Ship design optimization method and system based on equipment collaborative operation data analysis

Abstract

The invention discloses a ship design optimization method and system based on equipment collaborative operation data analysis, and aims to optimize breeding auxiliary ship design. The method comprises the following steps: firstly, acquiring historical operation data and swing coefficient data of a breeding auxiliary ship under different main control factors, and performing performance evaluation; analyzing the main control factor and a performance evaluation result, and determining the influence of the main control factor on the ship performance; then, according to the breeding scale data of the target breeding place, fish technology and fish art equipment matching is carried out, and equipment information is obtained; determining required ship performance demand data according to the equipment information; and finally, in combination with the wind wave change characteristic data, the main control factor-performance influence data and the ship performance demand data, optimizing and determining the design parameters of the ship in the target farm. According to the method, through equipment collaborative analysis, the accuracy and adaptability of ship design are improved, the requirements of different breeding environments are met, and the working efficiency and stability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of ship design, and in particular to a ship design optimization method and system based on equipment collaborative operation data analysis. Background Art

[0002] With the rapid development of the aquaculture industry, aquaculture auxiliary vessels are increasingly used in aquaculture operations. These auxiliary vessels are not only responsible for providing logistics support, but also need to carry a variety of fishing equipment, such as bait throwers, net washers, mechanical arms, cranes, fish pumps, etc., to perform different tasks. With the expansion of aquaculture scale and the complexity of the operating environment, the optimization of ship design has become a key factor in improving aquaculture efficiency and equipment stability.

[0003] Existing aquaculture auxiliary ship design methods usually rely on a single ship performance parameter or static design index, ignoring the need for coordinated work between the ship and various fishing equipment during actual operation, especially in complex wind, wave and current environments. Traditional design methods fail to fully consider the dynamic performance of the ship, especially the six-degree-of-freedom swing performance (i.e., the ship's wave resistance), as well as the maximum swing amplitude and acceleration that different equipment may withstand during the swing process. This single design method often leads to poor matching between the ship and the equipment, thus affecting working stability, equipment service life and aquaculture efficiency.

[0004] In addition, existing design methods usually do not fully utilize the environmental characteristics of the aquaculture site (such as wind, wave and current changes) as well as comprehensive information such as aquaculture scale and equipment requirements, resulting in the inability of ship design to accurately adapt to the actual needs of the target aquaculture site. Therefore, how to optimize ship design based on equipment collaborative operation data, environmental characteristics and ship dynamic performance has become a technical problem that needs to be solved urgently.

[0005] To this end, the present invention proposes a ship design optimization method and system based on equipment collaborative operation data analysis. By comprehensively analyzing the performance of the ship, environmental changes and equipment requirements, the ship design is optimized, the applicability, stability and work efficiency of aquaculture auxiliary ships are improved, thereby providing a more efficient and intelligent ship design solution for the aquaculture industry. Summary of the invention

[0006] In order to solve at least one of the above technical problems, the present invention proposes a ship design optimization method and system based on equipment collaborative operation data analysis.

[0007] A first aspect of the present invention provides a ship design optimization method based on equipment collaborative operation data analysis, comprising:

[0008] Obtaining historical operation data and historical ship sway coefficient data of aquaculture auxiliary ships with different main control factors under various wind and wave characteristics, and performing performance evaluation on aquaculture auxiliary ships with different main control factors according to the historical operation data and historical ship sway coefficient data to obtain performance evaluation results;

[0009] Comprehensively analyze the main control factors of the aquaculture auxiliary ship and the performance evaluation results to determine the impact of the main control factors of the ship on the ship performance, and obtain the main control factor-performance impact data;

[0010] Acquire the aquaculture scale data of the target aquaculture site, match the fishing techniques and equipment according to the aquaculture scale data, and determine the fishing techniques and equipment information of the target aquaculture site;

[0011] Determine the ship performance requirement data of the aquaculture auxiliary ship required for the operation of the fishery technology and fishery equipment according to the fishery technology and fishery equipment information;

[0012] The wind and wave variation characteristic data of the target aquaculture site are obtained, and the design parameters of the aquaculture auxiliary ship in the target aquaculture site are determined according to the main control factor-performance impact data, the ship performance requirement data, and the wind and wave variation characteristic data.

