CN115115142B - Ship emergency stop point planning method and device and electronic equipment - Google Patents

Ship emergency stop point planning method and device and electronic equipment Download PDF

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CN115115142B
CN115115142B CN202211015439.2A CN202211015439A CN115115142B CN 115115142 B CN115115142 B CN 115115142B CN 202211015439 A CN202211015439 A CN 202211015439A CN 115115142 B CN115115142 B CN 115115142B
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许端阳
葛全胜
曹巍
陶泽兴
吴茂炜
汪源
王焕炯
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Abstract

The invention discloses a planning method, a device and electronic equipment for a ship emergency stop point, and relates to the technical field of ship emergency stop action planning; screening a plurality of alternative areas from the areas to be parked based on the appropriate threshold values of indexes at all levels in the multi-level index system; and sequencing the multiple candidate areas based on the weight of each level of index in the multi-level index system, and screening out the optimal candidate area as a stop point. According to the method for planning the stop points, multiple elements in the stop environment can be comprehensively considered through a multi-level index system, the importance degree of different elements to emergency stop actions can be quantified by using the weights of indexes at all levels, and the optimal alternative area is intelligently selected as the stop points through overall consideration, so that the stop points are planned more reasonably and are consistent with actual conditions.

Description

Ship emergency stop point planning method and device and electronic equipment
Technical Field
The invention relates to the technical field of emergency berthing action planning of ships, in particular to a method and a device for planning emergency berthing points of ships and electronic equipment.
Background
The docking point of the ship emergency docking action plans important contents of tasks such as emergency rescue, land and sea infrastructure construction and the like. The stop point planning is a multi-attribute decision problem, and because various factors are involved and are comprehensively influenced by various geographic elements, the stop point planning is also a relatively complex system analysis process, and the optimal stop point is difficult to quantitatively determine based on various factors.
The conventional stop point planning technology is more qualitative judgment based on experience, and the final scheme has strong subjectivity; or, considering a single factor, the suitability evaluation is performed to select the optimal stop point, but there is no overall consideration of multiple factors. Therefore, the prior art can not meet the task of planning the docking points under the condition of more complex docking environment, and a new technology is needed to enable the selection of the docking points to be more reasonable and consistent with the reality.
Disclosure of Invention
The invention aims to provide a method, a device and electronic equipment for planning emergency stop points of ships, which are used for solving the problems that the conventional stop point planning method is greatly influenced by subjective factors, lacks of overall consideration of multiple elements and has large planning result errors.
According to a first aspect of the invention, a method for planning emergency berthing points of a ship is provided, which comprises the following steps:
constructing a multi-level index system, wherein the multi-level index system is used for representing the characteristics of a parking environment;
screening a plurality of alternative areas from the areas to be parked based on the appropriate threshold values of indexes at all levels in the multi-level index system;
and sequencing the multiple candidate areas based on the weight of each level of index in the multi-level index system, and screening out the optimal candidate area as a stop point.
In some embodiments, constructing a multi-level index hierarchy includes:
constructing first-level indexes, wherein the first-level indexes comprise beach factors, onshore factors, meteorological factors and ocean factors;
and constructing a lower-level index based on each first-level index.
In some embodiments, screening out a plurality of candidate areas from the area to be docked based on the suitable threshold of each level of index in the multi-level index system includes:
screening out a plurality of initial candidate areas with lower indexes of the beach factors and lower indexes of the onshore factors both at proper thresholds from the areas to be parked;
and screening a plurality of candidate areas in which the subordinate indexes of the meteorological factors and the subordinate indexes of the oceanic factors are both at proper thresholds from the plurality of initial candidate areas.
In some embodiments, screening out a plurality of initial candidate areas from the areas to be docked, where the lower indicators of the beach factors and the lower indicators of the onshore factors are both at the appropriate threshold, includes:
dividing the area to be parked into a beach area and an onshore area;
dividing the beach area into a plurality of beach sub-areas;
screening out alternative beach areas with lower indexes of the beach factors all at proper threshold values from the plurality of beach sub-areas;
dividing the onshore region into a plurality of onshore subregions;
screening out alternative onshore areas with lower indexes of the onshore factors all at proper threshold values from the plurality of onshore subregions;
and combining the adjacent alternative beach areas and the alternative on-shore areas to obtain the initial alternative area.
