A scheduling optimization method and system based on shipbuilding

Abstract

The present invention discloses a scheduling optimization method and system based on shipbuilding, which relates to the field of scheduling optimization technology. The specific steps are: collecting relevant data of shipbuilding production and preprocessing the relevant data; constructing a carrier network based on the preprocessed data, and calculating the dependencies and constraints between each node to generate an optimal overall section deployment plan; constructing a prior distribution of system behavior using the optimal overall section deployment plan and historical data according to a Gaussian process model; obtaining real-time status data and generating a posterior probability distribution through real-time status data updates; constructing a mathematical model based on the preprocessed data, and using an ant colony algorithm and the prior distribution or posterior probability distribution of system behavior to perform multi-objective optimization on the mathematical model to obtain an optimal deployment plan. The present invention combines the Gaussian process and the ant colony algorithm to flexibly adjust strategies to cope with changing conditions, thereby improving the efficiency and accuracy of scheduling.

Description

Technical Field

[0001] The present invention relates to the technical field of scheduling optimization, and more particularly to a scheduling optimization method and system based on shipbuilding. Background Art

[0002] Currently, maritime transport is a core pillar of global trade and commodity exchange, and its importance is self-evident. The construction and operational efficiency of ships directly impacts the smooth operation of the global supply chain. Especially in the current globalized economic environment, efficient ship operation is crucial to maintaining the stability of trade flows. With the continued expansion of international trade and the growing demand for ships, the shipbuilding industry has shown strong growth momentum, driving the rapid development of the maritime industry.

[0003] However, the shipbuilding process is complex and diverse, involving three major process flows: hull construction, outfitting, and painting, which include multiple key links, such as steel pretreatment, parts processing, component assembly, section assembly, and slipway assembly. This process faces many challenges, including but not limited to the management of the construction cycle, the effective use of funds, and the coordination between workshop equipment. In particular, the existing solutions are insufficient in improving construction efficiency and reducing operating costs. Although deterministic methods can provide optimal solutions for small-scale problems, and heuristic methods provide greater flexibility and scalability, most of these methods are based on static models and lack the dynamic adaptability required to cope with changes in on-site conditions (such as material shortages, equipment failures, or adjustments to production priorities).

[0004] Therefore, developing a scheduling optimization algorithm with dynamic adaptability to meet the needs of real-time adjustment and emergency response in shipbuilding scenarios is an urgent problem that technical personnel in this field need to solve. Summary of the Invention

[0005] In view of this, the present invention provides a scheduling optimization method and system based on shipbuilding, which overcomes the above-mentioned defects.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A scheduling optimization method based on shipbuilding, the specific steps are:

[0008] Collecting relevant data of shipbuilding and production, and preprocessing the relevant data;

[0009] Build a loading network based on pre-processed data, calculate the dependencies and constraints between nodes, and generate the optimal overall segment deployment plan;

[0010] Constructing a prior distribution of system behavior using the optimal overall segment deployment plan and historical data according to a Gaussian process model; acquiring real-time status data, and updating and generating a posterior probability distribution based on the real-time status data;

[0011] Based on the preprocessed data, a mathematical model is constructed with the goal of minimizing material handling costs, production cycles, and resource consumption, and meeting multiple constraints. The mathematical model is optimized for multiple objectives using an ant colony algorithm and a priori distribution or a posteriori probability distribution of system behavior to obtain an optimal deployment solution.

[0012] Optionally, the ship manufacturing production related data includes ship design drawings, bill of materials, process flow information, equipment resources and human resource information.

[0013] Optionally, the steps for obtaining the optimal overall segment deployment solution are:

[0014] Building a piggybacking network based on the relevant data, wherein each production link is a node and process constraints are edges in the piggybacking network;

[0015] A topological sorting algorithm is used to calculate the dependencies and constraints between the nodes based on the onboard network to generate the optimal overall segment deployment plan.

