Ship cabin layout method and system based on three-dimensional knowledge gene layer dynamic fusion

Abstract

The invention belongs to the field of ship engineering design, and discloses a ship cabin layout method and system based on three-dimensional knowledge gene layer dynamic fusion. The system comprises a cabin layout reasoning module based on priori knowledge, an intelligent cabin layout optimization algorithm module based on computational knowledge, a human intelligence knowledge cabin layout scheme online design module and a three-dimensional knowledge fusion solving cabin layout design module. Three-dimensional knowledge individuals of the priori knowledge scheme, the algorithm knowledge scheme and the human intelligence knowledge scheme which are obtained respectively are dynamically fused on the evolution algorithm gene level to form an evolution group, and a cabin layout scheme is obtained through operation in a replacement mode, a crossing mode and a mode of combining crossing and a multi-objective function tradeoff method; according to the method, an optimization target and constraint conditions which are difficult to be expressed by mathematical languages in the cabin layout process can be considered in the algorithm solving process, and a more practical ship cabin layout scheme is obtained.

Description

Technical Field

[0001] The present invention belongs to the field of ship engineering design, and in particular relates to a ship cabin layout method and system based on dynamic fusion of three-way knowledge gene layers. Background Art

[0002] During the shipbuilding cycle, ship design takes up a lot of time. Ship design is divided into preliminary design, detailed design, and production design. Ship general layout design is a key step in ship detailed design, and cabin layout design is an important part of general layout design. In the intelligent layout design of ship cabins (including cabins and internal equipment, pipelines, etc.), there are many types of objects to be laid, large quantities, complex coupling relationships, complex constraints, and multiple optimization goals. It has both computational complexity, engineering complexity, and engineering practical complexity. Traditional cabin layout design is carried out with the help of interactive methods such as mouse and keyboard provided by CAD system. It is a repetitive and spiral process. In the design, a local problem often causes the overall plan to be readjusted, which not only increases the workload, but also prolongs the design cycle and may even cause economic losses. With the development of computer technology, it is imperative to improve the intelligence and automation of cabin layout.

[0003] In recent years, existing technologies have conducted a lot of research using theories and methods such as intelligent algorithms, knowledge reasoning, and expert systems, but most of them use "machine intelligence" as the main technical route. The optimization process lacks human participation, and does not fully consider the parallel and effective use of prior knowledge and the designer's professional knowledge and experience. The optimization process lacks effective guidance, and the resulting design scheme lacks innovation, which is still a long way from actual engineering application. Therefore, it is necessary to establish a new layout design method based on intelligent algorithms that can effectively utilize prior knowledge in parallel and give full play to the designer's intelligence, so that humans and machines can cooperate better. This can fundamentally solve the problem of intelligent layout optimization design of ship cabins and improve the intelligence and practicality of ship cabin layout design schemes. Summary of the invention

[0004] In order to overcome the problems existing in the related art, the disclosed embodiments of the present invention provide a ship cabin layout method and system based on dynamic fusion of three-way knowledge gene layer, specifically relating to an intelligent layout design method for ship cabins based on dynamic fusion of three-way knowledge gene layer.

[0005] The technical solution is as follows: a ship cabin layout system based on dynamic fusion of three-way knowledge gene layers, the system comprising: The cabin layout reasoning module based on prior knowledge is used to organize the prior knowledge of cabin layout design, and uses the knowledge engineering reasoning expression method to perform knowledge representation and knowledge reasoning to obtain the prior knowledge solution; The intelligent cabin layout optimization algorithm module based on computational knowledge uses an improved ant colony-genetic algorithm to perform cabin layout and obtain an algorithm knowledge solution for obtaining the computational knowledge solution and realizing three-way layout knowledge fusion; The human intelligence knowledge cabin layout plan online design module is used to design the cabin layout plan online with a three-dimensional visualization solution to obtain the human intelligence knowledge plan; The three-way knowledge fusion solution for cabin layout design module dynamically fuses the three-way knowledge individuals of the prior knowledge scheme, algorithm knowledge scheme, and human intelligence knowledge scheme obtained respectively at the gene level of the improved ant colony-genetic algorithm to form an evolutionary group, and obtains the cabin layout scheme based on the three-way knowledge fusion solution through replacement, crossover, and a combination of crossover and multi-objective function trade-off method; the three-way knowledge individuals include prior knowledge individuals, artificial individuals, and algorithm individuals.

[0006] Furthermore, in the cabin layout reasoning module based on prior knowledge, the prior knowledge includes excellent design examples, professional knowledge, and rule data information; the solution to the ship cabin layout problem to be solved is obtained by acquiring and reusing prior knowledge based on knowledge engineering, and the solution code string of the problem is in the form of prior knowledge individuals.

[0007] In the intelligent cabin layout optimization algorithm module based on computational knowledge, the computational knowledge includes knowledge obtained by improved ant colony-genetic algorithm calculation; for the ship cabin layout problem to be solved, the solution of the problem is automatically generated, and the solution code string form of the problem is the algorithm individual.

[0008] In the online design module of cabin layout scheme based on human intelligence knowledge, human intelligence knowledge includes professional knowledge and experiential knowledge provided by designers. By using human intelligence knowledge, combining engineering practice and calculation conditions, and using the interactive information provided by the online design interface of human intelligence knowledge scheme, a solution to a cabin layout problem is obtained. The solution string form of the problem is called an artificial individual.

[0009] In the online design module of the human intelligence knowledge cabin layout plan, the three-dimensional visualization of the online design cabin layout plan is performed through the human intelligence knowledge plan online design interface, which specifically includes the online designer obtaining the cabin layout plan code string to be modified from the layout plan library and generating the corresponding three-dimensional layout plan, and after modifying the code string, updating the three-dimensional plan in real time to determine further modification or save the plan.

[0010] The online design interface of the human intelligence knowledge solution includes: a cabin sequence modification module, a functional module, and a layout solution update module. The online design interface of the human intelligence knowledge solution includes a cabin sequence modification module, a functional module, and a layout solution update module. The cabin sequence modification module refers to the three-dimensional layout solution corresponding to the cabin layout solution code string, and optimizes the cabin layout code string with the knowledge and experience of the online designer to achieve the optimization adjustment of the cabin sequence code string; the functional module can save, display, and switch the cabin sequence code string, and is responsible for obtaining the cabin layout solution sequence code string in Excel to the online design interface, and save it back to the Excel solution library after modification to form a solution set of artificial individuals that are different from prior knowledge and algorithm individuals; the layout solution update module generates a three-dimensional cabin layout solution according to the cabin layout solution code string, which is convenient for designers to intuitively understand the layout situation and can quickly update and generate a new solution after modification.