In some embodiments, before sorting the candidate regions based on the weights of the indexes of each stage in the multi-stage index system and screening out an optimal candidate region as a stop point, the method further includes:
establishing a fuzzy function;
setting a subjective preference value;
calculating a normalized decision matrix based on the fuzzy function and the subjective preference value;
and establishing a single-target optimization model to obtain the weight of each level of index.
In some embodiments, the normalized decision matrix
Figure 365361DEST_PATH_IMAGE001
The formula of (1) is as follows:
Figure 440764DEST_PATH_IMAGE002
where m and n represent the number of rows and columns of the fuzzy decision matrix A,
Figure 624008DEST_PATH_IMAGE003
is represented in
Figure 316021DEST_PATH_IMAGE004
Attribute down decision maker pair
Figure 302300DEST_PATH_IMAGE005
The normalized decision value of each of the schemes,
Figure 294527DEST_PATH_IMAGE006
is the lower limit of the normalized matrix values,
Figure 662054DEST_PATH_IMAGE007
is the upper limit of the normalized matrix values,
Figure 356210DEST_PATH_IMAGE008
is the most likely normalized matrix value,
Figure 100175DEST_PATH_IMAGE009
is represented in the first
Figure 946908DEST_PATH_IMAGE004
Decision maker pair under attribute
Figure 184DEST_PATH_IMAGE005
One of the triangular blur numbers of the scheme,
Figure 197947DEST_PATH_IMAGE010
(or
Figure 11182DEST_PATH_IMAGE011
），
Figure 978001DEST_PATH_IMAGE012
(or
Figure 952910DEST_PATH_IMAGE013
) Respectively the lower limit and the upper limit of the fuzzy number,
Figure 890167DEST_PATH_IMAGE014
(or
Figure 241514DEST_PATH_IMAGE015
) The most probable value.
The calculation formula of the weight is as follows:
Figure 328418DEST_PATH_IMAGE016
wherein
Figure 474229DEST_PATH_IMAGE017
Figure 630273DEST_PATH_IMAGE018
Calculated from the following equation
Figure 50890DEST_PATH_IMAGE019
。
The above formula is expressed in
Figure 461142DEST_PATH_IMAGE020
Decision maker pair under attribute
Figure 309013DEST_PATH_IMAGE005
Subjective preference value of individual scheme
Figure 703085DEST_PATH_IMAGE021
With corresponding objective preference value (attribute value)
Figure 645502DEST_PATH_IMAGE022
The similarity between them.
Figure 706999DEST_PATH_IMAGE023
And is meant to be a representation of the similarity,
Figure 460191DEST_PATH_IMAGE023
the larger the triangular blur number
Figure 75980DEST_PATH_IMAGE024
And
Figure 822088DEST_PATH_IMAGE025
the greater the degree of similarity.
Figure 472513DEST_PATH_IMAGE026
Is a vector of the weight of the attribute,
Figure 396606DEST_PATH_IMAGE027
is as follows
Figure 30850DEST_PATH_IMAGE028
Weight of attribute, single objective function
Figure 65802DEST_PATH_IMAGE029
Expressing the total similarity between the subjective preference value and the objective preference value (attribute value) of the decision maker under all attributes, solving the linear programming model to obtain the optimal attribute weight vector
Figure 822930DEST_PATH_IMAGE026
M represents the length of the attribute variable, n represents the length of the scheme variable, and T represents the vector transpose.
In some embodiments, before the step of screening out a plurality of candidate areas from the area to be docked based on the suitable threshold of each level of the index in the multi-level index system, the method further includes:
generating suitable threshold values of indexes at all levels in the multi-level index system based on the attributes of the emergency berthing actions of the ship;
the attributes include personnel size, equipment type, time period to dock, and action characteristics.
