009] Determining whether the target sea ice data meets the conditions for replacing the channel position data;

[0010] When the sea ice concentration parameter constitutes a passing obstacle on the channel position data, it is determined that the target sea ice data meets the conditions for replacing the channel position data;

[0011] Based on the determination result, determining route avoidance data according to the target sea ice data;

[0012] The course position data is modified based on the route avoidance data.

[0013] The present application also provides an intelligent route planning system based on sea ice density, including:

[0014] A first acquisition module is used to acquire marine environment data in real time and identify sea ice density parameters in the marine environment data;

[0015] a second acquisition module, configured to acquire currently preset channel position data, and mark target sea ice data within a preset range of the channel position data based on the channel position data and a sea ice density parameter;

[0016] A judgment module, configured to judge whether the target sea ice data satisfies the conditions for replacing the channel position data;

[0017] a determination module, configured to determine that the target sea ice data satisfies a condition for replacing the channel position data when the sea ice concentration parameter constitutes a passing obstacle on the channel position data;

[0018] a route avoidance module, configured to determine route avoidance data according to the target sea ice data based on a result of the determination;

[0019] A modification module is used to modify the channel position data based on the route avoidance data.

[0020] The present application also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the above methods when executing the computer program.

[0021] The present application also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0022] The sea ice density-based intelligent route planning method and system of the embodiment of the present application obtains navigation environment data in real time, identifies sea ice density parameters, and automatically determines whether the channel position data needs to be changed based on this data. This method can determine route avoidance data based on target sea ice data and modify the channel position data based on this data. In this way, it is possible to automatically determine whether the channel position data needs to be changed, reducing the workload of the crew and improving the accuracy of decision-making; determining route avoidance data based on target sea ice data and modifying the channel position data based on this data ensures that the ship avoids areas with dense sea ice during navigation and reduces navigation risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of a method for intelligent route planning based on sea ice density according to an embodiment of the present application;

[0024] Figure 2 This is a flow chart of a method for intelligent route planning based on sea ice density according to an embodiment of the present application;

[0025] Figure 3 This is a schematic block diagram of the structure of an intelligent route planning system based on sea ice density according to an embodiment of the present application;

[0026] Figure 4 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present application.

[0027] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0029] Reference Figure 1 In an embodiment of the present application, a method for intelligent route planning based on sea ice density is provided, the method comprising:

[0030] S1. Acquire marine environment data in real time and identify sea ice density parameters in the marine environment data;

[0031] S2. Obtaining currently preset channel position data, and marking target sea ice data within a preset range of the channel position data based on the channel position data and a sea ice density parameter;

[0032] S3, determining whether the target sea ice data meets the conditions for replacing the channel position data;

[0033] S4. When the sea ice concentration parameter constitutes a passing obstacle on the channel position data, determining that the target sea ice data meets the condition for replacing the channel position data;

[0034] S5. Based on the determination result, determining route avoidance data according to the target sea ice data;

[0035] S6. Modify the channel position data based on the route avoidance data.

[0036] As described in steps S1-S2 above, real-time sea ice density data is collected through satellite remote sensing, buoys, ship reports, and other means to reflect the current distribution of sea ice. This real-time data can promptly reflect changes in sea ice, helping crews make timely route adjustments and improve navigation safety. Combining pre-set channel location data with real-time sea ice density data can identify areas of sea ice that pose a threat to the channel. By marking target sea ice data, crews can clearly understand which areas need to be avoided, allowing them to plan safer routes.

