CN111340343A - Unmanned ship safety risk grey correlation degree evaluation method - Google Patents
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Abstract
The invention discloses a method for evaluating the gray correlation degree of safety risks of an unmanned ship, which comprises the following steps of: establishing an unmanned ship safety risk influence factor evaluation index system; carrying out weight calculation on the unmanned ship safety risk influence factor evaluation indexes; and establishing an unmanned ship grey correlation analysis model to evaluate the safety risk of the unmanned ship. The method adopts an objective calculation method, namely a whitening weight function method and a subjective calculation method, namely an expert scoring method, to calculate the unmanned ship safety risk influence factor index weight; the grey correlation degree analysis requires less data, has low requirements on the data, has a simple principle, and is suitable for analyzing the influence of various factors of the unmanned ship on quality safety indexes.
Description
Technical Field
The invention relates to the technical field of unmanned ship risk assessment, in particular to an unmanned ship safety risk grey correlation degree evaluation method.
Background
The unmanned ship is a multipurpose observation platform. The unmanned water surface survey ship is used as a comprehensive operation platform for executing real-time unmanned and automatic measurement, relieves the survey staff from heavy underwater topography survey work, and is the inevitable trend of the development of modern marine survey technical equipment. Unmanned ships need to navigate and operate autonomously in complex marine environments, and therefore, the unmanned ships impose more severe requirements on maneuverability, control performance and reliability. In order to ensure that the unmanned ship can complete various complex tasks safely, reliably and autonomously, the unmanned ship is required to complete various complex tasks autonomously, the unmanned ship is required to have flexible maneuverability and environment adaptability, and the risk of safety sealing of the unmanned ship is required to be evaluated for better task completion.
The application of safety risk assessment in oceanographic engineering occurred in the late seventies of the twentieth century, and research work on risk assessment was conducted successively in IMO, IACS and many countries to date. At present, the key points of domestic and foreign research are oil tankers, warships, scientific research ships, icebreakers and the like, and the research conclusion is mainly applied to the ship design stage. The safety risk assessment is applied to the research of the ship detection stage, and only the influence of the ship length and the ship age on the overall safety is seen at present. And the ship risk analysis and risk decision are applied to the research of ship detection, and because the influence factors are many, the comprehensive evaluation is difficult to carry out. In the research of the method for evaluating the safety risk of the unmanned ship, the unmanned ship is mostly evaluated aiming at a certain influence factor, and under the condition that the unmanned ship is in a multi-environment factor, the single factor evaluation has little effect on the safety risk evaluation of the unmanned ship, and the unmanned ship cannot be evaluated from the risks in various aspects such as human, machine, environment and management.
In view of this, a safety risk assessment method for unmanned ships becomes an urgent technical problem to be solved in the field.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method for evaluating the gray correlation degree of the safety risk of the unmanned ship.
The technical scheme adopted by the invention is as follows:
the method for evaluating the gray correlation degree of the safety risk of the unmanned ship is characterized by comprising the following steps of:
establishing an unmanned ship safety risk influence factor evaluation index system;
carrying out weight calculation on the unmanned ship safety risk influence factor evaluation indexes;
and establishing an unmanned ship grey correlation analysis model to evaluate the safety risk of the unmanned ship.
The further technical scheme of the invention is as follows: establishing an unmanned ship safety risk influence factor evaluation index system; the method specifically comprises the following steps:
setting n unmanned ship safety risk influence factor indexes and evaluating m ship types, wherein the unmanned ship safety risk influence factor indexes form a matrix expressed as:
after the index value composition matrix of the unmanned ship safety risk influence factors is subjected to normalized transformation, the formula (1) is changed into:
let x0＝[x0(1),x0(2),…,x0(n)]For reference to a sequence of ship shape influencing factor index values, xi＝[xi(1),xi(2),…,xi(n)]The index value sequence of the ith comparative ship model influence factor is 1,2, …, m;
let x '═ x' (1), x '(2), …, x' (n) ] be the cumulative subtraction generating sequence of the sequence x ═ x (1), x (2), …, x (n) ], where x '(1) ═ x (1), x' (k) ═ x (k) — x (k-1), k ═ 2, …, n;
the relevance is the difference of geometric shapes between curves, and the size of the difference between the curves is used as a measurement scale of the relevance degree; the following formula for calculating the point correlation coefficient is defined:
wherein ξ is resolution coefficient between 0 and 1, and λ1As a shift weighting factor, λ2Weighting coefficients for the rate of change of displacement, and 0 < lambda1,λ2＜1，λ1+λ2＝1；
Zeta in the formulai(k) The k-th influencing factor for the ith ship type is related to the reference ship type x0In the form of a relative differenceIs referred to as xiFor x0The correlation coefficient of the influencing factor at k.