[0013] In this scheme, the historical operation data and historical ship sway coefficient data of aquaculture auxiliary ships with different main control factors under various wind and wave characteristics are obtained, and the performance of aquaculture auxiliary ships with different main control factors is evaluated according to the historical operation data and historical ship sway coefficient data to obtain performance evaluation results, which are specifically:

[0014] Obtaining historical operation data of a preset number of aquaculture auxiliary vessels in a preset working time period under various wind and wave characteristics, wherein the wind and wave characteristics include wind speed, wind direction, wave height, wave direction, water flow speed and water flow direction, and the historical operation data includes navigation speed and navigation resistance;

[0015] Obtaining the main control factors of the aquaculture auxiliary ship, wherein the main control factors include ship length, ship width, draft, square coefficient, rhombus coefficient, cross-section coefficient and waterline area coefficient;

[0016] Based on the sway sensor, the six-degree-of-freedom sway condition data of the aquaculture auxiliary ship at each time point in the preset working time period is obtained, and the ship sway coefficient of the aquaculture auxiliary ship in the preset working time period is calculated according to the six-degree-of-freedom sway condition data, wherein the ship sway coefficient includes a roll amplitude coefficient, a pitch period coefficient, and a heave acceleration coefficient, and the historical ship sway coefficient data is obtained;

[0017] The acceleration performance and seakeeping performance of the aquaculture auxiliary ship are evaluated according to the historical operation data and the historical ship sway coefficient data to obtain a performance evaluation result.

[0018] In this scheme, the main control factors of the aquaculture auxiliary ship and the performance evaluation results are comprehensively analyzed to determine the impact of the main control factors of the ship on the ship performance, and obtain the main control factor-performance impact data, specifically:

[0019] Constructing a ship performance evaluation model based on a neural network, constructing an input layer, a hidden layer and an output layer of the ship performance evaluation model, wherein the number of neurons in the input layer corresponds to the number of main control factors, the number of neurons in the output layer corresponds to the number of ship performance indicators to be evaluated, the number of hidden layers and the number of neurons in each layer are preset values, initializing the weights and bias parameters of the ship performance evaluation model, and importing the main control factors as the input layer and the performance evaluation results as the output layer into the ship performance evaluation model for pre-training operation;

[0020] A genetic algorithm is introduced, a fitness function of the genetic algorithm is constructed based on a mean square error, a prediction error of a ship performance evaluation model on ship performance is used as an evaluation index of the fitness function, a population of the genetic algorithm is randomly generated, each individual in the population represents a set of weights and bias parameters of the ship performance evaluation model, and the number of iterations, crossover probability and mutation probability of the genetic algorithm are set;

[0021] For each iteration process, the ship performance evaluation model is forward calculated based on the main control factors and the performance evaluation results to obtain the predicted value of the ship performance, the fitness of each individual in the population is calculated according to the fitness function and the predicted value, individuals are selected according to the fitness, and crossover and mutation operations are performed on the selected individuals until the iteration is completed to obtain a set of optimal weights and bias parameters;

[0022] The ship performance evaluation model is trained according to the optimal weight and bias parameters, partial derivatives of each main control factor on the ship performance index are calculated during the training process, and the degree and direction of influence of each main control factor on the ship performance are determined according to the partial derivatives;

[0023] The main control factor-performance impact data is generated according to the degree and direction of the impact of each main control factor on the ship performance.