[0037] As described in steps S3-S6 above, an assessment is made as to whether sea ice poses a navigational obstacle based on the sea ice concentration parameter and the channel position data, thereby determining whether the channel position data needs to be changed. Automatically determining whether the channel position data needs to be changed reduces the crew's workload and improves decision-making accuracy. When the sea ice concentration parameter reaches a preset threshold, the condition for changing the channel position data is automatically triggered to prevent the vessel from navigating in high-risk areas. Automatically triggering the condition for changing the channel position data ensures that the vessel always navigates within a safe area, reducing navigation risks. Based on the target sea ice data, route avoidance data is determined to guide the vessel to avoid areas with dense sea ice. By determining the route avoidance data, the vessel can be guided to avoid high-risk areas during navigation, ensuring navigation safety. Based on the route avoidance data, the channel position data is modified to adjust the route, ensuring that the vessel avoids areas with dense sea ice during navigation. Modifying the channel position data ensures that the vessel always navigates within a safe area, improving navigation safety. Specifically, by acquiring navigation environment data in real time, the sea ice concentration parameter is identified, and based on this data, a determination is automatically made as to whether the channel position data needs to be changed. This method determines route avoidance data based on target sea ice data and modifies channel position data based on this data. This approach automatically determines whether channel position data needs to be changed, reducing the crew's workload and improving decision-making accuracy. By determining route avoidance data based on target sea ice data and modifying channel position data based on this data, the ship avoids dense sea ice areas during navigation, reducing navigation risks.

[0038] Reference Figure 2In one embodiment, the method for determining whether the target sea ice data satisfies the conditions for replacing the channel position data includes:

[0039] S31, controlling the buoy module to receive a measurement signal on the waterway, and determining observation parameters of target sea ice data according to the measurement signal, wherein the observation parameters include position information, sea ice density, and sea ice volume;

[0040] S32, calculating the edge distance between the sea ice position information and the channel position based on the position information and the channel position data;

[0041] S33, when the edge distance is greater than a preset first distance threshold, determining that the target sea ice data does not meet the condition for replacing the channel position data;

[0042] S34. When the edge distance is less than a preset first distance threshold, construct a risk prediction model, input the sea ice density and sea ice volume into the risk prediction model, and output a sea ice risk coefficient through the risk prediction model;

[0043] S35. When the risk coefficient is greater than a preset coefficient threshold, it is determined that the target sea ice data constitutes a passing obstacle for the channel position data, and the condition for replacing the channel position data is met.

[0044] As described above, by deploying a buoy module, sonar or other sensors are used to receive measurement signals along the waterway. Through signal processing and data analysis, the location, density, and volume of sea ice are determined. The buoy module monitors the location and characteristics of sea ice in real time, providing accurate data support for route planning and helping to avoid dense ice areas. Using spatial geometry calculations, the sea ice location information is compared with pre-set channel location data to calculate the shortest distance between the two. Calculating the edge distance helps assess the potential threat posed by sea ice to the waterway and provides a basis for determining whether to update the channel location data. A safety distance threshold is set. When the edge distance exceeds this threshold, the sea ice is deemed to pose no threat to the waterway and no channel location data needs to be updated. By setting a safety distance threshold, it is possible to automatically determine whether sea ice avoidance is necessary, reducing manual intervention and improving route planning efficiency. Using mathematical models and statistical analysis methods, sea ice density and volume are used as input parameters to predict the risk factor of sea ice and assess its threat to the waterway. The risk prediction model can predict the risk factor based on sea ice characteristics, providing a more comprehensive basis for route planning and helping to develop safer routes. A risk factor threshold is set. When the risk factor exceeds this threshold, sea ice is considered to be an obstacle to the navigation channel and the navigation channel position data needs to be changed. By setting the risk factor threshold, it can be automatically determined whether sea ice avoidance is necessary.

[0045] In one feasible embodiment, assume a ship is sailing along a route in the Arctic. Sea ice density in this region varies significantly with the seasons, and a forecast model indicates that the ice will continue to move southward over the next 15 days. A buoy module is configured to monitor the sea ice density along the route in real time, collecting observational parameters such as location, density, and volume. The buoy module compares the collected sea ice location information with pre-set route location data and calculates the distance between the edge of the sea ice and the route. For example, the distance between the edge of the sea ice and the route is 70 nautical miles, which is less than a pre-set first distance threshold of 100 nautical miles. Because the distance is less than the pre-set first distance threshold, the buoy module constructs a risk prediction model and inputs the sea ice density and volume. The risk prediction model outputs a sea ice risk coefficient of 0.7, which is higher than the pre-set coefficient threshold of 0.5. Because the risk coefficient is greater than the pre-set coefficient threshold, the target sea ice data is determined to constitute a barrier to the route location data, satisfying the conditions for changing the route location data. The navigation system automatically adjusts the route to guide the ship to a safe area, avoiding areas of dense sea ice. As sea ice shifts, the buoy module continuously monitors its position and density, feeding this new data into the risk prediction model. The navigation system dynamically adjusts the route based on the latest risk assessment, ensuring the vessel remains within a safe zone.

