CN113742843A - Sensor fuzzy optimization method for accurate positioning of ship component assembly - Google Patents

Sensor fuzzy optimization method for accurate positioning of ship component assembly Download PDF

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CN113742843A
CN113742843A CN202111034134.1A CN202111034134A CN113742843A CN 113742843 A CN113742843 A CN 113742843A CN 202111034134 A CN202111034134 A CN 202111034134A CN 113742843 A CN113742843 A CN 113742843A
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fuzzy
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牛延丹
姜军
马秋杰
陈好楠
储云泽
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Shipbuilding Technology Research Institute of CSSC No 11 Research Institute
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Abstract

The invention provides a fuzzy optimal selection method of a sensor for accurate positioning of ship component assembly, which comprises the following steps: determining a model selection scheme of a sensor of a ship component assembly unit; finding out an evaluation index set corresponding to the scheme; classifying and layering all evaluation indexes, and establishing a multi-target multi-layer structure model of the ship component assembly sensor; determining the membership degrees of all indexes in the model and the weight of each layer, and calculating the membership degrees and the weights of the evaluation indexes; taking a target layer as a first layer of the structure, forming a first layer fuzzy preferred membership matrix by the membership degree corresponding to each index in the first layer, calculating the fuzzy preferred membership degree of the first layer, and giving an index weight set of the first layer to obtain the fuzzy preferred membership degree of the layer; descending layer by layer from the first layer, carrying out fuzzy operation on each layer until the lowest layer obtains the membership degrees of all the schemes, and selecting the scheme with the maximum membership degree as the optimal scheme according to the maximum membership degree principle.

