CN111932074A - Intelligent ship coating process recommendation method based on knowledge - Google Patents
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- 238000000576 coating method Methods 0.000 title claims abstract description 160
- 238000000034 method Methods 0.000 title claims abstract description 35
- 239000011248 coating agent Substances 0.000 claims abstract description 109
- 238000011156 evaluation Methods 0.000 claims abstract description 18
- 238000007637 random forest analysis Methods 0.000 claims abstract description 8
- 238000010422 painting Methods 0.000 claims description 28
- 239000003973 paint Substances 0.000 claims description 16
- JEIPFZHSYJVQDO-UHFFFAOYSA-N iron(III) oxide Inorganic materials O=[Fe]O[Fe]=O JEIPFZHSYJVQDO-UHFFFAOYSA-N 0.000 claims description 14
- 238000005507 spraying Methods 0.000 claims description 11
- 239000000463 material Substances 0.000 claims description 8
- 239000013598 vector Substances 0.000 claims description 6
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- 238000012545 processing Methods 0.000 claims description 4
- 238000012800 visualization Methods 0.000 claims description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 3
- 229910000831 Steel Inorganic materials 0.000 claims description 2
- 238000005422 blasting Methods 0.000 claims description 2
- 238000005259 measurement Methods 0.000 claims description 2
- 238000007761 roller coating Methods 0.000 claims description 2
- 238000012163 sequencing technique Methods 0.000 claims description 2
- 239000007787 solid Substances 0.000 claims description 2
- 239000010959 steel Substances 0.000 claims description 2
- -1 plate shape Substances 0.000 claims 1
- 238000010276 construction Methods 0.000 description 6
- 239000007921 spray Substances 0.000 description 5
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 4
- 238000003066 decision tree Methods 0.000 description 3
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- 229910052742 iron Inorganic materials 0.000 description 2
- 238000007592 spray painting technique Methods 0.000 description 2
- 229910000851 Alloy steel Inorganic materials 0.000 description 1
- 239000003085 diluting agent Substances 0.000 description 1
- 238000005488 sandblasting Methods 0.000 description 1
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Abstract
The invention discloses a knowledge-based intelligent recommendation method for a ship coating process, which comprises the following steps of: extracting features and characteristic values from a ship coating process knowledge base and generating a ship coating knowledge map; classifying the ship coating object to be coated to a certain knowledge by using a random forest classifier; calculating the similarity between the object to be coated and the coated object contained in the knowledge to obtain a coating object set and a coating process set adjacent to K; scoring the neighboring coating process set by combining a ship coating multi-target evaluation function; the invention obtains a method for realizing intelligent recommendation of a coating process by selecting the Top-N coating processes which are more fit with ship objects to be coated in sequence. The invention adopts a knowledge map method, can effectively reduce the redundancy of coating knowledge and realize the rapid generation of the coating process.
Description
Technical Field
The invention relates to a ship coating process, in particular to an intelligent knowledge-based ship coating process recommendation method.
Background
The ship coating is an important link in ship construction and runs through the whole ship construction process.
The coating work of ships is mainly the steel surface treatment of hull structures and various outfitting and coating operation of paint, which comprises grinding, sand blasting, rust removal, surface painting and the like, and is one of the most important processes in ship construction. With the implementation of the shell, outfitting and coating integrated shipbuilding mode, the coating work is more and more high in shipbuilding status.
At present, most ship enterprises adopt the concept of regional coating, but the design depth of the ship coating process of domestic related enterprises is not enough, and the useful information of coating cannot be related, so that the coating working hours are increased, a large amount of materials are wasted, and the capability of improving and improving the coating design is an important and indispensable means for the enterprises to improve the market competitiveness and deal with the current complex market environment.
Because the ship coating knowledge has various forms, some coating knowledge is fuzzy or abstract, and a large amount of coating data/knowledge is poured in, so that the waste of computing resources is caused.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a knowledge-based intelligent recommendation method for a ship coating process, which effectively reduces redundancy of coating knowledge and realizes quick generation of the coating process.
