Ship cooling water system pipe network optimization design method and device and electronic equipment

Abstract

The invention provides a ship cooling water system pipe network optimization design method and device and electronic equipment, and the method comprises the steps: taking cooling uniformity maximization and cooling power minimization of a ship cooling water system pipe network as optimization design targets of the pipe network, and constructing a target function; obtaining water supply demand data of each cooling user of the ship cooling water system, and determining a constraint condition of the target function according to the water supply demand data of each cooling user; and solving the target function under constraint conditions by adopting a genetic algorithm to obtain an optimal design parameter set of the pipe network. The design efficiency of the ship cooling water system pipe network is improved, the cooling performance of the ship cooling water system pipe network can be optimal, and reasonable utilization of resources is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of ship design, and in particular to a method, device and electronic equipment for optimizing the design of a pipe network of a ship cooling water system. Background Art

[0002] The ship cooling water system is mainly used to provide cooling water that meets the requirements of various cooling users on the ship. It is a basic support system to ensure the safe and stable operation of the ship, and directly affects the comprehensive performance of the ship, such as safety, reliability, and compactness. The ship cooling water system has many cooling users, and the application scenarios are complex and changeable, and the pipe network design is relatively complex. At present, the existing ship cooling water system pipe network design method only considers a single factor, the design efficiency is low, which affects the cooling performance of the ship cooling water system pipe network and easily causes waste of resources. Summary of the invention

[0003] The present invention provides a method, device and electronic equipment for optimizing the design of a pipe network of a ship cooling water system, so as to solve the defects that the pipe network design method of a ship cooling water system in the prior art only considers a single factor, has low design efficiency, affects the cooling performance of the pipe network of the ship cooling water system, and easily causes waste of resources.

[0004] The present invention provides a method for optimizing the design of a pipe network of a ship cooling water system, comprising: Taking maximizing the cooling uniformity and minimizing the cooling power of the pipe network of the ship cooling water system as the optimization design goals of the pipe network, an objective function is constructed; Acquiring water supply demand data of each cooling user of the ship cooling water system, and determining the constraint conditions of the objective function according to the water supply demand data of each cooling user; A genetic algorithm is used to solve the objective function under the constraints to obtain an optimal design parameter set for the pipe network.

[0005] In some embodiments, the obtaining of water supply demand data of each cooling user of the ship cooling water system and determining the constraint conditions of the objective function according to the water supply demand data of each cooling user include: Acquire water supply demand data of each cooling user of the ship cooling water system under various working conditions, wherein the water supply demand data includes water volume demand data and flow resistance demand data; Calculate the change range of the water demand of each cooling user according to the water demand data of each cooling user under various working conditions, and calculate the change range of the flow resistance demand of each cooling user according to the flow resistance demand data of each cooling user under various working conditions; According to the water demand data and flow resistance demand data of each cooling user under various working conditions, as well as the water demand change amplitude and flow resistance demand change amplitude of each cooling user, the cooling users are globally classified, and each cooling user is divided into a plurality of cooling areas, wherein the plurality of cooling areas include a low flow resistance cooling area and a high flow resistance cooling area; The constraint conditions of the objective function are determined according to the cooling areas to which the cooling users belong.

[0006] In some embodiments, the globally classifying the cooling users and dividing the cooling users into a plurality of cooling areas includes: According to the variation range of the water demand of each cooling user, each cooling user is divided into different main cooling areas; Each main cooling area is divided into a plurality of cooling areas according to the flow resistance demand data of each cooling user under various working conditions.

[0007] In some embodiments, the constraint conditions of the objective function include: the water pipes of each cooling user in the low flow resistance cooling area are connected in series; the water pipes of each cooling user in the high flow resistance cooling area are connected in parallel.