In some embodiments, inferior indicators of the beach factors include a coast slope, a beach substrate, offshore obstacles, and a port;
the lower-level indexes of the onshore factors comprise vegetation, highways, airports, residential areas, soil bearing capacity, gradient, surface cutting degree, beach width and beach depth;
the lower-level indicators of the meteorological factors comprise visibility, precipitation and wind speed;
the subordinate indicators of the marine factors include 200m depth offshore, flow direction, flow velocity and near-shore waves.
The invention provides a ship emergency stop planning device in a second aspect, which comprises:
the system comprises a construction module, a display module and a display module, wherein the construction module is used for constructing a multi-level index system which is used for representing the characteristics of a parking environment;
the screening module is used for screening a plurality of alternative areas from the areas to be parked based on the appropriate threshold values of all levels of indexes in the multi-level index system;
and the sorting module is used for sorting the plurality of candidate areas based on the weight of each level of index in the multi-level index system and screening out the optimal candidate area as a stop point.
A third aspect of the invention provides an electronic device comprising a memory, a processor and a computer program stored in a storage medium of the memory and executable on the processor, the processor implementing the method according to any one of claims 1 to 8 when executing the computer program.
(III) advantageous effects
The technical scheme of the invention has the following beneficial technical effects:
according to the planning method for the emergency stop points of the ship, provided by the embodiment of the invention, various elements in the stop environment can be comprehensively considered through a multi-level index system, the importance degree of different elements on emergency stop actions can be quantized by using the weights of indexes of all levels, and the optimal alternative area is intelligently selected as the stop point through overall consideration, so that the planning of the stop point is more reasonable and is consistent with the actual situation.
Drawings
FIG. 1 is a flow chart of a method for planning emergency docking points of a ship according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a multi-level indexing architecture provided in accordance with an embodiment of the present invention;
FIG. 3 is a flow chart of a method for planning emergency docking points of a ship according to another embodiment of the invention;
fig. 4 is a structural diagram of an emergency docking point planning apparatus for a ship according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings in conjunction with the following detailed description. It is to be understood that these descriptions are only illustrative and are not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
Before describing the method provided by the first embodiment of the present invention in detail, a brief description will be given of the related art.
Fig. 1 is a flowchart of a ship emergency docking point planning method according to an embodiment of the present invention. As shown in FIG. 1, the invention provides a docking point planning method, which comprises the following steps:
step S101: and constructing a multi-level index system, wherein the multi-level index system is used for representing the characteristics of the parking environment.
Step S102: and screening a plurality of alternative areas from the areas to be parked based on the appropriate threshold values of indexes at all levels in the multi-level index system.
Step S103: and sequencing the multiple candidate areas based on the weight of each level of index in the multi-level index system, and screening out the optimal candidate area as a stop point.
According to the method for planning the docking points, multiple elements in the docking environment can be comprehensively considered through a multi-level index system, the importance degree of different elements on emergency docking actions of the ship can be quantified by using the weights of indexes of all levels, and the optimal alternative area is intelligently selected as the docking points through overall consideration, so that the planning of the docking points is more reasonable and is consistent with the actual situation.
As shown in FIG. 2, in some embodiments, the multi-level index system includes a first level index and a second level index. The first-level indicators include beach factors, onshore factors, meteorological factors, and oceanic factors.
The embodiment divides the emergency berthing action of the ship into an emergency berthing phase and an onshore advancing phase, and the beach factor comprehensively considers the action characteristics of the emergency berthing phase, and establishes secondary indexes associated with the beach factor, including a coast slope, a beach substrate, offshore barriers and a port. The shoreside factors comprehensively consider the action characteristics of the shoreside advancing stage, and secondary indexes related to the shoreside factors are established, including vegetation, highways, airports, residential areas, soil bearing capacity, gradient, surface cutting degree, beach width and beach depth. Secondary indicators associated with meteorological factors include visibility, precipitation, and wind speed. Secondary indicators associated with marine factors include 200m water depth offshore, flow direction, flow velocity, and near shore waves.