The technical scheme is as follows: the invention relates to a knowledge-based intelligent recommendation method for a ship coating process, which comprises the following steps of:
s1, extracting characteristics and characteristic values from the ship coating process knowledge base; generating a ship coating object knowledge map and a ship coating process knowledge map by using a knowledge map method, realizing ship coating knowledge visualization and finishing dimension reduction processing on ship coating data;
s2, classifying the ship painting object into a certain knowledge by using a random forest classifier according to the generated ship painting object knowledge map;
s3, after classification is finished, calculating the similarity between the class of painting objects contained in the classification result and the class of painting objects to be painted by utilizing the similarity, and obtaining a painting object set with a K neighbor;
s4, scoring the coating process corresponding to the coating object set of the neighboring K by combining a ship coating multi-target evaluation function;
and S5, obtaining a Top-N coating process which is more fit with the ship object to be coated after sequencing, and obtaining a recommendation list.
Has the advantages that: compared with the prior art, the invention has the advantages that: the ship painting knowledge and data can be visually displayed by adopting a knowledge map method, so that the requirements of the method on data processing and the calculation time of the data are reduced, and the dimension reduction of a large amount of heterogeneous data for ship painting is realized; when knowledge classification is carried out, random forests are adopted for knowledge classification, so that the classification precision of ship coating objects is improved; the cosine similarity is adopted for similarity calculation through the method, so that the similarity between ship coating objects can be effectively distinguished, and the recommendation of a coating process is facilitated; the invention adopts a multi-target evaluation function, can realize the selection of the coating process suitable for specific conditions under different targets, and provides more selection angles for the recommendation algorithm; the invention adopts a recommendation algorithm, can realize the intelligent design generation of the ship coating process, and provides a more effective means for scientific management of the coating process.
Drawings
FIG. 1 is a flow chart of a knowledge-based intelligent recommendation method for a ship painting process.
Detailed Description
As shown in fig. 1, an intelligent knowledge-based recommendation method for a ship coating process includes the following steps:
s1, extracting characteristics and characteristic values from the ship coating process knowledge base; and generating a ship coating object knowledge map and a ship coating process knowledge map by using a knowledge map method, so as to realize ship coating knowledge visualization and finish dimension reduction processing on ship coating data.
In step S1, the knowledge map of the ship painting object includes knowledge of characteristics of the material, the plate type, the painting area, the surface roughness, and the painting area.
The ship coating process knowledge map comprises coating matching knowledge and process parameter knowledge, wherein the coating matching knowledge comprises paint name, color, dry film thickness, wet film thickness, solid content, diluent and coating interval; the process parameter knowledge comprises rust removal equipment knowledge, coating equipment knowledge and intelligent equipment knowledge, the rust removal equipment knowledge comprises manual rust removal and machine rust removal knowledge, the manual rust removal knowledge comprises working hours and teams, and the machine rust removal knowledge comprises equipment knowledge of shot blasting rust removal such as iron shot diameter, jet distance, jet angle, air pressure and the like and equipment knowledge of a high-pressure water rust remover such as operation angle, water yield, jet distance and the like; the knowledge of the coating equipment comprises knowledge of manual coating and machine coating, the knowledge of the manual coating comprises knowledge of brush coating/roller coating, the knowledge of the machine coating comprises knowledge of airless spraying and air spraying, the knowledge of airless spraying comprises the knowledge of equipment of an airless sprayer with air pressure, paint spraying pressure, spray gun speed, connecting pipe material length, construction temperature and spraying flow hydraulic pressure, and the knowledge of equipment of a bi-component sprayer with air pressure, paint spraying pressure, spray gun speed, connecting pipe material length, construction temperature and spraying flow; the intelligent equipment knowledge comprises equipment knowledge of the rust removal robot with control precision, degree of freedom, control mode, iron shot diameter, spray distance, spray angle and air pressure, and equipment knowledge of the coating robot with control precision, degree of freedom, control mode, air pressure, spray painting pressure, spray gun speed, connecting pipe length, construction temperature and spray painting flow.
S2: according to the generated ship painting object knowledge graph, classifying a ship painting object into a certain knowledge by using a random forest classifier:
the coating is an object to be coated, which is made of alloy steel, has a coating area of 180 square meters, a plate type of straight plates, a coating area of outer plates, a rust removal grade of Sa2.5 and surface roughness of Ra25, and has the following characteristics and characteristic values.