[0016] Optionally, the specific steps of the optimal deployment solution are:

[0017] Step 41: Construct an optimization model for the shipbuilding process, determine the goal of minimizing material handling costs, production cycle time, and resource consumption, and define multiple constraints; decompose the shipbuilding process flow into state space nodes, where the state space nodes correspond to production steps, resource allocation, and operation procedures;

[0018] Step 42: Initialize the ant colony population, randomly generate multiple ant individuals, each of which represents a shipbuilding path solution, and initialize the initial path of each ant individual;

[0019] Step 43: In each iteration step, the individual ant selects the next state space node based on the path selection probability, where the path selection probability is determined by the pheromone concentration between nodes and the heuristic information.

[0020] Step 44: Update the path pheromone according to the objective function optimization value of the path completed by the individual ant;

[0021] Step 45: Evaluate the path plan of each ant individual, select the current optimal solution, and determine whether the iteration termination condition is met; if not, return to step 43 to continue iterative optimization; if so, output the optimal deployment plan.

[0022] Optionally, the expression of the path selection probability is:

[0023] ;

[0024] Where, For slave nodes To Node The probability of selection, For the path ( , )’s pheromone strength; For the path ( , ) heuristic information; is the weight of the pheromone; For the path ( , )’s pheromone strength; For the path ( , ) heuristic information; is the weight of the heuristic information; is a candidate path node; A collection of nodes.

[0025] A scheduling optimization system based on shipbuilding includes a data preprocessing module, a deployment sequence optimization module, a heuristic solution generation module and an ant colony optimization module connected in sequence;

[0026] The data preprocessing module is used to collect relevant data of shipbuilding production and preprocess the relevant data;

[0027] The deployment sequence optimization module is used to calculate the dependencies and constraints between nodes through the loading network constructed based on the preprocessed data, and generate the optimal overall segment deployment plan;

[0028] The heuristic solution generation module is used to construct a prior distribution of system behavior based on the Gaussian process model using the optimal overall segment deployment plan and historical data; obtain real-time status data, and update and generate a posterior probability distribution based on the real-time status data;

[0029] The ant colony optimization module is used to establish a mathematical model that meets multiple constraints with the goal of minimizing material handling costs, production cycles, and resource consumption. The ant colony algorithm and the prior distribution or posterior probability distribution of system behavior are used to perform multi-objective optimization on the mathematical model to obtain the optimal deployment plan.

[0030] Optionally, the deployment sequence optimization module includes:

[0031] A piggyback network construction unit, configured to construct a piggyback network based on the relevant data, wherein each production link in the piggyback network is a node and process constraints are edges;

[0032] A computing unit is configured to calculate the dependencies and constraints between the nodes based on the onboard network using a topological sorting algorithm to generate the optimal overall segment deployment solution.

[0033] Optionally, the ant colony optimization module includes:

[0034] A digital model building unit is used to construct an optimization model of the shipbuilding process, determine the goal of minimizing material handling costs, production cycle and resource consumption, and define multiple constraints; decompose the shipbuilding process into state space nodes, where the state space nodes correspond to production steps, resource allocation and operation procedures;

[0035] An ant colony initialization unit is used to initialize the ant colony population, randomly generate multiple ant individuals, each of which represents a shipbuilding path solution, and initialize the initial path of each of the ant individuals;

[0036] a path selection unit, configured to select, in each iteration step, the next state space node by the ant individual based on a path selection probability determined by the pheromone concentration between nodes and heuristic information;

[0037] A pheromone updating unit, configured to update the path pheromone according to the objective function optimization value of the path completed by the individual ant;

[0038] The deployment plan generating unit is used to evaluate the path plan of each ant individual, select the current optimal solution, and determine whether the iteration termination condition is met; if not, continue the iterative optimization; if so, output the optimal deployment plan.

[0039] From the above technical solutions, it can be seen that the present invention discloses a scheduling optimization method and system based on shipbuilding, which has the following beneficial effects compared with the existing technology:

[0040] 1. The prior distribution provided by the Gaussian process guides the ant colony algorithm's search, reducing unnecessary exploration and thus lowering the risk of falling into a local optimum. As the production environment changes, the ant colony algorithm can flexibly adjust its strategy by updating the posterior distribution to cope with changing conditions, thereby improving scheduling efficiency and accuracy.

