Ship type automatic identification and intelligent optimization system

Abstract

The invention discloses a ship type automatic identification and intelligent optimization system which effectively digitalizes experience of engineers in ship type optimization so as to reduce excessive dependence on personal design experience of the engineers and does not need high-performance cluster clamping. According to the technical scheme, the system is characterized by comprising the following modules: a data layer module (1), a functional layer module (2) and an interaction layer module (3), wherein the data layer module (1) is used for storing related sample data; the functional layer module (2) is used for performing ship type automatic identification and parametric modeling on the related data of the data layer module (1), updating an intelligent model and forecasting resistance performance; and the interaction layer module (3) is a user computer end and is used for interacting information with the functional layer module (2).

Description

Technical Field

[0001] The present invention belongs to the technical field of ship linear optimization systems, and particularly relates to a ship type automatic recognition and intelligent optimization system. Background Art

[0002] Existing ship line optimization methods are roughly divided into two categories: One method is to model the ship type manually and evaluate the prototype scheme at the same time. Then, based on the optimization design experience of engineers over the years, the ship type geometry is modified. Immediately afterwards, the modified ship type geometry is evaluated, and the modification and evaluation are carried out repeatedly until better performance is achieved. This type of method relies heavily on the personal work experience of engineers, and engineers who are not technically proficient are not competent.

[0003] Another method is to parametrically model the whole or part of the ship geometry through 3D modeling software. By changing some design parameters, the ship type geometry is changed, and then the modified geometry is evaluated. This type of method usually requires the support of a high-performance cluster to meet the evaluation calculation requirements of multiple samples. In addition, when parametrically modeling and designing optimization parameters, this type of method still requires the personal work experience of engineers as a guide. Summary of the Invention

[0004] The present invention solves the problem that the current traditional ship line optimization speed optimization heavily relies on the personal design experience of engineers, or the 3D software modeling optimization requires the support of a high-performance cluster and still requires the personal experience of engineers, thus affecting the ship type optimization effect. The present invention provides a ship type automatic recognition and intelligent optimization system that effectively digitalizes the experience of engineers in ship type optimization to reduce the over-reliance on the personal design experience of engineers and does not require the support of a high-performance cluster.

[0005] The technical solution of the present invention is as follows:

[0006] A ship type automatic recognition and intelligent optimization system, characterized in that it is composed of the following modules: a data layer module (1), a function layer module (2), and an interaction layer module (3).

[0007] The data layer module (1) collects and organizes data such as ship parameters, ship type information, geometric models, ship type parametric modeling, ship resistance calculation strategies, intelligent models, and ship resistance performance, and establishes corresponding databases, namely, establishing a ship type geometric parameterization database (11), a ship type geometric database (12), an intelligent model database (13), a ship type information database (14), a ship resistance calculation strategy database (15), and a ship resistance performance database (16). Data such as ship type information, geometric models, and ship resistance performance are respectively input into the ship type information database (14), the ship type geometric database (12), and the ship resistance performance database (16); data such as ship parameters and ship type parametric modeling are input into the ship type geometric parameterization database (11); intelligent models are input into the intelligent model database (13); ship resistance calculation strategies are input into the ship resistance calculation strategy database (15).

[0008] The function layer module (2) consists of a ship type information management module (21), a ship type geometric management module (22), a ship resistance calculation strategy management module (23), a ship resistance performance management module (24), a geometric parameterization modeling module (25), a geometric reconstruction module (26), a geometric fairing processing module (27), a geometric automatic correction module (28), a geometric script management module (29), an intelligent point filling module (210), an automatic training module (211), an intelligent model management module (212), and a ship resistance performance prediction module (213). The function layer module (2) uses the data in each database of the data layer module (1) to perform parametric modeling, ship type automatic recognition, update intelligent models, and resistance performance prediction, develops ship type performance evaluation and optimization functions, and forms a ship type automatic recognition and intelligent optimization platform;

[0009] Among them, the ship type information management module (21), the ship type geometric management module (22), the ship resistance calculation strategy management module (23), and the ship resistance performance management module (24) are respectively used to manage the data in the ship type information database (14), the ship type geometric database (12), the ship resistance calculation strategy database (15), and the ship resistance function database (16) in terms of addition, deletion, modification, and inspection;