In some embodiments, the step S102 specifically includes dividing the area to be parked into a beach area and an on-shore area, in this embodiment, the beach area and the on-shore area are respectively located at two sides of the coastline, the beach area is divided into a plurality of beach sub-areas along the direction of the coastline, and the plurality of beach sub-areas are arranged along the direction of the coastline. The alternative beach areas with the lower indexes of the beach factors all at the proper threshold value are screened out from the plurality of beach areas, and in the embodiment, the lower indexes of the beach factors comprise the coast slope, the beach substrate, the offshore barrier and the port. Divide the ashore region into a plurality of ashore subregion along the direction of coastline, a plurality of ashore regions arrange in proper order along the direction of coastline and set up, select the alternative ashore region that the subordinate index of onshore factor all is in suitable threshold value in a plurality of ashore subregion, and in this embodiment, the subordinate index of onshore factor includes vegetation, highway, airport, resident's place, soil property bearing capacity, slope, earth's surface cutting degree, beach width and beach degree of depth. And combining the adjacent alternative beach areas and the alternative on-shore areas to obtain an initial alternative area. In this embodiment, a time period to be parked is obtained first, it is then determined whether there is an alternative time period in which the subordinate indexes of the meteorological factors and the subordinate indexes of the oceanic factors are both at the appropriate threshold value in each initial alternative area within the parking time period, and the initial alternative area whose determination result is yes is taken as the alternative area. According to the embodiment, the planning scheme of the stop point can be associated with the time period to be stopped through the lower indexes of the meteorological factors and the lower indexes of the oceanic factors, so that the stop point can be further optimized, and the alternative areas suitable for the beach factors, the onshore factors, the meteorological factors and the oceanic factors in the time period to be stopped are selected.
Fig. 3 is a flowchart of a ship emergency docking point planning method according to another embodiment of the present invention. As shown in fig. 3, a method for planning a stop point according to an embodiment of the present invention includes:
constructing a multi-level index system, and constructing a first-level index, wherein the first-level index comprises beach factors, onshore factors, meteorological factors and ocean factors, and the subordinate indexes of the beach factors comprise a coast slope, beach bottom materials, offshore obstacles and ports; lower-level indexes of the onshore factors comprise vegetation, highways, airports, residential areas, soil bearing capacity, gradient, surface cutting degree, beach width and beach depth; the lower-level indicators of meteorological factors comprise visibility, precipitation and wind speed; lower indicators of marine factors include 200m depth offshore, flow direction, flow velocity and near shore waves.
And generating suitable threshold values of indexes at all levels in the multi-level index system based on the attributes of the emergency berthing actions of the ship. Attributes of a ship emergency berthing activity include personnel size, equipment type, time period to berth, and action characteristics.
In this embodiment, the weights of the beach factors, the onshore factors, the meteorological factors and the oceanic factors are as follows:
TABLE 1 beach factor weight and score settings
Figure 917925DEST_PATH_IMAGE030
TABLE 2 onshore factor weight and score settings
Figure 508306DEST_PATH_IMAGE031
TABLE 3 Meteorological factor weight and score settings
Figure 346949DEST_PATH_IMAGE032
TABLE 4 Marine factor weight and score settings
Figure 221233DEST_PATH_IMAGE033
The embodiment comprehensively considers the requirements of emergency berthing and onshore traveling on berth points, combines indexes of shoals (coast slopes, beach bottom materials, offshore barriers and ports) and onshore (vegetation, roads, airports, residential areas, soil bearing capacity, slopes, surface cutting degree, beach width and beach depth), and selects a plurality of initial alternative areas suitable for ship berthing through space superposition analysis. The formula of the spatial superposition analysis is as follows:
Figure 487130DEST_PATH_IMAGE034
wherein, V is the result after applying the spatial superposition analysis calculation, namely a plurality of initial candidate region sets suitable for parking in the research; f is a spatial superposition method; I.C. A k The k-th space index is represented, and in the research, the k-th space index refers to influence factors of various environmental indexes. And selecting the regions with the indexes suitable and generally suitable as initial candidate regions by applying a space superposition analysis method and combining with the parking suitability threshold range.
