Ship trajectory thermodynamic diagram generation method and device, electronic equipment and storage medium

Abstract

The invention discloses a ship track thermodynamic diagram generation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining AIS data of a target ship, and converting the AIS data into a plurality of AIS point data; projecting the AIS point data in the preset unit time to a preset two-dimensional pixel surface for color radiation, and generating an initial thermodynamic diagram corresponding to each segment of preset unit time; performing progressive pixel weighted average method operation on the initial thermodynamic diagram of each section of preset unit time to obtain a multi-level thermodynamic diagram of each time interval; and obtaining a to-be-searched time range, and performing pixel weighted average method operation on the target hierarchy thermodynamic diagram of the corresponding time interval based on the time span of the time range to obtain a target thermodynamic diagram corresponding to the time range. The method can efficiently and accurately generate the ship track thermodynamic diagram, and can be widely applied to the technical field of data processing.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a method, device, electronic device and storage medium for generating a ship trajectory heat map. Background Art

[0002] As the core data source for ship dynamic monitoring, the spatio-temporal trajectory heat visualization analysis of Automatic Identification System (AIS) data of ships has important value in the fields of maritime traffic management, waterway planning, ecological protection, etc. Traditional heat map generation methods generally include the following steps: First, discretize the AIS data in space and time, and map the longitude and latitude of the ship to a fixed grid or raster; Then, based on the number or density of ships in each grid, use methods such as weighted average or distance-based Gaussian kernel to calculate the heat value of the area and generate a heat map. In this way, the activity density distribution of ships within a certain time period can be effectively displayed, helping to analyze waterway congestion, potential collision risk areas, and special ship activity trends, etc.

[0003] Although the existing technology performs well in the application of small-scale data sets or short time spans, it has significant drawbacks when dealing with large-scale AIS data sets. First, the existing technology usually faces the problem of high computational cost. Especially when the number of ships is large or the time span is long, traditional methods need to process the data of each ship at each time point, which leads to a sharp increase in the amount of calculation, a long result generation time, and cannot meet the requirements of real-time processing. Summary of the Invention

[0004] The present invention aims to solve the problems of related technical limitations at least to a certain extent. For this purpose, the present invention provides a method, device, electronic device and storage medium for generating a ship trajectory heat map, which can efficiently and accurately generate a ship trajectory heat map.

[0005] On the one hand, an embodiment of the present invention provides a method for generating a ship trajectory heat map, including the following steps: Obtain the AIS data of the target ship and convert the AIS data into multiple AIS point data; Project the AIS point data within a preset unit time onto a preset two-dimensional pixel plane for color radiation to generate an initial heat map corresponding to each preset unit time; Perform progressive pixel weighted average method operations on the initial heat maps of each preset unit time to obtain multi-level heat maps for each time interval; Obtain the time range to be searched, and perform pixel weighted average method operations on the target-level heat map of the corresponding time interval based on the time span of the time range to obtain the target heat map corresponding to the time range.

[0006] Optionally, the method further comprises the following steps: The AIS point data is cleaned based on the preset threshold range.

[0007] Optionally, the AIS point data includes the latitude and longitude, speed and timestamp of the position point corresponding to the target ship; and data cleaning of the AIS point data based on a preset threshold range includes at least one of the following steps: Based on the longitude and latitude of the AIS point data, spatial position cleaning is performed through a preset position boundary range, and the AIS point data whose longitude and latitude are not within the position boundary range are removed; the position boundary range includes the effective longitude and latitude range and the ocean boundary range; Based on the speed of the AIS point data, speed cleaning is performed using a preset speed threshold, and the AIS point data whose speed exceeds the speed threshold is removed.

[0008] Optionally, the AIS point data includes the latitude and longitude and timestamp of the position point corresponding to the target ship; the two-dimensional pixel surface is constructed based on a preset number of pixel points divided based on the global longitude and latitude range; the AIS point data within the preset unit time is projected onto the preset two-dimensional pixel surface for color radiation, and an initial heat map corresponding to each preset unit time is generated, including the following steps: Based on longitude, latitude and timestamp, the AIS point data within a preset unit time is projected to the pixel points in the two-dimensional pixel plane; Taking the pixel point corresponding to the longitude and latitude as the center of the circle, the pixel points within the preset radius are radiated with color based on the preset color range; the color radiation gradually changes from dark to light in the preset color range from the center of the circle to the outside; Traverse each pixel point on the two-dimensional pixel surface, perform pixel weighted averaging on the results of all color radiation of a single pixel point, and obtain the initial thermal map corresponding to the preset unit time.

[0009] Optionally, a progressive pixel weighted average method is performed on the initial heat map of each preset unit time to obtain a multi-level heat map of each time interval, including the following steps: At the end of the day, the initial heat map of each preset unit time within the day is calculated by pixel weighted average method to obtain the heat map of the whole day of the corresponding date; At the end of the month, the pixel weighted average method is used to calculate the full-day heat map of each day in the month to obtain the full-month heat map of the corresponding month; Among them, the multi-level heat map includes an initial heat map corresponding to each preset unit time, a full-day heat map corresponding to each day, and a full-month heat map corresponding to each month.

[0010] Optionally, the multi-level heat map includes an initial heat map corresponding to each preset unit time, a whole-day heat map corresponding to each day, and a whole-month heat map corresponding to each month; performing a pixel weighted average method operation on the target-level heat map in its corresponding time interval based on the time span of the time range, including the following steps: When the time span is less than one day, perform a pixel weighted average method operation on the initial heat map of each preset unit time in the time interval corresponding to the time range; When the time span is greater than or equal to one day and less than one month, perform a pixel weighted average method operation on the whole-day heat map of each day in the time interval corresponding to the time range; When the time span is greater than or equal to one month, perform a pixel weighted average method operation on the whole-month heat map of each month in the time interval corresponding to the time range.