[0010] The geometric parameterization modeling module (25) performs parametric modeling on the ship type geometry in the ship type data database (14) and the ship type geometric database (12) in the data layer module (1), calls the ship type data (14) and the ship type geometric parameterization database (11) for comparison to achieve automatic ship type recognition, and then the geometric reconstruction module (26) performs ship type geometric reconstruction, the geometric fairing processing module (27) performs ship type geometric fairing, and the geometric automatic correction module (28) performs ship type geometric automatic correction to highly restore and reproduce the ship type geometry in the database. The geometric automatic correction module (28) synchronously updates the corresponding parametric modeling data of the ship type to the ship type geometric database (12) and the ship type geometric parameterization database (11), and inputs the corresponding ship type data after restoration and reproduction into the ship resistance performance management module (24). The ship resistance performance management module (24) evaluates the resistance performance of the corresponding ship type after restoration and reproduction based on numerical simulation technology to obtain a ship type model after resistance performance evaluation, and enters or updates the numerical calculation strategy that meets the engineering application accuracy into the resistance calculation strategy database (15);

[0011] The automatic training module (211) is used to perform machine learning based on four databases: the ship type data database (14), the ship type geometric parameterization database (11), the ship resistance calculation strategy database (15), and the ship resistance performance database (16). It calls the relevant data of these four databases to customize and train the ship type intelligent model for the ship type model after resistance performance evaluation obtained by the ship resistance performance management module (24), and enters or updates the trained ship type intelligent model into the intelligent model database (13). That is, the ship type intelligent model is a surrogate model obtained through machine learning training and is used for performance prediction and optimization of ship resistance. The automatic training module (211) also uses the machine learning to summarize and refine to form a calculation strategy model, and outputs or exports a script for ship resistance numerical simulation through the geometric script management module (29) for presetting the ship resistance numerical calculation strategy;

[0012] The intelligent interpolation module (210) is used to further perform machine learning on the ship type intelligent model trained by the automatic training module (211) based on the ship type data database (14), the ship type geometric parameterization database (11), the ship resistance calculation strategy database (15), and the ship resistance performance database (16), perform interpolation or filling operations on sparse samples to increase the sample density, thereby generating new ship type data, ship type geometric parameterization data, ship resistance calculation strategy data, and ship resistance performance data, and enter or update these data into the corresponding databases;

[0013] The interaction layer module (3) is the user's computer terminal and is used to interact with the function layer module (2).

[0014] The ship form geometric parameterization database (11) consists of a midship geometric parameterization database (111), a bow geometric parameterization database (112), and a stern geometric parameterization database (113). The bow geometric parameterization database (112) includes an upper bow geometric parameterization database (1121) and a lower bow geometric parameterization database (1122); the stern geometric parameterization database (113) includes an upper stern geometric parameterization database (1131) and a lower stern geometric parameterization database (1132).

[0015] The resistance calculation strategy database (15) is used to input and update calculation strategy data that meets the accuracy requirements of engineering applications; the calculation strategy data is the result of evaluating the resistance performance of the corresponding ship form based on numerical simulation technology.

[0016] The ship form parameters include: ship name, ship type, length between perpendiculars, breadth, draft, block coefficient, and scale ratio.

[0017] The ship resistance calculation strategy data includes: fluid medium parameters, mesh generation parameters, and solver parameters.

[0018] The effects of the present invention are as follows:

[0019] The ship form automatic recognition and intelligent optimization system consists of a data layer module (1), a function layer module (2), and an interaction layer module (3). The function layer module (2) performs parametric modeling on the newly imported ship form geometry through the geometric parameterization modeling module (25), outputs the material information of the ship form of the ship, such as ship type, length between perpendiculars, breadth, draft, block coefficient, etc., through the geometric script management module, and calls the ship form material data (14) and the ship form geometric parameterization database (11) for comparison, and automatically recognizes the ship geometric model by means of parametric modeling technology.