Aiming at the screened initial candidate areas, selecting areas with suitable and generally suitable indexes according to weather (visibility, precipitation and wind speed) indexes and ocean (200 m offshore water depth, flow speed, flow direction and near-shore wave) indexes by a space superposition method and by combining with suitable threshold values for docking, determining candidate time suitable for docking to form a candidate area set, wherein each candidate area comprises corresponding candidate time.
And performing spatial superposition analysis based on the subordinate indexes of the meteorological factors and the subordinate indexes of the oceanic factors, screening out a plurality of alternative areas in which the subordinate indexes of the meteorological factors and the subordinate indexes of the oceanic factors are both at proper thresholds in the same alternative time period from the plurality of initial alternative areas, wherein the alternative areas correspond to the alternative time periods one by one.
And calculating the weight of each level of index in the multi-level index system by using a fuzzy trigonometric function, sequencing the multiple candidate areas based on the weight, and screening out the optimal candidate area as a stop point.
The definition of the triangular blur number is: if it is
Figure 264941DEST_PATH_IMAGE035
Wherein
Figure 110538DEST_PATH_IMAGE036
Balance of
Figure 308170DEST_PATH_IMAGE037
Is a triangular blur number. In the formula
Figure 744967DEST_PATH_IMAGE038
Represents a number of triangular ambiguities which,
Figure 309941DEST_PATH_IMAGE039
and
Figure 490386DEST_PATH_IMAGE040
represents the first
Figure 342192DEST_PATH_IMAGE041
An attribute and
Figure 949891DEST_PATH_IMAGE042
according to the technical scheme, the method comprises the following steps of,
Figure 736581DEST_PATH_IMAGE043
(or
Figure 704406DEST_PATH_IMAGE044
），
Figure 158521DEST_PATH_IMAGE045
(or
Figure 405963DEST_PATH_IMAGE046
) Respectively the lower and upper limits of the blur number,
Figure 929217DEST_PATH_IMAGE047
(or
Figure 451465DEST_PATH_IMAGE048
) The most probable value, its characteristic function (membership function)
Figure 25666DEST_PATH_IMAGE049
Can be expressed as
Figure 709588DEST_PATH_IMAGE050
In some embodiments, step S103 comprises:
(1) Set of candidate regions
Figure 985718DEST_PATH_IMAGE051
Wherein
Figure 780499DEST_PATH_IMAGE052
Represents the jth candidate region.
(2) Attribute set
Figure 943627DEST_PATH_IMAGE053
Wherein
Figure 329609DEST_PATH_IMAGE054
Represents the jth attribute: (The influencing factors of the ship berth point selection, namely the subordinate indexes of the beach, shore, weather and ocean factors).
(3) Constructing fuzzy decision matrix
Figure 830385DEST_PATH_IMAGE055
And m and n represent the number of rows and columns of the fuzzy decision matrix. The attribute values of different schemes under different attributes are
Figure 694435DEST_PATH_IMAGE056
。
(4) Plan for decision maker
Figure 977649DEST_PATH_IMAGE057
Has certain subjective preference (among them)
Figure 268953DEST_PATH_IMAGE058
Represents the j-th candidate region or regions,
Figure 519675DEST_PATH_IMAGE059
representing a set of candidate regions) and setting the subjective preference value as a triangular fuzzy number
Figure 187417DEST_PATH_IMAGE060
，
Figure 325137DEST_PATH_IMAGE061
In the formula
Figure 787342DEST_PATH_IMAGE062
And
Figure 259781DEST_PATH_IMAGE063
is the lower and upper limits of the triangular blur number,
Figure 996793DEST_PATH_IMAGE064
is a subjective preference value.