[0008] In some embodiments, the use of a genetic algorithm to solve the objective function under the constraints to obtain an optimal design parameter set for the pipe network includes: According to the cooling areas to which the cooling users belong, a pipe network simulation model is constructed, a plurality of initial design parameter sets are randomly generated, the plurality of initial design parameter sets are determined as an initial population, and each initial design parameter set is determined as an individual of the initial population; Calculating the fitness value of each initial design parameter set according to the objective function; Determining the optimal individual from the initial population according to the fitness value of each initial design parameter set; Performing a crossover operation on the optimal individuals to generate new individuals, performing a mutation operation on the new individuals to obtain a next generation population, wherein the next generation population includes a plurality of new design parameter sets; The process starts with calculating the fitness value of each new design parameter set according to the objective function and performs cyclic iteration until an iteration termination condition is reached, and the optimal individual that reaches the iteration termination condition is determined as the optimal design parameter set.

[0009] In some embodiments, the use of a genetic algorithm to solve the objective function under the constraints to obtain an optimal design parameter set for the pipe network includes: A non-dominated sorting genetic algorithm is used to solve the objective function under the constraints to obtain an optimal design parameter set for the pipe network.

[0010] In some embodiments, the optimal design parameter set includes optimal flow rate, flow resistance, and pipe network series and parallel form.

[0011] In some embodiments, the method further comprises: Based on the optimal design parameter set of the pipe network, a two-dimensional plane design scheme of the pipe network is generated that matches the water supply demand of each cooling user.

[0012] The present invention also provides a device for optimizing the design of a pipe network of a ship cooling water system, comprising: A construction unit, for constructing an objective function with maximizing cooling uniformity and minimizing cooling power of a pipe network of a ship cooling water system as optimization design goals of the pipe network; A determination unit, configured to obtain water supply demand data of each cooling user of the ship cooling water system, and determine the constraint conditions of the objective function according to the water supply demand data of each cooling user; A solving unit is used to solve the objective function under the constraint conditions by using a genetic algorithm to obtain an optimal design parameter set for the pipe network.

[0013] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for optimizing the pipe network of a ship cooling water system as described above is implemented.

[0014] The ship cooling water system pipe network optimization design method, device and electronic equipment provided by the present invention take the maximization of cooling uniformity and the minimization of cooling power of the ship cooling water system pipe network as the optimization design goals of the pipe network, construct an objective function, obtain water supply demand data of each cooling user of the ship cooling water system, determine the constraint conditions of the objective function according to the water supply demand data of each cooling user, use a genetic algorithm to solve the objective function under the constraint conditions, and obtain the optimal design parameter set of the pipe network, thereby improving the design efficiency, enabling the cooling performance of the ship cooling water system pipe network to reach the best, and facilitating the rational use of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 It is a schematic flow chart of a method for optimizing the design of a pipe network of a ship cooling water system provided by an embodiment of the present invention; Figure 2 It is a flow chart of solving an objective function under constraint conditions using a genetic algorithm provided by an embodiment of the present invention; Figure 3 It is a structural schematic diagram of a pipe network optimization design device for a ship cooling water system provided by the present invention; Figure 4 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] At present, there are more and more users of ship cooling water systems, and their cooling needs vary greatly. User characteristics, operating conditions, usage conditions and heat dissipation conditions are different. On the one hand, the pipe network of the cooling water system is very complex, which greatly increases the difficulty of designing and regulating the cooling system. The traditional design method is inefficient. On the other hand, in the traditional design method, the cooling water pump is usually configured to meet the maximum head and maximum flow at the same time. Each branch user matches the user's cooling flow through valve throttling. However, the flow resistance and flow demand between different cooling users vary greatly, which can easily cause pump power over-matching, resulting in huge energy waste, and valve throttling noise and increased vibration.

[0019] To this end, the embodiment of the present invention provides a method, device and electronic device for optimizing the design of a pipe network of a ship cooling water system, by taking the optimization design objectives of the pipe network of the ship cooling water system to maximize the cooling uniformity and minimize the cooling power, constructing an objective function, obtaining the water supply demand data of each cooling user of the ship cooling water system, determining the constraint conditions of the objective function according to the water supply demand data of each cooling user, and solving the objective function under the constraint conditions using a genetic algorithm to obtain the optimal design parameter set of the pipe network. The present invention improves the design efficiency, can optimize the cooling performance of the pipe network of the ship cooling water system, and is conducive to the rational use of resources.