Further, the safety risk factors comprise ship seaworthiness factors, dynamic propulsion factors, navigation control factors, communication factors, human factors and environmental factors; the seaworthiness factors of the ship comprise longitudinal stability, transverse stability, slewing rings/ship length and ship body integrity; the power propulsion factors include: the power strength, the power redundancy, the power response acceleration, the power scram time and the rudder force strength; navigation control factors comprise sensor precision, obstacle sensing and an automatic collision avoidance system; the communication factors include: communication speed, communication channel (single/double), communication distance, error rate and data encryption; the human factors (remote control mode) include: experience of operating the unmanned ship, team ability, the number of team persons and physical conditions of personnel; environmental factors include test site vessel density, wind speed, wave height, and visibility.
The further technical scheme of the invention is as follows: the matrix for the index values of the unmanned ship safety risk influence factors is normalized by the benefit type; the method specifically comprises the following steps: the normalization uses the following formula:
the further technical scheme of the invention is as follows: the cost-type standardization is adopted for the index value composition matrix of the unmanned ship safety risk influence factors; the method specifically comprises the following steps: normalization takes the following formula:
the further technical scheme of the invention is as follows: carrying out weight calculation on the unmanned ship safety risk influence factor evaluation indexes; the method specifically comprises the following steps:
calculating a gray correlation coefficient of the unmanned ship safety risk influence factors;
and (4) carrying out weight calculation on the unmanned ship safety risk influence factors by adopting a whitening weight function method and an expert scoring method.
The further technical scheme of the invention is as follows: the establishing of the unmanned ship grey correlation analysis model for evaluating the safety risk of the unmanned ship specifically comprises the following steps: weighting and summing the grey correlation coefficients of all the influencing factors to obtain the ship shape xiAnd x0The gray correlation of (a) is shown in formula (6):
in the formula of gammaiIs xiFor x0W (k) is a weight coefficient of the influence factor index k: wherein the content of the first and second substances,
w(k)＝(1-α)wW(k)+αwD(k)； (7)
in the formula wW(k) And wD(k) The weights obtained by a whitening weight function method and an expert scoring method are respectively, α is a subjective coefficient, and 0 is more than or equal to α is less than or equal to 1.
Further, the subjective coefficient α is 0.5.
The further technical scheme of the invention is as follows: the whitening weight function method specifically comprises the following steps:
the whitening weight function is set as:
f(x)＝xln x； (8)
then the weight w is normalizedW(k) Is calculated as follows:
for n influencing factor indexes, m ship types and influencing factor index values, forming a matrix, and normalizing the value of the matrix:
the first step, the sum of the influencing factors of each ship type is calculated:
secondly, solving the entropy value of the influencing factors:
when in use
When it is used, order
Thirdly, solving the sum of entropy values of all the influencing factors:
fourthly, solving the relative weight among the influencing factors:
fifthly, normalizing the relative weight to obtain the weight coefficient w of each influence factor indexW(k)：
The further technical scheme of the invention is as follows: the expert scoring method specifically comprises the following steps:
first, judging a matrix:
the judgment matrix represents the relative importance degree of every two of the influencing factors of the level and is provided with n factors B1,B2,…,BnThus, a decision matrix can be constructed:
determining relative importance u in a matrixijEstablishing a 1-9 scale method adopting Saaty, and scoring the relative importance of each factor by experts through designing a scoring form;
and secondly, calculating the weight:
the weight calculation method adopts a sum-product method, and firstly, the judgment matrix is normalized according to columns:
then sum by row:
para-vector W ═ W'1,w′2,…,w′n]And (3) carrying out normalization:
vector WD＝[wD(1),wD(2),…,wD(n)]The obtained index weight is obtained;
thirdly, consistency check:
after the weight is obtained, the consistency of the previous judgment matrix needs to be checked, and the maximum radix lambda needs to be calculatedmaxThe formula is as follows:
calculating a consistency index CI:
the consistency ratio is then calculated:
in the formula, RI is an evaluation consistency index, and generally, when CR is less than 0.1, the consistency of the matrix is judged to be acceptable, otherwise, expert opinions need to be consulted again.
