Target water area ship traffic flow complexity analysis method, system and device and storage medium

Abstract

The embodiment of the invention provides a target water area ship traffic flow complexity analysis method, system and device and a storage medium, and belongs to the technical field of ship traffic management. The method comprises the steps that ship navigation data in a target evaluation water area are collected, the ship navigation data comprise ship track points, then ships in the target evaluation water area are clustered according to the ship track points to determine an interaction ship pair, ship interaction index calculation is conducted according to navigation data of the interaction ship pair, and first index data are obtained; and performing ship individual index calculation according to the ship navigation data to obtain second index data, and determining the ship traffic flow complexity of the target water area according to the second index data and the first index data. When the ship interaction class index is calculated, only the ships in the class need to be subjected to interaction analysis on the basis of the clustering result, all the ships in the target evaluation water area do not need to be traversed for interaction analysis, the ship traffic flow complexity analysis efficiency of the target water area is improved, and the calculation burden is reduced.

Description

Technical Field

[0001] This application relates to the technical field of vessel traffic management, and particularly to a method, system, device and storage medium for analyzing the complexity of vessel traffic flow in a target water area. Background Art

[0002] With the development of the global economy, the volume of waterway freight transportation has been continuously increasing, the number of navigating vessels has correspondingly increased, the navigation environment has become more complex, the navigation risk has also risen, and water area traffic management has become more complicated. Therefore, measuring the complexity of vessel traffic flow in a target water area helps to efficiently manage water area traffic and provides a reliable decision-making basis. The current assessment of the complexity of vessel traffic flow in a target water area considers factors such as vessel density, vessel speed, and vessel size. When calculating indicators related to vessel interaction, it is necessary to traverse all vessels in the water area for indicator calculation, resulting in low efficiency and heavy computational burden for analyzing the complexity of vessel traffic flow in the target water area. Summary of the Invention

[0003] The main purpose of the embodiments of this application is to propose a method, system, device and storage medium for analyzing the complexity of vessel traffic flow in a target water area, aiming to improve the analysis efficiency of the complexity of vessel traffic flow in the target water area and reduce the computational burden.

[0004] To achieve the above object, on the one hand, an embodiment of this application proposes a method for analyzing the complexity of vessel traffic flow in a target water area, which includes the following steps:

[0005] Collect vessel navigation data in a target evaluation water area, where the vessel navigation data includes vessel trajectory points;

[0006] Cluster the vessels in the target evaluation water area according to the vessel trajectory points to determine interacting vessel pairs;

[0007] Calculate vessel interaction type indicators based on the navigation data of the interacting vessel pairs to obtain first indicator data;

[0008] Calculate vessel individual indicators based on the vessel navigation data to obtain second indicator data;

[0009] Determine the complexity of vessel traffic flow in the target water area according to the second indicator data and the first indicator data.

[0010] In some embodiments, the clustering of the vessels in the target evaluation water area according to the vessel trajectory points to determine interacting vessel pairs includes the following steps:

[0011] Calculate the relative distance change rate between every two vessels according to the navigation data of the vessels corresponding to the vessel trajectory points in the point clusters, and determine the interaction state between every two vessels according to the relative distance change rate;

[0012] Determine pairs of ships with an interaction state of a converging state as interacting ship pairs.

[0013] In some embodiments, the determining of interacting ship pairs according to the ships corresponding to the ship trajectory points in the point cluster includes the following steps:

[0014] According to the navigation data of the ships corresponding to the ship trajectory points in the point cluster, calculate the relative distance change rate between pairs of ships, and determine the interaction state of pairs of ships according to the relative distance change rate;

[0015] Determine pairs of ships with an interaction state of a converging state as interacting ship pairs.

[0016] In some embodiments, the calculating of ship interaction type indicators according to the navigation data of the interacting ship pairs to obtain first indicator data includes the following steps:

[0017] Perform grid division on the target evaluation water area to obtain a plurality of sub-areas;

[0018] For the ships in the sub-areas, calculate the collision risk of the ships according to the navigation data of all the interacting ship pairs containing the ships to obtain the ship collision risk values of the sub-areas;

[0019] Calculate the relative navigation directions of the interacting ship pairs according to the navigation data of the interacting ship pairs located in the sub-areas, and count the number of crossing ships, the number of head-on meeting ships, and the number of overtaking ships in the sub-areas according to the relative navigation directions;

[0020] Form the first indicator data of the sub-areas according to the ship collision risk values, the number of crossing ships, the number of head-on meeting ships, and the number of overtaking ships in the sub-areas.

[0021] In some embodiments, the calculating of ship individual indicators according to the ship navigation data to obtain second indicator data includes the following steps:

[0022] Calculate the ship type dimensions, the proportion of target type ships, and the proportion of ships with abnormal speeds respectively according to the ship navigation data in the sub-areas to obtain the second indicator data of the sub-areas.

[0023] In some embodiments, the determining of the ship traffic flow complexity of the target water area according to the second indicator data and the first indicator data includes the following steps:

[0024] Integrate the second indicator data and the first indicator data of each sub-area to obtain the third indicator data of the sub-area, and the third indicator data includes the indicator values of the sub-areas of multiple evaluation indicators;

[0025] Assign weights to the evaluation indicators in the third indicator data, and determine the complexity of the ship traffic flow in the target waters of the sub-region according to the weights and indicator values of the evaluation indicators.

[0026] In some embodiments, the method for analyzing the complexity of the ship traffic flow in the target waters further includes the following steps:

[0027] Determine the color configuration of each sub-region according to the complexity of the ship traffic flow in the target waters of each sub-region;

[0028] Visualize the analysis results of the complexity of the ship traffic flow in the target waters of the target evaluation region on the map according to the color configurations of the respective sub-regions in the target evaluation region.

