CN110826891A - Relative collision risk degree obtaining method based on ship cluster situation - Google Patents
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Abstract
The embodiment of the invention relates to a relative collision risk degree obtaining method based on ship cluster situation, which comprises the following steps: dividing a ship perception area into a plurality of virtual dynamic grids; calculating the action granularity value of the virtual dynamic grid on the target ship by adopting a fuzzy logic rule; obtaining subjective evaluation weights by adopting a subjective weighting method in the virtual dynamic grids, and obtaining objective evaluation weights by adopting an objective weighting method; respectively calculating the comprehensive weight of the risk degree of the target ship for the virtual dynamic grids belonging to the main interference area and the secondary interference area; and calculating the relative collision risk of the target ship relative to the ship cluster situation according to the action granularity value, the comprehensive risk weight and the collision probability in the primary and secondary interference areas. The method provided by the embodiment of the invention can better improve the identification speed of the risk degree in the ship navigation process, improve the accuracy of recognizing the surrounding environment and more efficiently evaluate the risk of the current navigation state of the ship.
Description
Technical Field
The invention relates to the technical field of ships, in particular to a relative collision risk degree obtaining method based on ship cluster situation.
Background
The intelligent unmanned ship is an unmanned ship and has independent navigation, an intelligent engine room, energy efficiency management, cargo transportation and an intelligent integrated platform, the technology integrates the technologies of ship, communication, automation, robot control, remote monitoring, networking system and the like, and the functions of independent navigation, intelligent obstacle avoidance and the like can be realized. Compared with a manned ship, the intelligent unmanned ship has the advantages of high safety coefficient, economy, environmental protection, greenness and energy conservation. The path planning of the intelligent unmanned ship is the core content of the autonomous navigation system of the intelligent unmanned ship. The research on the collision risk of the ship can better provide reference basis for planning the ship path.
The method for acquiring the relative collision risk based on the ship cluster situation is the basis of the analysis of the ship cluster situation, and represents the accurate selection of the ship, so that the analysis of the ship cluster situation is simpler and more intuitive. At present, most of researches on ship encounters are carried out on ships in two encounters, the researches on multi-ship encounters in a complex navigation environment are lacked, the researches on the influence of the whole area on a target ship are not replaced by a single virtual representation, and the aims of complexity of unmanned ship navigation and autonomous safe navigation cannot be fulfilled.
The ship collision risk degree is a fuzzy concept, means the measurement of the collision possibility between ships, and is an early warning for possible danger in the future. The existing research on the collision risk of multiple ships mostly takes an evaluation method of the collision risk of two ships as a research basis, the collision risk of a target ship and each interference ship is respectively calculated, and a key avoidance ship is determined. The method has a large difference from the actual situation, has certain defects, has certain referential property in the situation that ships are few in a wide water area, but cannot well represent the danger degree of the environment where the target ship is located in a busy water area, and is lack of evaluation on the whole danger degree of the sensing area of the target ship.
The above drawbacks are expected to be overcome by those skilled in the art.
Disclosure of Invention
Technical problem to be solved
In order to solve the problems in the prior art, the invention provides a relative collision risk degree obtaining method based on a ship cluster situation, and solves the problem that the overall risk degree of a target ship sensing area is not evaluated.
(II) technical scheme
In order to achieve the purpose, the invention adopts the main technical scheme that:
an embodiment of the present invention provides a method for acquiring a relative collision risk based on a ship cluster situation, including:
dividing a ship perception area into a plurality of virtual dynamic grids;
analyzing the ship cluster situation in the virtual dynamic grids, determining the virtual dynamic grids belonging to a main interference area and a secondary interference area, and calculating the action granularity value of the virtual dynamic grids on a target ship by adopting a fuzzy logic rule;
obtaining subjective evaluation weights by adopting a subjective weighting method and obtaining objective evaluation weights by adopting an objective weighting method in the virtual dynamic grids;
calculating a comprehensive risk weight of the target ship for the virtual dynamic grids belonging to the main interference area and the secondary interference area according to the subjective evaluation weight and the objective evaluation weight respectively;
and calculating the relative collision risk of the target ship relative to the ship cluster situation according to the action granularity value, the comprehensive risk weight and the collision probability in the primary interference area and the secondary interference area.
In an exemplary embodiment of the invention, the dividing the vessel awareness area into a plurality of virtual dynamic meshes comprises:
dividing the ship sensing area by taking the target ship as a center and taking a meeting area of the target ship and an interference ship, and sequentially dividing the ship sensing area into a collision area, a main interference area and a secondary interference area from inside to outside;
and dividing by combining meeting angles of the collision area, the main interference area and the secondary interference area to obtain 24 virtual dynamic grids.