[0024] In this solution, the aquaculture scale data and wind and wave condition data of the target aquaculture site are obtained, and the fishing techniques and equipment are matched according to the aquaculture scale data to determine the fishing techniques and equipment information of the target aquaculture site, specifically:

[0025] Obtaining the breeding scale data of the target breeding site, wherein the breeding scale data includes breeding area, breeding species and quantity, and breeding density;

[0026] Obtaining working parameter information of various fishing equipment, including technical parameters, applicable scope, working principle, and production capacity;

[0027] Constructing a fishing technique and fishing equipment database, and importing the working parameter information of the various types of fishing technique and fishing equipment into the fishing technique and fishing equipment database;

[0028] Obtain the type of fishing technology and fishing art equipment required to be carried by the breeding auxiliary ship during the breeding process of the target breeding site, match the breeding scale data of the target breeding site and the type of fishing technology and fishing art equipment required to be carried by the breeding auxiliary ship with the fishing technology and fishing art equipment database, and determine the equipment parameters of the fishing technology and fishing art equipment, wherein the equipment parameters include equipment power or size;

[0029] The fishery technology and fishery equipment information of the target breeding site is determined according to the type and equipment parameters of the fishery technology and fishery equipment required to be carried by the breeding auxiliary vessel.

[0030] In this solution, the ship performance requirement data of the aquaculture auxiliary ship required for the operation of the fishery technology and fishery equipment is determined according to the fishery technology and fishery equipment information, specifically:

[0031] According to the fishing technique and fishing technology equipment information, equipment operation data of the fishing technique and fishing technology equipment required to be carried by the aquaculture auxiliary ship in the target aquaculture site under the performance characteristics of each ship are obtained, wherein the equipment operation data includes operation efficiency, equipment damage degree, and equipment shaking degree;

[0032] Performing a working stability evaluation on each of the carried fishing technique and fishing technology equipment according to the equipment operation data to obtain a stability evaluation result;

[0033] Acquire the collaborative operation relationship data between each carried fishery technology and fishery art equipment, construct the equipment collaborative operation relationship topology structure according to the collaborative operation relationship data, determine the influence degree of the working stability of each fishery technology and fishery art equipment on the stability of other fishery technology and fishery art equipment carried in the aquaculture auxiliary vessel according to the equipment collaborative operation relationship topology structure, and obtain the equipment collaborative operation stability influence data;

[0034] Obtaining work efficiency demand data of the target breeding site, and setting a comprehensive stability threshold for the coordinated operation of fishing technology and fishing equipment according to the work efficiency demand data;

[0035] A simulated annealing algorithm is introduced, the comprehensive stability threshold is used as the optimization target of the simulated annealing algorithm, and the data affecting the coordinated operation stability of the equipment is analyzed based on the simulated annealing algorithm to determine the minimum stability threshold required for each fishing technology and fishery equipment carried by the aquaculture auxiliary vessel under the condition of meeting the comprehensive stability threshold;

[0036] The minimum ship performance characteristics of the aquaculture auxiliary ship are determined according to the minimum stability threshold, and the ship performance requirement data of the aquaculture auxiliary ship required for the operation of the fishing technology and fishing equipment are obtained.

[0037] In this solution, the wind and wave change characteristic data of the target breeding site are obtained, and the design parameters of the breeding auxiliary ship in the target breeding site are determined according to the main control factor-performance impact data, ship performance demand data, and wind and wave change characteristic data, specifically:

[0038] Acquire wind and wave change data of the target breeding site within a preset time period, perform feature extraction on the wind and wave change data, determine the wind and wave change law of the target breeding site within the preset time period, and obtain wind and wave change feature data;

[0039] Determine the influence of different wind and wave characteristics on ship performance according to the performance evaluation results, and obtain wind and wave characteristics-performance influence data;

[0040] A ship design parameter analysis model is constructed based on the main control factor-performance impact data and the wind and wave characteristics-performance impact data, the ship performance requirement data is imported into the ship design parameter analysis model for comparison with the main control factor-performance impact data, and the optional range of the main control factors that meet the ship performance requirement data is preliminarily determined to obtain the range data of the candidate main control factors;

[0041] The wind and wave variation characteristic data are imported into the ship design parameter analysis model, and the wind and wave variation characteristic data and the candidate main control factor range data are analyzed according to the wind and wave characteristic-performance impact data to determine the final main control factor range that meets the ship performance requirement data under the wind and wave variation characteristics in the target breeding site, and the minimum value of each main control factor in the final main control factor range is selected as the design parameter of the breeding auxiliary ship in the target breeding site to obtain the ship design parameter combination.