The invention has the beneficial effects that:
the unmanned ship safety risk influence factor evaluation index system is established; calculating the unmanned ship safety risk influence factor index weight by adopting an objective calculation method, namely a whitening weight function method and a subjective calculation method, namely an expert scoring method; because the gray correlation degree analysis requires less data, the requirement on the data is low, the principle is simple, the unmanned ship gray correlation analysis model is established to evaluate the safety risk of the unmanned ship, and the method is suitable for analyzing the influence of each factor of the unmanned ship on the quality safety index.
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Fig. 1 is a flowchart of a method for evaluating a gray correlation degree of safety risks of an unmanned ship according to the present invention.
Detailed Description
The grey correlation degree analysis is a multi-factor statistical analysis method, and can solve the grey correlation degree of each influence factor in an unknown nonlinear problem, reflect the importance of each influence factor on a target function, and further determine the primary and secondary of each influence factor. The grey correlation degree analysis method uses the grey correlation degree to describe the strength, the size and the sequence of the relationship between the factors according to the sample data of each factor, and if the sample data reflects that the changing situations (the direction, the size, the speed and the like) of the two factors are basically consistent, the correlation degree between the two factors is larger. Otherwise, the degree of association is small. The method has the advantages of clear thought, capability of reducing loss caused by information asymmetry to a great extent, lower requirement on data and less workload; the main defects are that the optimal values of all indexes need to be determined currently, the subjectivity is too high, and the optimal values of part of the indexes are difficult to determine.
The safety risk of the unmanned ship belongs to the phenomenon of multi-factor atypical distribution characteristics, the difficulty of regression correlation analysis is quite high, relatively speaking, the data required by grey correlation degree analysis is less, the requirement on the data is lower, the principle is simple, and the method is suitable for analyzing the influence of each factor of the unmanned ship on quality safety indexes.
The foregoing is the core idea of the present application, and in order to make those skilled in the art better understand the scheme of the present application, the present application will be further described in detail with reference to the accompanying drawings. It should be understood that the specific features in the embodiments and examples of the present application are detailed description of the technical solutions of the present application, and are not limited to the technical solutions of the present application, and the technical features in the embodiments and examples of the present application may be combined with each other without conflict.
As shown in fig. 1, a flow chart of a method for evaluating a gray correlation degree of safety risks of an unmanned ship is provided.
Referring to fig. 1, a method for evaluating the gray correlation degree of the safety risk of an unmanned ship comprises the following steps:
102, calculating the weight of the unmanned ship safety risk influence factor evaluation indexes;
and 103, establishing an unmanned ship gray correlation analysis model to evaluate the safety risk of the unmanned ship.
In the embodiment of the invention, an unmanned ship safety risk influence factor evaluation index system is established; the method specifically comprises the following steps:
setting n unmanned ship safety risk influence factor indexes and evaluating m ship types, wherein the unmanned ship safety risk influence factor indexes form a matrix expressed as:
after the index value composition matrix of the unmanned ship safety risk influence factors is subjected to normalized transformation, the formula (1) is changed into:
let x0＝[x0(1),x0(2),…,x0(n)]For reference to a sequence of ship shape influencing factor index values, xi＝[xi(1),xi(2),…,xi(n)]The index value sequence of the ith comparative ship model influence factor is 1,2, …, m;
let x '═ x' (1), x '(2), …, x' (n) ] be the cumulative subtraction generating sequence of the sequence x ═ x (1), x (2), …, x (n) ], where x '(1) ═ x (1), x' (k) ═ x (k) — x (k-1), k ═ 2, …, n;
the relevance is the difference of geometric shapes between curves, and the size of the difference between the curves is used as a measurement scale of the relevance degree; the following formula for calculating the point correlation coefficient is defined:
wherein ξ is resolution coefficient with value of 0-1, generally 0.5, and λ1As a shift weighting factor, λ2Weighting coefficients for unique rates of change, and 0 < lambda1,λ2＜1，λ1+λ2The value of 1 can be adjusted according to the emphasis of a specific problem. If the relative displacement relation of the analyzed factors and each influencing factor is not large, but the relation with the overall change trend is large, the lambda is properly reduced1Increase of λ2(ii) a If the analyzed factors and the influence factors have small variation trend relationship, the lambda can be properly increased1Decrease λ2. In general, λ may be taken1＝λ20.5. Zeta in the formulai(k) The k-th influencing factor for the ith ship type is related to the reference ship type x0Is called x, this form of relative difference is called xiFor x0The correlation coefficient of the influencing factor at k.