In an exemplary embodiment of the invention, the meeting angle division includes:
and dividing meeting areas according to the azimuth angles of the interference ships on the target ship of 350-5 degrees, 5-67.5 degrees, 67.5-112.5 degrees, 112.5-175 degrees, 175-185 degrees, 185-247.5 degrees, 247.5-292.5 degrees and 292.5-350 degrees to obtain 8 fan-shaped areas.
In an exemplary embodiment of the present invention, the obtaining subjective evaluation weights by using subjective weighting method in the plurality of virtual dynamic grids includes:
comparing every two indexes of subjective evaluation according to a hierarchical analysis model to obtain a weight ratio of every two factors to obtain a weight vector;
obtaining a judgment matrix according to the weight ratio;
calculating a consistency index according to the maximum characteristic value of the judgment matrix, and calculating a relative consistency index based on the consistency index and the average random consistency index;
and comparing the relative consistency index with a preset range, and if the relative consistency index is within the preset range, taking the normalized feature vector of the weight as the subjective evaluation weight.
In an exemplary embodiment of the invention, the obtaining the objective evaluation weight by using the objective weighting method includes:
calculating the information content of the objective evaluation index by adopting an improved CRITIC method;
calculating the objective evaluation weight of the index according to the information amount.
In an exemplary embodiment of the present invention, the calculating, for the virtual dynamic grids belonging to the primary interference area and the secondary interference area, a risk degree comprehensive weight for the target ship according to the subjective evaluation weight and the objective evaluation weight respectively includes:
the subjective evaluation weight obtained in the main interference area is as follows:
ωAHP＝[ωA1ωA2ωA3ωA4ωA5ωA6ωA7ωA8]
the objective evaluation weights are:
ωN.CRITIC＝[ωC1ωC2ωC3ωC4ωC5ωC6ωC7ωC8]
calculating to obtain the comprehensive weight of the risk degree of the main interference area to the target ship as follows:
ωZ＝[ω1ω2ω3ω4ω5ω6ω7ω8]
wherein ω isA1～ωA8Are subjective evaluation weights, ω, in 8 virtual dynamic grids, respectively, belonging to the main interference regionc1～ωC8Are respectively the objective evaluation weights, omega, in 8 virtual dynamic grids belonging to the main interference region1～ω8Respectively integrating the risk degree in 8 virtual dynamic grids in the main interference area;
the subjective evaluation weight obtained in the secondary interference area is as follows:
ωAHP＝[ωA9ωA10ωA11ωA12ωA13ωA14ωA15ωA16]
the objective evaluation weights are:
ωN.CRITIC＝[ωC9ωC10ωC11ωC12ωC13ωC14ωC15ωC16]
and calculating the comprehensive weight of the risk degree of the secondary interference area to the target ship as follows:
ωZ＝[ω9ω10ω11ω12ω13ω14ω15ω16]
wherein ω isA9～ωA16Are subjective evaluation weights, ω, in 8 virtual dynamic grids, respectively, belonging to the secondary interference regionc9～ωC16Are respectively objective evaluation weights, omega, in 8 virtual dynamic grids belonging to the secondary interference region9～ω 168 virtual in the secondary interference regionRisk composite weights in a dynamic grid.
In an exemplary embodiment of the present invention, the calculation formula of the risk degree comprehensive weight is:
where k is one of n indices.
In an exemplary embodiment of the present invention, before calculating the relative risk of collision of the target ship with respect to the ship cluster situation according to the action granularity value and the risk comprehensive weight, the method further includes:
calculating the probability that the collision of the target ship in the main interference area and the secondary interference area in the ship cluster is X respectively according to the subjective and objective comprehensive weighting method1And X2Wherein X is1+X2＝1。
In an exemplary embodiment of the present invention, the calculating a relative collision risk of the target ship with respect to a ship cluster situation according to the action granularity value, the comprehensive risk weight, and the probability of collision in the primary interference area and the secondary interference area includes:
the probability of collision between the primary interference zone and the secondary interference zone is:
X＝[X1X2]
the action particle size values are:
f＝[f1f2... f16]
the relative collision risk is:
CRIX＝X1(f1·ω1+f2·ω2+...+f8·ω8)+X2(f9·ω9+f10·ω10+...+f16·ω16)。
(III) advantageous effects
The invention has the beneficial effects that: according to the method for acquiring the relative collision risk based on the ship cluster situation, provided by the embodiment of the invention, the subjective evaluation and the objective evaluation weight are respectively and comprehensively considered for the main interference area and the secondary interference area, so that the relative collision risk received by the target ship in the cluster situation is obtained, the identification speed of the risk in the ship navigation process can be better improved, the accuracy of recognizing the surrounding environment is improved, and the risk of the current ship navigation state is more efficiently judged and evaluated.