[0011] In the three-way knowledge fusion solution for cabin layout design module, prior knowledge individuals, artificial individuals and algorithm individuals are expressed in a unified coding string, and their fitness values ​​and diversity values ​​are used to determine whether to join the improved ant colony-genetic algorithm group; in the evolutionary process, the fusion of prior knowledge individuals, artificial individuals and algorithm individuals adopts three operation modes to complete continuous dynamic information fusion; the three operation modes include replacement mode, crossover mode, and a combination of crossover and multi-objective function trade-off method.

[0012] The replacement method includes: adding artificial individuals to the algorithm population and randomly replacing a certain number of algorithm solutions, where the algorithm solution is randomly selected from the algorithm population, or the algorithm solution is a poor solution of the algorithm population, and then performing an evolution operation on the algorithm population; The crossover method includes: randomly selecting an algorithm solution from the algorithm population, performing a crossover operation with an artificial individual respectively, and randomly selecting one of the two sub-individuals generated after each crossover operation, and putting it back into the algorithm population, so that the gene fragments of the artificial individual and the gene fragments of the algorithm solution can fully exchange information and merge; The combination of the crossover and multi-objective function trade-off method includes: first, adjusting the relative importance of each objective function by changing the weight coefficient of each objective function according to the functional requirements of the cabin layout and the algorithm operation status; secondly, forcing each individual in the algorithm to perform a crossover operation with the prior knowledge individual or the artificial individual in the current evolutionary generation, so that the individuals in the algorithm group inherit part of the genes of the prior knowledge individual or the artificial individual, thereby guiding the evolution of the entire algorithm group.

[0013] Another object of the present invention is to provide a ship cabin layout method based on three-way knowledge gene layer dynamic fusion, comprising: S1, organize the prior knowledge of cabin layout design, use the knowledge engineering reasoning expression method to perform knowledge representation and knowledge reasoning to obtain the prior knowledge scheme; use the improved ant colony-genetic algorithm to obtain the algorithm knowledge scheme and algorithm individuals; and perform three-dimensional visualization of the online design of cabin layout scheme to obtain the human intelligence knowledge scheme and artificial individuals; S2, the three-way knowledge individuals of the prior knowledge scheme, algorithm knowledge scheme and human wisdom knowledge scheme obtained respectively are dynamically integrated at the gene level of the improved ant colony-genetic algorithm to form an evolutionary group, and the cabin layout scheme based on the three-way knowledge fusion solution is obtained through replacement, crossover, and the combination of crossover and multi-objective function trade-off method; S3, applying the obtained cabin layout scheme to the layout design of multiple cabins to be arranged on the deck of the ship.

[0014] Further, in step S1, obtaining the prior knowledge solution includes: In step S1, obtaining a priori knowledge solution includes: The built-in VBA editor and Automation technology of CATIA are used to transfer parameter values ​​between layout boundary conditions and reasoning rules. Among them, the boundary conditions of the layout are fixed parameters stored in the CATIA model, and the reasoning rules are also stored in the model as the rules of the knowledge engineering module. A multi-objective reasoning system is established by integrating the cabin layout requirements in the specification convention and the cabin lighting, vibration noise and adjacency layout requirements. Specifically, it includes first completing the reasoning of the cabin layout area on the deck based on the layout rules, lighting, vibration noise in the specification, and then completing the reasoning of the specific layout position of the cabin in the area based on the adjacency and circulation between the cabins to obtain the prior knowledge scheme. Taking the cabin layout reasoning of lighting as an example, starting from the demand, cabins with high lighting requirements such as living cabins and leisure and entertainment rooms are given priority to be arranged in the outer area of ​​the deck. At the same time, by analyzing the architectural structure of the deck and the hull shape, it is determined which areas can obtain better natural lighting. For example, cabins on the side of the ship usually have better lighting than cabins near the center of the ship; The use of improved ant colony-genetic algorithm to obtain algorithm knowledge solutions includes: The ant colony algorithm is used to first initialize the cabin code and then the reverse area allocation to randomly generate the cabin layout plan. Then the roulette method is used to select the cabin layout plan with excellent fitness value to update the iteration of the pheromone guidance algorithm. The pheromone formula is as follows: ; In the formula, For the cabin With cabin Pheromone concentration between; variable The volatilization rate indicating the concentration of pheromone; Indicates Plans in the cabin and Increased pheromones between; For the A plan, The value range is ; In view of the fact that the pheromone mechanism is prone to fall into local optimality, the crossover and mutation operators of the genetic algorithm are introduced to increase the diversity of the solution population, guide the solution of the algorithm, and obtain the specific algorithm flow of the algorithm knowledge solution. Figure 6 As shown; The scheme for obtaining human intelligence knowledge includes: By using the GUI function provided by the built-in VB editor in CATIA, the EXCEl function library is added through the VB editor to call the EXCEL command, and the cabin layout plan stored in EXCEL in the form of a real number code string is obtained. Each real number in the code string represents a corresponding cabin, and the position of the cabin in the code string represents the layout order of the cabin. Then, the cabin layout is carried out in the corresponding deck area according to the recommended layout area of ​​the cabin and the strategy of evenly distributing the remaining area. Based on their own experience, the designer modifies the position of the corresponding real number in the code string in the online design interface of the human intelligence knowledge solution to obtain different cabin layout solutions. Combined with the deck boundary to be arranged and the area information of the cabin, the width of each cabin is determined according to the aisle position and then the length of the cabin is obtained; according to the situation of the cabin layout solution and modification suggestions, the position and size parameters of the cabin are updated in real time according to the designer's modification of the layout solution. The update realizes the real-time update of the three-dimensional layout solution by first determining the cabin code and then determining the cabin layout area and then allocating the area; finally, with the help of the GUI function provided by the VB editor, the online operation interface of the human intelligence knowledge solution is built.

[0015] In step S2, the cabin layout solution based on the three-way knowledge fusion solution includes: First, we establish methods for acquiring prior knowledge, human intelligence knowledge, and computational knowledge. The three methods for acquiring knowledge have been described before: In the cabin layout reasoning module based on prior knowledge, the layout knowledge includes: excellent design examples, professional knowledge, rules, and regulations. For the ship cabin layout problem to be solved, the solution to the problem is obtained by acquiring and reusing prior knowledge based on knowledge engineering, and the solution code string of the problem is in the form of prior knowledge individuals.

[0016] In the intelligent cabin layout optimization algorithm module based on computational knowledge, the computational knowledge includes knowledge obtained by improved ant colony-genetic algorithm calculation; for the ship cabin layout problem to be solved, the solution of the problem is automatically generated, and the solution code string form of the problem is the algorithm individual.

[0017] In the online design module of cabin layout scheme based on human intelligence knowledge, human intelligence knowledge includes the professional knowledge and experience of designers. For a specific problem of cabin layout, the designer provides a solution to the problem based on human intelligence knowledge, combined with engineering practice and calculation conditions, as well as the interactive information provided by the online design interface of human intelligence knowledge scheme. The solution to the problem in the form of a coded string is called an artificial individual.