[0029] To achieve the above object, on the other hand, an embodiment of the present application proposes a system for analyzing the complexity of the ship traffic flow in the target waters, including:

[0030] A first module for collecting ship navigation data in the target evaluation waters, where the ship navigation data includes ship trajectory points;

[0031] A second module for clustering the ships in the target evaluation waters according to the ship trajectory points to determine interacting ship pairs;

[0032] A third module for calculating ship interaction type indicators based on the navigation data of the interacting ship pairs to obtain first indicator data;

[0033] A fourth module for calculating ship individual indicators based on the ship navigation data to obtain second indicator data;

[0034] A fifth module for determining the complexity of the ship traffic flow in the target waters according to the second indicator data and the first indicator data.

[0035] To achieve the above object, on the other hand, an embodiment of the present application proposes an electronic device, where the electronic device includes a memory, a processor, a program stored on the memory and executable on the processor, and a data bus for realizing the connection and communication between the processor and the memory. When the program is executed by the processor, the method described in the above embodiments is implemented.

[0036] To achieve the above object, on the other hand, an embodiment of the present application proposes a storage medium, where the storage medium is a computer-readable storage medium for computer-readable storage. The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the method described in the above embodiments.

[0037] The method, system, device and storage medium for analyzing the complexity of ship traffic flow in a target water area proposed in this application collect the ship navigation data in the target evaluation water area. The ship navigation data includes ship trajectory points. Then, the ships in the target evaluation water area are clustered based on the ship trajectory points to determine interacting ship pairs. Next, ship interaction type indicators are calculated based on the navigation data of the interacting ship pairs to obtain first indicator data, and ship individual indicators are calculated based on the ship navigation data to obtain second indicator data. The complexity of the ship traffic flow in the target water area is determined based on the second indicator data and the first indicator data. When calculating the ship interaction type indicators in this application, only the ships within the class need to be analyzed for interaction based on the clustering result, without having to traverse all the ships in the target evaluation water area for interaction analysis, which improves the efficiency of analyzing the complexity of ship traffic flow in the target water area and reduces the calculation burden. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a flowchart of the method for analyzing the complexity of ship traffic flow in a target water area provided by an embodiment of this application;

[0039] Figure 2 is a schematic diagram of the target evaluation water area provided by an embodiment of this application;

[0040] Figure 3 is a schematic diagram of the rasterization of the target evaluation water area provided by an embodiment of this application;

[0041] Figure 4 is a flowchart of calculating the collision risk index based on the recognition of the ship convergence state provided by an embodiment of this application;

[0042] Figure 5 is a schematic diagram of the evaluation system for the complexity of ship traffic flow in a target water area provided by an embodiment of this application;

[0043] Figure 6 is a distribution diagram of the complexity of ship traffic flow in the target water area of the target evaluation water area provided by an embodiment of this application;

[0044] Figure 7 is the overall flowchart of the water area traffic complexity of the raster provided by an embodiment of this application;

[0045] Figure 8 is a schematic diagram of the system for analyzing the complexity of ship traffic flow in a target water area provided by an embodiment of this application;

[0046] Figure 9 is a schematic diagram of the hardware structure of the electronic device provided by an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] In order to make the objectives, technical solutions and advantages of the present application more clear and understandable, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0048] It should be noted that although functional modules are divided in the system and the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order from the module division in the system or the order in the flowchart. Terms such as "first" and "second" in the specification, claims and the above-mentioned drawings are used to distinguish similar objects and do not necessarily have to be used to describe a specific order or sequence.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0050] First, several terms involved in this application are analyzed:

[0051] DBSCAN (Density-Based Spatial Clustering of Applications with Noise, density-based spatial clustering with noise) is a density-based clustering algorithm that can identify clusters of any shape and can effectively process noisy data. Different from distance-based clustering methods such as K-means, DBSCAN discovers clusters by defining "density" and is suitable for processing datasets with complex structures.

[0052] Based on this, the embodiments of the present application provide a method, system, device and storage medium for analyzing the complexity of ship traffic flow in a target water area, aiming to improve the efficiency of analyzing the complexity of ship traffic flow in the target water area and reduce the computational burden.

[0053] The method, system, device and storage medium for analyzing the complexity of ship traffic flow in the target water area provided by the embodiments of the present application will be specifically described through the following embodiments. First, the method for analyzing the complexity of ship traffic flow in the target water area in the embodiments of the present application is described.

[0054] The method for analyzing the complexity of ship traffic flow in the target water area provided by the embodiments of the present application relates to the technical field of ship traffic management. The method for analyzing the complexity of ship traffic flow in the target water area provided by the embodiments of the present application can be applied to a terminal, or to a server side, or can also be software running on the terminal or the server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, or can also be configured as 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, and big data and artificial intelligence platforms; the software can be an application for implementing the method for analyzing the complexity of ship traffic flow in the target water area, etc., but is not limited to the above forms.

[0055] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0056] Figure 1 is an optional flowchart of the method for analyzing the complexity of ship traffic flow in the target water area provided by the embodiments of the present application, Figure 1 The method in may include but is not limited to steps S101 to S105.

[0057] Step S101, collect the ship navigation data in the target evaluation water area, and the ship navigation data includes ship trajectory points;

[0058] Step S102, cluster the ships in the target evaluation water area according to the ship trajectory points to determine the interacting ship pairs;

[0059] Step S103, calculate the ship interaction type indicators according to the navigation data of the interacting ship pairs to obtain the first indicator data;

[0060] Step S104: Calculate the individual indexes of the ship based on the ship navigation data to obtain the second index data;

[0061] Step S105: Determine the complexity of the ship traffic flow in the target water area according to the second index data and the first index data.