[0011] Optionally, the multi-level heat map includes an initial heat map corresponding to each preset unit time, a whole-day heat map corresponding to each day, and a whole-month heat map corresponding to each month; the method further includes the following steps: Normalize the preselected ship trajectory heat map to obtain the normalized heat value at each position of the ship trajectory heat map; Based on the normalized heat value, map the corresponding position of the target heat map to an RGB color through a preset mapping table to generate a pseudo-color image corresponding to the ship trajectory heat map; Wherein, the ship trajectory heat map includes an initial heat map, a whole-day heat map, a whole-month heat map, and a target heat map; the preset mapping table includes the mapping relationship between each numerical interval of the normalized heat value and the chromaticity of its corresponding RGB color.

[0012] On the other hand, an embodiment of the present invention provides a ship trajectory heat map generation device, including: The first module is used to obtain the AIS data of the target ship and convert the AIS data into multiple AIS point data; The second module is used to project the AIS point data within a preset unit time onto a preset two-dimensional pixel plane for color radiation to generate an initial heat map corresponding to each preset unit time; The third module is used to perform a progressive pixel weighted average method operation on the initial heat maps of each preset unit time to obtain a multi-level heat map for each time interval; The fourth module is used to obtain the time range to be searched, and perform a pixel weighted average method operation on the target-level heat map in its corresponding time interval based on the time span of the time range to obtain the target heat map corresponding to the time range.

[0013] Optionally, the device further includes: The fifth module is used to perform data cleaning on the AIS point data based on a preset threshold range.

[0014] Optionally, the multi-level heat map includes an initial heat map corresponding to each preset unit time period, a whole-day heat map corresponding to each day, and a whole-month heat map corresponding to each month; the apparatus further includes: A sixth module, configured to perform normalization processing on a preselected ship trajectory heat map to obtain a normalized heat value at each position of the ship trajectory heat map; A seventh module, configured to map the corresponding positions of the target heat map to RGB colors through a preset mapping table based on the normalized heat values, and generate a pseudo-color image corresponding to the ship trajectory heat map; Wherein, the ship trajectory heat map includes an initial heat map, a whole-day heat map, a whole-month heat map, and a target heat map; the preset mapping table includes the mapping relationship between each numerical interval of the normalized heat value and the chromaticity of the corresponding RGB color.

[0015] On the other hand, an embodiment of the present invention provides an electronic device, including: a processor and a memory; the memory is used to store a program; the processor executes the program to implement the above-mentioned ship trajectory heat map generation method.

[0016] On the other hand, an embodiment of the present invention provides a computer storage medium, in which a program executable by a processor is stored, and the program executable by the processor is used to implement the above-mentioned ship trajectory heat map generation method when executed by the processor.

[0017] In the embodiment of the present invention, by obtaining the AIS data of the target ship, the AIS data is converted into multiple AIS point data; the AIS point data within a preset unit time is projected onto a preset two-dimensional pixel plane for color radiation to generate an initial heat map corresponding to each preset unit time period; the initial heat maps of each preset unit time period are subjected to progressive pixel weighted average method operations to obtain a multi-level heat map for each time interval; the time range to be searched is obtained, and based on the time span of the time range, pixel weighted average method operations are performed on the target-level heat map of the corresponding time interval to obtain a target heat map corresponding to the time range. The embodiment of the present invention reduces the calculation cost through progressive pixel weighted average method operations, and further combines the operation of the target-level heat map corresponding to the time range to enhance the flexibility of data processing. The embodiment of the present invention can support large-scale data set processing, effectively solves the shortcomings of traditional AIS data processing methods, and has significant beneficial effects. Description of the Drawings

[0018] The drawings are used to provide a further understanding of the technical solutions of the present invention, and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the technical solutions of the present invention, and do not constitute a limitation to the technical solutions of the present invention.

[0019] Figure 1It is a schematic diagram of an implementation environment for generating a ship trajectory heat map provided by an embodiment of the present invention; Figure 2 It is a schematic flowchart of a method for generating a ship trajectory heat map provided by an embodiment of the present invention; Figure 3 It is a schematic diagram of an extended process of the method for generating a ship trajectory heat map provided by an embodiment of the present invention; Figure 4 It is a schematic diagram of the expanded process of step S200 provided by an embodiment of the present invention; Figure 5 It is a schematic diagram of the expanded process of step S300 provided by an embodiment of the present invention; Figure 6 It is a schematic diagram of the expanded process of performing pixel weighted average method operation on the target level heat map provided by an embodiment of the present invention; Figure 7 It is a schematic diagram of another extended process of the method for generating a ship trajectory heat map provided by an embodiment of the present invention; Figure 8 It is a schematic diagram of the overall process of the specific application of the method for generating a ship trajectory heat map provided by an embodiment of the present invention; Figure 9 It is a schematic diagram of the structure of a device for generating a ship trajectory heat map provided by an embodiment of the present invention; Figure 10 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Detailed implementation manners

[0020] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0021] It should be noted that although functional module division is performed in the system schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order from the module division in the system or the order in the flowchart. Terms such as "first / S100", "second / S200", etc. in the description, claims and the above-mentioned drawings are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence.

[0022] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments can be included in at least one embodiment of the invention. The phrase occurs in various places in the specification and is not necessarily referring to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive of other embodiments. Those skilled in the art will understand explicitly and implicitly that the embodiments described herein can be combined with other embodiments.