[0018] Then, some excellent prior knowledge solutions and human intelligence knowledge solutions are selected to form a hybrid solution set, which is represented in the form of a coding string; when the improved ant colony-genetic algorithm runs to a certain stage, the hybrid solution set is added to the population of the improved ant colony-genetic algorithm, and in each iterative solution of the improved ant colony-genetic algorithm, the fusion and dynamic optimization of three-way knowledge are realized; finally, through replacement and crossover operations, multiple simulation experiments are conducted to adjust the number and quantity of the hybrid solution set added, so as to obtain the best time, quantity and individual quality of the hybrid knowledge population to obtain the optimal three-way knowledge fusion solution mechanism; Each sub-objective function in the above multi-objective function trade-off method is weighted to obtain a cabin layout solution with excellent comprehensive performance.

[0019] Combined with all the above technical solutions, the beneficial effects of the present invention are as follows: in the process of optimizing the layout of ship cabins based on intelligent algorithms, the present invention gives full play to the guiding role of prior knowledge and human intelligence knowledge on the algorithm, gives full play to the role of various layout knowledge, and forms an evolutionary group based on the dynamic fusion of prior knowledge, human intelligence knowledge, and computational knowledge (three-way knowledge) at the genetic level of the evolutionary algorithm. After operations such as selection, crossover, and mutation, a cabin layout plan with high intelligence and strong engineering practicality is automatically obtained. In view of the layout optimization design problem of ship cabins, the present invention mainly includes a method for obtaining cabin layout prior knowledge, computational knowledge, and human intelligence knowledge solutions, a theory of dynamic fusion of the three-way knowledge genetic layer, and an effective fusion mechanism. In the process of algorithm solving, the present invention can take into account the optimization goals and constraints that are difficult to express in mathematical language in the cabin layout process, and obtain a more practical ship cabin layout plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings herein are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description, serve to explain the principles of the present disclosure; Figure 1 It is a schematic diagram of a ship cabin layout system based on dynamic fusion of three-way knowledge gene layers provided by an embodiment of the present invention; Figure 2 It is a schematic diagram of a ship cabin layout system based on dynamic fusion of three-way knowledge gene layers provided by an embodiment of the present invention; Figure 3It is a flow chart of a method for ship cabin layout based on dynamic fusion of three-way knowledge gene layers provided by an embodiment of the present invention; Figure 4 It is a schematic diagram of a ship cabin layout method based on dynamic fusion of three-way knowledge gene layers provided by an embodiment of the present invention; Figure 5 It is a schematic diagram of a cabin layout design fusion mechanism of three-way knowledge fusion provided by an embodiment of the present invention; Figure 6 is a flow chart of an improved ant colony-genetic optimization algorithm provided by an embodiment of the present invention; Figure 7 It is a schematic diagram of an online design interface built based on the GUI function of the VB editor provided in an embodiment of the present invention; Figure 8 is a two-dimensional rendering of the cabin layout obtained according to an embodiment of the present invention; Fig. 9 It is a schematic diagram of the cabin layout and three-dimensional effect provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific implementation disclosed below.

[0022] The innovation of the present invention is that it integrates multiple sources of knowledge, including a priori knowledge, human intelligence knowledge and computational knowledge, to provide a rich material basis for cabin layout schemes. The present invention proposes a layout design mechanism and implementation method that integrates a priori, computational and human intelligence knowledge, and verifies its superiority in intelligent cabin layout design through simulation comparative calculations. The present invention systematically discusses the methods for obtaining a priori knowledge schemes, computational knowledge schemes and human intelligence knowledge schemes for multi-layer decks, and verifies the feasibility and efficiency of this method in multi-layer deck layout design. These innovations promote the intelligence and automation of ship cabin layout design and improve the overall design efficiency and quality of ships.

[0023] Example 1. In the ship cabin layout system based on dynamic fusion of three-way knowledge gene layer provided by the embodiment of the present invention, the ship cabin includes the cabin and its internal equipment, pipelines, etc.; an evolutionary group is formed by dynamic fusion at the gene level of the evolutionary algorithm based on prior knowledge, human intelligence knowledge, and computational knowledge (three-way knowledge), and a cabin layout plan with high intelligence and strong engineering practicality is automatically obtained through operations such as selection, crossover, and mutation.

[0024] like Figure 1 As shown, it includes: a cabin layout reasoning module based on prior knowledge, an intelligent cabin layout optimization algorithm module based on computational knowledge, an online design module for cabin layout scheme based on human intelligence knowledge, and a cabin layout design module based on three-way knowledge fusion solution.

[0025] The cabin layout reasoning module based on prior knowledge is used to organize the prior knowledge of cabin layout design, and use the knowledge engineering expression method to perform knowledge representation and knowledge reasoning to obtain a prior knowledge solution. The basic form of the knowledge engineering expression is an "IF-THEN" structure, and the knowledge about the cabin layout acquired in the knowledge acquisition stage is represented by the production expression method, thereby forming an inference rule code library.

[0026] The intelligent cabin layout optimization algorithm module based on computational knowledge uses an improved ant colony-genetic algorithm to perform cabin layout and obtain an algorithm knowledge solution for obtaining the computational knowledge solution and realizing three-way layout knowledge fusion.

[0027] The human intelligence knowledge cabin layout solution online design module is used to design a three-dimensional visualization solution of the cabin layout solution online to obtain the human intelligence knowledge solution; Exemplarily, a three-dimensional visualization solution for online design of cabin layout solutions through an online design interface of human intelligence knowledge solutions specifically includes an online designer obtaining a code string of the cabin layout solution to be modified from a layout solution library and generating a corresponding three-dimensional layout solution, and updating the three-dimensional solution in real time after modifying the code string to determine further modification or saving the solution; The online design interface of the human intelligence knowledge solution includes: a cabin sequence modification module, a function module, and a layout solution update module; The cabin sequence modification module refers to the three-dimensional layout plan corresponding to the cabin layout plan code string, and optimizes the cabin layout code string with the knowledge and experience of the online designer to achieve the optimization adjustment of the cabin sequence code string; the functional module can save, display, and switch the cabin sequence code string, and is responsible for obtaining the cabin layout plan sequence code string in Excel to the online design interface; after modification, it is saved back to the Excel solution library to form a solution set of artificial individuals that is different from prior knowledge and algorithm individuals; the layout plan update module generates a three-dimensional cabin layout plan according to the cabin layout plan code string, which is convenient for designers to intuitively understand the layout situation and can quickly update and generate a new plan after modification.

[0028] Among them, human intelligence knowledge includes the professional knowledge and experiential knowledge provided by designers; by using human intelligence knowledge, combining engineering practice and calculation conditions, and using the interactive information provided by the online design interface of the human intelligence knowledge solution, a solution to a cabin layout problem is obtained. The solution string form of the problem is called an artificial individual.