[0041] 2. The global search capability of the ant colony algorithm and the local optimization capability of the Gaussian process complement each other. By utilizing the Gaussian process model and heuristic information, the strategy is dynamically adjusted based on historical data and current status, achieving flexible optimization of the production process. In the face of changes and uncertainties in production, the system can respond quickly and adjust its strategy, maintaining a high degree of flexibility and adaptability, which is crucial for maintaining the stability and efficiency of the production process.

[0042] 3. The system fully considers the various complex constraints encountered in the shipbuilding process, such as safety distance, equipment interference, and task priority, to ensure that the scheduling plan is not only scientific and reasonable, but also practical and feasible, and better adapts to the complex requirements of the actual production environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in 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 merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0044] Figure 1 A schematic flow chart of the method provided by the present invention;

[0045] Figure 2 This is a schematic diagram of the system structure provided by the present invention. DETAILED DESCRIPTION

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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 making creative efforts are within the scope of protection of the present invention.

[0047] On the one hand, an embodiment of the present invention discloses a scheduling optimization method based on shipbuilding, aiming to provide an efficient scheduling optimization method specifically for dealing with complex scheduling needs in large-scale shipbuilding industry. This method fully considers the multiple constraints in the shipbuilding process, such as safety distance, interference and production priority constraints, so that the optimization scheme is more in line with the actual production environment; by combining Gaussian process and ant colony algorithm, the accuracy and adaptability are significantly improved. The Gaussian process provides a priori distribution to guide the ant colony algorithm to avoid unnecessary exploration and reduce the risk of falling into local optimality; as the production environment changes and uncertainty increases, the Gaussian process updates the posterior distribution, allowing the ant colony algorithm to flexibly adjust its strategy; in addition, the global search of the ant colony algorithm is combined with the local optimization of the Gaussian process, so that the optimization process has both global exploration capabilities and can finely adjust the optimal solution. This method provides a scientific solution to the yard crane scheduling problem in shipbuilding, and provides a new theoretical basis and practical guidance for optimization problems in complex industrial environments; the steps are as follows: Figure 1 As shown, specifically:

[0048] Step 1: Collect relevant data of shipbuilding production and pre-process the relevant data;

[0049] Step 2: Build a loading network based on pre-processed data, calculate the dependencies and constraints between nodes, and generate the optimal overall segment deployment plan;

[0050] Step 3: Based on the Gaussian process model, the optimal overall segment deployment plan and historical data are used to construct a prior distribution of system behavior; real-time status data is obtained, and the posterior probability distribution is generated through real-time status data updates;

[0051] Step 4: Based on the preprocessed data, a mathematical model is constructed that meets multiple constraints with the goal of minimizing material handling costs, production cycle, and resource consumption. The ant colony algorithm and the prior distribution or posterior probability distribution of system behavior are used to perform multi-objective optimization on the mathematical model to obtain the optimal deployment plan.

[0052] In one embodiment, the relevant data of ship manufacturing production includes ship design drawings, bill of materials, process flow information, equipment resources and human resource information.

[0053] Furthermore, data preprocessing involves the following steps: The hull is rationally planned and divided into sections based on the ship type and manufacturing requirements. The sections, as intermediate products, undergo a manufacturing process that includes small assembly, intermediate assembly, large assembly, pre-outfitting, and painting. Small sections are first assembled into intermediate assemblies, which are then combined into large assemblies. After preheating, shot blasting, and rust removal in the pretreatment workshop, the profiles are transported to the processing area for cutting and bending to form small components. These small components are then hoisted, assembled, and welded to form small sections. These sections then undergo similar processes to form intermediate assemblies, which are then transported to the pre-outfitting and painting workshops for further processing.