(1) And setting an original painting object training set as D, and randomly selecting D painting object training subsets to be used for training a decision tree through bootstrap to serve as object samples at the root node of the decision tree.
(2) When each coating object sample has M attributes (material, plate type, surface roughness, coating area, rust removal grade and the like), when each node of the decision tree needs to be split, M attributes are randomly selected from the M attributes, and the condition M < < M is met. Then, a certain policy is adopted to select 1 attribute from the m attributes as the split attribute of the node.
(3) D results are obtained according to the trained random forest, and classification is carried out through voting to obtain the classification result of the object to be coated.
According to the attributes of the objects to be coated, random forest classification is utilized, the objects to be coated are directly classified into a matched piece of knowledge, and sample data matching is reduced by 80%.
S3: calculating the similarity between the class of coating objects contained in the classification result and the objects to be coated by utilizing the similarity;
according to data in a ship coating process database, cosine similarity is used for calculating an object to be coated and a coating object in the knowledge. The cosine between the two painting object vectors can be found by using the euclidean dot product formula:
a·b＝||a||||b||cosθ
given two paint object vectors a and B, the remaining chord similarity is given by the dot product and the vector length, as shown below:
in the formula, AiAnd BiRepresenting the respective components of the coating object a and the object B, respectively.
Wherein the result of the similarity calculation is [ -1, 1 [ ]]In the method, a coated object with similarity larger than a threshold value is selected by setting a similarity threshold value eta, and a coating object set with adjacent K can be obtained according to a ship coating object knowledge graph
And then combining the ship coating process knowledge map and the coating process database to form a coated object process set
And a painting object set
Correspond to each other.
S4: and (3) grading the coating process of the coating object set adjacent to the K by combining a ship coating multi-target evaluation function:
according to the established ship coating multi-target evaluation function, the coated object process set is scored by taking the coating quality, the coating consumption and the coating working hour as targets, and the formula is as follows:
in the formula: f. ofevalIs a multi-target evaluation function; f. ofH、fV、fTRespectively a coating quality objective function, a paint consumption objective function and a coating working hour objective function; lambda [ alpha ]H、λV、λTInfluence factors respectively related to coating quality, coating material consumption and coating working hours.
With paint film thickness uniformity as an evaluation criterion, a coating quality objective function was established as follows:
in the formula: h0Is the nominal dry film thickness; hmax，i、Hmin，iMaximum and minimum dry film thickness within zone i, respectively; a is the number of measurement regions,
s is the coating area; i is a region number; k is a radical ofH，iAre weighting coefficients associated with the regions.
The target function of the coating dosage is designed as follows:
in the formula: vactFor the actual paint dosage, VratIs the rated paint dosage.
The coating working hour objective function is designed as follows:
in the formula: t isactFor actual coating man-hours, TratRated coating man-hour.
Coating process sets under four targets of good comprehensive evaluation, high coating quality, low coating consumption and short coating time are respectively obtained according to multi-target evaluation functions
Scoring of the coating process in (1).
And (4) obtaining similar Top-N coating processes after sorting according to the obtained coating process scores. And (4) obtaining a coating process with high score under different evaluation targets by setting the value of N, and generating a recommendation list.
When the value of N is 8, two coating processes before comprehensive evaluation, high coating quality, small coating consumption and short, medium and short-term grading during coating are respectively taken, and the obtained recommendation list is as follows:
according to the characteristics and the target of an object to be coated, a recommended list is combined, and a coating process example with the best comprehensive evaluation is selected, wherein the specific process parameters are as follows.
Claims (6)
1. A knowledge-based intelligent recommendation method for a ship coating process is characterized by comprising the following steps:
s1, extracting characteristics and characteristic values from the ship coating process knowledge base; generating a ship coating object knowledge map and a ship coating process knowledge map by using a knowledge map method, realizing ship coating knowledge visualization and finishing dimension reduction processing on ship coating data;
s2, classifying the ship painting object into a certain knowledge by using a random forest classifier according to the generated ship painting object knowledge map;
s3, after classification is finished, calculating the similarity between the class of painting objects contained in the classification result and the class of painting objects to be painted by utilizing the similarity, and obtaining a painting object set with a K neighbor;
s4, scoring the coating process corresponding to the coating object set of the neighboring K by combining a ship coating multi-target evaluation function;
and S5, obtaining a Top-N coating process which is more fit with the ship object to be coated after sequencing, and obtaining a recommendation list.