[0029] The three-way knowledge fusion solution cabin layout design module is used to dynamically fuse the acquired three-way knowledge individuals (solutions) at the improved ant colony-genetic algorithm gene level to form an evolutionary group. Through operations such as selection, crossover, and mutation, the strengths of each group are complemented, and the role of various layout knowledge is fully utilized to obtain a highly intelligent and practical cabin layout solution based on the three-way knowledge fusion solution.

[0030] Exemplarily, the present invention proposes a research idea of ​​integrating prior knowledge, computational knowledge and human intelligence knowledge into three-way cabin layout knowledge to carry out intelligent layout design of ship cabins, and a method for acquiring cabin layout prior knowledge, computational knowledge and human intelligence knowledge solutions.

[0031] The present invention proposes a three-way knowledge fusion solution mechanism and implementation method, and studies the influence of important parameters of three-way knowledge fusion on cabin layout design results through comparative experiments, determines the three-way knowledge fusion parameters, and determines the layout design principles based on three-way knowledge fusion.

[0032] Based on the research of knowledge engineering reasoning layout, this paper proposes a research idea of ​​combining knowledge engineering with parametric design, which can realize reasoning layout for various layout scenarios. At the same time, in the process of reasoning, the vibration noise, lighting and adjacent conditions of the cabin are taken into account, and a more comprehensive and reasonable reasoning rule is established for the cabin to be arranged, which improves the quality of the design scheme.

[0033] The present invention builds an online design interface for human intelligence knowledge solutions based on the secondary development method of CATIA, so that online designers can obtain the situation of corresponding solutions more intuitively, thereby improving the convenience and quality of human intelligence knowledge solution design.

[0034] Figure 2 It is a schematic diagram of a ship cabin layout system based on dynamic fusion of three-way knowledge gene layers provided by an embodiment of the present invention.

[0035] It can be seen from the above embodiments that the technical solution of the present invention can improve the design efficiency after transformation, quickly generate the cabin layout plan, complete the design project in a shorter time, thereby taking on more orders, increasing business volume and revenue, and at the same time optimizing the layout quality, taking into account the optimization objectives and constraints that are difficult to express in mathematical language, and the obtained ship cabin layout plan is more practical.

[0036] The method based on three-way dynamic fusion of knowledge gene layer proposed in the present invention is different from the traditional method in the field of ship cabin layout. It does not focus on a single knowledge source or design method, but dynamically integrates prior knowledge schemes, algorithmic knowledge schemes and human intelligence knowledge schemes at the evolutionary algorithm gene level, filling the technical gap in cabin layout design using multiple knowledge sources.

[0037] The technical solution of the present invention solves to a certain extent the two technical problems that have long troubled ship engineering designers, namely, the difficult-to-quantify constraint processing in ship cabin layout and the need for multi-knowledge source fusion, by means of three-way knowledge fusion.

[0038] The existing technology in the field of ship cabin layout has an excessive reliance on a single traditional knowledge source, such as relying solely on computational knowledge for layout optimization, which limits designers from exploring other methods. The present invention adopts a three-way knowledge fusion method to break this reliance, demonstrate the advantages of integrating multiple knowledge sources, and overcome this bias; at the same time, there was a bias in the past that only factors that can be accurately expressed mathematically can be integrated into algorithm design, but the present invention pays attention to and successfully handles layout goals and constraints that are difficult to quantify, overcomes the problem of one-sided emphasis on quantitative factors, and provides a more comprehensive and scientific approach for ship cabin layout design.

[0039] Embodiment 2, as Figure 3 As shown, the ship cabin layout method based on three-way knowledge gene layer dynamic fusion provided by the embodiment of the present invention includes: S1, organize the prior knowledge of cabin layout design, use the knowledge engineering reasoning expression method to perform knowledge representation and knowledge reasoning to obtain the prior knowledge scheme; use the improved ant colony-genetic algorithm to obtain the algorithm knowledge scheme and algorithm individuals; and perform three-dimensional visualization of the online design of cabin layout scheme to obtain the human intelligence knowledge scheme and artificial individuals; S2, the three-way knowledge individuals of the prior knowledge scheme, algorithm knowledge scheme and human wisdom knowledge scheme obtained respectively are dynamically integrated at the gene level of the improved ant colony-genetic algorithm to form an evolutionary group, and the cabin layout scheme based on the three-way knowledge fusion solution is obtained through replacement, crossover, and the combination of crossover and multi-objective function trade-off method; S3, applying the obtained cabin layout scheme to the layout design of multiple cabins to be arranged on the deck of the ship.

[0040] Exemplarily, in step S1, obtaining a priori knowledge solution includes: The built-in VBA editor and Automation technology of CATIA are used to transfer parameter values ​​between layout boundary conditions and reasoning rules. Among them, the boundary conditions of the layout are fixed parameters stored in the CATIA model, and the reasoning rules are also stored in the model as the rules of the knowledge engineering module. A multi-objective reasoning system is established by integrating the cabin layout requirements in the specification convention and the cabin lighting, vibration noise and adjacency layout requirements. Specifically, it includes first completing the reasoning of the cabin layout area on the deck based on the layout rules, lighting, vibration noise in the specification, and then completing the reasoning of the specific layout position of the cabin in the area based on the adjacency and circulation between the cabins to obtain the prior knowledge scheme. Taking the cabin layout reasoning of lighting as an example, starting from the demand, cabins with high lighting requirements such as living cabins and leisure and entertainment rooms are given priority to be arranged in the outer area of ​​the deck. At the same time, by analyzing the architectural structure of the deck and the hull shape, it is determined which areas can obtain better natural lighting. For example, cabins on the side of the ship usually have better lighting than cabins near the center of the ship; The use of improved ant colony-genetic algorithm to obtain algorithm knowledge solutions includes: The ant colony algorithm is used to first initialize the cabin code and then the reverse area allocation to randomly generate the cabin layout plan. Then the roulette method is used to select the cabin layout plan with excellent fitness value to update the iteration of the pheromone guidance algorithm. The pheromone formula is as follows: ; In the formula, For the cabin With cabin Pheromone concentration between; variable The volatilization rate indicating the concentration of pheromone; Indicates Plans in the cabin and Increased pheromones between; For the A plan, The value range is ; In view of the fact that the pheromone mechanism is prone to fall into local optimality, the crossover and mutation operators of the genetic algorithm are introduced to increase the diversity of the solution population, guide the solution of the algorithm, and obtain the specific algorithm flow of the algorithm knowledge solution. Figure 6 As shown; The scheme for obtaining human intelligence knowledge includes: By using the GUI function provided by the built-in VB editor in CATIA, the EXCEl function library is added through the VB editor to call the EXCEL command, and the cabin layout plan stored in EXCEL in the form of a real number code string is obtained. Each real number in the code string represents a corresponding cabin, and the position of the cabin in the code string represents the layout order of the cabin. Then, the cabin layout is carried out in the corresponding deck area according to the recommended layout area of ​​the cabin and the strategy of evenly distributing the remaining area. Based on their own experience, the designer modifies the position of the corresponding real number in the code string in the online design interface of the human intelligence knowledge solution to obtain different cabin layout solutions. Combined with the deck boundary to be arranged and the area information of the cabin, the width of each cabin is determined according to the aisle position and then the length of the cabin is obtained; according to the situation of the cabin layout solution and modification suggestions, the position and size parameters of the cabin are updated in real time according to the designer's modification of the layout solution. The update realizes the real-time update of the three-dimensional layout solution by first determining the cabin code and then determining the cabin layout area and then allocating the area; finally, with the help of the GUI function provided by the VB editor, the online operation interface of the human intelligence knowledge solution is built.