[0046] In one embodiment, after the step of determining that the edge distance is greater than a preset first distance threshold, the method further includes:

[0047] Acquiring meteorological parameters in real time, and predicting the movement direction and distance of the target avoiding sea ice within a predicted time period based on the meteorological parameters and observation parameters;

[0048] determining position change data of the target avoiding sea ice based on the moving direction and the moving distance, updating the edge distance based on the position change data, and determining whether the edge distance is greater than a preset first distance threshold;

[0049] When the edge distance is greater than a preset first distance threshold, it is determined that the target sea ice data does not meet the conditions for replacing the channel position data;

[0050] When the edge distance is less than a preset first distance threshold, it is determined that the target sea ice data meets the condition for replacing the channel position data.

[0051] As mentioned above, meteorological sensors can be used to collect real-time meteorological parameters such as wind speed, direction, and water temperature. Combined with observed sea ice parameters (such as density and volume), mathematical models or statistical methods can be used to predict the direction and distance of sea ice movement within the predicted time period. Predicting the direction and distance of sea ice movement helps more accurately assess the threat posed by sea ice to shipping routes, thereby providing a more accurate basis for route planning. Based on the predicted direction and distance of sea ice movement, the sea ice position is updated, and the edge distance between the sea ice and the shipping route is recalculated. If the edge distance remains greater than a preset first distance threshold, the sea ice is deemed to pose no threat, and the shipping route position data does not need to be updated. By updating sea ice position change data in real time, the threat posed by sea ice to shipping routes can be more accurately assessed, thereby reducing unnecessary route changes and improving navigation efficiency. If the edge distance between the sea ice and the shipping route remains greater than the preset first distance threshold, the sea ice is deemed to pose no threat, and the shipping route position data does not need to be updated. This judgment can reduce unnecessary route changes and improve navigation efficiency. If the edge distance between the sea ice and the shipping route is less than the preset first distance threshold, the sea ice is deemed to pose a potential threat, and the shipping route position data needs to be updated. This judgment can ensure that ships avoid areas with dense sea ice and improve navigation safety.

[0052] In one embodiment, the method for determining route avoidance data based on the target sea ice data includes:

[0053] acquiring ship data, and calculating a safe distance between the ship and sea ice based on observation parameters of the ship data and target sea ice data;

[0054] Obtain meteorological parameters, and predict the movement direction and distance of the target avoiding sea ice within the predicted time period based on the meteorological parameters and observation parameters;

[0055] Determine the position change data of the target sea ice at a preset time based on the moving direction and moving distance of the target sea ice during the predicted time period;

[0056] Determining whether the distance traveled by the ship when reaching the target and avoiding sea ice is greater than the safety distance based on the position change data;

[0057] When the distance when the ship reaches the target to avoid sea ice is greater than the safety distance, it is determined that the ship meets the requirements for safe passage, and route avoidance data is generated based on the safety distance.

[0058] As described above, mathematical models or algorithms are used to calculate the safe distance between the ship and the sea ice using data such as ship size, type, load, speed, and maneuverability, combined with observed parameters of the target sea ice (such as density and volume). This ensures that a sufficient safe distance is maintained between the ship and the sea ice, reducing the risk of collision and grounding and improving navigation safety. Meteorological sensors collect real-time meteorological parameters such as wind speed, direction, and water temperature. Combined with observed sea ice parameters, mathematical models or statistical methods are used to predict the direction and distance of sea ice movement within the predicted timeframe. Predicting the direction and distance of sea ice movement helps to more accurately assess the threat posed by sea ice to shipping lanes, providing a more accurate basis for route planning. Based on the predicted direction and distance of sea ice movement, sea ice position change data is determined at a preset time. This determination of sea ice position change data allows for a more accurate assessment of the threat posed by sea ice to shipping lanes, thereby reducing unnecessary route changes and improving navigation efficiency. Based on this sea ice position change data, the distance the ship will pass when reaching the sea ice is calculated and compared with the safe distance. This assessment ensures that ships transit ice areas within a safe distance, reducing the risk of collision and grounding, and improving navigation safety. If the distance a ship reaches the ice exceeds the safe distance, the ship is deemed to have met the conditions for safe passage, and route avoidance data is generated based on the safe distance. This assessment ensures that ships transit ice areas within a safe distance, reducing the risk of collision and grounding, and improving navigation safety.