[0062] In step S101 of some embodiments, the target evaluation water area refers to the area where the complexity analysis of the ship traffic flow needs to be carried out. For example, the target evaluation water area can be a river water area as shown in Figure 2 . The ship navigation data in the target evaluation water area refers to the navigation data of all ships located in the target evaluation water area. The navigation data can be data collected based on AIS. AIS (Automatic Identification System) data is a kind of real-time information data used for ship navigation and maritime traffic management, and is transmitted through VHF radio communication technology. AIS data mainly includes the static information of the ship (such as MMSI number, ship name, ship type, size, etc.), dynamic information (such as longitude and latitude, speed, course, navigation status, etc.) and voyage-related information (such as destination, estimated arrival time, etc.). The ship navigation data includes data such as ship track points, speed over ground, course over ground, and ship type.

[0063] Due to electromagnetic interference, geographical environment interference, other equipment problems, and human operation problems during the ship navigation process, there will be cases of missing values, duplicate values, and abnormal values in the AIS data. Therefore, it is necessary to clean the data. The specific method is as follows:

[0064] Missing values: directly delete them.

[0065] Duplicate values: Traverse the AIS data through the two fields of MMSI and AIS message time to identify duplicate values. Duplicate values are divided into two categories: The first category is that the ship dynamic information such as longitude and latitude of two rows of data is exactly the same; the second category is that only the ship static information and time of two rows of data are the same. For the first category, retain the data row retrieved for the first time; for the second category, delete both rows of data.

[0066] Abnormal values: Divide them into error data and drift data. For error data, set the speed range to [0 knots, 25 knots], and the course range to [0, 360°), and delete the data rows outside the range; for drift data, delimit the research boundary and delete the data rows whose position information exceeds the boundary longitude and latitude.

[0067] In step S102 of some embodiments, the method for analyzing the complexity of ship traffic flow in the target water area according to the embodiments of the present application is a dynamic method for analyzing the complexity of ship traffic flow in the target water area, that is, collecting the ship navigation data in the target evaluation water area at different times, and analyzing the complexity of ship traffic flow in the target water area for the ship navigation data at different times. In this embodiment, considering that some evaluation indicators of traffic complexity need to involve the calculation of the relative relationship between two ships, if all ships in the entire water area are calculated pairwise, the calculation complexity will be significantly increased. Based on this, at a certain moment, in this embodiment, according to the ship trajectory points at the corresponding moment in the target evaluation water area, the ships in the target evaluation water area are clustered to cluster the ships with close positions together, and the interactive ship pairs are combined within the cluster, avoiding analyzing and calculating by combining all ship pairs in the entire target evaluation water area, and improving the calculation efficiency. For the clustering of ship trajectory points in the embodiments of the present application, clustering algorithms such as DBSCAN can be used, which is not specifically limited here. The clustering situation of each ship in the target evaluation water area is as Figure 2 shown, and the ships of the same color are represented as one class.

[0068] In step S103 of some embodiments, the navigation data according to the interactive ship pair refers to the navigation data of the two ships in the interactive ship pair. The ship interaction type index is an evaluation index of the complexity of ship traffic flow in the target water area. When calculating this index value, it is necessary to analyze the interaction situation between the two ships, such as relative distance, relative course, etc. For example, the ship interaction type indexes used in the embodiments of the present application to analyze the complexity of ship traffic flow in the target water area include collision risk index (CRI), number of crossing ships, and number of meeting head-on ships, etc. Through the navigation data of relevant ships, the calculation of ship interaction type indexes such as collision risk index, number of crossing ships, and number of meeting head-on ships is carried out respectively, and the first index data can be obtained.

[0069] In step S104 of some embodiments, the ship individual index is an evaluation index of the complexity of ship traffic flow in the target water area. When calculating this index value, it is not necessary to analyze the interaction situation between two ships. For example, the ship individual indexes used in the embodiments of the present application to analyze the complexity of ship traffic flow in the target water area include ship type scale, proportion of key ships, and proportion of ships with abnormal speed, etc. Through the navigation data of the ships in the water area, the calculation of ship interaction type indexes such as ship type scale, proportion of key ships, and proportion of ships with abnormal speed is carried out respectively, and the second index data can be obtained.

[0070] In step S105 of some embodiments, the second index data and the first index data jointly form the values of each evaluation index in the evaluation system, and the complexity of ship traffic flow in the target water area can be calculated according to the above index values.

[0071] In the calculation of the complexity of the ship traffic flow in the target waters from step S103 to step S105, it can be the calculation of the complexity of the ship traffic flow in the target evaluation waters, or after the grid division of the target evaluation waters, the calculation of the complexity of the ship traffic flow in the target waters for each grid (i.e., sub-region). When calculating the complexity of the ship traffic flow in the target evaluation waters, the first index data of the target evaluation waters is calculated by calculating the ship interaction type indicators based on the navigation data of all interacting ship pairs in the target evaluation waters, and the second index data of the target evaluation waters is calculated by calculating the ship individual indicators based on the ship navigation data in the target evaluation waters. Then, based on the second index data and the first index data of the target evaluation waters, the complexity of the ship traffic flow in the target waters of the target evaluation waters is determined. When calculating the complexity of the ship traffic flow in the target waters of the sub-region, the first index data of the sub-region is calculated by calculating the ship interaction type indicators based on the navigation data of the interacting ship pairs in the sub-region, and the second index data of the sub-region is calculated by calculating the ship individual indicators based on the ship navigation data in the sub-region. Then, based on the second index data and the first index data of the sub-region, the complexity of the ship traffic flow in the target waters of the sub-region is determined. The subsequent embodiments illustrate the detailed process of the analysis of the complexity of the ship traffic flow in the target waters of the sub-region by taking the sub-region as an example. The analysis process of the complexity of the ship traffic flow in the target evaluation waters is the same as that of the sub-region and will not be elaborated here.