[0054] In one embodiment, the steps for obtaining the optimal overall segment deployment solution are:

[0055] Step 21: Construct a loading network based on the relevant data, with each production link as a node and process constraints as edges in the loading network;

[0056] Step 22: Use a topological sorting algorithm to calculate the dependencies and constraints between nodes based on the network, and generate an optimal overall segment deployment plan.

[0057] Furthermore, by constructing a shipping network, the overall section deployment sequence of each production link in the shipbuilding process is optimized. The shipping network treats each production link as a node and calculates the dependencies and constraints between nodes to generate the optimal deployment sequence. This optimized deployment sequence helps reduce conflicts in material handling and equipment scheduling, improves production efficiency, and ensures that each link is carried out in order, effectively shortening production cycle time and reducing production costs.

[0058] Furthermore, the topological sorting algorithm has the following specific calculation steps:

[0059] Initialization: Based on the directed graph of the production process, determine the in-degree of each node (i.e. its predecessor task);

[0060] Select a node: Select a node with an in-degree of zero, indicating that the production link can start;

[0061] Delete nodes and related edges: remove the selected node from the graph and reduce the in-degree of its successor nodes;

[0062] Repeat: Continue selecting nodes with in-degree zero and repeat the above steps until all nodes have been visited.

[0063] In one embodiment, the steps of constructing the Gaussian process model are:

[0064] Select an appropriate kernel function: Select an appropriate Gaussian process kernel function based on historical data to construct the system's prior distribution;

[0065] Training Gaussian process model: Based on historical data, a Gaussian process is used to generate a preliminary prior distribution that reflects the underlying laws and expected behaviors of the shipbuilding process.

[0066] Furthermore, heuristic information and prior information are used to deploy the overall segment; a Gaussian process model is used to generate a prior distribution of system behavior based on historical data, and its posterior distribution is updated based on the current state; the Gaussian process model is then used to generate the probability distribution of different deployment plans. The specific steps are as follows:

[0067] 1) Prior information: Based on historical data (e.g., deployment data from past shipbuilding processes), a Gaussian process is used to generate a prior distribution of system behavior, expressed as:

[0068] ;

[0069] Where, is the objective function (optimal total segment deployment plan), is an asymptotic lower bound; is the mean function, is the covariance function, which is used to describe the correlation between different locations.

[0070] 2) Real-time data update: Update the posterior distribution of the Gaussian process model based on the real-time production status (e.g., current process completion status, resource availability, etc.). The posterior distribution is generated based on the known training data and the current input; the mean expression is:

[0071] ;

[0072] Where, Is a new input point The mean of (prior information of training data); Is a new input point and training data The covariance vector between ; is the covariance matrix, is the deviation of the training output from the mean.

[0073] The variance expression is:

[0074] ;

[0075] Where, Is a new input point The autocovariance of Is a new input point With training data The covariance between is the inverse of the covariance matrix of the training data; Is a new input point With training data The transposed matrix of the covariance between .

[0076] 3) Generate probability: Generate the probability of the optimization scheme (e.g., deployment sequence, spatial arrangement, etc.) through the posterior distribution, so that the deployment sequence and resource allocation in the shipbuilding process can be predicted and optimized. The expression is:

[0077] ;

[0078] Where, is the training data; is the current output; It is a new prediction given historical data; is the uncertainty about the new forecast; is a Gaussian distribution.

[0079] By using Gaussian processes, the effects of various deployment sequences and resource allocation schemes in the shipbuilding process can be predicted based on historical data and current conditions, and these schemes can be optimized. This approach not only improves scheduling and production efficiency but also handles uncertainty and complex constraints.

[0080] In one embodiment, the specific steps of the optimal deployment solution are:

[0081] Step 41: Construct an optimization model for the shipbuilding process, determine the goal of minimizing material handling costs, production cycle time, and resource consumption, and define multiple constraints; decompose the shipbuilding process flow into state space nodes, where the state space nodes correspond to production steps, resource allocation, and operation procedures;

[0082] Step 42: Initialize the ant colony population, randomly generate multiple ant individuals, each ant individual represents a shipbuilding path solution, and initialize the initial path of each ant individual;

[0083] Step 43: In each iteration, the individual ant selects the next state space node based on the path selection probability, which is determined by the pheromone concentration between nodes and the heuristic information.