[0042] The second aspect of the present invention further provides a ship design optimization system based on equipment collaborative operation data analysis, the system comprising: a memory, a processor, the memory comprising a ship design optimization method program based on equipment collaborative operation data analysis, the ship design optimization method program based on equipment collaborative operation data analysis when executed by the processor, implements the following steps:

[0043] Obtaining historical operation data and historical ship sway coefficient data of aquaculture auxiliary ships with different main control factors under various wind and wave characteristics, and performing performance evaluation on aquaculture auxiliary ships with different main control factors according to the historical operation data and historical ship sway coefficient data to obtain performance evaluation results;

[0044] Comprehensively analyze the main control factors of the aquaculture auxiliary ship and the performance evaluation results to determine the impact of the main control factors of the ship on the ship performance, and obtain the main control factor-performance impact data;

[0045] Acquire the aquaculture scale data of the target aquaculture site, match the fishing techniques and equipment according to the aquaculture scale data, and determine the fishing techniques and equipment information of the target aquaculture site;

[0046] Determine the ship performance requirement data of the aquaculture auxiliary ship required for the operation of the fishery technology and fishery equipment according to the fishery technology and fishery equipment information;

[0047] The wind and wave variation characteristic data of the target aquaculture site are obtained, and the design parameters of the aquaculture auxiliary ship in the target aquaculture site are determined according to the main control factor-performance impact data, the ship performance requirement data, and the wind and wave variation characteristic data.

[0048] The present invention discloses a ship design optimization method and system based on equipment collaborative operation data analysis, aiming to optimize the design of aquaculture auxiliary ships. The method comprises the following steps: first, the historical operation data and sway coefficient data of aquaculture auxiliary ships under different main control factors are obtained, and performance evaluation is performed; then, the main control factors and performance evaluation results are analyzed to determine the influence of the main control factors on the ship performance; then, the fishing technology and fishing art equipment are matched according to the aquaculture scale data of the target aquaculture site to obtain equipment information; the required ship performance demand data is determined according to the equipment information; finally, the design parameters of the ship in the target aquaculture site are optimized and determined by combining the wind and wave change characteristic data, the main control factor-performance influence data and the ship performance demand data. This method improves the accuracy and adaptability of ship design through equipment collaborative analysis, meets the needs of different aquaculture environments, and improves work efficiency and stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A flow chart of a ship design optimization method based on equipment collaborative operation data analysis of the present invention is shown;

[0050] Figure 2 A flow chart showing the performance evaluation results obtained by the present invention is shown;

[0051] Figure 3 A flow chart showing the design parameters of aquaculture auxiliary vessels determined according to the present invention is shown;

[0052] Figure 4 A block diagram of a ship design optimization system based on equipment collaborative operation data analysis of the present invention is shown. DETAILED DESCRIPTION

[0053] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0054] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0055] Figure 1 A flow chart of a ship design optimization method based on equipment collaborative operation data analysis of the present invention is shown.

[0056] like Figure 1 As shown, the first aspect of the present invention provides a ship design optimization method based on equipment collaborative operation data analysis, comprising:

[0057] S102, obtaining historical operation data and historical ship sway coefficient data of aquaculture auxiliary ships with different main control factors under various wind and wave characteristics, and performing performance evaluation on aquaculture auxiliary ships with different main control factors according to the historical operation data and historical ship sway coefficient data to obtain performance evaluation results;

[0058] S104, comprehensively analyzing the main control factors of the aquaculture auxiliary ship and the performance evaluation results, determining the impact of the main control factors of the ship on the ship performance, and obtaining main control factor-performance impact data;