[0020] The geometric parameter modeling module (25) uses the geometric reconstruction module (26) to perform ship form geometric reconstruction, the geometric fairing processing module (27) to perform ship form geometric fairing, and the geometric automatic correction module (28) to perform ship form geometric automatic correction, and performs bisection comparison analysis and fine adjustment on the reconstructed geometric model and the original geometry, which can ensure the consistency and fairing of ship form geometric reconstruction. The geometric automatic correction module (28) synchronously updates the corresponding parametric modeling data of the ship form to the ship form geometric database (12) and the ship form geometric parameterization database (11), and inputs the restored and reproduced corresponding ship form data into the ship resistance performance management module (24). The ship resistance performance management module (24) evaluates the resistance performance of the restored and reproduced corresponding ship form based on numerical simulation technology, obtains the ship form model after resistance performance evaluation, and inputs or updates the numerical calculation strategy that meets the accuracy requirements of engineering applications into the resistance calculation strategy database (15) to realize the automatic evaluation of the ship form resistance performance.

[0021] The function layer module (2) updates the intelligent model and the resistance performance prediction in a timely manner through the intelligent data filling module (210). The intelligent data filling module (210) is used to perform machine learning based on the ship type data database (14), the ship type geometric parameterization database (11), the ship resistance calculation strategy database (15) and the ship resistance performance database (16). At the same time, according to business requirements, interpolation or data filling operations are performed on sparse samples to increase the sample density, and machine learning is set to generate new ship type data, ship type geometric parameterization data, ship resistance calculation strategy data and ship resistance performance data, and these data are entered or updated into the corresponding databases, thereby generating a new intelligent model.

[0022] The automatic training module (211) of the present invention uses the ship type data (14), the ship type geometric parameterization database (11), the ship resistance numerical calculation strategy library (15) and the ship resistance performance database (16) to perform machine learning, calls the relevant data of these four databases to customize and train an effective intelligent model, and enters or updates the trained ship type intelligent model into the intelligent model database (13) for ship resistance performance prediction and optimization. At the same time, the automatic training module (211) is also used to perform machine learning using the ship type data database (14), the ship type geometric parameterization database (11) and the ship resistance performance database (16), summarize and refine to form a calculation strategy model for presetting the ship resistance numerical calculation strategy.

[0023] The function layer module (2) uses the data in each database of the data layer module (1) for automatic ship type recognition, parametric modeling, updating the intelligent model and resistance performance prediction, develops the ship type performance and optimization functions, forms an automatic ship type recognition and intelligent optimization platform through digital means, and organizes and records the experience of engineers in ship type optimization in the prior art through modules and databases such as ship type parameters, ship type parametric geometric models, ship resistance calculation strategies and ship resistance performance, getting rid of the dependence on the personal work experience of engineers.

[0024] The data layer module (1) and the function layer module (2) establish a mapping correspondence relationship between ship geometry deformation and ship geometric parameterization, ship resistance calculation strategy and ship resistance performance through relevant modules and databases, thereby effectively digitizing the experience of engineers in ship type optimization.

[0025] The ship type geometry in the data layer module (1) is divided into five major regions: the midship region, the upper bow region, the lower bow region, the upper stern region, and the lower stern region. Parametric modeling settings are performed for each region, and local geometry and overall ship optimization design can be achieved.

[0026] The following further describes the present invention with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is the structural block diagram of the present invention;

[0028] Figure 2 is the application flow chart of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0029] Figure 1 In the ship type automatic identification and intelligent optimization system, it consists of the following modules: data layer module 1, function layer module 2, and interaction layer module 3.

[0030] The data layer module 1 collects and collates data such as ship parameters, ship type information, geometric models, ship type parametric modeling, ship resistance calculation strategies, intelligent models, and ship resistance performance, and establishes corresponding databases, namely, establishing a ship type geometric parameterization database 11, a ship type geometric database 12, an intelligent model database 13, a ship type information database 14, a ship resistance calculation strategy database 15, and a ship resistance performance database 16. Data such as ship type information, geometric models, and ship resistance performance are respectively input into the ship type information database (14), the ship type geometric database (12), and the ship resistance performance database (16); data such as ship parameters and ship type parametric modeling are input into the ship type geometric parameterization database (11); the intelligent model is input into the intelligent model database (13); and the ship resistance calculation strategy is input into the ship resistance calculation strategy database (15).

[0031] The ship type geometric parameterization database 11 consists of a midship geometric parameterization database 111, a bow geometric parameterization database 112, and a stern geometric parameterization database 113. The bow geometric parameterization database 112 includes a bow upper geometric parameterization database 1121 and a bow lower geometric parameterization database 1122; the stern geometric parameterization database 113 includes a stern upper geometric parameterization database 1131 and a stern lower geometric parameterization database 1132; the data layer module 1 is used to store relevant data; the sample data includes: ship type geometric parameterization data, ship type geometric data, intelligent model data, ship type information, ship resistance calculation strategy data, intelligent models, and ship resistance performance data.