[0023] It can be understood that the method for generating a ship trajectory heat map provided by the embodiments of the present invention can be applied to any computer device with data processing and computing capabilities, and this computer device can be various types of terminals or servers. When the computer device in the embodiment is a server, the server is an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. Optionally, the terminal is a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto.

[0024] As Figure 1 shown, it is a schematic diagram of an implementation environment provided by the embodiments of the present invention. Referring to Figure 1 , this implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be network-connected wirelessly or wiredly to complete data transmission and exchange.

[0025] The server 101 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0026] In addition, the server 101 can also be a node server in a blockchain network. Among them, the blockchain is a new application mode of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithms.

[0027] The terminal 102 may be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited thereto. The terminal 102 and the server 101 may be directly or indirectly connected via wired or wireless communication, which is not limited in the embodiment of the present invention.

[0028] Based on the example Figure 1 In the implementation environment shown, an embodiment of the present invention provides a method for generating a ship trajectory heat map. The following is explained using the example of the ship trajectory heat map generation method applied to the server 101. It can be understood that the ship trajectory heat map generation method can also be applied to the terminal 102.

[0029] Reference Figure 2 , Figure 2 The flowchart of the method for generating a ship trajectory heat map applied to a server provided in an embodiment of the present invention, the execution subject of the method for generating a ship trajectory heat map can be any of the aforementioned computer devices (including a server or a terminal). Figure 2 , the method comprises the following steps: S100, obtaining AIS data of a target ship, and converting the AIS data into multiple AIS point data; For example, in some specific implementations, the target ship AIS data is obtained, and the data may be historical data or dynamic data accessed through an interface. The name, MMSI, longitude and latitude, timestamp and other information of the ship may be read from the target ship AIS data. In some specific application scenarios, the longitude and latitude information in the ship AIS data may be converted into AIS point data in the WGS84 coordinate system.

[0030] In some embodiments, the method may further include the following steps: performing data cleaning on the AIS point data based on a preset threshold range.

[0031] For example, in order to filter out abnormal data and avoid affecting the accuracy of heat map generation, the AIS point data may be cleaned before subsequent data processing steps.

[0032] It should be noted that the AIS point data includes the latitude and longitude, speed and timestamp of the corresponding position of the target ship; in some embodiments, such as Figure 3 As shown, performing data cleaning on the AIS point data based on a preset threshold range may include at least one of the following steps: T100, based on the longitude and latitude of the AIS point data, perform spatial position cleaning through the preset position boundary range, and remove the AIS point data whose longitude and latitude are not within the position boundary range; The location boundary range includes the effective range of longitude and latitude and the ocean boundary range; Exemplarily, in some specific embodiments, spatial location cleaning can be performed, and the specific implementation can be as follows: First, perform the first location cleaning on the longitude and latitude. The effective range of longitude is [-180, 180], and the effective range of latitude is [-90, 90]. Data with longitude and latitude outside the effective range are excluded. Then, perform the second location cleaning on the longitude and latitude. Using the global coastline data as the ocean boundary, obtain the global ocean distribution range. By superimposing the AIS data and the global ocean distribution range in the same coordinate system, exclude the AIS data with longitude and latitude outside the ocean boundary range.

[0033] T200. Based on the speed of the AIS point data, perform speed cleaning through a preset speed threshold, and exclude the AIS point data with a speed exceeding the speed threshold.

[0034] Exemplarily, in some specific embodiments, speed cleaning can be performed, and the specific implementation can be as follows: Set a speed threshold and exclude data with a speed outside the threshold range. Usually, the maximum speed of a ship does not exceed 35 knots. Generally, the threshold can be set to 35 knots (the specific speed threshold can be adjusted according to application requirements, and this is only an example for illustration). The speed is calculated by dividing the distance between the current position and the previous position of the ship (unit: nautical miles) by the time difference of the ship's movement (unit: hours).

[0035] S200. Project the AIS point data within a preset unit time onto a preset two-dimensional pixel plane for color radiation to generate an initial heat map corresponding to each segment of the preset unit time; It should be noted that the AIS point data includes the longitude, latitude, and timestamp of the corresponding position point of the target ship; the two-dimensional pixel plane is constructed based on a preset number of pixel points divided according to the global longitude and latitude range; in some embodiments, as Figure 4 shown, step S200 may include the following steps: S201. Based on the longitude, latitude, and timestamp, project the AIS point data within a preset unit time onto the pixel points in the two-dimensional pixel plane; S202. Using the pixel points corresponding to the longitude and latitude as the center, perform color radiation on the pixel points within a preset radius range based on a preset color interval; the color radiation gradually changes from deep to light in the preset color interval from the center outwards; S203. Traverse each pixel point of the two-dimensional pixel plane, and perform pixel weighted average method operation on the results of all color radiations of a single pixel point to obtain the initial heat map corresponding to the preset unit time.

[0036] Exemplarily, in some specific embodiments, the global scope (i.e., longitude [-180, 180], latitude [-90, 90]) can be divided into 2880 * 1440 pixel points. Preferably, the unit time can be in hours (specifically, in practical applications, the unit time can be adaptively adjusted according to specific accuracy requirements, and the adjustment result is a factor of 24 hours a day, such as adjusted to a higher precision of half an hour, 15 minutes or 10 minutes, etc., or adjusted to a lower precision of one and a half hours, 2 hours or 3 hours, etc.). At the end of each whole hour, the AIS point data after cleaning within the unit time is projected onto the global scope. For each AIS point, a circle with a radius of 10 pixels is set with the longitude and latitude as the center, and at the same time, each point is set with a uniform gradient color, that is, the color at the center position is RGB(0.03, 0, 0), and it gradually changes uniformly to the color at the edge position (0, 0, 0). Among them, the 2880 * 1440 pixel points divided, the pixel radius of the AIS point, and the center position color RGB(0.03, 0, 0) can all be adjusted according to the actual situation. The numerical values used this time are numerical examples with better visualization effects obtained after multiple experiments and should not be regarded as a limitation to the embodiments of the present invention.