[0059] S106, acquiring the breeding scale data of the target breeding site, matching the fishing techniques and equipment according to the breeding scale data, and determining the fishing techniques and equipment information of the target breeding site;

[0060] S108, determining the ship performance requirement data of the aquaculture auxiliary ship required for the operation of the fishery technology and fishery equipment according to the fishery technology and fishery equipment information;

[0061] S110, obtaining the wind and wave variation characteristic data of the target aquaculture site, and determining the design parameters of the aquaculture auxiliary ship in the target aquaculture site according to the main control factor-performance impact data, the ship performance requirement data, and the wind and wave variation characteristic data.

[0062] It should be noted that by obtaining historical operation data and sway coefficient data, the actual performance of aquaculture auxiliary ships in real environments can be quantitatively evaluated, especially the dynamic response under different wind and wave characteristics. Through this step, the stability, seakeeping and sway performance of ships in different environments can be more accurately grasped; by comprehensively analyzing the relationship between the main design factors of the ship (such as ship length, ship width, draft, etc.) and its performance evaluation results, the specific impact of each main control factor on the ship performance can be determined. Through systematic analysis, it is possible to clearly understand how each design factor affects the key performance parameters of the ship, such as seakeeping, stability and sway coefficient. Obtaining the aquaculture scale data of the target aquaculture site is helpful to match the appropriate type of fishing technology and fishing equipment (such as bait throwing machine, net washing machine, etc.) according to the actual needs of the aquaculture site. This matching can not only optimize the efficiency of equipment use and ensure that the functions of each equipment work in the best state, but also effectively avoid equipment overload or underload and reduce equipment failure rate. Through precise matching, the overall efficiency and reliability of aquaculture operations can be improved. According to the working parameters of the fishing technology and fishing equipment, the ship performance demand data required for these equipment is determined to ensure that the ship has sufficient performance support when carrying these equipment. By considering the working efficiency, stability and dynamic response requirements of the equipment, the optimal performance requirements of the ship in different operating environments can be effectively determined. This helps to avoid low operating efficiency or equipment damage caused by mismatched ship performance. By comprehensively considering the wind and wave change characteristics of the aquaculture site, the ship performance requirements and the main control factor-performance impact data, the ship design parameters can be dynamically optimized. Under different wind and wave conditions, the performance requirements of the ship will change. Therefore, by analyzing the characteristics of wind and wave changes, the design parameters of the ship (such as ship type, draft, power configuration, etc.) can be accurately adjusted to ensure that the ship can still maintain stable and efficient operating performance in complex environments.

[0063] Figure 2 A flow chart of obtaining performance evaluation results of the present invention is shown.

[0064] According to an embodiment of the present invention, the historical operation data and historical ship sway coefficient data of aquaculture auxiliary ships with different main control factors under various wind and wave characteristics are obtained, and the performance of aquaculture auxiliary ships with different main control factors is evaluated according to the historical operation data and historical ship sway coefficient data to obtain performance evaluation results, which are specifically:

[0065] S202, obtaining historical operation data of a preset number of aquaculture auxiliary vessels in a preset working time period under various wind and wave characteristics, wherein the wind and wave characteristics include wind speed, wind direction, wave height, wave direction, water flow speed and water flow direction, and the historical operation data includes navigation speed and navigation resistance;

[0066] S204, obtaining main control factors of the aquaculture auxiliary ship, wherein the main control factors include ship length, ship width, draft, square coefficient, rhombus coefficient, cross-section coefficient and waterplane area coefficient;

[0067] S206, obtaining six-degree-of-freedom sway data of the aquaculture auxiliary ship at each time point in a preset working time period based on the sway sensor, and calculating the ship sway coefficient of the aquaculture auxiliary ship in the preset working time period according to the six-degree-of-freedom sway data, wherein the ship sway coefficient includes a roll amplitude coefficient, a pitch period coefficient, and a heave acceleration coefficient, and obtaining historical ship sway coefficient data;

[0068] S208, evaluating the acceleration performance and seakeeping performance of the aquaculture auxiliary ship according to the historical operation data and the historical ship sway coefficient data, and obtaining a performance evaluation result.