(5) Fuzzy decision matrix
Figure 989019DEST_PATH_IMAGE065
(m and n represent the number of rows and columns of the fuzzy decision matrix A) into a normalized matrix
Figure 622126DEST_PATH_IMAGE066
(wherein
Figure 332593DEST_PATH_IMAGE067
For normalizing the values of the matrix, m and n represent the number of rows and columns of the normalized matrix), where
Figure 591405DEST_PATH_IMAGE068
(wherein
Figure 703717DEST_PATH_IMAGE069
Is the lower limit of the normalized matrix values,
Figure 773305DEST_PATH_IMAGE070
is the upper limit of the normalized matrix values,
Figure 705489DEST_PATH_IMAGE071
is the most likely normalized matrix value), the formula is as follows
Figure 770921DEST_PATH_IMAGE072
(6) Due to the restriction of various conditions, a certain deviation exists between the subjective preference and the objective preference of a decision maker, and in order to ensure the reasonability of decision making, the attribute weight vector
Figure 472161DEST_PATH_IMAGE073
The selection should minimize the total deviation of the subjective preference value and the objective preference value (attribute value) of the decision maker. Taking into account a normalized decision matrix
Figure 712649DEST_PATH_IMAGE074
The elements in the method and the subjective preference value of a decision maker are all given in the form of triangular fuzzy number, and the similarity concept of triangular fuzzy number comparison is utilized to establish the following single-target optimizationModel:
Figure 132129DEST_PATH_IMAGE075
wherein
Figure 749055DEST_PATH_IMAGE076
In the formula
Figure 819648DEST_PATH_IMAGE077
Calculated from the following equation
Figure 231038DEST_PATH_IMAGE078
Is expressed in
Figure 137814DEST_PATH_IMAGE079
Decision maker pair under attribute
Figure 292852DEST_PATH_IMAGE080
Subjective preference value of individual scheme
Figure 217952DEST_PATH_IMAGE081
With corresponding objective preference value (attribute value)
Figure 65822DEST_PATH_IMAGE082
The similarity between them. And is
Figure 459894DEST_PATH_IMAGE083
The larger the triangular blur number
Figure 153044DEST_PATH_IMAGE084
And
Figure 198229DEST_PATH_IMAGE085
the greater the degree of similarity of (a) and (b),
Figure 685842DEST_PATH_IMAGE086
representing the phase between themSimilarity.
Figure 567210DEST_PATH_IMAGE087
Is as follows
Figure 329630DEST_PATH_IMAGE079
Weight of attribute, single objective function
Figure 980054DEST_PATH_IMAGE088
Expressing the total similarity between the subjective preference value and the objective preference value (attribute value) of the decision maker under all attributes, solving the linear programming model to obtain the optimal attribute weight vector
Figure 879047DEST_PATH_IMAGE089
M represents the length of the attribute weight vector, and T represents the vector transpose.
(7) In finding optimal weight vector of attribute
Figure 247712DEST_PATH_IMAGE090
Then, the comprehensive attribute value of each scheme is calculated
Figure 282664DEST_PATH_IMAGE091
Figure 53174DEST_PATH_IMAGE092
(8) Due to the fact that
Figure 663015DEST_PATH_IMAGE093
Still, the triangular fuzzy number is inconvenient to directly sort the schemes, so the possibility degree between the triangular fuzzy number can be calculated by using the possibility degree formula of comparison of the triangular fuzzy numbers
Figure 253397DEST_PATH_IMAGE094
The calculation of the calculation possibility is as follows
Figure 92040DEST_PATH_IMAGE095
Wherein,
Figure 717056DEST_PATH_IMAGE096
the choice of value depends on the risk attitude of the decision maker when
Figure 232220DEST_PATH_IMAGE097
The decision maker is called to pursue the risk; when in use
Figure 309897DEST_PATH_IMAGE098
The decision maker is said to be risk neutral; when in use
Figure 686652DEST_PATH_IMAGE099
Sometimes, the decision maker is said to be at aversive risk.