Drawings
Fig. 1 is a flowchart of a method for acquiring a relative collision risk based on a ship cluster situation according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of basic information of a meeting between two ships according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating the division of the target vessel perception area according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating virtual dynamic meshing in accordance with an embodiment of the present invention;
FIG. 5 is a graph illustrating a dominant interference region dynamic grid weight distribution according to an embodiment of the present invention;
FIG. 6 is a diagram illustrating a dynamic grid weight distribution of a secondary interference region according to an embodiment of the present invention;
FIG. 7 is a schematic diagram illustrating a probability distribution of an interference region in a classification of a sensing region of a ship according to an embodiment of the present invention;
fig. 8 is a schematic diagram of the risk in each virtual dynamic grid in the sensing region according to an embodiment of the present invention.
Detailed Description
For the purpose of better explaining the present invention and to facilitate understanding, the present invention will be described in detail by way of specific embodiments with reference to the accompanying drawings.
All technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
In the related embodiment of the invention, the calculation of the collision risk of the ship is mostly the research on the collision risk of two ships, and then the decision is obtained by one-to-one comparison, so that the efficiency is low, the recognition speed is low, the current navigation state of the ship cannot be accurately evaluated efficiently, and a reliable basis cannot be provided for avoiding the ship.
Fig. 1 is a flowchart of a method for acquiring a relative collision risk based on a ship cluster situation according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
as shown in fig. 1, in step S110, a vessel perception area is divided into a plurality of virtual dynamic grids;
as shown in fig. 1, in step S120, analyzing the ship cluster situation in the plurality of virtual dynamic grids, determining virtual dynamic grids belonging to a primary interference area and a secondary interference area, and calculating an action granularity value of the virtual dynamic grids on a target ship by using a fuzzy logic rule;
as shown in fig. 1, in step S130, subjective weighting is performed to obtain subjective evaluation weights in the plurality of virtual dynamic grids, and objective evaluation weights are performed by using objective weighting;
as shown in fig. 1, in step S140, for the virtual dynamic grids belonging to the primary interference area and the secondary interference area, a risk degree comprehensive weight for the target ship is calculated according to the subjective evaluation weight and the objective evaluation weight, respectively;
as shown in fig. 1, in step S150, a relative collision risk of the target ship with respect to the ship cluster situation is calculated according to the action granularity value, the risk comprehensive weight, and the probability of collision between the primary interference area and the secondary interference area.
Based on the method, the relative collision risk degree of the target ship in the cluster is obtained by respectively and comprehensively considering the subjective evaluation and the objective evaluation weight in the main interference area and the secondary interference area, so that the identification speed of the risk degree in the ship navigation process can be better improved, the accuracy of the cognition of the surrounding environment is improved, and the risk of the current navigation state of the ship is more efficiently evaluated.
The specific implementation of the steps of the embodiment shown in fig. 1 is described in detail below:
in step S110, the vessel awareness area is divided into a plurality of virtual dynamic meshes.
In an embodiment of the present invention, the step specifically includes: firstly, with the target ship as a center, dividing the ship sensing area by the meeting area of the target ship and the interference ship, and sequentially dividing the ship sensing area into a collision area, a main interference area and a secondary interference area from inside to outside; and then, dividing by combining meeting angles of the collision area, the main interference area and the secondary interference area to obtain 24 virtual dynamic grids.
In one embodiment of the invention, the ship sensing area is a range of the target ship with important attention, and when interference ships enter the sensing area, the interference ships are considered to have influence on each other. The division of the target vessel's perception area is described below with reference to an example:
FIG. 2 is a schematic diagram of basic information about meeting of two ships in an embodiment of the present invention, as shown in FIG. 2, a coordinate axis is established with the center of a target ship as the origin of coordinates, the east longitude direction as the positive direction of the X-axis, the north latitude direction as the positive direction of the Y-axis, and the speed of the target ship A at this time is set as vaCourse of being
The geographic coordinate is (x)a,ya) (ii) a The velocity of the interfering vessel B at this time is vbCourse of being
The geographic coordinate is (x)b,yb). The relevant calculation process is as follows:
relative velocity component v of two vessels on the X axisXrThe relative velocity component v of the two vessels on the Y axisYr：
True orientation α of interfering vessel relative to target vesselT：
Relative orientation of interfering vessel with respect to target vessel θ:
the dynamic boundary is the super ship field which is set for keeping the target ship field from being invaded and needs to make the road ship take proper collision avoidance action in advance, and comprises the ship field. Dynamic range radius R of the target vessel relative to the interfering vessel:
the safe meeting distance of the ship generally refers to the distance between two ships when collision avoidance measures such as steering and speed change are taken by the yielding ship in order to ensure that the two ships can be finally safely prevented from colliding in the collision avoidance process, and the safe meeting distance of the ship is smaller than the dynamic boundary. Safe encounter distance d of the target vessel relative to the interfering vessel:
fig. 3 is a schematic diagram of the division of the sensing area of the target ship in an embodiment of the invention, as shown in fig. 3, fig. 3(a) is a schematic diagram of the division of the sensing area of the ship, a marine ship driver usually uses the visibility distance of a ship mast as a distance limit for forming a collision risk, and the minimum visibility distance of the mast is 6n mile for a ship with a ship length greater than 50 m. When situation complexity analysis is met, ships with the ship length less than or equal to 50m exist in the sensing area, and collision danger distance limits of the ships are different.