[0072] According to some embodiments of the present application, step S102 may include but is not limited to the following steps:

[0073] Step S201, for the ship trajectory points in the target evaluation waters, determine the number of ship trajectory points within the expected neighborhood radius of the ship trajectory points;

[0074] Step S202, in the case where the number is greater than the expected number, create a point cluster based on the ship trajectory points, and add the ship trajectory points within the expected neighborhood radius of the ship trajectory points to the point cluster;

[0075] Step S203, determine the interacting ship pairs according to the ships corresponding to the ship trajectory points in the point cluster.

[0076] In this embodiment, the DBSCAN method can be used to cluster ship trajectory points. DBSCAN is a density-based unsupervised learning algorithm that can discover clusters of arbitrary shapes in a dataset and identify noise points in the dataset. The two core parameters of DBSCAN are (ε, Pmin), where ε represents the neighborhood range and Pmin represents the minimum number of samples (i.e., the expected number) within the neighborhood. At time t, by performing DBSCAN clustering on ship trajectory points, according to the characteristics of ship navigation in the inland river basin, ε is set to 1 nautical mile and Pmin is set to 2. Here, ε can be modified according to different jurisdictional waters. For a certain ship trajectory point in the target evaluation waters, determine the number of ship trajectory points within the expected neighborhood radius of the ship trajectory point. If the number is greater than the expected number, create a point cluster with this ship trajectory point as the core point, and add other ship trajectory points within the expected neighborhood radius of this ship trajectory point to the point cluster. After traversing all ship trajectory points in the target evaluation waters, multiple point clusters as shown in Figure 2 can be obtained. Determine the interacting ship pairs according to the ships corresponding to the ship trajectory points in the point cluster. Specifically, the ships corresponding to the ship trajectory points in the point cluster can be pairwise determined as interacting ship pairs, or pairwise convergence or separation interaction state analysis can be performed on the ships corresponding to the ship trajectory points in the point cluster, and then the interacting ship pairs can be determined. Through the clustering process of this embodiment, ships can be divided into multiple clusters according to spatial distance, thereby effectively removing noise points and irrelevant ships. In addition, when calculating the mutual relationship between two ships, only the relevant parameters of the ships within the same cluster need to be calculated, avoiding the redundant operation of pairwise calculation of all ships in the entire waters, and significantly improving the calculation efficiency.

[0077] According to some embodiments of the present application, step S203 may include but is not limited to the following steps:

[0078] Step S301, calculate the relative distance change rate between pairwise ships according to the navigation data of the ships corresponding to the ship trajectory points in the point cluster, and determine the interaction state between pairwise ships according to the relative distance change rate;

[0079] Step S302, determine the pairwise ships with the interaction state of the convergence state as the interacting ship pairs.

[0080] In this embodiment, ships within the same cluster may exhibit two states: the convergence state and the separation state. When calculating ship interaction type indicators, the research object should be pairwise ships in the convergence state. Therefore, judging the ships in the convergence state within each cluster can further reduce the calculation amount and shorten the operation time of the program. The specific judgment method is as follows: represents the relative distance between ship i and j. If the change rate of the relative distance It indicates that the relative distance between the two ships increases, and at this time, the two ships are in a separated state; if the change rate of the relative distance then it means that the relative distance between the two ships is decreasing, and the two ships are in a converging state. By combining the DBSCAN clustering algorithm with the converging state determination method, the mutual relationship between ships can be analyzed more efficiently and accurately.

[0081] According to some embodiments of the present application, step S103 may include but is not limited to the following steps:

[0082] Step S401, dividing the target evaluation water area into grids to obtain at least one sub-region;

[0083] Step S402, for the ships in the sub-region, calculate the collision risk of the ships according to the navigation data of all interactive ship pairs containing the ships, and obtain the ship collision risk value of the sub-region;

[0084] Step S403, calculate the relative navigation direction of the interactive ship pairs located in the sub-region according to the navigation data of the interactive ship pairs, and count the number of crossing ships, head-on meeting ships and overtaking ships in the sub-region according to the relative navigation direction.

[0085] In some embodiments of step S401, as Figure 3 shown, divide the target evaluation water area into grids to obtain at least one sub-region (i.e., grid), and then analyze the complexity of the ship traffic flow in the target water area for each grid.

[0086] In some embodiments of step S402, it is necessary to calculate the collision risk value of each ship in the sub-region, and then synthesize the collision risk values of the ships in the sub-region to obtain the ship collision risk value of the sub-region. When calculating the collision risk value of a ship, it is necessary to determine all interactive ship pairs containing the ship from all the foregoing interactive ship pairs, and then use the ship as the analysis object, and use the other ships in all the interactive ship pairs containing the ship as the target ships for analysis. The process of the collision risk value of the ship is as follows:

[0087] The Collision Risk Index (CRI) is one of the more important parameters in the field of unmanned ships, and it can measure the probability of a ship colliding with other surrounding ships. In this study, the method based on the Closest Point of Approach is used to calculate the CRI, and the calculation method is as follows:

[0088] First, convert the ship's longitude and latitude coordinates (λ, φ) to the Cartesian coordinate system (x, y), and the conversion process is as follows:

[0089] x = R·λ;

[0090]

[0091] Among them, R represents the radius of the earth.