[0037] Each AIS point is set with a weight of 1, and the pixel weighted average method is used to calculate the final color value of each pixel:

[0038] In the formula, is the contribution color value of the th AIS point to the pixel (determined based on the result of color radiation. For the edge position of the circle formed by the AIS point and the pixel points not radiated by it, the contribution color value is (0, 0, 0)), is the weight of the th AIS point (taking the value 1), is the total number of AIS points in the pixel .

[0039] By traversing and calculating one by one, the 2880 * 1440 pixel values of the whole world are obtained, which is the heat map within the unit time and is saved in the tif format.

[0040] S300. Perform progressive pixel weighted average method operations on the initial heat maps of each preset unit time to obtain multi-level heat maps for each time interval; It should be noted that in some embodiments, such as Figure 5As shown in the figure, step S300 may include the following steps: S301. At the end of the whole day, perform pixel weighted average method operation on the initial heat maps of each preset unit time within the day time to obtain the whole day heat map corresponding to the date; S302. At the end of the whole month, perform pixel weighted average method operation on the whole day heat maps of each day within the month time to obtain the whole month heat map corresponding to the month. Among them, the multi-level heat maps include the initial heat maps corresponding to each preset unit time, the whole day heat maps corresponding to each day, and the whole month heat maps corresponding to each month.

[0041] Exemplarily, in some specific embodiments, taking the preset unit time as 1 hour as an example, at the end of the whole day, perform pixel weighted average method operation on the hourly heat map pixels within the day time, and perform operation on 24 (1 per hour) tif format heat maps of the day to obtain the whole day heat map; at the end of the whole month, perform pixel weighted average method operation on the whole day heat map pixels within the month time, and perform operation on 28 / 29 / 30 / 31 (1 per day) tif format heat maps of the month to obtain the whole month heat map. The naming format of the hourly heat map is "hour - XX:XX:XX, XX / XX / XXXX", the naming format of the whole day heat map is "day - XX / XX / XXXX", and the naming format of the whole month heat map is "month - XX / XXXX" to form a heat map data set.

[0042] S400. Obtain the time range to be searched, and perform pixel weighted average method operation on the target level heat map of its corresponding time interval based on the time span of the time range to obtain the target heat map corresponding to the time range.

[0043] It should be noted that the multi-level heat maps include the initial heat maps corresponding to each preset unit time, the whole day heat maps corresponding to each day, and the whole month heat maps corresponding to each month; in some embodiments, as Figure 6 shown, performing pixel weighted average method operation on the target level heat map of its corresponding time interval based on the time span of the time range may include the following steps: S401. When the time span is less than one day, perform pixel weighted average method operation on the initial heat maps of each preset unit time in the time interval corresponding to the time range; S402. When the time span is greater than or equal to one day and less than one month, perform pixel weighted average method operation on the whole day heat maps of each day in the time interval corresponding to the time range; S403. When the time span is greater than or equal to one month, perform pixel weighted average method operation on the whole month heat maps of each month in the time interval corresponding to the time range.

[0044] Exemplarily, in some specific embodiments, when there is a specific need to calculate a heat map, a time range is selected according to the need. Furthermore, the optimal unit time can be selected according to the time range. If the time range is short (< 24 hours), the optimal unit is hour; if the time range is long but does not exceed one month (≥ 1 day and < 1 month), the optimal unit is day; if the time range is long (≥ 1 month), the optimal unit is month. For example, when calculating the heat map from 00:00 on May 1, 2024 to 12:00 on May 1, 2024, the optimal unit is hour; when calculating the heat map from May 1, 2024 to May 20, 2024, the optimal unit is day; when calculating the heat map from May 1, 2024 to December 31, 2024, the optimal unit is month. The heat map data corresponding to the time range in the heat map dataset is extracted according to the optimal unit, and then the extracted heat map data is operated by the pixel weighted average method.

[0045] Among them, in some embodiments, the multi-level heat map includes an initial heat map corresponding to each preset unit time, a daily heat map corresponding to each day, and a monthly heat map corresponding to each month; as Figure 7 shown, the method may further include the following steps: S500, normalizing the preselected ship trajectory heat map to obtain the normalized heat value of each position of the ship trajectory heat map; S600, based on the normalized heat value, mapping the corresponding position of the target heat map to RGB colors through a preset mapping table to generate a pseudo-color image corresponding to the ship trajectory heat map; wherein, the ship trajectory heat map includes an initial heat map, a daily heat map, a monthly heat map, and a target heat map; the preset mapping table includes the mapping relationship between each numerical interval of the normalized heat value and the chromaticity of the corresponding RGB color.

[0046] Exemplarily, in some specific embodiments, the preselected trajectory heat map can be normalized to obtain the heat map within the required time range. Furthermore, the normalized heat value ∈[0,1] can be mapped to the RGB color color mapping scheme (preset mapping table) as shown in Table 1 below: Table 1

[0047] The "blue → yellow → red" gradient color scheme can effectively highlight the hot spots of ship activities while maintaining the uniformity of visual perception, ensuring that users can intuitively interpret the heat map data. In addition, the preset mapping table can also be implemented through a color mapping function, specifically for converting the normalized heat value into RGB colors:

[0048] Colormap is a color lookup table containing 256 levels of color. Color index (integer value 0-255) used to look up RGB colors.