(9) Based on
Figure 166175DEST_PATH_IMAGE100
A likelihood matrix may be established
Figure 852240DEST_PATH_IMAGE101
(m and n represent the number of rows and columns of the probability matrix,
Figure 417214DEST_PATH_IMAGE102
representing the numerical value of the likelihood matrix), obtaining the sorting vector of the likelihood matrix, and sorting the schemes according to the component sizes of the likelihood matrix, thus obtaining the optimal scheme. Rank vector of a likelihood matrix
Figure 863239DEST_PATH_IMAGE103
Is calculated by the formula
Figure 197268DEST_PATH_IMAGE104
Is expressed in
Figure 804967DEST_PATH_IMAGE105
Decision maker pair under attribute
Figure 843854DEST_PATH_IMAGE106
The possibility degrees of the schemes are sorted, N is the total number of the schemes, N is a natural number)
The planning method for the emergency berthing points of the ship, provided by the embodiment of the invention, is based on different berthing stages, combines the characteristics of hydrological, meteorological, topographic and geomorphic factors of a berthing sea area, establishes a comprehensive berthing time and place evaluation multistage index system, and determines the appropriate threshold value of each stage of index by combining personnel scale, equipment type, time period to be berthed, action characteristics and the like, thereby preliminarily determining the optional area set suitable for berthing. On the basis, a multi-attribute comprehensive decision algorithm based on a fuzzy trigonometric function is introduced, the weight of each index is determined according to the preference of a decision maker, the candidate areas are optimally sorted by using weighting calculation, and quantitative research based on the indexes is fully combined with the requirements of the decision maker, so that the result of the location selection of the stop point is more in line with the actual requirements.
Fig. 4 is a structural diagram of an emergency docking point planning apparatus for a ship according to an embodiment of the present invention. As shown in fig. 4, based on the same inventive concept, an embodiment of the present invention provides a stop point planning apparatus, including:
the building module 11 is used for building a multi-level index system, and the multi-level index system is used for representing the characteristics of a parking environment;
the screening module 12 is configured to screen a plurality of candidate areas from the areas to be parked based on appropriate thresholds of indexes of each level in the multi-level index system;
and the sorting module 13 is configured to sort the multiple candidate areas based on the weight of each level of index in the multi-level index system and screen out an optimal candidate area as a stop point.
Based on the same inventive concept, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program that is stored in a storage medium of the memory and is executable on the processor, and is characterized in that the processor implements the method of any of the above embodiments when executing the computer program.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explaining the principles of the invention and are not to be construed as limiting the invention. Therefore, any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. Further, it is intended that the appended claims cover all such variations and modifications as fall within the scope and boundary of the appended claims, or the equivalents of such scope and boundary.

Claims (8)

1. A planning method for emergency stop points of ships is characterized by comprising the following steps:
constructing a multi-level index system, wherein the multi-level index system is used for representing the environmental characteristics of the stop points;
screening a plurality of alternative areas from the areas to be parked based on the appropriate threshold values of indexes at all levels in the multi-level index system;
sorting the multiple candidate areas based on the weight of each level of index in the multi-level index system and screening out the optimal candidate area as a stop point;
the method further comprises the following steps of sorting the plurality of candidate areas based on the weight of each level of index in the multi-level index system, and screening out the optimal candidate area as a stop point:
establishing a fuzzy trigonometric function;
setting a subjective preference value;
calculating a normalized decision matrix based on the fuzzy trigonometric function;
establishing a single-target optimization model to obtain the weight of each level of index;
the normalized decision matrix R = (R) ij ) m×n The formula of (1) is as follows:
Figure FDA0003915703020000011
where m and n represent the number of rows and columns of the normalized decision matrix, r ij Represents the normalized decision value of the decision maker on the jth scheme under the ith attribute, and is the clientThe value of the viewing preference is set to,
Figure FDA0003915703020000021
is the lower limit of the normalized matrix values,
Figure FDA0003915703020000022
is the upper limit of the normalized matrix values,
Figure FDA0003915703020000023
is the most likely normalized matrix value, a ij A triangular blur number derived from a blur trigonometric function representing the decision maker for the jth solution under the ith attribute,
Figure FDA0003915703020000024
respectively the lower and upper limits of the blur number,
Figure FDA0003915703020000025
is the most probable value;
the calculation formula of the single-target optimization model is as follows:
Figure FDA0003915703020000026
wherein
Figure FDA0003915703020000027
s(r ij ，v j ) Calculated from the following equation
Figure FDA0003915703020000028
The above formula represents the subjective preference value v of the decision maker for the jth scheme under the ith attribute j Corresponding objective preference value r ij Similarity between them, subjective preference value v j Is a triangular fuzzy number, v j ＝(v j L ，v j M ，v j U )，s(r ij ，v j ) Representing similarity, s (r) ij ，v j ) The larger the triangular blur number r ij And v j The greater the degree of similarity, v j L Lower bound of triangular blur number, v, for subjective preference value j U V is 0 or more, which is the upper limit of the triangular blur number of the subjective preference value j L ≤v j M ≤v j U 1 or less, omega is an attribute weight vector, omega i And (3) solving the linear single-target optimization model to obtain an optimal attribute weight vector omega, wherein m represents the length of an attribute variable, n represents the length of a scheme variable, and T represents vector transposition.