[0059] It is worth noting that calculating the edge distance between sea ice location information and the channel location is a step designed to determine the minimum safe distance between sea ice and the channel. By calculating the edge distance between sea ice location information and the channel location, a preliminary assessment can be made as to whether sea ice poses a threat to the channel. If the edge distance is greater than a preset first distance threshold, the sea ice is deemed not to pose a threat and the channel location data does not need to be updated. This step helps quickly assess the potential threat of sea ice to the channel and provides preliminary decision-making basis for route planning. Calculating the safe distance between the ship and sea ice is a step designed to ensure that the ship maintains a sufficient safe distance from sea ice during navigation. By considering data such as ship size, type, load, speed, and maneuverability, combined with observed sea ice parameters such as density and volume, the safe distance between the ship and sea ice can be calculated. This step helps ensure that ships transit through sea ice areas within a safe distance, reducing the risk of collision and grounding, and improving navigation safety. The edge distance is calculated based on the direct distance between the sea ice location information and the channel location, providing a preliminary safety assessment. The calculation of the safe distance between a ship and sea ice is more comprehensive, taking into account the specific parameters of the ship and the characteristics of the sea ice, thereby providing a more accurate safety assessment.

[0060] In one embodiment, after the step of determining whether the safety distance satisfies the safe passage of the ship based on the position change data, the method further comprises:

[0061] When the passing distance of the ship when reaching the target to avoid sea ice is less than the safe distance, obtaining the current speed parameter and the speed increase range of the ship, and determining whether the ship can safely pass by increasing the speed according to the current speed parameter and the speed increase range;

[0062] When the ship cannot pass safely by increasing its speed, the safe distance between the ship and the sea ice and the ship's speed parameters are recalculated based on the predicted moving direction and distance of the target sea ice at the preset time.

[0063] The route avoidance data is generated based on the recalculated safety distance and the speed parameters of the vessel.

[0064] As described above, the ship's current speed parameters are acquired through its sensors and control system, and the range of speed increases is determined based on the ship's performance data. This assessment assesses whether the ship can maintain a safe distance by increasing its speed when passing through sea ice, providing a basis for decision-making. By comparing the ship's current speed parameters with the range of speed increases, it is determined whether the ship can maintain a safe distance by accelerating. This determination guides the ship's decision on whether to accelerate to maintain a safe distance when passing through sea ice. Using a predictive model, the safe distance between the ship and the sea ice and the ship's speed parameters are recalculated based on the movement direction and distance of the target sea ice at a preset time. This recalculation helps the ship find a new safe path if accelerating to maintain a safe distance is no longer possible. Based on the recalculated safe distance and ship's speed parameters, new route avoidance data is generated to guide the ship to avoid dense sea ice areas.

[0065] In one possible embodiment, assume that ship B is navigating a channel near an area of dense sea ice. Ship B's navigation system monitors the location, density, and volume of the sea ice in real time, as well as the ship's current speed parameter and the range within which it can increase its speed. Ship B's navigation system calculates that the distance between the edge of the sea ice and the channel is 60 nautical miles, which is less than the preset first distance threshold of 100 nautical miles. When passing through the sea ice area, ship B calculates a safe distance from the ice to be 80 nautical miles. Ship B's navigation system obtains a current speed parameter of 20 knots, with a speed increase range of 5 knots. Because the current speed parameter is less than the safe distance, ship B's navigation system determines that it can maintain the safe distance by increasing its speed. Ship B's navigation system predicts the movement direction and distance of the target sea ice within a preset timeframe, predicting that the sea ice will move in the direction of ship B's navigation by 10 nautical miles. Due to the movement of the sea ice, ship B's navigation system recalculates the safe distance to 90 nautical miles and increases its speed parameter to 25 knots. Based on the recalculated safety distance and ship speed parameters, the navigation system of ship B generates new route avoidance data to guide the ship to avoid areas with dense sea ice.