[0041] In step S2, the cabin layout solution based on the three-way knowledge fusion solution includes: Firstly, the acquisition methods based on prior knowledge, human intelligence knowledge and computational knowledge are established respectively. Then, some excellent prior knowledge solutions and human intelligence knowledge solutions are selected to form a hybrid solution set, which is represented in the form of a coding string. When the ant colony-genetic algorithm runs to a certain stage, the hybrid solution set is added to the population of the improved ant colony-genetic algorithm. In each iterative solution of the improved ant colony-genetic algorithm, the fusion and dynamic optimization of three-way knowledge are realized. Finally, through replacement and crossover operations, multiple simulation experiments are performed to adjust the number and quantity of the hybrid solution set added, so as to obtain the best time, quantity and individual quality of the hybrid knowledge population, and obtain the optimal three-way knowledge fusion solution mechanism. Each sub-objective function in the above-mentioned multi-objective function trade-off method is weighted to obtain a cabin layout scheme with excellent comprehensive performance.

[0042] Example 3, as another embodiment of the present invention, Figure 4As shown, the ship cabin layout method based on the dynamic fusion of three-way knowledge gene layer provided by the embodiment of the present invention is a ship cabin intelligent layout optimization design method based on the dynamic fusion of prior knowledge, human intelligence knowledge and computational knowledge (three-way knowledge) gene layer. In the process of cabin intelligent layout optimization algorithm, it can give full play to the guiding role of cabin layout prior knowledge and human intelligence knowledge on intelligent algorithm, give full play to the respective strengths of man and machine, and adopt the strategy of effectively using excellent design examples, rules, regulations, etc. (prior knowledge) of ship cabin layout design, professional knowledge and experience of designers (human intelligence knowledge), and improved ant colony-genetic algorithm (computational knowledge) in parallel in the ship cabin intelligent layout design. In the process of algorithm solving, it can take into account the optimization goals and constraints that are difficult to express in mathematical language in the cabin layout process, so as to obtain a more practical ship cabin layout plan.

[0043] Prior knowledge and prior knowledge individuals: Prior knowledge mainly includes excellent design examples, professional knowledge, rule data information, etc. The solution to the ship cabin layout problem to be solved is obtained by acquiring and reusing prior knowledge based on knowledge engineering. The solution code string of the problem is in the form of prior knowledge individuals (prior knowledge solutions).

[0044] Human knowledge and artificial individuals: Human knowledge mainly refers to the professional knowledge and experiential knowledge mastered by designers. For a specific cabin layout problem, the solution provided by the designer based on human knowledge, taking into account the actual engineering and calculation conditions, as well as the interactive information provided by the algorithm, is called an artificial individual (artificial individual) in the form of a coded string.

[0045] Computational knowledge and algorithm individuals: Computational knowledge mainly refers to the knowledge obtained by the improved ant colony-genetic algorithm. For the ship cabin layout problem to be solved, the solution to the problem is completely automatically generated by the algorithm without human participation, and its coded string form is called an algorithm individual (algorithm solution).

[0046] Exemplarily, the present invention constructs an intelligent layout design method for ship cabins based on the dynamic fusion of prior knowledge, human intelligence knowledge, and computational knowledge (three-way knowledge) at the genetic level of the evolutionary algorithm to form an evolutionary population, and through operations such as selection, crossover, and mutation, the method evolves and optimizes the cabin layout. The cabin layout reasoning module based on prior knowledge is used to obtain the prior knowledge solution through knowledge engineering and parametric design cabin layout reasoning. In view of the characteristics of ship cabin layout design, a cabin layout design prior knowledge construction method based on ontology is proposed, and a complete layout design knowledge representation model based on ontology is established to realize the sharing and reuse of layout design prior knowledge, and provide prior knowledge individuals (prior knowledge solutions) for three-way knowledge fusion.

[0047] The cabin layout optimization algorithm module based on computational knowledge is used to obtain the computational knowledge scheme and the fusion algorithm of the three-way cabin layout knowledge by using the improved ant colony-genetic algorithm. The computational knowledge is mainly obtained through the efficient improved ant colony-genetic algorithm constructed for the cabin layout, providing algorithm individuals (algorithm solutions) for the three-way knowledge fusion.

[0048] Using the human intelligence knowledge solution online design module, designers use their professional knowledge and experience to design online to obtain human intelligence knowledge solutions. Based on the diversity of feasible solutions for ship cabin layout, a certain number of manual design solutions are obtained through the designer's online design, manual modification of prior knowledge individuals, and manual modification of algorithm individuals, providing artificial individuals (artificial individuals) for three-way knowledge fusion.

[0049] Increase the diversity of prior knowledge individuals and artificial individuals: Prior knowledge individuals and artificial individuals added to the algorithm will affect the evolution of algorithm individuals to a certain extent. For example, when the performance of the added artificial individuals is excessively superior to that of the algorithm individuals, the excellent genes of the algorithm individuals are easily submerged in the iteration process, resulting in the performance of the algorithm individuals not being fully utilized, which leads to the algorithm converging to a common solution containing only the excellent genes of artificial individuals. Therefore, by expanding the diversity of prior knowledge individuals and artificial individuals through human intelligence or automation technology, prior knowledge individuals and artificial individuals with different performance are obtained, and their diversity is evaluated to increase the probability of effective integration of prior knowledge individuals, artificial individuals and algorithm individuals in subsequent research, and to continuously and effectively combine with the algorithm.

[0050] The cabin layout design module using three-way knowledge fusion is used to dynamically integrate prior knowledge, computational knowledge and human intelligence knowledge at the algorithm gene layer to design the cabin layout plan.