[0020] Figure 1 The following is a flow chart of a method for optimizing the design of a pipe network for a ship cooling water system provided by an embodiment of the present invention. Figure 1 As shown, a method for optimizing the design of a pipe network of a ship cooling water system is provided, comprising the following steps: step 110, step 120, and step 130. The steps of the method flow are only a possible implementation of the present invention.

[0021] Step 110: construct an objective function by taking maximizing the cooling uniformity and minimizing the cooling power of the pipe network of the ship cooling water system as the optimization design goals of the pipe network.

[0022] Among them, the ship cooling water system is a system used to control and maintain the operating temperature of various equipment on the ship.

[0023] Among them, cooling uniformity refers to whether the temperature distribution of each part of the system is uniform and whether the cooling effect is consistent; cooling power refers to the energy consumption of the system per unit time.

[0024] Step 120: Obtain water supply demand data of each cooling user of the ship cooling water system, and determine the constraint conditions of the objective function according to the water supply demand data of each cooling user.

[0025] Optionally, the water supply demand data includes at least demand data such as water volume, flow resistance, and water temperature.

[0026] In some embodiments, water supply demand data of each cooling user of the ship cooling water system is obtained, and the constraint conditions of the objective function are determined according to the water supply demand data of each cooling user, including: Obtain water supply demand data of each cooling user of the ship cooling water system under various working conditions, the water supply demand data including water volume demand data and flow resistance demand data; According to the water demand data of each cooling user under various working conditions, the change range of the water demand of each cooling user is calculated; according to the flow resistance demand data of each cooling user under various working conditions, the change range of the flow resistance demand of each cooling user is calculated; According to the water demand data and flow resistance demand data of each cooling user under various working conditions, as well as the change range of the water demand and the flow resistance demand of each cooling user, each cooling user is globally classified, and each cooling user is divided into multiple cooling areas, and the multiple cooling areas include a low flow resistance cooling area and a high flow resistance cooling area; The constraints of the objective function are determined according to the cooling area to which each cooling user belongs.

[0027] Optionally, according to the cooling area to which each cooling user belongs, the series-parallel connection mode of each water pipe corresponding to each cooling user is determined, and the constraint condition of the objective function is determined according to the series-parallel connection mode of each water pipe corresponding to each cooling user.

[0028] In some embodiments, each cooling user is globally classified and divided into a plurality of cooling areas, including: According to the variation of water demand of each cooling user, each cooling user is divided into different main cooling areas; Each main cooling area is divided into multiple cooling areas according to the flow resistance demand data of each cooling user under various working conditions.

[0029] Optionally, cooling users with large water demand variation and cooling users with small water demand variation are divided into different main cooling areas.

[0030] In some embodiments, the constraints of the objective function include: the water pipes of each cooling user in the low flow resistance cooling area are connected in series; the water pipes of each cooling user in the high flow resistance cooling area are connected in parallel.

[0031] Optionally, the constraint condition also includes: for the local high flow resistance cooling area, an additional low flow and high head compact pump is used as the auxiliary booster pump of the cooling area. The advantage of this configuration is that it can effectively reduce the demand for the flow and head of the main cooling water pump, thereby effectively reducing the power of the main cooling water pump, laying the foundation for the ultimate realization of the cooling water system flow self-distribution.

[0032] Step 130: Using a genetic algorithm to solve the objective function under constraints to obtain an optimal design parameter set for the pipe network.

[0033] Among them, genetic algorithm is a search heuristic algorithm based on natural selection and genetic mechanism.

[0034] Figure 2 A schematic diagram of a flow chart of solving an objective function under constraints using a genetic algorithm provided by an embodiment of the present invention, such as Figure 2 As shown, in some embodiments, in step 130, a genetic algorithm is used to solve the objective function under constraints to obtain an optimal design parameter set of the pipe network, including: Step 131: construct a pipe network simulation model according to the cooling area to which each cooling user belongs, randomly generate multiple initial design parameter sets, determine the multiple initial design parameter sets as an initial population, and determine each initial design parameter set as an individual of the initial population; Step 132: Calculate the fitness value of each initial design parameter set according to the objective function; Step 133, determining the best individual from the initial population according to the fitness value of each initial design parameter set; Step 134: performing a crossover operation on the optimal individual to generate a new individual, performing a mutation operation on the new individual to obtain a next generation population, wherein the next generation population includes a plurality of new design parameter sets; Step 135, starting from calculating the fitness value of each new design parameter set according to the objective function, and performing loop iteration until the iteration termination condition is reached, and determining the optimal individual that reaches the iteration termination condition as the optimal design parameter set.