In this embodiment for meetingThe potential division is simple, and the non-collision danger stage is set to be limited by 6 nmile. And acquiring basic information of the current navigation states of the target ship and the interference ship through navigation aids such as an electronic chart, and calculating the dynamic boundary radius R and the safe encountering distance d of the target ship relative to different interference ships according to the formula (4) and the formula (5). As shown in fig. 3(a), boundary points A, B, C, D of the moving boundary are respectively taken on the connecting line of the target ship and the interfering ship bow, the safe meeting distance point E, F, G, H is respectively fitted to the two groups of points, at this time, three dotted circle regions with the target ship as the center of the circle are formed, and the ship sensing region is sequentially divided into sub-regions N by the three circle regions1Sub-region N2And sub-region N3I.e. the sub-region N1Corresponding to the impact region, sub-region N2Corresponding to the dominant interference region, subregion N3Corresponding to the secondary interference region.
Fig. 3(b) is an angle division diagram of a sensing area combining a theoretical basis of the international collision avoidance rules for forecasting simulations at Sea (collegs) for dividing the meeting situation of a ship and an actual experience of a navigation captain, wherein the meeting angle division includes: and dividing meeting areas according to the azimuth angles of the interference ships on the target ship of 350-5 degrees, 5-67.5 degrees, 67.5-112.5 degrees, 112.5-175 degrees, 175-185 degrees, 185-247.5 degrees, 247.5-292.5 degrees and 292.5-350 degrees to obtain 8 fan-shaped areas.
Fig. 3(c) is a superimposed graph of fig. 3(a) and fig. 3(b), the ship sensing area is divided from the meeting area and the meeting angle respectively, the ship sensing area is divided into a plurality of disc-shaped virtual dynamic grids, and the influence of interfering ships in different virtual dynamic grids on the target ship is different, so that the relative collision risk of the ship cluster situation is researched.
In step S120, the ship cluster situation is analyzed in the plurality of virtual dynamic grids, the virtual dynamic grids belonging to the primary interference area and the secondary interference area are determined, and the action granularity value of the virtual dynamic grids on the target ship is calculated by using the fuzzy logic rule.
In an embodiment of the invention, the ship cluster situation collision risk is a measure of the risk degree of the cluster situation where the ship is located, and is an overall concept. Whether the relative field of the ship is invaded or not is used as an entry point, the influence weight of the risk degree of different virtual dynamic grids in the sensing area relative to the target ship is analyzed, and the relative collision risk degree of the ship cluster situation is further evaluated.
According to the research on the complexity of the ship cluster situation, the current cluster situation of the ship needs to be analyzed. And virtualizing a virtual representative ship capable of representing the influence of the dynamic grid on the target ship according to the distribution condition of the interference ships in different virtual dynamic grids and the characteristics of the interference ships.
During actual vessel navigation, the driver's perception of the surroundings is fuzzy and incomplete. The fuzzy logic method can adopt a language variable form to carry out rule type approximate reasoning and is suitable for describing a subjective judgment process established on the basis of driver knowledge and experience, so that the acting force of ships in each dynamic grid area in a target ship sensing area on a target ship can be obtained by means of the fuzzy logic method, and further the acting force borne by the target ship in the driving process is obtained, and therefore the fuzzy logic rule is adopted in the embodiment to calculate the acting force value of the target in the virtual dynamic grid.
In one embodiment of the invention, the reference factors mainly considered by the action granularity are the shapes of the target ship and the ship representing the interference ship, the driving tendencies of the target ship and the ship representing the interference ship, the time interval between ships, the dynamic grid service level, the type of conflict between ships and the like. The magnitude of the force is described by the action particle size, the action particle size with the greatest attraction force is represented by 1, the action particle size with the greatest repulsion force is represented by-1, and the action particle sizes of different forces are represented by real numbers in the interval in which the action particle sizes are located, as shown in table 1.