[0051] The method of combining prior knowledge individuals, artificial individuals and computational individuals at the genetic level of the evolutionary algorithm is used to perform three-way knowledge deep fusion. The principle of the cabin layout design fusion mechanism of three-way knowledge fusion is as follows: Figure 5 As shown, including: Prior knowledge individuals, artificial individuals and algorithm individuals generated by evolutionary algorithms are expressed in a unified coding string, and whether to join the evolutionary algorithm group is determined based on their fitness value and diversity value. In the evolutionary process, the fusion of prior knowledge individuals, artificial individuals and algorithm individuals mainly adopts three operation modes to achieve a continuous dynamic information fusion: ① "Replacement" mode. For example: add artificial individuals to the algorithm group and randomly replace a certain number of algorithm solutions. This algorithm solution can be randomly selected from the algorithm group or can be a poor solution of the algorithm group, and then the algorithm group is evolved; ② "Crossover" mode. For example: randomly select algorithm solutions from the algorithm group, crossover operations are performed with certain artificial individuals respectively, and one of the two sub-individuals generated after each crossover operation is randomly selected and put back into the algorithm group, so that the gene fragments of the artificial individuals and the gene fragments of the algorithm solution can fully exchange and integrate information; ③ "Crossover" is combined with the multi-objective function trade-off method. Firstly, according to the functional requirements of cabin layout and the running status of the algorithm, the relative importance of each objective function is adjusted by changing the weight coefficient of each objective function. Secondly, in the current evolutionary generation, each individual in the algorithm is forced to perform a cross operation with the prior knowledge individual or the artificial individual, so that the individuals in the algorithm group inherit part of the genes of the prior knowledge individual or the artificial individual, thereby guiding the evolution of the entire algorithm group.

[0052] As another example, the cabin layout reasoning module based on prior knowledge designed by the present invention proposes a cabin layout reasoning method that combines knowledge engineering with parametric design. It uses CATIA's built-in editor and Automation technology to transfer parameter values ​​between layout boundary conditions and reasoning rules, thereby improving the versatility of layout reasoning rules. A multi-objective reasoning system is established by comprehensively considering the cabin layout requirements in the specifications and conventions and the layout requirements such as lighting, vibration noise and adjacency of the cabin. That is, first, the reasoning of the cabin layout area on the deck is completed based on the layout rules, lighting, vibration noise in the specifications, and then the reasoning of the specific layout position of the cabin in the area is completed based on the adjacency and circulation between the cabins.

[0053] The cabin layout optimization algorithm module designed by the present invention based on computational knowledge establishes a cabin layout algorithm mathematical model, and proposes an improved ant colony-genetic cabin layout optimization algorithm. The algorithm is mainly based on the idea of ​​solving the traveling salesman problem by the ant colony algorithm, and selects a sequential coding method. Each cabin is represented by a natural number, and a fixed-length real number code string is used to represent the layout plan of the cabin on the deck. First, the cabin layout plan is randomly generated, and then the cabin layout plan with excellent fitness value is selected to update the pheromone to guide the iteration of the algorithm. Since the pheromone mechanism is prone to fall into the local optimum, the crossover and mutation operators of the genetic algorithm are introduced to increase the diversity of the solution population, and the pheromone mechanism of the algorithm is better used to guide the solution of the algorithm. The ant colony algorithm, the genetic algorithm and the algorithm are used to solve the cabin layout design problem, respectively, and the superiority of the algorithm in the ship cabin layout design is verified.

[0054] in, Figure 6 The main flow chart of the improved ant colony-genetic optimization algorithm provided by the embodiment of the present invention.

[0055] The human intelligence knowledge scheme online design module designed by the present invention uses the GUI function provided by the built-in VB editor in CATIA, adds the EXCEl function library through the VB editor to call the EXCEL command, obtains the cabin layout scheme stored in the EXCEL in the form of a real number code string, each real number in the code string represents a corresponding cabin, and the position of the cabin in the code string represents the layout order of the cabin. Then, the corresponding deck area and layout method are clarified, and the cabin layout of the deck can be obtained. Designers can obtain different cabin layout schemes by modifying the position of the corresponding real number in the code string, and the length, width and other parameter values ​​of each cabin can be calculated by combining the area information of the deck boundary and the cabin to be arranged. In order to facilitate designers to better understand the situation of the cabin layout scheme and put forward reasonable modification suggestions, the software functions and commands provided in the Automation technology provided by CATIA are used to obtain the corresponding cabin model, and then the length and width parameter information of the cabin are assigned, so that the position and size of the cabin and other parameters can be updated in real time according to the designer's modification of the layout scheme, and the real-time update of the three-dimensional layout scheme is realized, so that designers can provide human intelligence knowledge schemes more intuitively and reasonably. Finally, with the help of the GUI function provided by the VB editor, the above development codes were classified and organized, and the online operation interface of the human intelligence knowledge solution was built.

[0056] The cabin layout design module of the three-way knowledge fusion solution designed in the present invention proposes a cabin layout design theory and implementation method based on the dynamic fusion of prior knowledge, human intelligence knowledge and computational knowledge (three-way knowledge) at the genetic level, and establishes a dynamic optimization and evaluation model for ship cabin layout based on three-way knowledge fusion, that is, firstly, acquisition methods based on prior knowledge, human intelligence knowledge and computational knowledge are established respectively, and then some excellent prior knowledge solutions and human intelligence knowledge solutions are selected to form a hybrid solution set, and expressed in the form of an algorithm coding string. When the algorithm runs to a certain stage, the hybrid solution set is added to the algorithm's population, and the three-way knowledge fusion and dynamic optimization can be achieved in each iterative solution of the algorithm. Finally, the optimal three-way knowledge fusion solution mechanism is obtained by exploring the best time, quantity and individual quality of the hybrid knowledge population. Figure 5 shown.

[0057] The parameters of three-way knowledge fusion are determined as follows: the prior knowledge scheme and the human intelligence knowledge scheme in the hybrid knowledge population are preferably 50% each; the number of hybrid knowledge population is about 20% of the algorithm population size; the individual quality range of the scheme in the hybrid knowledge population is 0.46~0.56; the timing of adding the hybrid knowledge population is after the fusion algorithm iterates and solves for 20 generations. The above provides an important reference for the design of the three-way knowledge fusion layout.

[0058] Exemplarily, the dynamic optimization model must consider the complex coupling relationship of the ship cabin layout, the complicated constraints, the requirements of multiple optimization objectives, and the need for three-way knowledge fusion. The three-way knowledge can be dynamically fused as needed at different time points such as the beginning, middle, and local optimum of the algorithm. When establishing the objective function for the solution, factors such as adjacency, circulation, vibration and noise, lighting conditions, and ergonomics are considered comprehensively. Among them, adjacency and circulation evaluate the relationship between each cabin to be arranged and all other cabins to be arranged. In the process of algorithm solution, each of the above sub-objective functions is weighted to obtain a cabin layout solution with excellent comprehensive performance.

[0059] Take the layout design of 28 cabins to be arranged on the deck of a teaching practice boat as an example.