[0066] In one embodiment, after the step of determining route avoidance data based on the target sea ice data, the method includes:

[0067] Real-time monitoring of navigation environment data and meteorological data during the ship's voyage;

[0068] determining current wind and wave parameters and visibility parameters based on the navigation environment data and meteorological data;

[0069] The navigation strategy of the ship is adjusted based on the wind and wave parameters and the visibility parameters, where the navigation strategy includes emergency docking, emergency avoidance and speed limit.

[0070] As mentioned above, sensors and data acquisition equipment installed on ships collect real-time marine environmental data (such as sea ice density and current velocity) and meteorological data (such as wind speed, wind direction, and air pressure). Real-time monitoring of marine environmental and meteorological data helps to promptly understand changes in navigation conditions, providing accurate environmental information for ships to make appropriate adjustments. Using mathematical models or statistical methods, the collected marine environmental and meteorological data is analyzed to calculate current wind and wave parameters (such as wind and wave level and height) and visibility parameters (such as visibility distance). Determining current wind and wave parameters helps assess navigation conditions and provides a basis for adjusting the ship's navigation strategy. Based on these wind and wave parameters, combined with the ship's performance data and navigation regulations, the ship's navigation strategy is adjusted. For example, when wind and waves are strong, emergency avoidance or speed restrictions may be necessary. Adjusting the navigation strategy ensures safe navigation under various navigation conditions, reducing the risk of accidents such as collisions and groundings caused by adverse weather and sea conditions.

[0071] In one embodiment, before the step of determining the observation parameters of the target sea ice data based on the measurement signal, the method further includes verifying the credibility of the observation parameters. The method includes:

[0072] Build a data fusion model to receive shared observation parameters from different ships in real time;

[0073] Inputting the observation parameters of the current ship and the shared observation parameters of other ships into the data fusion model respectively, performing time series analysis on the observation parameters and the shared observation parameters through the data fusion model, and determining the error parameters of the observation parameters by comparing the data values at the same time point;

[0074] Based on the time series, the change trend is identified by using the observation parameters and shared observation parameters of multiple time points of the data fusion model, and the error range of the observation parameters is determined according to the change trend;

[0075] quantifying the error parameter and the error range by the data fusion model to obtain an error quantization value, and converting the quantization value result into credibility;

[0076] When the credibility is greater than a preset credibility threshold, the observation parameter is determined to be a credible result, and the observation parameter of the target sea ice data is determined based on the credible result.

[0077] As described above, a model is established to process and analyze observation data from different ships. By integrating data from multiple sources, the diversity and comprehensiveness of the data are increased, thereby improving the accuracy of the final results. Shared observation parameters from other ships are collected in real time to ensure the data is up-to-date. Real-time data updates enable the system to promptly respond to changes in sea ice, improving data accuracy and real-time performance. Time series analysis is performed on the observed and shared observation parameters. By comparing data values at the same time point, the error parameters of the observation parameters are determined. Trends and patterns in the data are identified to help determine the error range of the observation parameters, thereby improving data accuracy. Based on the time series, the changing trends of the observed and shared observation parameters are identified. By identifying the changing trends in the data, we can better understand the dynamic characteristics of sea ice and improve the accuracy of the observation parameters. The data fusion model is used to quantify the error parameters and error ranges, resulting in a quantified error value. This quantified error allows for a more accurate assessment of the uncertainty of the observation parameters. The quantified values are converted into the confidence level of the observation parameters. This confidence level conversion allows for an intuitive assessment of the reliability of the observation parameters, thereby improving data accuracy. When the confidence level of an observation parameter exceeds a preset confidence threshold, the observation parameter is deemed reliable. By setting a confidence threshold, we ensure that only accurate data is used for route planning. Observational parameters for target sea ice data are determined based on the confidence results. Through confidence assessment, accurate observational parameters are determined, thereby improving the accuracy of route planning. It is worth noting that the movement and distribution of sea ice are random and uncertain, and observational data from a single ship may not fully reflect the true sea ice conditions. By building a data fusion model and receiving shared observational parameters from different ships in real time, the diversity and comprehensiveness of the data can be increased, thereby improving the accuracy of the final results. By receiving data from multiple ships in real time, the data fusion model provides data redundancy and verification. Even if data from a single ship is delayed or erroneous, data from other ships can be used to supplement it, improving the overall accuracy of the data. The data fusion model can comprehensively analyze data from different ships and identify outliers or inconsistencies in the data. This helps to detect and correct errors in the data of a single ship, thereby improving the accuracy of the overall data.