In the embodiment of the invention, the safety risk factors comprise seaworthiness factors of ships, dynamic propulsion factors, navigation control factors, communication factors, human factors and environmental factors; the seaworthiness factors of the ship comprise longitudinal stability, transverse stability, slewing rings/ship length and ship body integrity; the power propulsion factors include: the power strength, the power redundancy, the power response acceleration, the power scram time and the rudder force strength; navigation control factors comprise sensor precision, obstacle sensing and an automatic collision avoidance system; the communication factors include: communication speed, communication channel (single/double), communication distance, error rate and data encryption; the human factors (remote control mode) include: experience of operating the unmanned ship, team ability, the number of team persons and physical conditions of personnel; environmental factors include test site vessel density, wind speed, wave height, and visibility. The specific levels are shown in table 1 below:
TABLE 1
In the embodiment of the invention, the benefit type standardization is adopted for the index value composition matrix of the unmanned ship safety risk influence factors; the method specifically comprises the following steps: the normalization uses the following formula:
in the embodiment of the invention, the cost-type standardization is adopted for the index value composition matrix of the unmanned ship safety risk influence factors; the method specifically comprises the following steps: normalization takes the following formula:
in the embodiment of the invention, the weight calculation is carried out on the evaluation indexes of the influence factors of the unmanned ship safety risk; the method specifically comprises the following steps:
calculating a gray correlation coefficient of the unmanned ship safety risk influence factors;
and (4) carrying out weight calculation on the unmanned ship safety risk influence factors by adopting a whitening weight function method and an expert scoring method.
In the embodiment of the invention, an unmanned ship gray correlation analysis model is established to evaluate the safety risk of the unmanned ship, and the method specifically comprises the following steps: weighting and summing the grey correlation coefficients of all the influencing factors to obtain the ship shape xiAnd x0The gray correlation of (a) is shown in formula 6:
in the formula of gammaiIs xiFor x0W (k) is a weight coefficient of the influence factor index k: wherein the content of the first and second substances,
w(k)＝(1-α)wW(k)+αwD(k)； (7)
in the formula wW(k) And wD(k) α are subjective coefficients, 0 is equal to or more than α is equal to or less than 1, if the weight needs to better meet the subjective preference, α is increased, otherwise, if the coefficient is expected to avoid the subjective influence as much as possible, α is reduced, and 0.5 is generally adopted.
In the embodiment of the invention, the weighting of the unmanned ship safety risk influence factor indexes is calculated by adopting an objective calculation method whitening weight function method and a subjective calculation method expert scoring method to respectively obtain WWAnd WD。
The whitening weight function method is used for calculating the safety risk influence factors of the unmanned ship, and specifically comprises the following steps:
let the weighting function (whitening weight function) be:
f(x)＝x ln x； (8)
then the weight w is normalizedW(k) Is calculated as follows:
for n influencing factor indexes, m ship types and influencing factor index values, forming a matrix, and normalizing the value of the matrix:
the first step, the sum of the influencing factors of each ship type is calculated:
secondly, solving the entropy value of the influencing factors:
when in use
When it is used, order
Thirdly, solving the sum of entropy values of all the influencing factors:
fourthly, solving the relative weight among the influencing factors:
fifthly, normalizing the relative weight to obtain the weight coefficient w of each influence factor indexW(k)：
Entropy is a measure of the degree of disorder of the system and can be used to measure the amount of useful information contained in known data and to determine weights. When the difference of some index values of each evaluation object is large, the entropy value is small, which indicates that the effective information provided by the index is large, and the weight of the index is also large; on the contrary, if the difference between the index values is small and the entropy value is large, it indicates that the information provided by the index is small, and the weight of the index should be small.
The main idea of the expert scoring Method (Delphi Method) is to make questions to be answered into a questionnaire and individually distribute the questionnaire to experts, the experts are required to answer the form according to relevant professional knowledge and work experience of the experts, researchers carry out statistical treatment on the opinions of the experts, and the opinions are analyzed for consistency. The expert scoring method mainly comprises the following steps: establishing a judgment matrix, calculating weight and checking consistency.