[0060] In the specific implementation process, Figure 2 As shown, the layout knowledge such as standard layout rules, cabin vibration and noise requirements, cabin lighting requirements, cabin adjacency, cabin area and furniture configuration, etc. are collected and sorted from the cabin layout design specifications.

[0061] The knowledge acquisition, knowledge representation, and knowledge reasoning collected for the cabin to be arranged are integrated in the form of parameters and added to the parametric model and layout reasoning rule code of the cabin in the form of reasoning rules to form the corresponding cabin parametric model library and layout rule code library. Figure 2 The reasoning layout process shown performs reasoning on the deck configuration of the cabin, reasoning on the layout area of ​​the cabin on the deck, and reasoning on the specific position of the cabin in the layout area to obtain the corresponding prior knowledge layout design plan.

[0062] In the specific implementation process, the main process of the improved ant colony-genetic algorithm proposed in the present invention is as follows: Figure 6 As shown in the figure, after determining the population of cabin layout schemes according to the solution mechanism of the ant colony algorithm, the crossover and mutation operators of the genetic algorithm are introduced, and the new layout scheme is used to replace the corresponding schemes with poor quality in the original scheme population, which effectively improves the layout search space of the algorithm. For the 28 cabins to be arranged in the case, real number coding is adopted, and they are represented by numbers from 1 to 28 respectively. The coding string of the cabin layout scheme is as follows: [2 4 14 16 3 20 18 9 10 11 19 5 1 6 8 15 13 12 177 25 23 22 24 28 27 26 21]. The algorithm objective function adopts the weighted sum of adjacency, circulation, vibration noise, lighting and ergonomics.

[0063] In the specific implementation process, the online design interface of the human intelligence knowledge solution proposed by the present invention is as follows Figure 7 The module is mainly divided into three modules: cabin sequence modification module, function module, and layout scheme update module. The online designer can obtain the cabin layout scheme code string to be modified from the layout scheme library and generate the corresponding three-dimensional layout scheme. After modifying the code string, the three-dimensional scheme can be updated in real time to determine further modification or saving the scheme. This design module enables the designer to intuitively obtain the layout of the scheme and make reasonable suggestions. Figure 7 The code string on the left side is the layout plan of 28 cabins to be arranged from the EXCEL plan library.

[0064] In the specific implementation process, the cabin layout design solved by the three-way knowledge fusion proposed by the present invention is as follows: Figure 5 The improved genetic algorithm proposed in the present invention is selected as the three-way knowledge fusion algorithm. According to the important fusion parameters determined by the comparative experiment, the prior knowledge scheme and the human intelligence knowledge scheme are added in the algorithm solution process to realize the effective fusion of the three-way layout knowledge. The two-dimensional scheme and the three-dimensional effect schematic diagram of the cabin layout obtained for the 28 cabins to be arranged on the deck of a teaching practice boat in the case are shown in the figure. Figure 8 , Fig. 9 As shown, the design scheme comprehensively considers factors such as the adjacency between cabins, circulation and cabin lighting, and has excellent comprehensive performance and strong practicality.

[0065] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered within the protection scope of the present invention.

Claims

1. A ship cabin layout system based on dynamic fusion of three-way knowledge gene layer, characterized in that: The system includes: The cabin layout reasoning module based on prior knowledge is used to organize the prior knowledge of cabin layout design, and use the knowledge engineering reasoning expression method to perform knowledge representation and knowledge reasoning to obtain the prior knowledge scheme. The knowledge engineering expression is an IF-THEN structure. The knowledge about cabin layout acquired in the knowledge acquisition stage is represented by the production expression method to form a reasoning rule code library; The intelligent cabin layout optimization algorithm module based on computational knowledge uses the improved ant colony-genetic algorithm to perform cabin layout and obtain the algorithm knowledge solution, which is used to obtain the computational knowledge solution and realize the three-way layout knowledge fusion; The human intelligence knowledge cabin layout plan online design module is used to design the cabin layout plan online with a three-dimensional visualization solution to obtain the human intelligence knowledge plan; The three-way knowledge fusion solution for cabin layout design module dynamically fuses the three-way knowledge individuals of the prior knowledge scheme, algorithm knowledge scheme, and human intelligence knowledge scheme obtained respectively at the gene level of the improved ant colony-genetic algorithm to form an evolutionary group, and obtains the cabin layout scheme based on the three-way knowledge fusion solution through replacement, crossover, and a combination of crossover and multi-objective function trade-off method; the three-way knowledge individuals include prior knowledge individuals, artificial individuals, and algorithm individuals.

2. The ship cabin layout system based on three-way knowledge gene layer dynamic fusion according to claim 1 is characterized in that: In the cabin layout reasoning module based on prior knowledge, prior knowledge includes excellent design examples, professional knowledge, and rule data information; the solution to the ship cabin layout problem to be solved is obtained by acquiring and reusing prior knowledge based on knowledge engineering, and the solution code string of the problem is in the form of prior knowledge individuals.

3. The ship cabin layout system based on three-way knowledge gene layer dynamic fusion according to claim 1 is characterized in that: In the intelligent cabin layout optimization algorithm module based on computational knowledge, the computational knowledge includes knowledge obtained by improved ant colony-genetic algorithm calculation; for the ship cabin layout problem to be solved, the solution of the problem is automatically generated, and the solution code string form of the problem is the algorithm individual.

4. The ship cabin layout system based on three-way knowledge gene layer dynamic fusion according to claim 1 is characterized in that: In the online design module of cabin layout plan based on human knowledge, human knowledge includes the professional knowledge and experiential knowledge provided by the designer; By utilizing human intelligence knowledge, combining engineering practice and calculation conditions, and utilizing the interactive information provided by the online design interface of the human intelligence knowledge solution, a solution to a cabin layout problem is obtained. The solution string form of the problem is called an artificial individual.

5. The ship cabin layout system based on three-way knowledge gene layer dynamic fusion according to claim 1 is characterized in that: In the online design module of cabin layout scheme of human intelligence knowledge, a three-dimensional visualization solution of cabin layout scheme is designed online through the online design interface of human intelligence knowledge scheme, which specifically includes that the online designer obtains the cabin layout scheme code string to be modified from the layout scheme library and generates the corresponding three-dimensional layout scheme, and updates the three-dimensional scheme in real time after modifying the code string to determine further modification or saving the scheme; The online design interface of the human intelligence knowledge solution includes: a cabin sequence modification module, a function module, and a layout solution update module; The cabin sequence modification module refers to the three-dimensional layout scheme corresponding to the cabin layout scheme code string, and optimizes the cabin layout code string to achieve optimized adjustment of the cabin sequence code string; The functional module is used to save, display, and switch cabin sequence code strings, and is responsible for obtaining cabin layout scheme sequence code strings in Excel to the online design interface, and then saving them back to the Excel scheme library after modification to form a solution set of artificial individuals that are different from prior knowledge and algorithm individuals; The layout scheme updating module generates a three-dimensional cabin layout scheme according to the cabin layout scheme coding string, which is used for designers to understand the layout situation and update and generate a new scheme after modification.