Description

Sensor fuzzy optimization method for accurate positioning of ship component assembly
Technical Field
The invention relates to the field of ship construction, in particular to a fuzzy optimal selection method for a sensor for accurate positioning of ship component assembly.
Background
The assembly of the ship body is a key link in the ship building process, the quality, the building cost and the period of the ship are greatly influenced, the ship building process is complex, the ship is formed by assembling and welding thousands of parts, the workload is huge, the manual assembly cannot meet the production requirement, and at present, the ship body assembly technology in China is changing to a digital application stage. The positioning technology is the premise and the condition for realizing the digital accurate assembly of the ship, the ship body assembly positioning has a plurality of influencing factors, the selection of a sensor with a proper model is particularly important for the assembly positioning accuracy of the ship parts, the selection of the current sensor has the problems of strong subjectivity and strong randomness, and a user cannot perform comprehensive comparison when facing a plurality of index parameters.
Disclosure of Invention
The invention aims to provide a sensor fuzzy optimization method for accurate positioning of ship component assembly.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a sensor fuzzy optimization method for accurate positioning of ship component assembly is characterized by comprising the following steps:
step S1) determining a sensor model selection scheme of the ship component assembly unit;
step S2) finding out an evaluation index set of the corresponding scheme;
step S3), classifying and layering all evaluation indexes, and establishing a multi-target multi-layer structure model of the ship component assembly sensor;
step S4) calculating the membership degree and the weight of the evaluation index;
step S5) taking the target layer as a first layer of the structure, forming a first layer fuzzy preferred membership degree matrix by the membership degree corresponding to each evaluation index in the first layer, giving an evaluation index weight set of the first layer, and carrying out point multiplication on the first layer fuzzy preferred membership degree matrix and the first layer evaluation index weight set to obtain the first layer fuzzy preferred membership degree;
step S6) descending layer by layer from the first layer, carrying out fuzzy operation on each layer until the lowest layer obtains the membership degree of all the schemes, and selecting the scheme with the maximum membership degree as the optimal scheme according to the maximum membership degree principle.
Further, the set of schemes in step S1 is a ═ a1,A2,…Ai,An}，AiRepresents the ith scheme; in step S2, the set of evaluation indexes is d ═ d1,d2,…di,dm}，diIndicates the ith evaluation index.
Further, the step S4 includes,
step S41) dividing the evaluation index into quantitative factors and qualitative factors;
step S42) calculating the relative membership degree of the quantitative factors;
step S43), calculating the relative membership of qualitative factors, qualitatively ranking the superiority of decision set, and ranking some factor ciDecision A of the decision setkAnd AlPerforming binary priority relation comparison;
step S44) quantitatively calculating the superiority of decision set, and combining each decision with decision AjComparing superiority and giving quantitative scale;
step S45), the index weight is calculated, and the relative importance of the index factors of the same layer is obtained.
Further, in the step S42, the relative membership degree of the quantitative factor is
Figure BDA0003246300730000021
Further, in the step S43, if it is determined that the detected signal is not correct
AkRatio AlIs superior to the method taking ekl＝1,elk＝0；
AlRatio AkIs superior to the method taking ekl＝0,elk＝1；
AkRatio AlIs superior to the method taking ekl＝elk＝0.5；
Obtaining a binary priority relationship matrix
Figure BDA0003246300730000022
Determining c according to decision set superiority rankingiIs an optimal decision aj。
Further, in step S44, the quantitative scale is as shown in table 1:
TABLE 1 language operators, quantitative scales and relative membership
Tone operator Also, the same applies to Slightly less Is relatively Is obvious Is remarkable in that Is very good at Is prepared from Incomparable to
Quantitative scale 0.5 0.6 0.65 0.7 0.75 0.8 0.9 1.00
Relative degree of membership 1.00 0.667 0.538 0.429 0.333 0.25 0.111 0
Deriving decision set relative AjComparing the row vectors
Uj＝[uj1 uj2 … ujj… ujn]；
The conditions are satisfied: u is more than or equal to 0.5jk≤1，ujj＝0.5，ujkThe larger, AjComparison AkThe stronger the superiority;
for a certain factor ciThe relative membership quantization formula in the decision set is
Figure BDA0003246300730000031
Further, in the step S45, the evaluation index weight calculation formula is
Figure BDA0003246300730000032
Further, in the step S5, the membership matrix is
Figure BDA0003246300730000033
Calculating fuzzy optimal membership degree of the first layer, and giving a weight set of evaluation indexes of the first layer as