TABLE 1 action particle size for different actions
Table 2 is a fuzzy inference rule table, and a fuzzy inference rule in a partially parallel non-conflicting state is selected as an example.
TABLE 2 fuzzy inference rules under parallel non-conflicting conditions
When a ship perception area is divided into virtual dynamic grids, three meeting areas are a collision area, a main interference area and a secondary interference area. No vessels are generally present in the collision zone and are not considered here. The main interference area and the secondary interference area respectively correspond to an urgent situation and an urgent danger situation of danger situation division, in this embodiment, the research on the relative collision risk of the ship cluster situation mainly evaluates the danger of the interference ship to the target ship in the main interference area and the secondary interference area, the related dynamic grid distribution graph is shown in fig. 4, and fig. 4 is a schematic diagram of virtual dynamic grid division in one embodiment of the present invention.
The acting force of the ship cluster on the target ship is calculated in 1-16 virtual dynamic grids marked in the figure 4 as follows:
f＝[f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f11,f12,f13,f14,f15,f16]。
in step S130, subjective evaluation weights are obtained by a subjective weighting method and objective evaluation weights are obtained by an objective weighting method in the plurality of virtual dynamic grids.
In the step, the research on the collision risk degree of the ship cluster situation is a new idea for evaluating the collision risk degree of multiple ships. The influence weight of different meeting areas on the risk of the target ship is analyzed by adopting an objective and subjective comprehensive weighting method, and the action granularity of the corresponding dynamic grid area on the target ship is researched, so that the purposes of modeling and solving the risk of the ship cluster situation collision are achieved. The analysis of the risk degree weight of the virtual dynamic grid in the ship perception area needs to consider the experience of experts, analyze relevant rules according to actual data and comprehensively research from multiple angles, and achieve the effect of comprehensively analyzing problems.
In an embodiment of the present invention, obtaining the subjective evaluation weight by using the subjective weighting method in the plurality of virtual dynamic grids includes:
comparing every two indexes of subjective evaluation according to a hierarchical analysis model to obtain a weight ratio of every two factors to obtain a weight vector;
obtaining a judgment matrix according to the weight ratio;
calculating a consistency index according to the maximum characteristic value of the judgment matrix, and calculating a relative consistency index based on the consistency index and the average random consistency index;
and comparing the relative consistency index with a preset range, and if the relative consistency index is within the preset range, taking the normalized feature vector of the weight as the subjective evaluation weight. Based on the above, the subjective weighting method is a method in which an evaluator artificially weights according to the importance of each index, and sufficiently reflects the experience of an expert. Analytic Hierarchy Process (AHP) is a multi-criteria weight decision analysis method that can perform qualitative analysis and quantitative analysis.
The idea of the AHP method is to regard a multi-objective decision problem as a system, further divide the system into a general objective, sub objectives of each layer, a specific scheme and an evaluation criterion according to the hierarchy, carry out optimization decision based on the general objective, the sub objectives of each layer and the evaluation criterion, and finally obtain a global optimal scheme, which specifically comprises the following steps:
1) construction of decision matrices
And comparing every two influencing factors according to the hierarchical analysis model diagram and the scale of the proportion of the nine quantiles to obtain the weight ratio of every two influencing factors and form a judgment matrix.
① the weight vector is recorded as follows:
W＝(w1,w2,...,wn)T(formula 6)
In the formula wi(i ═ 1, 2.., n) is the weight of the index i.
② the decision matrix is as follows:
2) the calculation method of the matrix weight is as follows:
① calculate the consistency index:
in the formula ofmaxIs the largest eigenvalue of matrix a.
② calculate the relative consistency index:
CR is CI/RI (equation 9)
In the formula, RI is an average random consistency index, which is an average value of enough consistency indexes calculated according to a randomly occurring judgment matrix, and the related values are shown in table 3 below:
TABLE 3 average random consistency index
| Number n of |
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
| RI | 0.00 | 0.00 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.40 |
For example, assuming that the threshold of the preset unit is 0.1, when the consistency index is less than or equal to 0.1, the degree of inconsistency of the matrix is considered to be within the allowable range, and the normalized feature vector thereof can be used as the vector of the subjective evaluation weight, otherwise, the matrix needs to be reconstructed, and the above steps are repeated.
In an embodiment of the present invention, the obtaining the objective evaluation weight by using the objective weighting method includes:
calculating the information content of the objective evaluation index by adopting an improved CRITIC method;
calculating the objective evaluation weight of the index according to the information amount.