[0092] Then, define the states of the own ship (i.e., the analysis object) and the target ship at time t as S t ={x t , y t , v t , θ t}, S t T ={x t T , y t T , v t T , θ t T}. The calculation processes of the closest distance of approach (DCPA) and the time to the closest point of approach (TCPA) are as follows:

[0093]

[0094]

[0095] DCPA = D × sin(θ OT -β OT -π);

[0096]

[0097] Among them, D represents the distance between ships, β T represents the absolute azimuth angle of the target ship relative to the own ship, β OT represents the relative azimuth angle of the target ship relative to the own ship, θ OT represents the course angle of the target ship relative to the own ship.

[0098] In the design of the ship automatic collision avoidance system, the CRI usually ranges within the interval [0,1]. In the actual application scenario, the DCPA and TCPA are the most critical parameters for evaluating the collision risk between ships. Considering the negative correlation between the CRI and the DCPA and TCPA; and the trend that the CRI increases exponentially as the DCPA and TCPA decrease. In this embodiment, a negative exponential function is used to calculate the CRI, and the specific calculation formula is as follows:

[0099] CRI D = a d × exp(b d × DCPA);

[0100] CRI D = a d × exp(b d × DCPA);

[0101] Among them, a and b are coefficients estimated according to the opinions of maritime experts and ship traffic monitoring duty officers. In this embodiment, for the selected inland water area, a is set to 1.248 and b is set to -4.674. Specific calculation examples are shown in Table 1 and Table 2.

[0102] Table 1 CRI D Parameter settings

[0103]

[0104] Table 2 CRI T Parameter settings

[0105]

[0106] CRI is obtained by weighting CRI D and CRI T as shown in the following formula:

[0107] CRI = αCRI D + βCRI T ;

[0108] Among them, α and β are the weights of CRI D and CRI T respectively. In this embodiment, it is considered that the influence of time and space relationship on CRI is the same, so both α and β are set to 0.5. If there is a collision risk between the own ship and multiple target ships at the same moment, the highest CRI value is taken as the CRI of the own ship to increase the contribution value of CRI to complexity.

[0109] Exemplarily, please combine Figure 4 to illustrate the calculation process of the collision risk index based on the recognition of the ship convergence state in this embodiment.

[0110] Step S11: Obtain the ship AIS data, then generate all clusters based on the DBSCAN clustering algorithm, screen out the clusters with the number of ships in the cluster greater than 1 for subsequent analysis, and delete other clusters.

[0111] Step S12: Mark the own ship as the analysis object for collision risk calculation, find the cluster where the own ship is located, mark the other ships in the cluster as target ships, use the relative distance change rate to screen the target ships in the convergent state with the own ship, then calculate the collision sub-risk coefficients between the own ship and the screened target ships respectively, and take the maximum value to obtain the final collision sub-risk coefficient of the own ship.

[0112] In step S13, it is determined whether all the ships within the cluster have been traversed. If not, a new own ship is determined and step S12 is executed again. If so, the collision risk coefficients of all ships are output. Subsequently, the ship collision risk coefficient of the area is determined according to the collision risk coefficients of all ships in the area.

[0113] In some embodiments of step S403, if one of the ships in the interacting ship pair is located in a certain sub - area, it is considered that the interacting ship pair is located in that sub - area. In the interacting ship pair, the ships are mutually called the own ship and the target ship. The number of crossing ships, head - on meeting ships, overtaking ships, and ship compactness in the sub - area are described as follows:

[0114] Number of crossing ships: It refers to the ships that sail from one side of the waterway to the other side horizontally or nearly horizontally, or cross the bow direction of the ships sailing along the waterway horizontally. In this embodiment, the crossing ship angle considered when calculating the CRI is used, that is, when the traveling direction of the target ship is within the interval of [60°, 150°] ∪ [210°, 300°] of the own ship, the target ship is considered a crossing ship. The more the number of crossing ships in the area, the greater the complexity of the ship traffic flow in the area.

[0115] Number of head - on meeting ships: It includes the number of ships in head - on or nearly head - on encounters, meeting each other from the starboard or port side, and meeting in a curved waterway, but does not include the encounter of two crossing ships. In this embodiment, when the traveling direction of the target ship is within the interval of [0°, 60°] ∪ [300°, 360°] of the own ship, the target ship is considered a head - on meeting ship, and the number of head - on meeting ships in the area is proportional to the complexity.

[0116] Number of overtaking ships: When the traveling direction of the target ship is within the interval of [150°, 210°] of the own ship, the target ship is considered an overtaking ship. The more the number of overtaking ships in the area, the greater the complexity of the ship traffic flow in the area.

[0117] Ship compactness: An index to measure the degree of closeness between ships in a certain area, which is a derivative form of traffic density. In this embodiment, the average distance between ships (unit: km) is calculated to measure the ship compactness. Since the greater the average distance between ships, the smaller the complexity, the reciprocal of the average distance between ships should be taken in practical applications. And since there is an intersection between the CRI and this index when there are two ships within the calculation range, the ship compactness is only calculated when there are more than three ships within the calculation range.

[0118] According to some embodiments of the present application, step S104 may include but is not limited to the following steps:

[0119] In step S501, according to the ship navigation data in the sub - area, the ship type dimensions, the proportion of target - type ships, and the proportion of ships with abnormal speeds are calculated respectively to obtain the second index data of the sub - area.