W1 1＝{w1,w2,…,wm}；
Will be provided with
Figure BDA0003246300730000034
And W1 1Performing dot product operation to obtain the fuzzy optimal membership degree of the first layer
Figure BDA0003246300730000035
The invention establishes a fuzzy optimal selection model of the ship component assembly accurate positioning sensor, introduces the concept of influence factors of evaluation indexes on decision-making into a fuzzy optimal selection scheme based on relative membership, solves the sensor optimal selection scheme by utilizing an improved multi-objective function genetic algorithm, realizes objective and accurate selection of the ship component assembly sensor, and improves the assembly positioning accuracy.
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FIG. 1 is a flow chart of the present invention;
FIG. 2 is a diagram of a multi-objective multi-level structure model according to the present invention. .
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention discloses a fuzzy optimal selection method for a sensor for accurate positioning of ship component assembly, which comprises the following steps of:
step S1) firstly determining the sensor type selection scheme of the ship component assembly unit, namely the decision-making preference set is
A＝{A1,A2,…Ai,An}；
Wherein A isiIndicating the ith possible scenario.
Step S2) finds out the main evaluation index set of the corresponding scheme as
d＝{d1,d2,…di,dm}；
Wherein d isiIndicates the ith evaluation index.
Step S3), classifying and layering all evaluation indexes, and establishing a multi-target multi-level structure model optimized by a ship component assembly sensor, wherein the evaluation indexes comprise economic indexes, applicable indexes and performance indexes, the economic indexes specifically comprise price and quantity, the applicable indexes specifically comprise sensing modes and sensing objects, and the performance indexes specifically comprise sensitivity, stability and compatibility as shown in FIG. 2;
step S4), determining the membership degree of all the evaluation indexes in the model and the weight of each layer, and calculating the membership degree and the weight of the evaluation indexes.
The step S4 specifically includes the steps of,
step S41) dividing the evaluation index into quantitative factors and qualitative factors, wherein the economic index is the quantitative factor, and the applicable index and the performance index are the qualitative factors;
step S42) calculating the relative membership of the quantitative factors
Figure BDA0003246300730000041
Wherein, Ximax、XiminIs the maximum sum of the target featuresA minimum value.
Step S43) calculating the relative membership of qualitative factors, firstly qualitatively ordering the superiority of the decision set, and then determining a certain factor ciIn other words, decision A in the decision setkAnd AlMaking a binary priority comparison if
AkRatio AlIs superior to the method taking ekl＝1,elk＝0；
AlRatio AkIs superior to the method taking ekl＝0,elk＝1；
AkRatio AlIs superior to the method taking ekl＝elk＝0.5；
Obtaining a binary priority relation matrix:
Figure BDA0003246300730000051
determining c according to decision set superiority rankingiIs an optimal decision aj。
Step S44) decision set superiority quantitative calculation, each decision is compared with decision AjSuperiority comparisons were made and empirically given quantitative scales as shown in table 1:
TABLE 1 language operators, quantitative scales and relative membership
Tone operator Also, the same applies to Slightly less Is relatively Is obvious Is remarkable in that Is very good at Is prepared from Incomparable to
Quantitative scale 0.5 0.6 0.65 0.7 0.75 0.8 0.9 1.00
Relative degree of membership 1.00 0.667 0.538 0.429 0.333 0.25 0.111 0
Deriving decision set relative AjComparing the row vectors
Uj＝[uj1 uj2 … ujj… ujn]；
The conditions are satisfied: u is more than or equal to 0.5jk≤1，ujj0.5, general ujkThe larger the description, AjComparison AkThe stronger the superiority.
With a certain factor ciIn other words, the relative membership quantization formula in the decision set is
Figure BDA0003246300730000052
Step S45), the index weight is calculated to obtain the relative importance of the index factors of the same layer, and the calculation formula is
Figure BDA0003246300730000053
Step S5) taking the target layer as a first layer of the structure, wherein the membership degree corresponding to each evaluation index in the first layer forms a first layer fuzzy preferred membership degree matrix
Figure BDA0003246300730000061
Calculating fuzzy optimal membership degree of the first layer, and giving an evaluation index weight set of the first layer as follows:
W1 1＝{w1,w2,…,wm}；
will be provided with
Figure BDA0003246300730000062
And W1 1Performing dot product operation to obtain the fuzzy optimal membership degree of the first layer
Figure BDA0003246300730000063
Step S6) descending layer by layer from the first layer, each layer carrying out fuzzy operation until the lowest layer obtains the membership degree of all the schemes, and selecting the scheme with the largest membership degree as the best scheme according to the principle of the largest membership degree.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (8)