In view of the above, the objective weighting method is a method for determining a weight using objective information reflected by an index value, based on actual data. In the embodiment, an improved CRITIC weighting method is selected for research through comparison of multiple objective weighting methods.
The CRITIC method (criterion impact Through inter criterion Correlation) is an objective weighting method proposed by Diakoulaki. The objective weight of the features is comprehensively determined according to the contrast intensity of the features and the conflict of the features.
Firstly, the contrast strength of the characteristics refers to the difference of the same index in values of different samples, and the larger the difference of the evaluation index in values of different samples is, the larger the contrast strength of the characteristics is. The magnitude of the contrast intensity is typically measured by the standard deviation. Next, the conflict of the indexes refers to the magnitude of the correlation between the indexes, and the magnitude and direction of the conflict are generally measured by a correlation coefficient.
The traditional CRITIC method has certain defects, and firstly, the discrimination of indexes is reflected by adopting standard deviation indexes of dimensions. The standard deviation can not compare the contrast strength of the characteristics of the indexes due to different dimensions and orders of magnitude; and secondly, calculating the conflict of indexes of the traditional method. Negative values may occur in the correlation coefficient between indexes, and the result is not reasonable when the absolute values are the same for positive and negative correlation. The method is improved by aiming at the defects of the traditional CRITIC method, and the following improved CRITIC method is obtained.
Let Cj' represents the amount of information contained in the j-th index, and can be expressed by the following formula:
in the formula: r isij-representing the correlation of the ith evaluation index and the jth evaluation index;
σj-representing the standard deviation of the data;
-expressing the expectation of the data.
So the jth index objective weight WjThe improved calculation formula is as follows:
in step S140, for the virtual dynamic grids belonging to the primary interference area and the secondary interference area, a comprehensive risk weight for the target ship is calculated according to the subjective evaluation weight and the objective evaluation weight, respectively.
According to the above, 8 dynamic grids in a main interference area in a ship cluster situation are analyzed, and subjective evaluation weights obtained in the main interference area are as follows:
ωAHP＝[ωA1ωA2ωA3ωA4ωA5ωA6ωA7ωA8](formula 12)
The objective evaluation weights are:
ωN.CRITIC＝[ωC1ωC2ωC3ωC4ωC5ωC6ωC7ωC8](formula 13)
Comprehensively calculating the regional danger weight coefficient obtained by the subjective weighting method and the regional danger weight coefficient obtained by the objective weighting method to obtain the comprehensive danger weight of the main interference region to the target ship, wherein the comprehensive danger weight is as follows:
ωZ＝[ω1ω2ω3ω4ω5ω6ω7ω8](formula 14)
Wherein ω isA1～ωA8Are subjective evaluation weights, ω, in 8 virtual dynamic grids, respectively, belonging to the main interference regionc1～ωC8Are respectively the objective evaluation weights, omega, in 8 virtual dynamic grids belonging to the main interference region1～ω8The risk integrated weights in the 8 virtual dynamic grids in the main interference region are respectively. FIG. 5 is a graph of dominant interference region dynamic grid weight distribution according to an embodiment of the present invention.
Similarly, 8 dynamic grids in a secondary interference area in a ship cluster situation are analyzed, and subjective evaluation weights obtained in the secondary interference area are as follows:
ωAHP＝[ωA9ωA10ωA11ωA12ωA13ωA14ωA15ωA16](formula 15)
The objective evaluation weights are:
ωN.CRITIC＝[ωC9ωC10ωC11ωC12ωC13ωC14ωC15ωC16](formula 16)
Comprehensively calculating the regional danger weight coefficient obtained by the subjective weighting method and the regional danger weight coefficient obtained by the objective weighting method to obtain the comprehensive danger weight of the secondary interference region to the target ship, wherein the comprehensive danger weight is as follows:
ωZ＝[ω9ω10ω11ω12ω13ω14ω15ω16](equation 17) where ω isA9～ωA16Are subjective evaluation weights, ω, in 8 virtual dynamic grids, respectively, belonging to the secondary interference regionc9～ωC16Are respectively objective evaluation weights, omega, in 8 virtual dynamic grids belonging to the secondary interference region9～ω16Fig. 6 is a graph illustrating the distribution of the weights of the secondary interference region dynamic grids according to an embodiment of the present invention.
The calculation formula of the risk degree comprehensive weight, namely the calculation formula of the formula (14) obtained according to the formula (12) and the formula (13), is:
where k is one of n indices.
Considering that the study on the same problem is not comprehensive from a single point of view, in the embodiment, the subjective weight analysis and the objective weight analysis are integrated, and the advantages of the subjective weight analysis and the objective weight analysis are combined to realize the comprehensive weight analysis on the meeting area.