[0049] Finally, a pseudo-color image is generated and visualized.

[0050] It should be noted that the embodiments of the present invention include at least the following beneficial effects: 1. Reduce computational costs: Traditional heat map generation methods require processing the data of each ship at each time point. The amount of computation increases exponentially with the increase in the number of ships and the time span, resulting in high computational costs. The present invention reduces the amount of data for a single calculation by converting AIS data into multiple AIS point data and generating an initial heat map within a preset unit time. The progressive pixel weighted average method further reduces the computational complexity, avoids repeated calculations, and significantly reduces the computational cost.

[0051] 2. Improve real-time processing capability: Due to the large amount of calculation, the traditional method takes a long time to generate results when processing large-scale AIS data, which cannot meet the needs of real-time processing. However, the present invention can quickly generate the target thermal map by generating the initial thermal map and multi-level thermal map in stages, and performing pixel weighted average calculation within the time range to be searched. This staged processing method improves the real-time processing capability of the system and can respond to the needs of maritime traffic management and waterway planning more quickly.

[0052] 3. Enhance the flexibility of data processing: Traditional methods usually need to process all data at once, lack flexibility, and are difficult to adapt to the needs of different time spans and data sizes. The present invention generates multi-level heat maps in a progressive manner, and can select different time spans and levels as needed to flexibly generate target heat maps. This flexibility enables the system to better adapt to the needs of different application scenarios, such as short-term waterway congestion analysis and long-term ecological protection research.

[0053] 4. Optimize the visualization effect of the heat map: The heat map generated by the traditional method may be noisy or not smooth enough, which affects the visualization effect and analysis accuracy. The present invention can effectively smooth the heat map, reduce noise and improve the visualization effect through the pixel weighted average method. The progressive processing method can also retain more detailed information, making the heat map more accurate and intuitive.

[0054] 5. Support for processing large-scale datasets: Traditional methods often face problems of insufficient computing resources and excessive generation time when dealing with large-scale AIS datasets. However, through phased processing and progressive operations, the present invention can effectively process large-scale AIS datasets and generate high-quality heatmaps. This method is not only applicable to small-scale datasets but also can be extended to large-scale datasets to meet the requirements in fields such as maritime traffic management, waterway planning, and ecological protection.

[0055] To explain the principle of the technical solution of the present invention in detail, the overall process of the present invention will be described below in conjunction with some specific embodiments. It is easy to understand that the following is an explanation of the technical principle of the present invention and should not be regarded as a limitation of the present invention.

[0056] First of all, it should be noted that existing methods usually lack efficient time aggregation and multi-scale processing capabilities, making it difficult to effectively integrate and optimize data within different time scales (such as daily, monthly, and yearly), resulting in a lack of consistency and reliability in the comparison and analysis of heatmaps between different time periods. In short, when faced with a huge amount of data, the existing technology has excessive storage pressure and computing resource consumption, making it difficult to meet the requirements in practical applications. Therefore, how to improve the efficiency, accuracy, and flexibility of heatmap generation remains an urgent problem to be solved in the existing technology.

[0057] In view of this, the present invention addresses the problems of high computational cost, low efficiency, and excessive storage and computing resource consumption in the existing technology for generating ship trajectory heatmaps when dealing with large-scale and long-time-series AIS data, and proposes an optimized heatmap generation algorithm. Through data preprocessing, pixel weighted average method calculation, an efficient time aggregation mechanism, and a memory optimization strategy, this algorithm effectively improves the generation efficiency and reduces resource consumption, and is particularly suitable for real-time processing of large-scale and long-time-series datasets, solving multiple key problems of the existing technology. As Figure 8 shown, the present invention can be implemented through the following process steps: Obtain the AIS data of the target ship, and the data can be historical data or dynamic data accessed through an interface.

[0058] Read information such as the name, MMSI, longitude and latitude, and timestamp of the ship from the AIS data of the target ship.

[0059] According to the longitude and latitude information in the ship's AIS data, it is converted into AIS point data in the WGS84 coordinate system, and the point data is cleaned. 1) Perform spatial position cleaning. First, perform the first position cleaning on the longitude and latitude. The valid range of longitude is [-180, 180], and the valid range of latitude is [-90, 90]. Data that are not in the valid range of longitude and latitude are eliminated. Then perform the second position cleaning on the longitude and latitude. Use the global coastline data as the ocean boundary to obtain the global ocean distribution range. By superimposing the AIS data and the global ocean distribution range in the same coordinate system, eliminate the AIS data whose longitude and latitude are not within the ocean boundary. 2) Perform speed cleaning. Set a speed threshold and eliminate data whose speed is not within the threshold range. Usually the maximum speed of a ship does not exceed 35 knots, and the threshold can generally be set to 35 knots. The speed calculation is obtained by dividing the distance between the current position of the ship and the previous position (unit: nautical miles) by the time difference (unit: hour) of the ship's movement.

[0060] The global scope (i.e. longitude [-180, 180], latitude [-90, 90]) is divided into 2880*1440 pixels. The unit time is hour, and at the end of the hour, the cleaned AIS point data in the unit time is projected onto the global scope. For each AIS point, the longitude and latitude are set as the center of the circle, and the radius is 10 pixels. At the same time, each point is set with a uniform gradient color, that is, the color at the center of the circle is RGB (0.03, 0, 0), and the color uniformly gradients to the edge position is (0, 0, 0). Among them, the divided 2880*1440 pixels, the AIS point radius pixels, and the center position color RGB (0.03, 0, 0) can all be adjusted according to actual conditions. The values ​​used this time are the values ​​with better visualization effects obtained after multiple tests.