[0035] Optionally, a pipeline network simulation model is constructed according to the cooling area to which each cooling user belongs, and a multi-circuit thermal simulation method of the cooling water system is used to perform refined simulation calculations on the pipeline network to understand the thermal characteristics of the pipeline network under different working conditions. Based on a large amount of calculation data under each working condition, genetic algorithms and genetic programming are used to predict performance indicators such as flow and temperature of the secondary cooling water system.

[0036] Optionally, the pipe network is designed according to different sets of design parameters, and the pipe network is scored to obtain the optimal layout plan.

[0037] In some embodiments, a genetic algorithm is used to solve the objective function under constraints to obtain an optimal design parameter set for the pipe network, including: The non-dominated sorting genetic algorithm is used to solve the objective function under constraints and obtain the optimal design parameter set of the pipeline network.

[0038] Among them, the non-dominated sorting genetic algorithm is a multi-objective optimization algorithm based on the Pareto optimal concept. It is used to solve optimization problems with multiple conflicting objectives. Through Pareto improvement, that is, improving the interests of at least one individual without harming the interests of other individuals, it gradually approaches the Pareto optimal state.

[0039] Optionally, a non-dominated solution sorting genetic algorithm is used to perform multi-objective optimization on parameters such as cooling water system pipe network flow, flow resistance, temperature, pipe network series-parallel form, pump power, etc., to obtain the Pareto optimal solution set of parameters such as flow, flow resistance, pipe network series-parallel form, etc.

[0040] In some embodiments, the optimal design parameter set includes optimal flow rate, flow resistance, and series and parallel connection of the pipe network.

[0041] In some embodiments, the above method further comprises: Based on the optimal design parameter set of the pipe network, a two-dimensional plan design of the pipe network is generated to match the water supply demand of each cooling user.

[0042] In an embodiment of the present invention, by taking maximizing the cooling uniformity and minimizing the cooling power of the pipe network of the ship cooling water system as the optimization design goals of the pipe network, an objective function is constructed, and the water supply demand data of each cooling user of the ship cooling water system is obtained. According to the water supply demand data of each cooling user, the constraint conditions of the objective function are determined, and the objective function is solved under the constraint conditions using a genetic algorithm to obtain the optimal design parameter set of the pipe network, thereby improving the design efficiency, enabling the cooling performance of the pipe network of the ship cooling water system to be optimized, and facilitating the rational use of resources.

[0043] The following is a description of a ship cooling water system pipe network optimization design device provided by an embodiment of the present invention. The ship cooling water system pipe network optimization design device described below and the ship cooling water system pipe network optimization design method described above can be referenced to each other.

[0044] Figure 3 The schematic diagram of the structure of the ship cooling water system pipe network optimization design device provided by the present invention is as follows: Figure 3 As shown, the ship cooling water system pipe network optimization design device 300 includes: A construction unit 310 is used to construct an objective function by taking the maximization of cooling uniformity and the minimization of cooling power of the pipe network of the ship cooling water system as the optimization design objectives of the pipe network; The determination unit 320 is used to obtain water supply demand data of each cooling user of the ship cooling water system, and determine the constraint conditions of the objective function according to the water supply demand data of each cooling user; The solving unit 330 is used to solve the objective function under the constraint conditions by using a genetic algorithm to obtain an optimal design parameter set of the pipe network.