[0069] It should be noted that by comprehensively analyzing the historical operation of the ship under different wind and wave characteristics, such as navigation speed, navigation resistance, etc., combined with the ship's sway data (including six-degree-of-freedom sway conditions such as roll, pitch and heave), the acceleration performance and wave resistance of the ship can be accurately evaluated. Specifically, based on the real-time data obtained by the sway sensor, the sway coefficient of the ship within the preset working time period can be accurately calculated, thereby evaluating the stability and carrying capacity of the ship under the action of wind and waves. This evaluation process can reveal the dynamic response of the ship in different environments and clarify the navigation ability and wave resistance of the ship under specific wind and wave conditions. The sway sensor includes an accelerometer and a gyroscope. The six-degree-of-freedom sway data includes the angles, angular velocities, angular accelerations and linear accelerations of roll, pitch, heave, sway, pitch and bow.

[0070] According to an embodiment of the present invention, the main control factors of the aquaculture auxiliary ship and the performance evaluation results are comprehensively analyzed to determine the impact of the main control factors of the ship on the ship performance, and obtain the main control factor-performance impact data, specifically:

[0071] Constructing a ship performance evaluation model based on a neural network, constructing an input layer, a hidden layer and an output layer of the ship performance evaluation model, wherein the number of neurons in the input layer corresponds to the number of main control factors, the number of neurons in the output layer corresponds to the number of ship performance indicators to be evaluated, the number of hidden layers and the number of neurons in each layer are preset values, initializing the weights and bias parameters of the ship performance evaluation model, and importing the main control factors as the input layer and the performance evaluation results as the output layer into the ship performance evaluation model for pre-training operation;

[0072] A genetic algorithm is introduced, a fitness function of the genetic algorithm is constructed based on a mean square error, a prediction error of a ship performance evaluation model on ship performance is used as an evaluation index of the fitness function, a population of the genetic algorithm is randomly generated, each individual in the population represents a set of weights and bias parameters of the ship performance evaluation model, and the number of iterations, crossover probability and mutation probability of the genetic algorithm are set;

[0073] For each iteration process, the ship performance evaluation model is forward calculated based on the main control factors and the performance evaluation results to obtain the predicted value of the ship performance, the fitness of each individual in the population is calculated according to the fitness function and the predicted value, individuals are selected according to the fitness, and crossover and mutation operations are performed on the selected individuals until the iteration is completed to obtain a set of optimal weights and bias parameters;

[0074] The ship performance evaluation model is trained according to the optimal weight and bias parameters, partial derivatives of each main control factor on the ship performance index are calculated during the training process, and the degree and direction of influence of each main control factor on the ship performance are determined according to the partial derivatives;

[0075] The main control factor-performance impact data is generated according to the degree and direction of the impact of each main control factor on the ship performance.