[0078] In one embodiment, after the steps of quantifying the error parameter and error range using the data fusion model to obtain a quantized error value, and converting the quantized value into a credibility value, the method further includes:

[0079] When the credibility is less than or equal to a preset credibility threshold, the observation parameter is judged to be an unreliable result;

[0080] Obtain the shared observation parameters of different ships, calculate the average value of all shared observation parameters, and obtain the mean value of the overall parameters;

[0081] According to the mean, the shared observation parameter closest to the mean is extracted from all shared observation parameters as the observation parameter of the current ship.

[0082] As mentioned above, when the credibility of an observation parameter is lower than the preset credibility threshold, the system will automatically determine that the observation parameter is an unreliable result. By setting the credibility threshold, the system can automatically identify unreliable data and prevent it from having a negative impact on route planning. The average value of all shared observation parameters is calculated to obtain the mean of the overall parameter. By calculating the mean of the overall parameter, a relatively stable parameter value can be obtained as an alternative observation parameter. Based on the mean of the overall parameter, the observation parameter closest to the mean is extracted from all shared observation parameters as the observation parameter of the current ship. By extracting the observation parameter closest to the mean of the overall parameter, the accuracy of the current ship's observation parameters can be improved, thereby improving the accuracy of route planning.

[0083] In one embodiment, the route avoidance data includes a safe distance, a navigation direction, a navigation speed, and a navigation time. Based on the movement direction of the sea ice and the predicted data, combined with the maneuverability of the ship and the navigation rules, the navigation direction that the ship needs to take during navigation is determined. Determining the correct navigation direction helps ships avoid areas with dense sea ice and improves navigation safety. Based on the performance data of the ship, the speed of movement of the sea ice, and the navigation rules, the navigation speed that the ship needs to maintain during navigation is determined. Reasonable control of the navigation speed can ensure that the ship remains safe when passing through the sea ice area, reduce the risk of collision and grounding, and improve navigation safety. Based on the performance data of the ship, the speed of movement of the sea ice, and the navigation rules, the time required for the ship to pass through the sea ice area is determined. Determining the navigation time helps ships to arrange navigation plans reasonably, ensure safe passage through the sea ice area within the expected time, and improve navigation safety.

[0084] The sea ice density-based intelligent route planning method of the present application obtains navigation environment data in real time, identifies sea ice density parameters, and automatically determines whether the channel position data needs to be changed based on this data. This method can determine route avoidance data based on target sea ice data and modify the channel position data based on this data. In this way, it is possible to automatically determine whether the channel position data needs to be changed, reducing the workload of the crew and improving the accuracy of decision-making; determining route avoidance data based on target sea ice data and modifying the channel position data based on this data ensures that the ship avoids sea ice-dense areas during navigation and reduces navigation risks.

[0085] Reference Figure 3 , the embodiment of the present application also provides a route intelligent planning system based on sea ice density, including:

[0086] The first acquisition module 1 is used to acquire marine environment data in real time and identify sea ice density parameters in the marine environment data;

[0087] The second acquisition module 2 is used to obtain the current preset channel position data, and mark the target sea ice data within the preset range of the channel position data according to the channel position data and the sea ice density parameter;

[0088] A judgment module 3 is used to judge whether the target sea ice data meets the conditions for replacing the channel position data;

[0089] A determination module 4 is configured to determine that the target sea ice data satisfies a condition for replacing the channel position data when the sea ice concentration parameter constitutes a passing obstacle on the channel position data;

[0090] A route avoidance module 5 is configured to determine route avoidance data according to the target sea ice data based on the result of the determination;

[0091] The modification module 6 is configured to modify the channel position data based on the route avoidance data.