[0061] The weight of each AIS point is set to 1, and the final color value of each pixel is calculated using the pixel weighted average method:

[0062] In the formula, is the i-th AIS point pair pixel The contribution color value, is the weight of the i-th AIS point (value 1), n ​​is the number of pixels The total number of AIS points in the

[0063] The global 2880*1440 pixel values ​​are calculated one by one, which is the heat map per unit time and saved in tif format.

[0064] Select whether to calculate the hourly or monthly heat map according to specific requirements. Specific requirements usually only view the heat map across months. For example, from September 1, 2024 to January 31, 2025, you can choose to calculate the monthly heat map; specific requirements usually only view the heat map across days. For example, from September 5, 2024 to September 12, 2024, you can choose to calculate the hourly heat map; specific requirements usually only view the heat map across hours, and you can choose not to calculate the hourly or monthly heat map. If you are unsure of the requirements or have both requirements, you can select all.

[0065] Taking the selection of all as an example, at the end of the hourly calculation, perform the pixel weighted average method operation on the hourly heat map within the day. Operate on 24 (1 per hour) tif-format heat maps of the day to obtain the hourly heat map; at the end of the monthly calculation, perform the pixel weighted average method operation on the daily heat map within the month. Operate on 28 / 29 / 30 / 31 (1 per day) tif-format heat maps of the month to obtain the monthly heat map.

[0066] Normalize the hourly, daily, and monthly heat maps so that the numerical range is 0 - 1. The normalization formula:

[0067] In the formula, is the normalized value at the longitude and latitude of ; is the heat value calculated at the longitude and latitude of ; and are the maximum and minimum values in the heat map respectively.

[0068] The naming format of the hourly heat map is "hour-YYYY-MM-DD HH:00"; the naming format of the daily heat map is "day-YYYY-MM-DD"; the naming format of the monthly heat map is "month-YYYY-MM", forming a heat map data set.

[0069] When calculating the heat map with specific requirements, select the time range according to the requirements.

[0070] Select the optimal unit time according to the time range. If the time range is short (< 24 hours), the optimal unit is hour; if the time range is long but does not exceed one month (≥ 1 day and < 1 month), the optimal unit is day; if the time range is long (≥ 1 month), the optimal unit is month. For example, when calculating the heat map from 00:00 on May 1, 2024 to 12:00 on May 1, 2024, the optimal unit is hour; when calculating the heat map from May 1, 2024 to May 20, 2024, the optimal unit is day; when calculating the heat map from May 1, 2024 to December 31, 2024, the optimal unit is month. Extract the heat map data corresponding to the time range from the heat map dataset according to the optimal unit.

[0071] Perform pixel weighted average method operation on the extracted heat map data and normalize it to obtain the heat map within the required time range.

[0072] The normalized heat values ∈[0,1] are mapped to RGB colors. This method adopts the following color mapping scheme as described in Table 1 above. The gradient color scheme of "blue → yellow → red" can effectively highlight the hot spots of ship activities while maintaining the uniformity of visual perception, ensuring that users can intuitively interpret the heat map data.

[0073] Color mapping function is used to convert the normalized heat values into RGB colors:

[0074] Colormap is a color lookup table that contains 256 levels of colors. Color index (integer value 0 - 255), used to look up and obtain RGB colors.

[0075] Finally, generate a pseudo-color image and visualize it.

[0076] In some specific application scenarios, for the traditional method of the prior art and the method of the embodiment of the present invention, heat map generation tests are conducted and compared under the same test configuration. An example of the test result comparison is shown in Table 2 below: Table 2

[0077] In summary, the present invention enables efficient time aggregation and adaptive time unit selection: The present invention proposes to adaptively select the optimal time unit (such as hour, day, month) according to requirements for generating heat maps. This technology can dynamically select the most suitable aggregation granularity according to the time range of the data, improving the calculation efficiency. Moreover, when dealing with cross-hour, cross-day, or cross-month requirements, it can respond quickly, avoiding repeated calculations and resource waste in traditional methods. In addition, the present invention enables adaptive time unit selection and an efficient time aggregation mechanism: The present invention provides a technology for selecting the optimal time unit based on the time range, flexibly handling heat map calculation requirements for different time scales (hour, day, month), and avoiding redundant calculations on the data.

[0078] Compared with the disadvantages of the prior art, the beneficial effects of the present invention are compared as follows: Problems of the prior art: Traditional heat map generation methods need to process a large amount of ship AIS data. Especially when the number of ships is large and the time span is long, the calculation cost is very high, and the generation process is slow, unable to meet the requirements of real-time monitoring.

[0079] Advantages of the present invention: The present invention significantly reduces the consumption of computing resources and improves the efficiency of generating heat maps by optimizing data cleaning and aggregation algorithms. Through adaptive selection of the optimal unit time and efficient algorithm design, even when dealing with large-scale data, the system can respond quickly, reducing the calculation time and storage requirements.

[0080] Problems of the prior art: When generating large-scale heat maps, existing methods usually require a large amount of computing resources and storage space. Especially in the calculation of long-term data or large-scale regions, it is easy to cause bottlenecks in memory and computing power.

[0081] Advantages of the present invention: The present invention effectively reduces the consumption of computing and storage resources through technologies such as reasonable adaptive selection, efficient data storage structures, and block processing. In the face of large amounts of data, the system can operate efficiently, without being affected by resource bottlenecks in terms of calculation efficiency, ensuring the stability and scalability of the system.