[0076] It should be noted that the neural network model can efficiently handle complex nonlinear relationships. By taking the main control factors (such as ship length, ship width, draft, etc.) as input and the ship performance evaluation results (such as seakeeping, acceleration performance, etc.) as output, the mathematical model of ship performance is obtained through training. The multi-layer structure of the neural network (including input layer, hidden layer and output layer) can automatically learn and extract potential features that affect ship performance, and improve the prediction accuracy and generalization ability of the model. The performance of the model can be effectively improved by combining the genetic algorithm to optimize the weight and bias parameters of the neural network. The genetic algorithm can avoid the trap of local optimal solutions and find the global optimal model parameters by simulating the natural selection process, randomly generating populations and iteratively optimizing. This optimization process enables the model to more accurately predict ship performance indicators when facing complex and changeable ship performance data, and effectively improve the accuracy and reliability of ship performance evaluation. During the training process of the neural network, the back propagation algorithm is used to calculate the gradients (i.e. partial derivatives) of each layer of neurons to optimize the weights and biases of the model. Each main control factor (such as ship length, ship width, draft, etc.) is used as an input to affect the change of network output (ship performance index). By calculating the partial derivative of the network output with respect to each input, the contribution of each main control factor to the ship performance is obtained. The partial derivative represents the rate of change of the ship performance index when the main control factor changes slightly, reflecting the sensitivity of the factor to the ship performance. The absolute value of the partial derivative reflects the sensitivity or influence of the main control factor on the ship performance. A larger partial derivative indicates that the main control factor has a stronger influence on the ship performance; a smaller partial derivative indicates that the influence of the factor is weaker. The sign of the partial derivative (positive or negative) reflects the direction of the relationship between the main control factor and the ship performance. A positive partial derivative indicates that an increase in the main control factor will lead to an improvement in ship performance (for example, increasing the length of the ship may improve the seakeeping performance); a negative partial derivative indicates that an increase in the main control factor will lead to a decrease in performance (for example, increasing the draft may reduce the stability of the ship); the ship performance indicators are acceleration performance indicators and seakeeping performance indicators, and the weights and bias parameters of the initialized ship performance evaluation model are assigned initial values; the degree and direction of influence are the magnitude of the change in the main control factor on the ship performance indicator and the positive and negative relationship between the change in the main control factor and the change in the ship performance indicator.

[0077] According to an embodiment of the present invention, the aquaculture scale data and wind and wave condition data of the target aquaculture site are obtained, and the fishing technique and equipment are matched according to the aquaculture scale data to determine the fishing technique and equipment information of the target aquaculture site, specifically:

[0078] Obtaining the breeding scale data of the target breeding site, wherein the breeding scale data includes breeding area, breeding species and quantity, and breeding density;

[0079] Obtaining working parameter information of various fishing equipment, including technical parameters, applicable scope, working principle, and production capacity;

[0080] Constructing a fishing technique and fishing equipment database, and importing the working parameter information of the various types of fishing technique and fishing equipment into the fishing technique and fishing equipment database;

[0081] Obtain the type of fishing technology and fishing art equipment required to be carried by the breeding auxiliary ship during the breeding process of the target breeding site, match the breeding scale data of the target breeding site and the type of fishing technology and fishing art equipment required to be carried by the breeding auxiliary ship with the fishing technology and fishing art equipment database, and determine the equipment parameters of the fishing technology and fishing art equipment, wherein the equipment parameters include equipment power or size;

[0082] The fishery technology and fishery equipment information of the target breeding site is determined according to the type and equipment parameters of the fishery technology and fishery equipment required to be carried by the breeding auxiliary vessel.

[0083] It should be noted that the appropriate equipment can be accurately matched according to the aquaculture scale data of the target aquaculture site (such as aquaculture area, aquaculture species and quantity, aquaculture density) and the type of aquaculture equipment required. This matching process not only takes into account the working parameters of the equipment (such as power, scope of application, production capacity, etc.), but also ensures that the type and function of the equipment are consistent with the actual needs of the aquaculture site, thereby avoiding over-configuration or insufficient equipment; the technical parameters include equipment power, structural dimensions, equipment working efficiency, and working mode; the aquaculture equipment information is the equipment type and equipment parameters.

[0084] According to an embodiment of the present invention, the ship performance requirement data of the aquaculture auxiliary ship required for the operation of the fishing technology and fishing equipment is determined according to the fishing technology and fishing equipment information, specifically:

[0085] According to the fishing technique and fishing technology equipment information, equipment operation data of the fishing technique and fishing technology equipment required to be carried by the aquaculture auxiliary ship in the target aquaculture site under the performance characteristics of each ship are obtained, wherein the equipment operation data includes operation efficiency, equipment damage degree, and equipment shaking degree;

[0086] Performing a working stability evaluation on each of the carried fishing technique and fishing technology equipment according to the equipment operation data to obtain a stability evaluation result;