1. A sensor fuzzy optimization method for accurate positioning of ship component assembly is characterized by comprising the following steps:
step S1) determining a sensor model selection scheme of the ship component assembly unit;
step S2) finding out an evaluation index set of the corresponding scheme;
step S3), classifying and layering all evaluation indexes, and establishing a multi-target multi-layer structure model of the ship component assembly sensor;
step S4) calculating the membership degree and the weight of the evaluation index;
step S5) taking the target layer as a first layer of the structure, forming a first layer fuzzy preferred membership degree matrix by the membership degree corresponding to each evaluation index in the first layer, giving an evaluation index weight set of the first layer, and carrying out point multiplication on the first layer fuzzy preferred membership degree matrix and the first layer evaluation index weight set to obtain the first layer fuzzy preferred membership degree;
step S6) descending layer by layer from the first layer, carrying out fuzzy operation on each layer until the lowest layer obtains the membership degree of all the schemes, and selecting the scheme with the maximum membership degree as the optimal scheme according to the maximum membership degree principle.
2. The method for fuzzy sensor optimization of accurate positioning facing ship component assembly according to claim 1, wherein the set of solutions in step S1 is a ═ { a ═1,A2,…Ai,An}，AiRepresents the ith scheme;
in step S2, the set of evaluation indexes is d ═ d1,d2,…di,dm}，diIndicates the ith evaluation index.
3. The method for fuzzy sensor optimization of accurate positioning of marine vessel component assembly according to claim 2, wherein said step S4 includes,
step S41) dividing the evaluation index into quantitative factors and qualitative factors;
step S42) calculating the relative membership degree of the quantitative factors;
step S43), calculating the relative membership of qualitative factors, qualitatively ranking the superiority of decision set, and ranking some factor ciDecision A of the decision setkAnd AlPerforming binary priority relation comparison;
step S44) quantitatively calculating the superiority of decision set, and combining each decision with decision AjComparing superiority and giving quantitative scale;
step S45), the index weight is calculated, and the relative importance of the index factors of the same layer is obtained.
4. The fuzzy optimization method of the sensor for accurate positioning of the ship component assembly according to claim 3, wherein in the step S42, the relative membership degree of the quantitative factor is
Figure FDA0003246300720000021
5. The method for fuzzy sensor optimization of accurate positioning of marine component assembly according to claim 4, wherein in step S43, if yes, the step
AkRatio AlIs superior to the method taking ekl＝1,elk＝0；
AlRatio AkIs superior to the method taking ekl＝0,elk＝1；
AkRatio AlIs superior to the method taking ekl＝elk＝0.5；
Obtaining a binary priority relationship matrix
Figure FDA0003246300720000022
Determining c according to decision set superiority rankingiIs an optimal decision aj。
6. The method for fuzzy optimization of sensor facing to accurate positioning of ship parts assembly according to claim 5, wherein in step S44, quantitative scale is shown in Table 1:
TABLE 1 language operators, quantitative scales and relative membership
Tone operator Also, the same applies to Slightly less Is relatively Is obvious Is remarkable in that Is very good at Is prepared from Incomparable to Quantitative scale 0.5 0.6 0.65 0.7 0.75 0.8 0.9 1.00 Relative degree of membership 1.00 0.667 0.538 0.429 0.333 0.25 0.111 0
Deriving decision set relative AjComparing the row vectors
Uj＝[uj1 uj2 … ujj … ujn]；
The conditions are satisfied: u is more than or equal to 0.5jk≤1，ujj＝0.5，ujkThe larger, AjComparison AkThe stronger the superiority;
for a certain factor ciThe relative membership quantization formula in the decision set is
Figure FDA0003246300720000031
7. Sensor fuzzy optimization method for accurate positioning of ship component assembly according to claim 6In the step S45, the evaluation index weight calculation formula is
Figure FDA0003246300720000032
8. The fuzzy optimization method for the sensor oriented to the accurate positioning of the ship component assembly according to claim 7, wherein in the step S5, the membership matrix is
Figure FDA0003246300720000033
Calculating fuzzy optimal membership degree of the first layer, and giving a weight set of evaluation indexes of the first layer as
W1 1＝{w1,w2,…,wm}；
R is to be1 1And W1 1Performing dot product operation to obtain the fuzzy optimal membership degree of the first layer
Figure FDA0003246300720000034
CN202111034134.1A 2021-09-03 2021-09-03 Sensor fuzzy optimization method for accurate positioning of ship component assembly Pending CN113742843A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114201837A (en) * 2022-02-15 2022-03-18 杭州杰牌传动科技有限公司 Speed reducer model selection method and system based on scene virtual matching

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114201837A (en) * 2022-02-15 2022-03-18 杭州杰牌传动科技有限公司 Speed reducer model selection method and system based on scene virtual matching

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