Firstly, judging a matrix;
the judgment matrix represents the relative importance degree of every two of the influencing factors of the level and is provided with n factors B1,B2,…,BnThus, a decision matrix can be constructed:
determining relative importance u in a matrixijThe construction adopts Saaty 1-9 scale method, and the scoring is carried out through designForm, the relative importance of the expert to each factor is scored; it accords with the psychological habit of people when comparing and judging. Experimental psychology shows that when ordinary people compare certain attributes of a group of things simultaneously and keep basically consistent judgment, the maximum number of things which can be correctly distinguished is between 5 and 9, and the specific meanings are as follows:
TABLE 2
| uij | Definition of |
| 1 | ui is equally important compared to uj |
| 3 | ui is slightly more important than uj |
| 5 | ui is significantly more important than uj |
| 7 | ui is strongly important compared to uj |
| 9 | ui is extremely important compared to uj |
| Even number of | The importance degree is between two adjacent odd numbers |
| 1/n | ui is compared with uj, and the degree of importance is n |
Secondly, calculating the weight;
the weight calculation method adopts a sum-product method, and firstly, the judgment matrix is normalized according to columns:
then sum by row:
para-vector W ═ W'1,w′2,…,w′n]And (3) carrying out normalization:
vector WD＝[wD(1),wD(2),…,wD(n)]I.e. the found index weight.
Thirdly, checking consistency;
after the weight is obtained, the consistency of the previous judgment matrix needs to be checked, and the maximum radix lambda needs to be calculatedmaxThe formula is as follows:
calculating a consistency index CI:
the consistency ratio is then calculated:
in the formula, RI is an evaluation consistency index, and is obtained by looking up a table, see table 3, and generally when CR is less than 0.1, it is determined that the consistency of the matrix is acceptable, otherwise, expert opinions need to be consulted again.
TABLE 3
| n | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
| RI | 0 | 0 | 0.58 | 0.9 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 |
The above examples are typical examples of the present invention, but the embodiments of the present invention are not limited to the above examples. Other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principles of the invention are intended to be included within the scope of the invention.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only intended to illustrate the technical solution of the present invention and not to limit the same, and a person of ordinary skill in the art can make modifications or equivalents to the specific embodiments of the present invention with reference to the above embodiments, and such modifications or equivalents without departing from the spirit and scope of the present invention are within the scope of the claims of the present invention as set forth in the claims.
Claims (9)
1. The method for evaluating the gray correlation degree of the safety risk of the unmanned ship is characterized by comprising the following steps of:
establishing an unmanned ship safety risk influence factor evaluation index system;
carrying out weight calculation on the unmanned ship safety risk influence factor evaluation indexes;
and establishing an unmanned ship grey correlation analysis model to evaluate the safety risk of the unmanned ship.
2. The method according to claim 1, wherein the unmanned ship safety risk influencing factor evaluation index system is established; the method specifically comprises the following steps:
setting n unmanned ship safety risk influence factor indexes and evaluating m ship types, wherein the unmanned ship safety risk influence factor indexes form a matrix expressed as:
after the index value composition matrix of the unmanned ship safety risk influence factors is subjected to normalized transformation, the formula (1) is changed into:
let x0＝[x0(1),x0(2),…,x0(n)]For reference to a sequence of ship shape influencing factor index values, xi＝[xi(1),xi(2),…,xi(n)]The index value sequence of the ith comparative ship model influence factor is 1,2, …, m;
let x '═ x' (1), x '(2), …, x' (n) ] be the cumulative subtraction generating sequence of the sequence x ═ x (1), x (2), …, x (n) ], where x '(1) ═ x (1), x' (k) ═ x (k) — x (k-1), k ═ 2, …, n;
the relevance is the difference of geometric shapes between curves, and the size of the difference between the curves is used as a measurement scale of the relevance degree; the following formula for calculating the point correlation coefficient is defined:
wherein ξ is resolution coefficient between 0 and 1, and λ1As a shift weighting factor, λ2Weighting coefficients for the rate of change of displacement, and 0 < lambda1,λ2＜1，λ1+λ2＝1；
Zeta in the formulai(k) The k-th influencing factor for the ith ship type is related to the reference ship type x0Is called x, this form of relative difference is called xiFor x0The correlation coefficient of the influencing factor at k.
3. The method of claim 2, wherein the safety risk factors include seaworthiness factors, dynamic propulsion factors, voyage control factors, communication factors, human factors, environmental factors; the seaworthiness factors of the ship comprise longitudinal stability, transverse stability, slewing rings/ship length and ship body integrity; the power propulsion factors include: the power strength, the power redundancy, the power response acceleration, the power scram time and the rudder force strength; navigation control factors comprise sensor precision, obstacle sensing and an automatic collision avoidance system; the communication factors include: communication speed, communication channel, communication distance, bit error rate and data encryption; the human factors include: experience of operating the unmanned ship, team ability, the number of team persons and physical conditions of personnel; environmental factors include test site vessel density, wind speed, wave height, and visibility.