2. The planning method for emergency berth points of ships according to claim 1, characterized in that the construction of the multilevel index system comprises:
constructing a first-level index, wherein the first-level index comprises a beach factor, an onshore factor, a meteorological factor and a marine factor;
and constructing a lower-level index based on each first-level index.
3. The method for planning emergency berthing points of ships according to claim 2, wherein screening out a plurality of candidate areas from the areas to be berthed based on suitable thresholds of indexes of each level in the multi-level index system comprises:
screening out a plurality of initial alternative areas of which the lower indexes of the beach factors and the lower indexes of the onshore factors are both at proper thresholds from the areas to be docked;
and screening out a plurality of candidate areas in which the subordinate indexes of the meteorological factors and the subordinate indexes of the oceanic factors are both at proper threshold values from the plurality of initial candidate areas.
4. The planning method for emergency berth points of a ship according to claim 3, wherein screening out a plurality of initial candidate areas in which the subordinate indexes of the beach factors and the subordinate indexes of the onshore factors are both at suitable thresholds from the areas to be berthed comprises:
dividing the area to be parked into a beach area and an onshore area;
dividing the beach area into a plurality of beach sub-areas;
screening out alternative beach areas with lower indexes of the beach factors all at proper threshold values from the plurality of beach sub-areas;
dividing the onshore region into a plurality of onshore subregions;
screening out alternative onshore areas with lower indexes of the onshore factors all at a proper threshold from the plurality of onshore sub areas;
and combining the adjacent alternative beach areas and the alternative on-shore areas to obtain the initial alternative area.
5. The method for planning emergency berthing points of ships according to claim 1, wherein before screening out a plurality of candidate areas from the areas to be berthed based on the suitable threshold of each level of indexes in the multi-level index system, the method further comprises:
generating suitable threshold values of indexes at all levels in the multi-level index system based on the attributes of the emergency berthing actions of the ship;
the attributes include personnel size, equipment type, time period to dock, and action characteristics.
6. The method of claim 2, wherein the subordinate indicators of the beach factors include a coast slope, a beach substrate, offshore obstacles, and a port;
the lower-level indexes of the onshore factors comprise vegetation, highways, airports, residential areas, soil bearing capacity, gradient, surface cutting degree, beach width and beach depth;
the lower-level indicators of the meteorological factors comprise visibility, precipitation and wind speed;
the lower indicators of the marine factors include 200m depth offshore, flow direction, flow velocity and near shore waves.
7. A ship emergency stop planning device, for use in the method of any one of claims 1-6, comprising:
the system comprises a construction module, a storage module and a display module, wherein the construction module is used for constructing a multi-level index system, and the multi-level index system is used for representing the characteristics of a parking environment;
the screening module is used for screening a plurality of alternative areas from the areas to be parked based on the appropriate threshold values of all levels of indexes in the multi-level index system;
and the sorting module is used for sorting the plurality of candidate areas based on the weight of each level of index in the multi-level index system and screening out the optimal candidate area as a stop point.
8. An electronic device comprising a memory, a processor, and a computer program stored in a storage medium of the memory and executable on the processor, characterized in that the processor implements the method according to any one of claims 1 to 6 when executing the computer program.
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