[0120] In this embodiment, the ship type scale, the proportion of target type ships, and the proportion of ships with abnormal speeds in the sub-region are described as follows:

[0121] Ship type scale: In this embodiment, the influence of ship width and ship length in the ship type scale on complexity is mainly considered, and the ship type scale is quantitatively represented by calculating the length-width ratio of each ship. According to relevant research, ships with a high length-width ratio have good course stability and less wave-making resistance when sailing in water, but the ship's turning performance is poor; ships with a low length-width ratio have good turning performance, better operability, and higher lateral stability, but the ship's course stability is poor. Under the theme of the complexity of ship traffic flow in the target water area, more attention is paid to the operability of ships in complex situations, and ships with low operability will increase complexity. Therefore, the greater the length-width ratio of a ship, the greater its influence on complexity. The ship type scale index value of the area can be obtained by integrating the ship type scales of each ship in the comprehensive area (sub-region or target evaluation water area).

[0122] Proportion of key ships: Four types of key ships stipulated by the Ministry of Transport are selected, namely passenger ships, dangerous goods ships, sand and gravel ships, and ships transporting easily flowable solid bulk goods. Calculate the proportion of key ships in the total number of ships in the area (sub-region or target evaluation water area). The proportion of key ships is directly proportional to complexity.

[0123] Proportion of ships with abnormal speeds: According to the high-speed limit and low-speed limit standards set for ships in a specific water area, the operating speed of the ship is detected. If it exceeds the speed regulation range of the water area, it is considered a ship with abnormal speed. The greater the proportion of ships with abnormal speeds in the total number of ships in the area, the greater the complexity in the area.

[0124] According to some embodiments of the present application, step S105 may include but is not limited to the following steps:

[0125] Step S601, integrating the second index data and the first index data of each sub-region to obtain the third index data of the sub-region. The third index data includes the index values of the sub-region of multiple evaluation indicators;

[0126] Step S602, assign weights to the evaluation indicators in the third index data, and determine the complexity of ship traffic flow in the target water area of the sub-region according to the weights and index values of the evaluation indicators.

[0127] In this embodiment, the first index data and the second index data are integrated together to form the third index data, that is, the third index data includes the index values of ship interaction type indicators and ship individual indicators. Exemplarily, the third index data includes, for example Figure 5The index values of indicators such as ship size, proportion of key ships, proportion of abnormal ship speeds, ship collision risk index, number of crossing ships, number of meeting head-on ships, number of overtaking ships, and ship compactness shown in the index layer.

[0128] In this embodiment, based on the above descriptions of various influencing factors, a ship traffic flow complexity evaluation system as shown in Figure 5 can be constructed. That is, the evaluation system is a hierarchical structure model, which reasonably divides the target, criterion, and index layers according to the characteristics of the decision-making problem. Based on the evaluation system, the analytic hierarchy process is used to determine the weights of various factors (i.e., evaluation indicators). The analytic hierarchy process is a method that combines quantitative and qualitative analysis and is commonly used in decision analysis and problem-solving, and is widely applied in multi-criteria decision-making problems. The main steps of the analytic hierarchy process are as follows:

[0129] Sort out the expert scores to obtain the judgment matrix: Collect the scores of experts on the relative importance of various factors, and construct the judgment matrix accordingly.

[0130] Conduct a consistency test on the judgment matrix: By calculating the consistency ratio (CR) of the judgment matrix, if CR > 0.1, it indicates that the judgment matrix is inconsistent and needs to be tested.

[0131] Calculate the weight vectors of each indicator: On the basis of passing the consistency test, calculate the weight vectors of each indicator to determine the weights of various factors in the decision-making. Based on Figure 5 the evaluation system, the indicator weights are shown in Table 3.

[0132] Table 3 Indicator Weights of the Evaluation System

[0133]

[0134] After determining the weights, each indicator is fused based on the Dynamic Density (DD) method to quantify the complexity. The calculation method of DD is shown in the following formula:

[0135]

[0136] where n is the number of indicators, Wi is the weight of the i-th indicator, and TCi is the influencing factor of ship traffic flow complexity.

[0137] According to the weights and indicator values of each indicator, weighted calculation can be performed to obtain the ship traffic flow complexity of the target water area of the region.

[0138] According to some embodiments of the present application, the ship traffic flow complexity analysis method of the embodiments of the present application may further include but is not limited to the following steps:

[0139] Step S701: Determine the color configuration of each sub-region according to the target water area ship traffic flow complexity of each sub-region.

[0140] Step S702: Visualize the analysis result of the target water area ship traffic flow complexity of the target evaluation area on the map according to the color configurations of the respective sub-regions in the target evaluation area.

[0141] In this embodiment, to achieve an intuitive display of the target water area ship traffic flow complexity, the rasterization method is used to spatially divide the target evaluation water area into several regular grid cells. Based on the ship AIS data, the target water area ship traffic flow complexity value within each grid cell is calculated in the above manner, and grids with different ship traffic flow complexity values are represented by different colors to generate the target water area ship traffic flow complexity distribution map. The target water area ship traffic flow complexity distribution map of the target evaluation water area is as Figure 6 shown.

[0142] According to some embodiments of the present application, the rasterization method is used to spatially divide the research water area to achieve the visualization of the target water area ship traffic flow complexity. Compared with the method of first calculating the complexity between ships and then interpolating the surrounding areas, the rasterization method significantly improves the processing efficiency and real-time update ability of large-scale ship data by focusing on the ship information statistics and analysis within each grid cell, better meeting the requirements of real-time monitoring. In addition, the rasterization method can effectively integrate the discretely distributed ship data into each grid cell and perform statistical calculations based on the cells, thus accurately reflecting the spatial distribution characteristics of ships and avoiding interpolation errors and calculation deviations caused by uneven ship distribution. This method not only improves the calculation efficiency but also enhances the accuracy and reliability of the complexity assessment, providing more efficient technical support for water area traffic supervision.

[0143] The rasterization visualization method requires calculating the complexity of each grid, so the ship AIS data needs to be converted into data for each grid. As Figure 7 shown, the specific process is as follows:

[0144] S21: For each ship position point, use the ray method to determine the grid cell it belongs to.