[0084] Step 44: Update the path pheromone according to the objective function optimization value of the path completed by the individual ant;

[0085] Step 45: Evaluate the path plan of each ant individual, select the current optimal solution, and determine whether the iteration termination condition is met; if not, return to step 43 to continue iterative optimization; if so, output the optimal deployment plan.

[0086] Furthermore, the site optimization is converted into a mathematical model and optimized through the ant colony algorithm. In the ship site optimization problem, the ant colony algorithm can effectively optimize the key links such as resource allocation and deployment sequence in the hull manufacturing process. The specific steps are:

[0087] In step 41, the problem is modeled: the optimization objectives are determined (reducing material handling costs, improving production efficiency, and reducing resource consumption); multiple constraints in shipbuilding are considered (safety distance, equipment interference, job priority, and resource limitations); and the ant colony state space is defined: each step, resource allocation, and production link in the shipbuilding process is used as the state space of the ant colony.

[0088] Material handling costs are quantified based on the distance the material is transported and the number of times it is moved; assuming Indicates that the slave site To the site distance, Indicates that the slave site To the site The quantity of materials transported, the optimization objective can be expressed as:

[0089] ;

[0090] Production efficiency is usually measured by minimizing the production cycle time; the production cycle time is the total time to complete the shipbuilding task, which is composed of the time of each process. Assume Indicates the process time, the optimization objective can be expressed as:

[0091] ;

[0092] Indicates the number of operations.

[0093] Resource consumption is measured by the consumption of materials, energy, and labor. It is a resource The consumption, It is a resource The unit cost, the optimization objective can be expressed as:

[0094] .

[0095] In step 42, the ant population is initialized, with a certain number of ants randomly generated. Each ant's path is initialized. Specifically, each ant represents a potential optimization solution, and the ants search for the optimal path according to pre-set rules. Each ant determines its path based on heuristic information (the time or space requirements of the production process) and prior information (historical data or expert knowledge).

[0096] In step 43, at each step, the ant selects the next node to visit (the next production link in the deployment sequence) based on the path selection rule. This selection is typically based on two factors: pheromone concentration and heuristic information. Each path has a pheromone value that indicates the quality of the path. The higher the pheromone concentration, the better the path. In shipyard optimization, heuristic information (such as process time and resource availability) can be used to help the ants make decisions. The expression is usually:

[0097] ;

[0098] Where, Represents a slave node To Node The probability of selection; is the path ( , )’s pheromone strength; For the path ( , ) heuristic information; For the path ( , )’s pheromone strength; is the path ( , ) heuristic information (path feasibility or process completion time), and are the weights of pheromone and heuristic information, respectively; is an index representing a candidate path node; A collection of nodes.

[0099] In step 44, after each ant completes a round of path selection, it updates the pheromone content along its path based on the optimization performance of its path (total manufacturing time or resource utilization). The pheromone update rule is typically divided into two phases: volatilization and enhancement. Pheromones along all paths will volatilize, simulating the pheromone decay process in nature. If an ant's path is optimal (i.e., meets the optimization objective function), the pheromone content along that path will increase. The pheromone update expression is:

[0100] ;

[0101] Where, It is the volatilization factor of pheromone, controlling the speed of pheromone decay; is the path at time t ( , )’s pheromone strength; It's the ants on the path Increased pheromone amount.

[0102] In step 45, after all ants have completed their path selection and pheromone update, they evaluate the quality of each solution. The quality of each path is typically evaluated using an objective function (such as minimizing manufacturing time or resource consumption). The optimal path is selected as the solution for the current iteration, and further optimization is performed based on this solution. Steps 43 to 45 are repeated for multiple rounds until the stopping condition is met and the optimal solution that meets the objective function is found.