[0045] Optionally, water supply demand data of each cooling user of the ship cooling water system is obtained, and the constraint conditions of the objective function are determined according to the water supply demand data of each cooling user, including: Obtain water supply demand data of each cooling user of the ship cooling water system under various working conditions, the water supply demand data including water volume demand data and flow resistance demand data; According to the water demand data of each cooling user under various working conditions, the change range of the water demand of each cooling user is calculated; according to the flow resistance demand data of each cooling user under various working conditions, the change range of the flow resistance demand of each cooling user is calculated; According to the water demand data and flow resistance demand data of each cooling user under various working conditions, as well as the change range of the water demand and the flow resistance demand of each cooling user, each cooling user is globally classified, and each cooling user is divided into multiple cooling areas, and the multiple cooling areas include a low flow resistance cooling area and a high flow resistance cooling area; The constraints of the objective function are determined according to the cooling area to which each cooling user belongs.

[0046] Optionally, each cooling user is globally classified and divided into multiple cooling areas, including: According to the variation of water demand of each cooling user, each cooling user is divided into different main cooling areas; Each main cooling area is divided into multiple cooling areas according to the flow resistance demand data of each cooling user under various working conditions.

[0047] Optionally, the constraint conditions of the objective function include: the water pipes of each cooling user in the low flow resistance cooling area are connected in series; the water pipes of each cooling user in the high flow resistance cooling area are connected in parallel.

[0048] Optionally, a genetic algorithm is used to solve the objective function under constraints to obtain an optimal design parameter set for the pipe network, including: According to the cooling area to which each cooling user belongs, a pipe network simulation model is constructed, a plurality of initial design parameter sets are randomly generated, the plurality of initial design parameter sets are determined as an initial population, and each initial design parameter set is determined as an individual of the initial population; According to the objective function, the fitness value of each initial design parameter set is calculated; According to the fitness value of each initial design parameter set, the optimal individual is determined from the initial population; Perform crossover operation on the best individuals to generate new individuals, perform mutation operation on the new individuals to obtain the next generation population, which includes multiple new design parameter sets; The optimal design parameter set is determined by calculating the fitness value of each new design parameter set according to the objective function and performing cyclic iteration until the iteration termination condition is reached. The optimal individual that reaches the iteration termination condition is determined as the optimal design parameter set.

[0049] Optionally, a genetic algorithm is used to solve the objective function under constraints to obtain an optimal design parameter set for the pipe network, including: The non-dominated sorting genetic algorithm is used to solve the objective function under constraints and obtain the optimal design parameter set of the pipeline network.

[0050] Optionally, the optimal design parameter set includes optimal flow rate, flow resistance, and pipe network series and parallel forms.

[0051] Optionally, the ship cooling water system pipe network optimization design device 300 further includes: The generation unit is used to generate a two-dimensional plan design scheme of the pipe network that matches the water supply demand of each cooling user based on the optimal design parameter set of the pipe network.

[0052] It should be noted here that the ship cooling water system pipeline optimization design device provided in the embodiment of the present invention can implement all the method steps implemented in the above-mentioned ship cooling water system pipeline optimization design method embodiment, and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as the method embodiment will not be described in detail here.

[0053] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 4As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430 and a communication bus 440, wherein the processor 410, the communication interface 420 and the memory 430 communicate with each other through the communication bus 440. The processor 410 may call the logic instructions in the memory 430 to execute the ship cooling water system pipe network optimization design method, the method comprising: taking the cooling uniformity maximization and cooling power minimization of the pipe network of the ship cooling water system as the optimization design objectives of the pipe network, constructing an objective function; obtaining the water supply demand data of each cooling user of the ship cooling water system, and determining the constraint conditions of the objective function according to the water supply demand data of each cooling user; using a genetic algorithm to solve the objective function under the constraint conditions to obtain the optimal design parameter set of the pipe network.

[0054] In addition, the logic instructions in the above-mentioned memory 430 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0055] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0056] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0057] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the design of a pipe network for a ship cooling water system, characterized in that: include: Taking maximizing the cooling uniformity and minimizing the cooling power of the pipe network of the ship cooling water system as the optimization design goals of the pipe network, an objective function is constructed; Acquire water supply demand data of each cooling user of the ship cooling water system, and determine the constraint conditions of the objective function according to the water supply demand data of each cooling user; A genetic algorithm is used to solve the objective function under the constraints to obtain an optimal design parameter set for the pipe network.