[0145] S22. For each ship, calculate the values of all its complexity influencing factors. After the calculation is completed, integrate the relevant information of the ship into the grid it belongs to. It should be noted that the ship compactness index is not an individual attribute of the ship but an attribute of the grid, so it needs to be calculated separately. Moreover, to reflect the impact of the number of ships on the complexity within the grid when multiple ships converge, the CRI of each ship within the grid is added together. To calculate the number of crossing ships, the number of meeting head-on ships, and the number of overtaking ships, add up the ships with the above behaviors within the grid. For the remaining indicators, calculate the average value or find the ratio;

[0146] S23. After obtaining the values of all indicators, since the scales of each indicator are different, normalization processing needs to be carried out first. Subsequently, based on the dynamic density algorithm, multiply and add the normalized indicator values with the weight vector W obtained from the analytic hierarchy process model to obtain the complexity of a single grid;

[0147] S24: Repeat S22 and S23 until the complexity of all grids is obtained.

[0148] This embodiment comprehensively considers factors related to ships and ship traffic flow. The analytic hierarchy process (AHP) is used to determine the weights of these factors, and the dynamic density calculation method is used to evaluate the complexity of the ship traffic flow. Finally, a visualization map of the complexity of the ship traffic flow in the target water area is generated. This implementation can assist the regulatory agency in comprehensively grasping the water traffic conditions and has strong generalization performance. For different water areas, only a small number of parameters need to be adjusted to be applicable. By real-time identifying potential risks, this embodiment effectively improves the overall safety of waterway transportation and provides a scientific and efficient decision-making support tool for maritime supervision.

[0149] By comparing Figure 3 and Figure 6 it can be seen that the degree of complexity is positively correlated with the number of ships. This is consistent with the general understanding that the more ships there are, the more complex the interaction between ships and the higher the collision risk. The complexity distribution map not only intuitively reflects the current water traffic conditions but also provides strong data support for subsequent risk assessment and management. In practical applications, regulatory personnel can quickly identify high-complexity areas through the complexity map of the ship traffic flow in the target water area and accurately locate the ships at risk in combination with the grid water area map, so as to take targeted supervision measures and effectively improve the water traffic safety management level.

[0150] Through experiments, the program of this embodiment can shorten the average running time, proving the efficiency of the proposed method in complexity detection and well meeting the real-time requirements. In practical applications, real-time is a key factor because the navigation status of ships and the surrounding environment change dynamically. The supervision system needs to have the ability to respond quickly to evaluate the complexity of ship traffic flow in real time and identify potential risks, thus effectively ensuring navigation safety. And by optimizing the calculation process and algorithm design, the calculation efficiency has been significantly improved, providing strong technical support for water area traffic supervision.

[0151] Please refer to Figure 8 , this embodiment of the present application also proposes a system for analyzing the complexity of ship traffic flow in a target water area, including:

[0152] The first module is used to collect ship navigation data in the target evaluation water area, and the ship navigation data includes ship trajectory points and ship headings;

[0153] The second module is used to cluster the ships in the target evaluation water area according to the ship trajectory points to determine interacting ship pairs;

[0154] The third module is used to calculate ship interaction type indicators based on the navigation data of the interacting ship pairs to obtain first indicator data;

[0155] The fourth module is used to calculate ship individual indicators based on the ship navigation data to obtain second indicator data;

[0156] The fifth module is used to determine the complexity of ship traffic flow in the target water area according to the second indicator data and the first indicator data.

[0157] It can be understood that the content in the above embodiments of the method for analyzing the complexity of ship traffic flow in a target water area is applicable to this system embodiment. The functions specifically implemented by this system embodiment are the same as those in the above embodiments of the method for analyzing the complexity of ship traffic flow in a target water area, and the beneficial effects achieved are also the same as those in the above embodiments of the method for analyzing the complexity of ship traffic flow in a target water area.

[0158] This embodiment of the present application also provides an electronic device, which includes: a memory, a processor, a program stored on the memory and executable on the processor, and a data bus for realizing the connection and communication between the processor and the memory. When the program is executed by the processor, it implements the above method for analyzing the complexity of ship traffic flow in a target water area. This electronic device can be any intelligent terminal including a tablet computer, an in-vehicle computer, etc.

[0159] Please refer to Figure 9 , Figure 9 schematically shows the hardware structure of an electronic device in another embodiment. The electronic device includes:

[0160] The processor 901 can be implemented in ways such as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0161] The memory 902 can be implemented in forms such as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 902 and are called by the processor 901 to execute the target water area ship traffic flow complexity analysis method of the embodiments of the present application;

[0162] The input / output interface 903 is used to implement information input and output;

[0163] The communication interface 904 is used to implement communication interaction between this device and other devices, and can implement communication through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.);

[0164] The bus 905 transmits information between various components of the device (such as the processor 901, the memory 902, the input / output interface 903, and the communication interface 904);

[0165] Among them, the processor 901, the memory 902, the input / output interface 903, and the communication interface 904 achieve communication connections with each other inside the device through the bus 905.

[0166] The embodiments of the present application also provide a storage medium. The storage medium is a computer-readable storage medium for computer-readable storage. The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the above-mentioned target water area ship traffic flow complexity analysis method.

[0167] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory may optionally include memories remotely disposed relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0168] The embodiments described in the embodiments of the present application are for more clearly illustrating the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art will know that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.

[0169] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than those shown in the figures, or combine certain steps, or different steps.

[0170] The system 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.

[0171] Those of ordinary skill in the art can understand that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices can be implemented as software, firmware, hardware, and appropriate combinations thereof.