[0032] The function layer module 2 consists of a ship type data management module 21, a ship type geometry management module 22, a ship resistance calculation strategy management module 23, a ship resistance performance management module 24, a geometric parameterization modeling module 25, a geometric reconstruction module 26, a geometric fairing processing module 27, a geometric automatic correction module 28, a geometric script management module 29, an intelligent point supplement module 210, an automatic training module 211, an intelligent model management module 212, and a ship resistance performance prediction module 213. The function layer module 2 uses each database in the data layer module 1 to perform parametric modeling, automatic ship type identification, update the intelligent model, and predict the resistance performance, develop the ship type performance evaluation and optimization functions, and form an automatic ship type identification and intelligent optimization platform.

[0033] Among them, the ship type data management module 21, the ship type geometry management module 22, the ship resistance calculation strategy management module 23, and the ship resistance performance management module 24 are respectively used to manage the data in the ship type data database 14, the ship type geometry database 12, the ship resistance calculation strategy database 15, and the ship resistance performance database 16 in terms of addition, deletion, modification, and inspection. The geometric script management module 29 manages the geometric script data, and the intelligent model management module 212 manages the intelligent model data.

[0034] The geometric parameterization modeling module 25 performs parametric modeling on the ship type geometry in the ship type data database 14 and the ship type geometry database 12 stored in the data layer module 1, and calls the ship type data 14 and the ship type geometric parameterization database 11 for comparison to achieve automatic ship type identification. At the same time, in combination with the geometric reconstruction module 26 for ship type geometric reconstruction, the geometric fairing processing module 27 for ship type geometric fairing, and the geometric automatic correction module 28 for ship type geometric automatic correction, to highly accurately restore and reproduce the ship type geometry stored in the database. And the geometric automatic correction module 28 synchronously updates the corresponding parametric modeling data of the ship type to the ship type geometry database 12 and the ship type geometric parameterization database 11, and inputs the restored and reproduced corresponding ship type data into the ship resistance performance management module 24. The ship resistance performance management module 24 evaluates the resistance performance of the corresponding ship type based on numerical simulation technology to obtain a ship type model after resistance performance evaluation, and enters or updates the numerical calculation strategy that meets the engineering application accuracy into the resistance calculation strategy database 15.

[0035] The automatic training module 211 is used to perform machine learning based on the ship type data database 14, the ship type geometric parameterization database 11, the ship resistance calculation strategy database 15, and the ship resistance performance database 16. It calls these four databases to customize and train the ship type intelligent model for the relevant data of the ship type model obtained by the ship resistance performance management module 24 after resistance performance evaluation, and enters or updates the trained ship type intelligent model into the intelligent model database 13. That is, the ship type intelligent model is a surrogate model obtained through machine learning training and is used for performance prediction and optimization of ship resistance.

[0036] The automatic training module 211 is also used to perform machine learning by combining the ship type data database 14, the ship type geometric parameterization database 11, and the ship resistance performance database 16, summarize and refine to form a ship resistance calculation strategy model, and output or export scripts for ship resistance numerical simulation through the geometric script management module 29 for presetting ship resistance numerical calculation strategies.

[0037] The intelligent interpolation module 210 is used to further perform machine learning on the ship type intelligent model trained by the automatic training module 211 based on the ship type data database 14, the ship type geometric parameterization database 11, the ship resistance calculation strategy database 15, and the ship resistance performance database 16, perform interpolation or point supplementation operations on sparse samples to increase the sample density, thereby generating new ship type data, ship type geometric parameterization data, ship resistance calculation strategy data, and ship resistance performance data, and entering or updating these data into the corresponding databases.

[0038] The interaction layer module 3 is the user's computer terminal and is used to interact with the function layer module 2.

[0039] The resistance calculation strategy database 15 is used to enter and update calculation strategy data that meets the engineering application accuracy; the calculation strategy data is the result of evaluating the resistance performance of the corresponding ship type based on numerical simulation technology.

[0040] The intelligent model database 13 optimizes the ship type geometric model according to the newly input ship type data and geometric model by the user, and at the same time uses the ship resistance performance prediction module (213) to predict the ship resistance performance of the optimized geometric model.