[0092] As described above, it can be understood that the various components of the route intelligent planning system based on sea ice density proposed in this application can realize the functions of any of the above-mentioned route intelligent planning methods based on sea ice density, and the specific structure will not be repeated.

[0093] Reference Figure 4 In the embodiment of the present application, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as monitoring data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a route intelligent planning method based on sea ice density is implemented.

[0094] The processor executes the sea ice density-based intelligent route planning method, which includes: acquiring navigation environment data in real time and identifying sea ice density parameters in the navigation environment data; acquiring currently preset channel position data and marking target sea ice data within a preset range of the channel position data based on the channel position data and the sea ice density parameters; determining whether the target sea ice data satisfies a condition for replacing the channel position data; when the sea ice density parameters constitute a passing obstacle on the channel position data, determining that the target sea ice data satisfies the condition for replacing the channel position data; determining route avoidance data based on the target sea ice data based on a result of the determination; and modifying the channel position data based on the route avoidance data.

[0095] The aforementioned intelligent route planning method based on sea ice density acquires real-time navigational environmental data, identifies sea ice density parameters, and automatically determines whether the channel position data needs to be changed based on this data. This method can determine route avoidance data based on target sea ice data and modify the channel position data based on this data. This approach automatically determines whether the channel position data needs to be changed, reducing the crew's workload and improving decision-making accuracy. By determining route avoidance data based on target sea ice data and modifying the channel position data based on this data, the ship avoids areas of dense sea ice during navigation, reducing navigation risks.

[0096] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for intelligent route planning based on sea ice density, comprising the steps of: acquiring navigation environment data in real time, and identifying a sea ice density parameter in the navigation environment data; acquiring currently preset channel position data, and marking target sea ice data within a preset range of the channel position data based on the channel position data and the sea ice density parameter; determining whether the target sea ice data meets a condition for replacing the channel position data; when the sea ice density parameter constitutes a passing obstacle on the channel position data, determining that the target sea ice data meets the condition for replacing the channel position data; determining route avoidance data based on the target sea ice data based on a result of the determination; and modifying the channel position data based on the route avoidance data.

[0097] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided in this application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct RAM bus dynamic RAM (DRDRAM), and RAM bus dynamic RAM (RDRAM), etc.

[0098] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, apparatus, article, or method comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, apparatus, article, or method. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, apparatus, article, or method comprising the element.

[0099] The above description is only a preferred embodiment of the present application and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A route intelligent planning method based on sea ice density, characterized in that: The method comprises: acquiring marine environment data in real time and identifying sea ice concentration parameters in the marine environment data; Obtaining currently preset channel position data, and marking target sea ice data within a preset range of the channel position data based on the channel position data and a sea ice density parameter; Determining whether the target sea ice data meets the conditions for replacing the channel position data; When the sea ice concentration parameter constitutes a passing obstacle on the channel position data, it is determined that the target sea ice data meets the conditions for replacing the channel position data; Based on the determination result, determining route avoidance data according to the target sea ice data; modifying the course position data based on the route avoidance data; The method for judging whether the target sea ice data meets the conditions for replacing the channel position data includes: controlling a buoy module to receive a measurement signal on the channel, determining observation parameters of the target sea ice data according to the measurement signal, the observation parameters including position information, sea ice density and sea ice volume; calculating an edge distance between the sea ice position information and the channel position according to the position information and the channel position data; when the edge distance is greater than a preset first distance threshold, judging that the target sea ice data does not meet the conditions for replacing the channel position data; when the edge distance is less than the preset first distance threshold, constructing a risk prediction model, inputting the sea ice density and sea ice volume into the risk prediction model, and outputting a risk coefficient of sea ice through the risk prediction model; when the risk coefficient is greater than a preset coefficient threshold, judging that the target sea ice data constitutes a passing obstacle for the channel position data and meets the conditions for replacing the channel position data; Before the step of determining the observation parameters of the target sea ice data based on the measurement signal, the method also includes verifying the credibility of the observation parameters. The method includes: constructing a data fusion model to receive shared observation parameters of different ships in real time; inputting the observation parameters of the current ship and the shared observation parameters of other ships into the data fusion model respectively, performing time series analysis on the observation parameters and shared observation parameters through the data fusion model, and determining the error parameters of the observation parameters by comparing the data values at the same time point; based on the time series, identifying the change trend of the observation parameters and shared observation parameters at multiple time points through the data fusion model, and determining the error range of the observation parameters according to the change trend; quantifying the error parameters and error range through the data fusion model to obtain an error quantization value, and converting the quantization value result into credibility; when the credibility is greater than a preset credibility threshold, the observation parameter is judged to be a credible result, and the observation parameters of the target sea ice data are determined based on the credible result.