[0172] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0173] It should be understood that in this application, "at least one (item)" means one or more, and "a plurality" means two or more. "And / or" is used to describe the association relationship of associated objects and indicates that there can be three relationships. For example, "A and / or B" can mean: only A exists, only B exists, and both A and B exist at the same time. Here, A and B can be singular or plural. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single item (item) or plural items (items). For example, at least one (item) of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0174] In several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the above units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of systems or units can be in electrical, mechanical, or other forms.

[0175] The units described above as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0176] In addition, in each embodiment of this application, the functional units can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0177] When the integrated unit 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 this application, in essence, or the part that contributes to the prior art, or all or 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 multiple 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 various embodiments of this application. The foregoing storage medium includes: various media that can store programs such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0178] The preferred embodiments of the embodiments of this application have been described above with reference to the accompanying drawings, and thus do not limit the scope of the rights of the embodiments of this application. Any modifications, equivalent replacements, and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of this application shall be within the scope of the rights of the embodiments of this application.

Claims

1. A method for analyzing the complexity of ship traffic flow in a target water area, characterized in that It includes the following steps: Collect the ship navigation data in the target evaluation water area, where the ship navigation data includes ship trajectory points; Cluster the ships in the target evaluation water area according to the ship trajectory points to determine interacting ship pairs; Calculate ship interaction type indicators based on the navigation data of the interacting ship pairs to obtain first indicator data; Calculate ship individual indicators based on the ship navigation data to obtain second indicator data; Determine the complexity of ship traffic flow in the target water area according to the second indicator data and the first indicator data.

2. The method for analyzing the complexity of ship traffic flow in a target water area according to claim 1, wherein The step of clustering the ships in the target evaluation water area according to the ship trajectory points to determine interacting ship pairs includes the following steps: For the ship trajectory points in the target evaluation water area, determine the number of ship trajectory points within the expected neighborhood radius of the ship trajectory points; In the case where the number is greater than the expected number, create a point cluster according to the ship trajectory points, and add the ship trajectory points within the expected neighborhood radius of the ship trajectory points to the point cluster; Determine interacting ship pairs according to the ships corresponding to the ship trajectory points in the point cluster.

3. The method for analyzing the complexity of ship traffic flow in the target water area according to claim 2, wherein The step of determining interacting ship pairs according to the ships corresponding to the ship trajectory points in the point cluster includes the following steps: Calculate the relative distance change rate between every two ships according to the navigation data of the ships corresponding to the ship trajectory points in the point cluster, and determine the interaction state between every two ships according to the relative distance change rate; Determine the pairs of ships with a converging state as interacting ship pairs.

4. The method for analyzing the complexity of ship traffic flow in a target water area according to claim 3, wherein The step of calculating ship interaction type indicators based on the navigation data of the interacting ship pairs to obtain first indicator data includes the following steps: Divide the target evaluation water area into grids to obtain at least one sub-region; For the ships in the sub-region, calculate the collision risk of the ships according to the navigation data of all the interacting ship pairs containing the ships to obtain the ship collision risk value of the sub-region; Calculate the relative navigation direction of the interacting ship pairs according to the navigation data of the interacting ship pairs located in the sub-region, and count the number of crossing ships, head-on meeting ships and overtaking ships in the sub-region according to the relative navigation direction; Form the first indicator data of the sub-region according to the ship collision risk value, number of crossing ships, number of head-on meeting ships and number of overtaking ships in the sub-region.

5. The method for analyzing the complexity of ship traffic flow in the target water area according to claim 4, wherein The step of calculating ship individual indicators based on the ship navigation data to obtain second indicator data includes the following steps: Calculate the ship type scale, proportion of target type ships and proportion of ships with abnormal speed respectively according to the ship navigation data in the sub-region to obtain the second indicator data of the sub-region.

6. The method for analyzing the complexity of ship traffic flow in the target water area according to claim 5, wherein The step of determining the complexity of ship traffic flow in the target water area according to the second indicator data and the first indicator data includes the following steps: Integrate the second indicator data and the first indicator data of each sub-region to obtain the third indicator data of the sub-region, and the third indicator data includes the indicator values of multiple evaluation indicators of the sub-region; Assign weights to the evaluation indicators in the third indicator data, and determine the complexity of ship traffic flow in the target water area of the sub-region according to the weights and indicator values of the evaluation indicators.

7. The method for analyzing the complexity of ship traffic flow in the target water area according to claim 6, wherein The method for analyzing the complexity of ship traffic flow in the target water area further includes the following steps: Determine the color configuration of each sub-region according to the complexity of ship traffic flow in the target water area of each sub-region; Visualize the analysis result of the complexity of ship traffic flow in the target water area of the target evaluation area on the map according to the color configurations of the respective sub-regions in the target evaluation area.

8. A system for analyzing the complexity of ship traffic flow in a target water area, characterized in that, It includes: A first module for collecting ship navigation data in the target evaluation water area, where the ship navigation data includes ship trajectory points; A second module for clustering the ships in the target evaluation water area according to the ship trajectory points to determine interacting ship pairs; A third module for calculating ship interaction type indicators based on the navigation data of the interacting ship pairs to obtain first indicator data; A fourth module for calculating ship individual indicators based on the ship navigation data to obtain second indicator data; A fifth module for determining the complexity of ship traffic flow in the target water area according to the second indicator data and the first indicator data.

9. An electronic device, characterized in that, The electronic device includes a memory, a processor, a program stored on the memory and executable on the processor, and a data bus for realizing connection communication between the processor and the memory. When the program is executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A storage medium, which is a computer-readable storage medium for computer-readable storage, and is characterized in that, The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the method according to any one of claims 1 to 7.

Images