[0041] The ship type parameters in the ship resistance calculation strategy include: ship name, ship type, ship length, ship width, draft, block coefficient, and scale ratio.

[0042] The ship resistance calculation strategy data includes: fluid medium parameters, mesh division parameters, and solver parameters.

[0043] Figure 2 In this invention, the specific implementation process is as follows:

[0044] Users access the ship type automatic identification and intelligent optimization platform through the client, and the specific operations that can be achieved are as follows:

[0045] In the first step, based on the ship type data management module 21, the management of ship type data can be realized. The ship type geometry management module 22 is used to manage the ship type geometry model data. The ship resistance calculation strategy module management module 23 is used to manage the ship resistance calculation strategy data. The ship resistance performance management module 24 manages the ship resistance performance data. The geometry script management module 29 manages the geometry script data. The data management content includes operations such as adding, deleting, modifying, and querying such data.

[0046] In the second step, combined with the geometric parametric modeling module 25, parametric modeling is carried out on the ship type geometry (midship area, upper bow area, lower bow area, upper stern area, lower stern area) stored in the database, and the ship type data 14 and the ship type geometric parametric database 11 are called for comparison to realize automatic ship type identification. At the same time, with the help of the geometric reconstruction module 26, the geometric fairing processing module 27, and the geometric automatic correction module 28, the high-precision restoration and reproduction of the ship type geometry stored in the database are realized, and the corresponding parametric modeling data of the ship type are synchronously updated to the ship type geometric parametric database 11.

[0047] In the third step, the ship resistance performance management module 24 evaluates the resistance performance of the corresponding ship type based on numerical simulation technology, and enters / updates the numerical calculation strategy that meets the engineering application accuracy into the resistance calculation strategy database 15.

[0048] In the fourth step, the automatic training module 211 and the intelligent model management module 212 are called to perform machine learning based on the ship type data, ship type geometric parametric data, resistance calculation strategy data, and ship resistance performance data. Combined with the business objectives and restrictive requirements, an intelligent model is customized and trained to meet different business needs, and the trained intelligent model is entered / updated into the intelligent model database 13.

[0049] In the fifth step, the intelligent interpolation module 210 is called to perform machine learning based on the ship type data, ship type geometric parametric data, resistance calculation strategy data, and ship resistance performance data, and perform interpolation / point filling operations on the sparse samples to increase the sample density, thereby generating new ship type data, ship type geometric parametric data, resistance calculation strategy, and ship resistance performance data, and entering / updating these data into the corresponding databases.

[0050] Step 6: Based on the automatic training module 211, perform machine learning by combining the ship type data database, the ship type geometric parameterization database, and the ship resistance performance data, summarize and refine to form a calculation strategy model, and combine with the script management module to output / export the script for using resistance numerical simulation.

[0051] Step 7: With the help of the intelligent model generated in Step 4, optimize the ship type geometric model based on the newly input ship type data and geometric model by the user, and at the same time use the ship resistance performance prediction module 213 to predict the ship resistance performance of the optimized geometric model.