6. The ship cabin layout system based on three-way knowledge gene layer dynamic fusion according to claim 1 is characterized in that: In the three-way knowledge fusion solution for cabin layout design module, prior knowledge individuals, artificial individuals and algorithm individuals are expressed in a unified coding string, and whether to join the improved ant colony-genetic algorithm group is determined based on the fitness value and diversity value; in the evolutionary process, the fusion of prior knowledge individuals, artificial individuals and algorithm individuals adopts three operation modes to complete continuous dynamic information fusion; the three operation modes include replacement mode, crossover mode, and a combination of crossover and multi-objective function trade-off method.

7. The ship cabin layout system based on three-way knowledge gene layer dynamic fusion according to claim 6 is characterized in that: The replacement method includes: adding artificial individuals to the algorithm population and randomly replacing a certain number of algorithm solutions, where the algorithm solution is randomly selected from the algorithm population, or the algorithm solution is a poor solution of the algorithm population, and then performing an evolution operation on the algorithm population; The crossover method includes: randomly selecting an algorithm solution from the algorithm population, performing a crossover operation with an artificial individual respectively, and randomly selecting one of the two sub-individuals generated after each crossover operation, and returning it to the algorithm population, so that the gene fragments of the artificial individual and the gene fragments of the algorithm solution can fully exchange information and merge; The combination of the crossover and multi-objective function trade-off method includes: first, adjusting the relative importance of each objective function by changing the weight coefficient of each objective function according to the functional requirements of the cabin layout and the algorithm operation status; second, forcing each individual in the algorithm to perform a crossover operation with the prior knowledge individual or the artificial individual in the current evolutionary generation, so that the individuals in the algorithm group inherit part of the genes of the prior knowledge individual or the artificial individual, thereby guiding the evolution of the entire algorithm group.

8. A method for ship cabin layout based on dynamic fusion of three-way knowledge gene layer, characterized in that: The method implements the ship cabin layout system based on dynamic fusion of three-way knowledge gene layer as described in any one of claims 1 to 7, and the method comprises: S1, organize the prior knowledge of cabin layout design, use the knowledge engineering reasoning expression method to perform knowledge representation and knowledge reasoning to obtain the prior knowledge scheme; use the improved ant colony-genetic algorithm to obtain the algorithm knowledge scheme and algorithm individuals; and perform three-dimensional visualization of the online design of cabin layout scheme to obtain the human intelligence knowledge scheme and artificial individuals; S2, the three-way knowledge individuals of the prior knowledge scheme, algorithm knowledge scheme and human wisdom knowledge scheme obtained respectively are dynamically integrated at the gene level of the improved ant colony-genetic algorithm to form an evolutionary group, and the cabin layout scheme based on the three-way knowledge fusion solution is obtained through replacement, crossover, and the combination of crossover and multi-objective function trade-off method; S3, applying the obtained cabin layout scheme to the layout design of multiple cabins to be arranged on the deck of the ship.

9. The method for ship cabin layout based on three-way knowledge gene layer dynamic fusion according to claim 8 is characterized in that: In step S1, obtaining a priori knowledge solution includes: The built-in VBA editor and Automation technology of CATIA are used to transfer parameter values ​​between layout boundary conditions and reasoning rules; the layout boundary conditions are stored in the CATIA model as fixed parameters, and the reasoning rules are also stored in the model as the rules of the knowledge engineering module; a multi-objective reasoning system is established by integrating the cabin layout requirements in the specification conventions and the cabin lighting, vibration noise and adjacency layout requirements, specifically including first completing the reasoning of the cabin layout area on the deck based on the layout rules, lighting, vibration noise in the specifications, and then completing the reasoning of the specific layout position of the cabin in the area based on the adjacency and circulation between the cabins, and obtaining a priori knowledge plan; The use of improved ant colony-genetic algorithm to obtain algorithm knowledge solutions includes: The ant colony algorithm is used to first initialize the cabin code and then the reverse area allocation to randomly generate the cabin layout plan. Then the roulette method is used to select the cabin layout plan with excellent fitness value to update the iteration of the pheromone guidance algorithm. The pheromone formula is as follows: ； In the formula, For the cabin With cabin Pheromone concentration between; variable The volatilization rate indicating the pheromone concentration; Indicates Plans in the cabin and Increased pheromones between; For the A plan, The value range is ; As the pheromone mechanism is prone to fall into local optimality, the crossover and mutation operators of the genetic algorithm are introduced to increase the diversity of the solution population, guide the solution of the algorithm, and obtain the algorithm knowledge solution; The scheme for obtaining human intelligence knowledge includes: By using the GUI function provided by the built-in VB editor in CATIA, the EXCEl function library is added through the VB editor to call the EXCEL command, and the cabin layout plan stored in EXCEL in the form of a real number code string is obtained. Each real number in the code string represents a corresponding cabin, and the position of the cabin in the code string represents the layout order of the cabin. Then, the cabin layout is carried out in the corresponding deck area according to the recommended layout area of ​​the cabin and the strategy of evenly distributing the remaining area. Different cabin layout schemes are obtained by modifying the position of the corresponding real number in the code string in the online design interface of the human intelligence knowledge solution. Combined with the deck boundary and the area information of the cabin to be arranged, the width of each cabin is determined according to the aisle position and then the length value of the cabin is obtained; according to the situation of the cabin layout plan and modification suggestions, the position and size parameters of the cabin are updated in real time according to the modified layout plan. The update realizes real-time update of the three-dimensional layout plan by first determining the cabin code and then determining the layout area of ​​the cabin and then allocating the area; the GUI function provided by the VB editor is used to build the online operation interface of the human intelligence knowledge solution.

10. The method for ship cabin layout based on three-way knowledge gene layer dynamic fusion according to claim 8 is characterized in that: In step S2, the cabin layout solution based on the three-way knowledge fusion solution includes: Firstly, the acquisition methods based on prior knowledge, human intelligence knowledge and computational knowledge are established respectively. Then, some excellent prior knowledge solutions and human intelligence knowledge solutions are selected to form a hybrid solution set, which is represented in the form of a coding string. When the algorithm runs to a certain stage, the hybrid solution set is added to the population of the improved ant colony-genetic algorithm. In each iterative solution of the improved ant colony-genetic algorithm, the fusion and dynamic optimization of three-way knowledge are realized. Finally, through replacement and crossover operations, multiple simulation experiments are conducted to adjust the number and quantity of the hybrid solution set added, so as to obtain the best time, quantity and individual quality of the hybrid knowledge population, and obtain the optimal three-way knowledge fusion solution mechanism. Each sub-objective function in the above multi-objective function trade-off method is weighted to obtain a cabin layout solution with excellent comprehensive performance.

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