[0082] On the other hand, as Figure 9 shown, an embodiment of the present invention provides a ship trajectory heat map generation device 900, which may include: A first module 901, configured to obtain AIS data of a target ship and convert the AIS data into a plurality of AIS point data; A second module 902, configured to project the AIS point data within a preset unit time onto a preset two-dimensional pixel plane for color radiation to generate an initial heat map corresponding to each period of the preset unit time; The third module 903 is used to perform progressive pixel weighted average method operations on the initial heat maps of each preset unit time period to obtain multi-level heat maps for each time interval; The fourth module 904 is used to obtain the time range to be searched, and perform pixel weighted average method operations on the target-level heat maps of the corresponding time intervals based on the time span of the time range to obtain the target heat map corresponding to the time range.

[0083] In some embodiments, the device may further include: The fifth module is used to perform data cleaning on the AIS point data based on a preset threshold range.

[0084] In some embodiments, the multi-level heat maps include the initial heat maps corresponding to each preset unit time period, the daily heat maps corresponding to each day, and the monthly heat maps corresponding to each month; the device may further include: The sixth module is used to perform normalization processing on the preselected ship trajectory heat map to obtain the normalized heat values at each position of the ship trajectory heat map; The seventh module is used to map the corresponding positions of the target heat map to RGB colors through a preset mapping table based on the normalized heat values to generate a pseudo-color image corresponding to the ship trajectory heat map; Among them, the ship trajectory heat map includes the initial heat map, the daily heat map, the monthly heat map, and the target heat map; the preset mapping table includes the mapping relationship between each numerical interval of the normalized heat value and the chromaticity of the corresponding RGB color.

[0085] The content of the method embodiments of the present invention is applicable to the device embodiments of the present invention. The functions specifically implemented by the device embodiments of the present invention are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above methods.

[0086] On the other hand, the embodiments of the present invention also provide an electronic device. The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the above-mentioned ship trajectory heat map generation method is implemented. The electronic device may be any intelligent terminal including a tablet computer, a vehicle-mounted computer, etc.

[0087] It can be understood that the content in the above method embodiments is applicable to the device embodiments of the present invention. The functions specifically implemented by the device embodiments of the present invention are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0088] As Figure 10 shown, Figure 10 illustrates the hardware structure of an electronic device 1000 in another embodiment. The electronic device 1000 includes: The processor 1001 can be implemented in the form of a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided by the embodiments of the present invention; The memory 1002 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM), etc. The memory 1002 can store an operating system and other application programs. When implementing the technical solutions provided by the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1002 and are called by the processor 1001 to execute the network node population optimization method of the embodiments of the present invention; The input / output interface 1003 is used to implement information input and output; The communication interface 1004 is used to implement communication interaction between this device and other devices, and can implement communication through a wired method (such as USB, network cable, etc.) or through a wireless method (such as mobile network, WIFI, Bluetooth, etc.); The bus 1005 transmits information between the various components of the device (such as the processor 1001, the memory 1002, the input / output interface 1003, and the communication interface 1004); Among them, the processor 1001, the memory 1002, the input / output interface 1003, and the communication interface 1004 achieve communication connections with each other inside the device through the bus 1005.

[0089] The electronic device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, that is, they may be located in one place or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0090] The content of the method embodiments of the present invention is applicable to the electronic device embodiments of the present invention. The functions specifically implemented by the electronic device embodiments of the present invention are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method.

[0091] Another aspect of the embodiments of the present invention further provides a computer-readable storage medium. The storage medium stores a program, and the program is executed by a processor to implement the previous method.

[0092] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, and this computer-readable medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0093] The content of the method embodiments of the present invention is applicable to the embodiments of this computer-readable storage medium. The functions specifically implemented by the embodiments of this computer-readable storage medium are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those of the above method.

[0094] The embodiments of the present invention also disclose a computer program product or a computer program. This computer program product or computer program includes computer instructions, and these computer instructions are stored in a computer-readable storage medium. The processor of a computer device can read these computer instructions from the computer-readable storage medium, and the processor executes these computer instructions, causing the computer device to execute the method described above.

[0095] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in an order different from that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, as well as combinations of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0096] It should be noted that although several modules of devices for action execution are mentioned in the above detailed description, such a division is not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more of the above-described modules or units can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0097] From the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software, or by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a portable hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present invention.

[0098] In some alternative embodiments, the functions / operations mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the functions / operations involved, two consecutive blocks shown may actually be executed substantially simultaneously or the blocks can sometimes be executed in the reverse order. In addition, the embodiments presented and described in the flowcharts of the present invention are provided by way of example for the purpose of providing a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logical flows presented herein. Alternative embodiments are foreseeable, in which the order of various operations is changed and the sub-operations described as part of a larger operation are executed independently.

[0099] In addition, although the present invention has been described in the context of functional modules, it should be understood that, unless otherwise stated to the contrary, one or more of the functions and / or features may be integrated in a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It should also be understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. Rather, considering the attributes, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the modules will be understood within the ordinary skills of an engineer. Therefore, those skilled in the art can implement the present invention as set forth in the claims without undue experimentation. It should also be understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0100] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0101] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution device, apparatus, or equipment (such as a computer-based device, a device including a processor, or other devices that can fetch instructions from and execute instructions by the instruction execution device, apparatus, or equipment), or in combination with these instruction execution devices, apparatuses, or equipment. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in combination with an instruction execution device, apparatus, or equipment.

[0102] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which a program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0103] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution device. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0104] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0105] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the claims and their equivalents.