2. The ship cooling water system pipe network optimization design method according to claim 1 is characterized in that: The step of obtaining water supply demand data of each cooling user of the ship cooling water system and determining the constraint conditions of the objective function according to the water supply demand data of each cooling user includes: Acquire water supply demand data of each cooling user of the ship cooling water system under various working conditions, wherein the water supply demand data includes water volume demand data and flow resistance demand data; Calculate the change range of the water demand of each cooling user according to the water demand data of each cooling user under various working conditions, and calculate the change range of the flow resistance demand of each cooling user according to the flow resistance demand data of each cooling user under various working conditions; According to the water demand data and flow resistance demand data of each cooling user under various working conditions, as well as the water demand change amplitude and flow resistance demand change amplitude of each cooling user, the cooling users are globally classified, and each cooling user is divided into a plurality of cooling areas, wherein the plurality of cooling areas include a low flow resistance cooling area and a high flow resistance cooling area; The constraint conditions of the objective function are determined according to the cooling areas to which the cooling users belong.

3. The ship cooling water system pipe network optimization design method according to claim 2 is characterized in that: The globally classifying the cooling users and dividing the cooling users into a plurality of cooling areas includes: According to the variation range of the water demand of each cooling user, each cooling user is divided into different main cooling areas; Each main cooling area is divided into a plurality of cooling areas according to the flow resistance demand data of each cooling user under various working conditions.

4. The ship cooling water system pipe network optimization design method according to claim 2 is characterized in that: The constraint conditions of the objective function include: the water pipes of each cooling user in the low flow resistance cooling area are connected in series; and the water pipes of each cooling user in the high flow resistance cooling area are connected in parallel.

5. The ship cooling water system pipe network optimization design method according to claim 2 is characterized in that: The method of using a genetic algorithm to solve the objective function under the constraint conditions to obtain an optimal design parameter set for the pipe network includes: According to the cooling areas to which the cooling users belong, a pipe network simulation model is constructed, a plurality of initial design parameter sets are randomly generated, the plurality of initial design parameter sets are determined as an initial population, and each initial design parameter set is determined as an individual of the initial population; Calculating the fitness value of each initial design parameter set according to the objective function; Determining the optimal individual from the initial population according to the fitness value of each initial design parameter set; Performing a crossover operation on the optimal individuals to generate new individuals, performing a mutation operation on the new individuals to obtain a next generation population, wherein the next generation population includes a plurality of new design parameter sets; The process starts with calculating the fitness value of each new design parameter set according to the objective function and performs cyclic iteration until an iteration termination condition is reached, and the optimal individual that reaches the iteration termination condition is determined as the optimal design parameter set.

6. The ship cooling water system pipe network optimization design method according to claim 1, characterized in that: The method of using a genetic algorithm to solve the objective function under the constraint conditions to obtain an optimal design parameter set for the pipe network includes: A non-dominated sorting genetic algorithm is used to solve the objective function under the constraints to obtain an optimal design parameter set for the pipe network.

7. The ship cooling water system pipe network optimization design method according to any one of claims 2 to 6, characterized in that: The optimal design parameter set includes optimal flow rate, flow resistance, and pipe network series and parallel forms.

8. The ship cooling water system pipe network optimization design method according to claim 1, characterized in that: The method further comprises: Based on the optimal design parameter set of the pipe network, a two-dimensional plane design scheme of the pipe network is generated that matches the water supply demand of each cooling user.

9. A device for optimizing the design of pipe network for ship cooling water system, characterized in that: include: A construction unit is used to construct an objective function by taking maximizing cooling uniformity and minimizing cooling power of a pipe network of a ship cooling water system as optimization design goals of the pipe network; A determination unit, configured to obtain water supply demand data of each cooling user of the ship cooling water system, and determine the constraint conditions of the objective function according to the water supply demand data of each cooling user; A solving unit is used to solve the objective function under the constraint conditions by using a genetic algorithm to obtain an optimal design parameter set for the pipe network.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the ship cooling water system pipeline optimization design method as described in any one of claims 1 to 8 is implemented.

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