2. The intelligent knowledge-based recommendation method for marine painting process according to claim 1, wherein in step S1, the knowledge map of marine painting object comprises knowledge of characteristics of material, plate type, painting area, surface roughness and painting area.
3. The intelligent knowledge-based recommendation method for marine painting process according to claim 1, wherein in step S1, the knowledge map of marine painting process includes knowledge of coating matching and knowledge of process parameters, wherein the knowledge of coating matching includes paint name, color, dry film thickness, wet film thickness, solid content, thinner and painting interval; the process parameter knowledge comprises derusting equipment knowledge, coating equipment knowledge and intelligent equipment knowledge, the derusting equipment knowledge comprises manual derusting knowledge and machine derusting knowledge, the manual derusting knowledge comprises working hours and teams, and the machine derusting knowledge comprises shot blasting derusting equipment knowledge and high-pressure water deruster equipment knowledge; the knowledge of coating equipment comprises knowledge of manual coating and knowledge of machine coating, the knowledge of manual coating comprises knowledge of brush coating/roller coating, the knowledge of machine coating comprises knowledge of airless spraying and air spraying, the knowledge of airless spraying comprises the knowledge of equipment of a hydraulic airless sprayer, and the knowledge of air spraying comprises the knowledge of equipment of a bi-component sprayer; the intelligent equipment knowledge includes equipment knowledge of the rust removal robot and equipment knowledge of the painting robot.
4. The intelligent knowledge-based recommendation method for ship coating process according to claim 1, wherein in step S2, the knowledge of the coating object includes material, plate shape, steel area, rust removal grade and surface roughness, and the object to be coated is classified into a knowledge by classifying the random forest according to the above features.
5. The intelligent knowledge-based recommendation method for ship coating process as claimed in claim 1, wherein in step S3, the cosine similarity between the object to be coated and the coating object in the knowledge is calculated according to the data in the ship coating process database, and the cosine value between two coating object vectors can be obtained by using euclidean dot product formula:
a·b＝||a||||b||cosθ
given two paint object vectors a and B, the remaining chord similarity is given by the dot product and the vector length, as shown below:
in the formula, AiAnd BiRepresenting the respective components of the coating object A and the object B respectively; wherein the result of the similarity calculation is [ -1, 1 [ ]]In the method, a coated object with similarity larger than a threshold value is selected by setting a similarity threshold value eta, and a coating object set with adjacent K can be obtained according to a ship coating object knowledge graph
And then combining the ship coating process knowledge map and the coating process database to form a coated object process set
And a painting object set
Correspond to each other.
6. The intelligent knowledge-based recommendation method for ship coating process as claimed in claim 1, wherein in step S4, the multi-objective evaluation function for ship coating is established, and the coated object process set is scored with the coating quality, paint usage and coating time as the target, wherein the formula is as follows:
in the formula: f. ofevalIs a multi-target evaluation function; f. ofH、fV、fTRespectively a coating quality objective function, a paint consumption objective function and a coating working hour objective function; lambda [ alpha ]H、λV、λTInfluence factors respectively related to the coating quality, the coating consumption and the coating working hour,
with paint film thickness uniformity as an evaluation criterion, a coating quality objective function was established as follows:
in the formula: h0Is the nominal dry film thickness; hmax，i、Hmin，iMaximum and minimum dry film thickness within zone i, respectively; a is the number of measurement regions,
s is the coating area; i is a region number; k is a radical ofH，iAre weighting coefficients associated with the regions.
The target function of the coating dosage is designed as follows:
in the formula: vactFor the actual paint dosage, VratIs the rated paint dosage.
The coating working hour objective function is designed as follows:
in the formula: t isactFor actual coating man-hours, TratRated coating working hours;
finally, coating process sets under four targets of good comprehensive evaluation, high coating quality, low coating consumption and short coating time are respectively obtained according to the multi-target evaluation function
Scoring of the coating process in (1).
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| CN114969553B (en) * | 2022-07-19 | 2022-10-28 | 苏芯物联技术(南京)有限公司 | Welding cost and process parameter comprehensive intelligent recommendation method based on knowledge graph |
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