In step S150, a relative collision risk of the target ship with respect to the ship cluster situation is calculated according to the action granularity value, the risk comprehensive weight, and the probability of collision between the primary interference area and the secondary interference area.
In an embodiment of the present invention, before the step of calculating the relative collision risk of the target ship with respect to the ship cluster situation according to the action granularity value and the risk comprehensive weight, the method further includes:
calculating the main interference area and the secondary interference area of the target ship in the ship cluster according to an objective and subjective comprehensive weighting methodThe probability of collision in the region to be interfered is X1And X2Wherein X is1+X2＝1。
FIG. 7 is a schematic diagram illustrating a dangerous probability distribution of an interference region in a classification of a ship sensing region according to an embodiment of the present invention, where as shown in FIG. 7, a probability of a main interference region is X1The probability of the secondary interference region is X2. Analyzing a main interference area and a secondary interference area in a target ship sensing area as two influence factors to obtain the weights occupied by the danger probability of the main interference area and the secondary interference area respectively as the following formula:
X＝[X1X2](formula 19)
When the situation of the ship cluster is analyzed, determining the action granularity value of the dynamic grid on the target ship through a fuzzy logic rule as follows:
f＝[f1f2... f16](formula 20)
The ship cluster situation collision risk is a set of ship cluster situation action granularity and dynamic grid risk weight, and then the relative collision risk under the ship cluster situation is:
CRIX＝X1(f1·ω1+f2·ω2+...+f8·ω8)+X2(f9·ω9+f10·ω10+...+f16·ω16) (formula 21)
Fig. 8 is a schematic diagram of the risk in each virtual dynamic grid in the sensing area according to an embodiment of the present invention, and as shown in fig. 8, the collision risk of the sector area formed by the dynamic grids of the primary interference area and the secondary interference area in the same angle range can be calculated by the above method, so as to provide a more intuitive basis for collision avoidance decision.
The numerical value of the collision risk of the ship cluster situation is determined, and the numerical value is further required to be subjected to range analysis, so that the risk of the cluster situation is more intuitively described. The action granularity of the dynamic grid deduced according to the fuzzy rule is (-1,1), the range of the ship cluster situation collision risk degree is also (-1,1), and a related cluster situation collision risk degree division numerical table is as follows:
TABLE 4 Cluster situation collision risk degree division table
Through the research on the collision risk of the ship cluster situation, the overall risk is defined as the relative collision risk in the embodiment, and the effect of evaluating the integral risk of the ship sailing in the complex meeting environment is achieved through modeling and solving the relative collision risk.
In summary, by using the method for acquiring relative collision risk based on the ship cluster situation provided by the embodiment of the invention, the collision risk is comprehensively researched according to two aspects of different meeting types around the ship, the relation between the meeting ships and the regional characteristics of different directions in the ship sensing region; and in the ship navigation process, the overall danger degree of the ship sensing area is analyzed, and the overall danger in the ship motion process is described more comprehensively. The representing ship replaces the influence of the whole area on the target ship, and the situation overall risk degree is described on the basis of the influence, so that the calculation difficulty is reduced; and comprehensively carrying out weight analysis on the meeting area by an subjective and objective comprehensive method. Based on the method, the research on the relative risk of the ship cluster situation can better improve the identification speed of the risk in the ship navigation process, improve the accuracy of recognizing the surrounding environment and evaluate the current navigation state risk of the ship more efficiently.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the invention. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment 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 disk, a removable hard disk, etc.) or on a network, and includes 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 embodiment of the present invention.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (9)
1. A relative collision risk degree obtaining method based on ship cluster situation is characterized by comprising the following steps:
dividing a ship perception area into a plurality of virtual dynamic grids;
analyzing the ship cluster situation in the virtual dynamic grids, determining the virtual dynamic grids belonging to a main interference area and a secondary interference area, and calculating the action granularity value of the virtual dynamic grids on a target ship by adopting a fuzzy logic rule;
obtaining subjective evaluation weights by adopting a subjective weighting method and obtaining objective evaluation weights by adopting an objective weighting method in the virtual dynamic grids;
calculating a comprehensive risk weight of the target ship for the virtual dynamic grids belonging to the main interference area and the secondary interference area according to the subjective evaluation weight and the objective evaluation weight respectively;
and calculating the relative collision risk of the target ship relative to the ship cluster situation according to the action granularity value, the comprehensive risk weight and the collision probability in the primary interference area and the secondary interference area.
2. The method for acquiring relative collision risk based on ship cluster situation according to claim 1, wherein the dividing the ship sensing area into a plurality of virtual dynamic grids comprises:
dividing the ship sensing area by taking the target ship as a center and taking a meeting area of the target ship and an interference ship, and sequentially dividing the ship sensing area into a collision area, a main interference area and a secondary interference area from inside to outside;
and dividing by combining meeting angles of the collision area, the main interference area and the secondary interference area to obtain 24 virtual dynamic grids.