4. The method according to claim 2, wherein the risk factor index value composition matrix for the unmanned ship is normalized by benefit type; the method specifically comprises the following steps: the normalization uses the following formula:
5. the method according to claim 2, wherein the cost-based normalization is adopted for the index value composition matrix of the unmanned ship safety risk influencing factors; the method specifically comprises the following steps: normalization takes the following formula:
6. the method according to claim 1, wherein the weighting calculation is performed on unmanned ship safety risk influencing factor evaluation indexes; the method specifically comprises the following steps:
calculating a gray correlation coefficient of the unmanned ship safety risk influence factors;
and (4) carrying out weight calculation on the unmanned ship safety risk influence factors by adopting a whitening weight function method and an expert scoring method.
7. The method according to claim 1, wherein the establishing of the unmanned ship grey correlation analysis model is used for evaluating the safety risk of the unmanned ship, and specifically comprises the following steps: weighting and summing the grey correlation coefficients of all the influencing factors to obtain the ship shape xiAnd x0The gray correlation of (a) is shown in formula 6:
in the formula of gammaiIs xiFor x0W (k) is a weight coefficient of the influence factor index k: wherein the content of the first and second substances,
w(k)＝(1-α)wW(k)+αwD(k)； (7)
in the formula wW(k) And wD(k) Obtained by the whitening-weight-function method and the expert scoring method respectivelyThe weight α is a subjective coefficient, 0 is more than or equal to α is less than or equal to 1.
8. The method according to claim 7, wherein the whitening weight function method is specifically:
the whitening weight function is set as:
f(x)＝xlnx； (8)
then the weight w is normalizedW(k) Is calculated as follows:
for n influencing factor indexes, m ship types and influencing factor index values, forming a matrix, and normalizing the value of the matrix:
the first step, the sum of the influencing factors of each ship type is calculated:
secondly, solving the entropy value of the influencing factors:
when in use
When it is used, order
Thirdly, solving the sum of entropy values of all the influencing factors:
fourthly, solving the relative weight among the influencing factors:
fifthly, normalizing the relative weights to obtain the weightsWeight coefficient w of influence factor indexW(k)：
9. The method according to claim 7, wherein the expert scoring specifically comprises:
first, judging a matrix:
the judgment matrix represents the relative importance degree of every two of the influencing factors of the level and is provided with n factors B1,B2,…,BnThus, a decision matrix can be constructed:
determining relative importance u in a matrixijEstablishing a 1-9 scale method adopting Saaty, and scoring the relative importance of each factor by experts through designing a scoring form;
and secondly, calculating the weight:
the weight calculation method adopts a sum-product method, and firstly, the judgment matrix is normalized according to columns:
then sum by row:
para-vector W ═ W'1,w′2,…,w′n]And (3) carrying out normalization:
vector WD＝[wD(1),wD(2),…,wD(n)]The obtained index weight is obtained;
thirdly, consistency check:
after the weight is obtained, the consistency of the previous judgment matrix needs to be checked, and the maximum radix lambda needs to be calculatedmaxThe formula is as follows:
calculating a consistency index CI:
the consistency ratio is then calculated:
in the formula, RI is an evaluation consistency index, and generally, when CR is less than 0.1, the consistency of the matrix is judged to be acceptable, otherwise, expert opinions need to be consulted again.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112488587A (en) * | 2020-12-21 | 2021-03-12 | 北京航空航天大学 | Priori probability evaluation method and system for degradation degree of efficiency of human body of sailor |
| CN112950028A (en) * | 2021-03-02 | 2021-06-11 | 江苏科技大学 | Production management risk assessment method for semi-submersible type hoisting and disassembling platform |
| CN115880844A (en) * | 2023-02-21 | 2023-03-31 | 江苏称金智能科技有限公司 | Ship security intelligent management system based on multi-source perception |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112488587A (en) * | 2020-12-21 | 2021-03-12 | 北京航空航天大学 | Priori probability evaluation method and system for degradation degree of efficiency of human body of sailor |
| CN112488587B (en) * | 2020-12-21 | 2022-06-24 | 北京航空航天大学 | Priori probability evaluation method and system for degradation degree of efficiency of human body of sailor |
| CN112950028A (en) * | 2021-03-02 | 2021-06-11 | 江苏科技大学 | Production management risk assessment method for semi-submersible type hoisting and disassembling platform |
| CN115880844A (en) * | 2023-02-21 | 2023-03-31 | 江苏称金智能科技有限公司 | Ship security intelligent management system based on multi-source perception |
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