Claims

1. An automatic ship type identification and intelligent optimization system, characterized in that It consists of the following modules: data layer module (1), function layer module (2) and interaction layer module (3). The data layer module (1) collects and organizes data such as ship parameters, ship type information, geometric models, ship type parametric modeling, ship resistance calculation strategies, intelligent models, and ship resistance performance, and establishes corresponding databases, namely, establishing a ship type geometric parameterization database (11), a ship type geometric database (12), an intelligent model database (13), a ship type information database (14), a ship resistance calculation strategy database (15), and a ship resistance performance database (16). Data such as ship type information, geometric models, and ship resistance performance are respectively input into the ship type information database (14), the ship type geometric database (12), and the ship resistance performance database (16); data such as ship parameters and ship type parametric modeling are input into the ship type geometric parameterization database (11); the intelligent model is input into the intelligent model database (13); the ship resistance calculation strategy is input into the ship resistance calculation strategy database (15). The function layer module (2) consists of a ship type information management module (21), a ship type geometric management module (22), a ship resistance calculation strategy management module (23), a ship resistance performance management module (24), a geometric parameterization modeling module (25), a geometric reconstruction module (26), a geometric fairing processing module (27), a geometric automatic correction module (28), a geometric script management module (29), an intelligent point filling module (210), an automatic training module (211), an intelligent model management module (212), and a ship resistance performance prediction module (213). The function layer module (2) uses the data in each database of the data layer module (1) to perform parametric modeling, ship type automatic recognition, update the intelligent model and resistance performance prediction, develop ship type performance evaluation and optimization functions, and form a ship type automatic recognition and intelligent optimization platform. Among them, the ship type information management module (21), the ship type geometric management module (22), the ship resistance calculation strategy management module (23), and the ship resistance performance management module (24) are respectively used to manage the data in the ship type information database (14), the ship type geometric database (12), the ship resistance calculation strategy database (15), and the ship resistance function database (16) in terms of addition, deletion, modification, and inspection. The geometric parameterization modeling module (25) performs parametric modeling on the ship form geometries stored in the ship form data database (14) and the ship form geometric database (12) in the data layer module (1), and calls the ship form data (14) and the ship form geometric parameterization database (11) for comparison to achieve automatic ship form recognition. Then, the geometric reconstruction module (26) performs ship form geometric reconstruction, the geometric fairing processing module (27) performs ship form geometric fairing, and the geometric automatic correction module (28) performs ship form geometric automatic correction to highly restore and reproduce the ship form geometries stored in the database. The geometric automatic correction module (28) synchronously updates the corresponding parametric modeling data of the ship form to the ship form geometric database (12) and the ship form geometric parameterization database (11), and inputs the restored and reproduced corresponding ship form data into the ship resistance performance management module (24). The ship resistance performance management module (24) evaluates the resistance performance of the restored and reproduced corresponding ship form based on numerical simulation technology to obtain a ship form model after resistance performance evaluation, and enters or updates the numerical calculation strategy that meets the engineering application accuracy into the resistance calculation strategy database (15); The automatic training module (211) is used to perform machine learning based on four databases: the ship form data database (14), the ship form geometric parameterization database (11), the ship resistance calculation strategy database (15), and the ship resistance performance database (16). It calls the relevant data of these four databases to customize and train the ship form intelligent model for the ship form model obtained by the ship resistance performance management module (24) after resistance performance evaluation, and enters or updates the trained ship form intelligent model into the intelligent model database (13). That is, the ship form intelligent model is a surrogate model obtained through machine learning training and is used for performance prediction and optimization of ship resistance. The automatic training module (211) also uses the machine learning to summarize and form a calculation strategy model, and outputs or exports a script for ship resistance numerical simulation through the geometric script management module (29) for presetting the ship resistance numerical calculation strategy; The intelligent interpolation module (210) is used to further perform machine learning on the ship form intelligent model trained by the automatic training module (211) based on the ship form data database (14), the ship form geometric parameterization database (11), the ship resistance calculation strategy database (15), and the ship resistance performance database (16), and perform interpolation or point supplementation operations on sparse samples to increase the sample density, thereby generating new ship form data, ship form geometric parameterization data, ship resistance calculation strategy data, and ship resistance performance data, and entering or updating these data into the corresponding databases; The interaction layer module (3) is the user's computer terminal and is used to interact with the function layer module (2).

2. The ship type automatic identification and intelligent optimization system according to claim 1, characterized in that The ship form geometric parameterization database (11) consists of a midship geometric parameterization database (111), a bow geometric parameterization database (112), and a stern geometric parameterization database (113). The bow geometric parameterization database (112) includes an upper bow geometric parameterization database (1121) and a lower bow geometric parameterization database (1122); the stern geometric parameterization database (113) includes an upper stern geometric parameterization database (1131) and a lower stern geometric parameterization database (1132).

3. The ship type automatic identification and intelligent optimization system according to claim 1 or 2, characterized in that The resistance calculation strategy database (15) is used to input and update calculation strategy data that meets the accuracy requirements of engineering applications; the calculation strategy data is the result of evaluating the resistance performance of the corresponding ship form based on numerical simulation technology.

4. The ship type automatic identification and intelligent optimization system according to claim 1 or 2, characterized in that The ship form parameters include: ship name, ship type, length between perpendiculars, beam, draft, block coefficient, and scale ratio.

5. The ship type automatic identification and intelligent optimization system according to claim 1 or 2, characterized in that The ship resistance calculation strategy data includes: fluid medium parameters, mesh generation parameters, and solver parameters.

Images