3. The method for acquiring relative collision risk based on ship cluster situation according to claim 2, wherein the meeting angle division comprises:
and dividing meeting areas according to the azimuth angles of the interference ships on the target ship of 350-5 degrees, 5-67.5 degrees, 67.5-112.5 degrees, 112.5-175 degrees, 175-185 degrees, 185-247.5 degrees, 247.5-292.5 degrees and 292.5-350 degrees to obtain 8 fan-shaped areas.
4. The method for acquiring relative collision risk based on ship cluster situation according to claim 1, wherein the obtaining of subjective evaluation weight by using subjective weighting method in the plurality of virtual dynamic grids comprises:
comparing every two indexes of subjective evaluation according to a hierarchical analysis model to obtain a weight ratio of every two factors to obtain a weight vector;
obtaining a judgment matrix according to the weight ratio;
calculating a consistency index according to the maximum characteristic value of the judgment matrix, and calculating a relative consistency index based on the consistency index and the average random consistency index;
and comparing the relative consistency index with a preset range, and if the relative consistency index is within the preset range, taking the normalized feature vector of the weight as the subjective evaluation weight.
5. The method for acquiring relative collision risk based on ship cluster situation according to claim 1, wherein the obtaining objective evaluation weight by objective weighting comprises:
calculating the information content of the objective evaluation index by adopting an improved CRITIC method;
calculating the objective evaluation weight of the index according to the information amount.
6. The method according to claim 1, wherein the calculating a risk comprehensive weight for the target ship according to the subjective evaluation weight and the objective evaluation weight for the virtual dynamic grids belonging to the primary interference area and the secondary interference area respectively comprises:
the subjective evaluation weight obtained in the main interference area is as follows:
ωAHP＝[ωA1ωA2ωA3ωA4ωA5ωA6ωA7ωA8]
the objective evaluation weights are:
ωN.CRITIC＝[ωC1ωC2ωC3ωC4ωC5ωC6ωC7ωC8]
calculating to obtain the comprehensive weight of the risk degree of the main interference area to the target ship as follows:
ωZ＝[ω1ω2ω3ω4ω5ω6ω7ω8]
wherein ω isA1～ωA8Are subjective evaluation weights, ω, in 8 virtual dynamic grids, respectively, belonging to the main interference regionc1～ωC8Are respectively the objective evaluation weights, omega, in 8 virtual dynamic grids belonging to the main interference region1～ω8Respectively integrating the risk degree in 8 virtual dynamic grids in the main interference area;
the subjective evaluation weight obtained in the secondary interference area is as follows:
ωAHP＝[ωA9ωA10ωA11ωA12ωA13ωA14ωA15ωA16]
the objective evaluation weights are:
ωN.CRITIC＝[ωC9ωC10ωC11ωC12ωC13ωC14ωC15ωC16]
and calculating the comprehensive weight of the risk degree of the secondary interference area to the target ship as follows:
ωZ＝[ω9ω10ω11ω12ω13ω14ω15ω16]
wherein ω isA9～ωA16Are subjective evaluation weights, ω, in 8 virtual dynamic grids, respectively, belonging to the secondary interference regionc9～ωC16Are respectively objective evaluation weights, omega, in 8 virtual dynamic grids belonging to the secondary interference region9～ω16The risk integrated weights in the 8 virtual dynamic grids in the secondary interference region are respectively.
7. The method for acquiring relative collision risk based on ship cluster situation according to claim 6, wherein the calculation formula of the risk comprehensive weight is as follows:
where k is one of n indices.
8. The method according to claim 6, wherein before calculating the relative risk of collision of the target ship with respect to the ship cluster situation according to the action particle size value and the risk comprehensive weight, the method further comprises:
calculating the probability that the collision of the target ship in the main interference area and the secondary interference area in the ship cluster is X respectively according to the subjective and objective comprehensive weighting method1And X2Wherein X is1+X2＝1。
9. The method according to claim 8, wherein the calculating the relative risk of collision of the target ship with respect to the ship cluster situation according to the action granularity value, the risk comprehensive weight and the probability of collision between the primary interference area and the secondary interference area comprises:
the probability of collision between the primary interference zone and the secondary interference zone is:
X＝[X1X2]
the action particle size values are:
f＝[f1f2...f16]
the relative collision risk is:
CRIX＝X1(f1·ω1+f2·ω2+...+f8·ω8)+X2(f9·ω9+f10·ω10+...+f16·ω16)。
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