2. The method for intelligent route planning based on sea ice density according to claim 1, characterized in that: After the step of determining that the edge distance is greater than a preset first distance threshold, the method further includes: Acquiring meteorological parameters in real time, and predicting the movement direction and distance of the target avoiding sea ice within a predicted time period based on the meteorological parameters and observation parameters; determining position change data of the target avoiding sea ice based on the moving direction and the moving distance, updating the edge distance based on the position change data, and determining whether the edge distance is greater than a preset first distance threshold; When the edge distance is greater than a preset first distance threshold, it is determined that the target sea ice data does not meet the conditions for replacing the channel position data; When the edge distance is less than a preset first distance threshold, it is determined that the target sea ice data meets the condition for replacing the channel position data.

3. The method for intelligent route planning based on sea ice density according to claim 2, characterized in that: The method for determining route avoidance data based on the target sea ice data includes: acquiring ship data, and calculating a safe distance between the ship and sea ice based on observation parameters of the ship data and target sea ice data; Obtain meteorological parameters, and predict the movement direction and distance of the target avoiding sea ice within the predicted time period based on the meteorological parameters and observation parameters; Determine the position change data of the target sea ice at a preset time based on the moving direction and moving distance of the target sea ice during the predicted time period; Determining whether the distance traveled by the ship when reaching the target and avoiding sea ice is greater than the safety distance based on the position change data; When the distance when the ship reaches the target to avoid sea ice is greater than the safety distance, it is determined that the ship meets the requirements for safe passage, and route avoidance data is generated based on the safety distance.

4. The method for intelligent route planning based on sea ice density according to claim 3, characterized in that: After the step of determining whether the safety distance satisfies the safe passage of the ship based on the position change data, the method further comprises: When the passing distance of the ship when reaching the target to avoid sea ice is less than the safe distance, obtaining the current speed parameter and the speed increase range of the ship, and determining whether the ship can safely pass by increasing the speed according to the current speed parameter and the speed increase range; When the ship cannot pass safely by increasing its speed, the safe distance between the ship and the sea ice and the ship's speed parameters are recalculated based on the predicted moving direction and distance of the target sea ice at the preset time. The route avoidance data is generated based on the recalculated safety distance and the speed parameters of the vessel.

5. The method for intelligent route planning based on sea ice density according to claim 1, characterized in that: After the steps of quantifying the error parameter and error range by the data fusion model to obtain an error quantization value, and converting the quantization value result into credibility, the method further includes: When the credibility is less than or equal to a preset credibility threshold, the observation parameter is judged to be an unreliable result; Obtain the shared observation parameters of different ships, calculate the average value of all shared observation parameters, and obtain the mean value of the overall parameters; According to the mean, the shared observation parameter closest to the mean is extracted from all shared observation parameters as the observation parameter of the current ship.

6. An intelligent route planning system based on sea ice density, used in the method according to any one of claims 1 to 5, characterized in that: include: A first acquisition module is used to acquire marine environment data in real time and identify sea ice density parameters in the marine environment data; a second acquisition module, configured to acquire currently preset channel position data, and mark target sea ice data within a preset range of the channel position data based on the channel position data and a sea ice density parameter; A judgment module, configured to judge whether the target sea ice data satisfies the conditions for replacing the channel position data; a determination module, configured to determine that the target sea ice data satisfies a condition for replacing the channel position data when the sea ice concentration parameter constitutes a passing obstacle on the channel position data; a route avoidance module, configured to determine route avoidance data according to the target sea ice data based on a result of the determination; A modification module is used to modify the channel position data based on the route avoidance data.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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