[0103] Furthermore, ants select paths at each iteration, guided by pheromone intensity and the prior distribution provided by the Gaussian process. Paths with higher pheromone concentrations are more likely to be chosen by ants. The Gaussian process modifies pheromone intensity using the prior distribution, enabling ants to prioritize high-probability, high-quality paths. As uncertainty in the production process increases, the Gaussian process adapts to new environmental changes by updating the posterior distribution in real time. This means that whenever the production environment changes (such as equipment failures or production delays), the Gaussian process can adjust the posterior distribution to influence the ant colony algorithm's path selection and pheromone update strategy in real time.

[0104] On the other hand, this embodiment also discloses a scheduling optimization system based on shipbuilding, such as Figure 2 As shown, it includes a data preprocessing module, a deployment sequence optimization module, a heuristic solution generation module and an ant colony optimization module connected in sequence;

[0105] Data preprocessing module, used to collect relevant data of shipbuilding production and preprocess the relevant data;

[0106] The deployment sequence optimization module is used to calculate the dependencies and constraints between nodes through the loading network built based on preprocessed data, and generate the optimal overall segment deployment plan;

[0107] The heuristic solution generation module is used to construct the prior distribution of system behavior based on the Gaussian process model using the optimal total segment deployment plan and historical data; obtain real-time status data and generate the posterior probability distribution based on the real-time status data update;

[0108] The ant colony optimization module is used to establish a mathematical model that meets multiple constraints with the goal of minimizing material handling costs, production cycle and resource consumption. It uses the ant colony algorithm and the prior distribution or posterior probability distribution of system behavior to perform multi-objective optimization on the mathematical model to obtain the optimal deployment plan.

[0109] In one embodiment, the deployment sequence optimization module includes:

[0110] The piggyback network construction unit is used to construct a piggyback network based on relevant data, with each production link as a node and process constraints as edges in the piggyback network;

[0111] The computing unit is used to calculate the dependencies and constraints between nodes based on the network using a topological sorting algorithm to generate an optimal overall segment deployment plan.

[0112] In one embodiment, the ant colony optimization module includes:

[0113] The digital model building unit is used to construct an optimization model of the shipbuilding process, determine the goal of minimizing material handling costs, production cycle and resource consumption, and define multiple constraints; decompose the shipbuilding process into state space nodes, which correspond to production steps, resource allocation and operation procedures;

[0114] The ant colony initialization unit is used to initialize the ant colony population, randomly generate multiple ant individuals, each of which represents a shipbuilding path plan, and initialize the initial path of each ant individual;

[0115] The path selection unit is used to select the next state space node based on the path selection probability in each iteration step. The path selection probability is determined by the pheromone concentration between nodes and heuristic information.

[0116] A pheromone updating unit, used to update the path pheromone according to the optimization value of the objective function of the path completed by the individual ants;

[0117] The deployment plan generation unit is used to evaluate the path plan of each ant individual, select the current optimal solution, and determine whether the iteration termination condition is met; if not, continue iterative optimization; if so, output the optimal deployment plan.

[0118] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0119] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A scheduling optimization method based on shipbuilding, characterized in that: The specific steps are: Collecting relevant data of shipbuilding and production, and preprocessing the relevant data; A loading network is constructed based on the preprocessed data, and dependencies and constraints between nodes are calculated to generate an optimal overall segment deployment plan. The optimal overall segment deployment plan is obtained by: constructing a loading network based on the relevant data, wherein each production link is a node and process constraints are edges in the loading network; and using a topological sorting algorithm to calculate dependencies and constraints between nodes in the loading network to generate the optimal overall segment deployment plan. Constructing a prior distribution of system behavior using the optimal overall segment deployment plan and historical data according to a Gaussian process model; acquiring real-time status data, and updating and generating a posterior probability distribution based on the real-time status data; Based on the preprocessed data, a mathematical model is constructed with the goal of minimizing material handling costs, production cycles, and resource consumption, and meeting multiple constraints. The mathematical model is optimized for multiple objectives using an ant colony algorithm and a priori distribution or a posteriori probability distribution of system behavior to obtain an optimal deployment solution.