[0106] The above has specifically described the preferred embodiments of the present invention, but the present invention is not limited to the embodiments. Those skilled in the art can also make various equivalent deformations or substitutions without departing from the spirit of the present invention, and these equivalent deformations or substitutions are all included in the scope defined by the claims of the present invention.

Claims

1. A method for generating a ship trajectory heat map, characterized in that: The following steps are involved: Acquire AIS data of a target ship, and convert the AIS data into a plurality of AIS point data; Projecting the AIS point data within a preset unit time onto a preset two-dimensional pixel surface for color radiation, and generating an initial heat map corresponding to each segment of the preset unit time; Performing a progressive pixel weighted average operation on the initial heat map of each preset unit time segment to obtain a multi-level heat map of each time interval; The time range to be searched is obtained, and based on the time span of the time range, a pixel weighted average method is performed on the target level heat map corresponding to the time interval to obtain a target heat map corresponding to the time range.

2. The method for generating a ship trajectory heat map according to claim 1, characterized in that: The method further comprises the following steps: The AIS point data is cleaned based on a preset threshold range.

3. The method for generating a ship trajectory heat map according to claim 2, characterized in that: The AIS point data includes the latitude and longitude, speed and timestamp of the position point corresponding to the target ship; and the data cleaning of the AIS point data based on a preset threshold range includes at least one of the following steps: Based on the longitude and latitude of the AIS point data, spatial position cleaning is performed through a preset position boundary range, and the AIS point data whose longitude and latitude are not within the position boundary range is removed; the position boundary range includes a valid longitude and latitude range and an ocean boundary range; Based on the speed of the AIS point data, speed cleaning is performed using a preset speed threshold, and the AIS point data whose speed exceeds the speed threshold is removed.

4. The method for generating a ship trajectory heat map according to claim 1, characterized in that: The AIS point data includes the latitude and longitude and timestamp of the location point corresponding to the target ship; the two-dimensional pixel surface is constructed based on a preset number of pixel points divided based on the global longitude and latitude range; The step of projecting the AIS point data within the preset unit time onto a preset two-dimensional pixel surface for color radiation to generate an initial thermal map corresponding to each segment of the preset unit time includes the following steps: Based on the longitude and latitude and the timestamp, project the AIS point data within the preset unit time to the pixel point in the two-dimensional pixel plane; Taking the pixel point corresponding to the longitude and latitude as the center of the circle, the pixel points within a preset radius are radiated with colors based on a preset color range; the color radiation gradually changes from dark to light in the preset color range from the center of the circle to the outside; Each pixel point of the two-dimensional pixel surface is traversed, and the pixel weighted average method is performed on the results of all the color radiations of a single pixel point to obtain the initial thermal map corresponding to the preset unit time.

5. The method for generating a ship trajectory heat map according to claim 1, characterized in that: The step of performing a progressive pixel weighted average operation on the initial heat map of each preset unit time to obtain a multi-level heat map of each time interval includes the following steps: At the end of the day, the initial heat map of each preset unit time within the day is calculated by pixel weighted average method to obtain the whole day heat map of the corresponding date; At the end of a month, the pixel weighted average method is used to calculate the full-day heat map of each day in the month to obtain the full-month heat map of the corresponding month; The multi-level heat map includes the initial heat map corresponding to each preset unit time, the full-day heat map corresponding to each day, and the full-month heat map corresponding to each month.

6. The method for generating a ship trajectory heat map according to claim 1, characterized in that: The multi-level heat map includes the initial heat map corresponding to each preset unit time, the whole day heat map corresponding to each day, and the whole month heat map corresponding to each month; the time span based on the time range is subjected to pixel weighted average calculation on the target level heat map corresponding to the time interval, including the following steps: When the time span is less than one day, a pixel weighted average operation is performed on the initial thermal map of each preset unit time in the time interval corresponding to the time range; When the time span is greater than / equal to one day and less than one month, a pixel weighted average operation is performed on the whole-day heat map of each day in the time interval corresponding to the time range; When the time span is greater than / equal to one month, a pixel weighted average operation is performed on the full-month heat map of each month in the time interval corresponding to the time range.

7. The method for generating a ship trajectory heat map according to claim 1, characterized in that: The multi-level heat map includes the initial heat map corresponding to each preset unit time, the whole day heat map corresponding to each day, and the whole month heat map corresponding to each month; the method further includes the following steps: Normalizing the preselected ship track heat map to obtain a normalized heat value for each position of the ship track heat map; Based on the normalized thermal value, the corresponding position of the target thermal map is mapped to RGB color through a preset mapping table to generate a pseudo-color image corresponding to the ship track thermal map; Among them, the ship trajectory heat map includes the initial heat map, the whole day heat map, the whole month heat map and the target heat map; the preset mapping table includes the mapping relationship between each numerical interval of the normalized thermal value and the chromaticity of the corresponding RGB color.

8. A ship trajectory heat map generating device, characterized in that: include: The first module is used to obtain AIS data of the target ship and convert the AIS data into multiple AIS point data; The second module is used to project the AIS point data within the preset unit time onto a preset two-dimensional pixel surface for color radiation, and generate an initial heat map corresponding to each segment of the preset unit time; The third module is used to perform a progressive pixel weighted average operation on the initial heat map of each preset unit time segment to obtain a multi-level heat map of each time interval; The fourth module is used to obtain the time range to be searched, and based on the time span of the time range, perform pixel weighted averaging on the target level heat map corresponding to the time interval to obtain the target heat map corresponding to the time range.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 1 to 7.

10. A computer storage medium storing a program executable by a processor, characterized in that: The program executable by the processor is used to implement the method according to any one of claims 1 to 7 when executed by the processor.

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