2. A scheduling optimization method based on shipbuilding according to claim 1, characterized in that: The relevant data of ship manufacturing and production include ship design drawings, bill of materials, process flow information, equipment resources and human resource information.

3. The scheduling optimization method based on shipbuilding according to claim 1 is characterized in that: The specific steps of the optimal deployment solution are: Step 41: Construct an optimization model for the shipbuilding process, determine the goal of minimizing material handling costs, production cycle time, and resource consumption, and define multiple constraints; decompose the shipbuilding process flow into state space nodes, where the state space nodes correspond to production steps, resource allocation, and operation procedures; Step 42: Initialize the ant colony population, randomly generate multiple ant individuals, each of which represents a shipbuilding path solution, and initialize the initial path of each ant individual; Step 43: In each iteration step, the individual ant selects the next state space node based on the path selection probability, where the path selection probability is determined by the pheromone concentration between nodes and the heuristic information. Step 44: Update the path pheromone according to the objective function optimization value of the path completed by the individual ant; Step 45: Evaluate the path plan of each ant individual, select the current optimal solution, and determine whether the iteration termination condition is met; if not, return to step 43 to continue iterative optimization; if so, output the optimal deployment plan.

4. The scheduling optimization method based on shipbuilding according to claim 3 is characterized in that: The expression of the path selection probability is: Where, P ij is the selection probability from node i to node j, τ ij is the pheromone intensity of path (i, j); η ij is the heuristic information of path (i, j); α is the weight of pheromone; τ ik is the pheromone intensity of path (i, k); η ik is the heuristic information of path (i, k); β is the weight of heuristic information; k is the candidate path node; allowed is the node set.

5. A scheduling optimization system based on shipbuilding, characterized in that: It includes a data pre-processing module, a deployment sequence optimization module, a heuristic solution generation module and an ant colony optimization module which are connected in sequence; The data preprocessing module is used to collect relevant data of shipbuilding production and preprocess the relevant data; The deployment sequence optimization module is used to calculate the dependencies and constraints between nodes through the loading network constructed based on the preprocessed data, and generate the optimal overall segment deployment plan; The heuristic solution generation module is used to construct a prior distribution of system behavior based on the Gaussian process model using the optimal overall segment deployment plan and historical data; obtain real-time status data, and update and generate a posterior probability distribution based on the real-time status data; The ant colony optimization module is used to establish a mathematical model that meets multiple constraints with the goal of minimizing material handling costs, production cycle and resource consumption. The ant colony algorithm and the prior distribution or posterior probability distribution of system behavior are used to perform multi-objective optimization on the mathematical model to obtain the optimal deployment plan; Wherein, the deployment sequence optimization module includes: A piggyback network construction unit, configured to construct a piggyback network based on the relevant data, wherein each production link in the piggyback network is a node and process constraints are edges; A computing unit is configured to calculate the dependencies and constraints between the nodes based on the onboard network using a topological sorting algorithm to generate the optimal overall segment deployment solution.

6. A scheduling optimization system based on shipbuilding according to claim 5, characterized in that: The ant colony optimization module includes: A digital model building unit is used to construct an optimization model of the shipbuilding process, determine the goal of minimizing material handling costs, production cycle and resource consumption, and define multiple constraints; decompose the shipbuilding process into state space nodes, where the state space nodes correspond to production steps, resource allocation and operation procedures; An ant colony initialization unit is used to initialize the ant colony population, randomly generate multiple ant individuals, each of which represents a shipbuilding path solution, and initialize the initial path of each of the ant individuals; a path selection unit, configured to select, in each iteration step, the next state space node by the ant individual based on a path selection probability determined by the pheromone concentration between nodes and heuristic information; A pheromone updating unit, configured to update the path pheromone according to the objective function optimization value of the path completed by the individual ant; The deployment plan generating unit is used to evaluate the path plan of each ant individual, select the current optimal solution, and determine whether the iteration termination condition is met; if not, continue the iterative optimization; if so, output the optimal deployment plan.

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