[0087] Acquire the collaborative operation relationship data between each carried fishery technology and fishery art equipment, construct the equipment collaborative operation relationship topology structure according to the collaborative operation relationship data, determine the influence degree of the working stability of each fishery technology and fishery art equipment on the stability of other fishery technology and fishery art equipment carried in the aquaculture auxiliary vessel according to the equipment collaborative operation relationship topology structure, and obtain the equipment collaborative operation stability influence data;

[0088] It should be noted that the collaborative operation relationship data refers to the interaction, mutual influence and joint action relationship on the overall system performance between multiple fishing technology and fishing technique equipment when they are operated simultaneously or sequentially. The collaborative work of these devices often produces a mutual influence effect, which may affect the working efficiency, stability of the equipment and the overall performance of the ship. The equipment collaborative operation relationship topology structure refers to the representation of the mutual relationship and influence of various fishing technology and fishing technique equipment on the ship when working together in a structured way through a network diagram. The topology structure mainly describes how the devices are connected and interact with each other, and how they affect each other's working status and the performance of the overall system during the collaborative operation process.

[0089] Obtaining work efficiency demand data of the target breeding site, and setting a comprehensive stability threshold for the coordinated operation of fishing technology and fishing equipment according to the work efficiency demand data;

[0090] A simulated annealing algorithm is introduced, the comprehensive stability threshold is used as the optimization target of the simulated annealing algorithm, and the data affecting the coordinated operation stability of the equipment is analyzed based on the simulated annealing algorithm to determine the minimum stability threshold required for each fishing technology and fishery equipment carried by the aquaculture auxiliary vessel under the condition of meeting the comprehensive stability threshold;

[0091] It should be noted that the simulated annealing algorithm can determine the minimum stability threshold required for each fishing technology and fishery equipment carried by aquaculture auxiliary ships by optimizing the data affecting the coordinated operation of equipment stability, because the simulated annealing algorithm is a global optimization algorithm, which is particularly suitable for solving complex, multi-variable optimization problems. Specifically, first, the comprehensive stability threshold is used as the optimization target. The simulated annealing algorithm gradually explores the combination of different equipment stability thresholds by iteratively searching and adjusting the data affecting the coordinated operation of equipment stability, so as to minimize the overall stability risk of the system. In this process, the simulated annealing algorithm can effectively avoid falling into the local optimal solution, and gradually find the global optimal solution through appropriate randomness and variation adjustment. By analyzing the coordinated operation relationship between equipment, the simulated annealing algorithm can accurately determine the minimum stability threshold required for each fishing technology and fishery equipment under the premise of meeting the comprehensive stability requirements; the comprehensive stability threshold represents the maximum instability that can be tolerated when the equipment is in coordinated operation.

[0092] The minimum ship performance characteristics of the aquaculture auxiliary ship are determined according to the minimum stability threshold, and the ship performance requirement data of the aquaculture auxiliary ship required for the operation of the fishing technology and fishing equipment are obtained.

[0093] It should be noted that by obtaining equipment operation data (such as work efficiency, degree of damage and degree of shaking), the working stability of each device is evaluated to understand their operating characteristics on the ship. Next, the collaborative operation relationship between the equipment is analyzed, the topological structure of the equipment collaborative operation relationship is constructed, and the stability of the mutual influence between the equipment is quantified to ensure that the equipment can maintain the predetermined stability when working collaboratively. Then, according to the work efficiency requirements of the target breeding site, the comprehensive stability threshold is set to ensure that the collaborative operation of all equipment will not affect the overall work efficiency. Finally, the simulated annealing algorithm is introduced to optimize the stability of the equipment collaborative operation, determine the minimum stability threshold required for each device, and then derive the minimum performance requirements required for the ship.

[0094] Figure 3 A flow chart of determining design parameters of aquaculture auxiliary vessels according to the present invention is shown.

[0095] According to an embodiment of the present invention, the wind and wave change characteristic data of the target aquaculture site is obtained, and the design parameters of the aquaculture auxiliary ship in the target aquaculture site are determined according to the main control factor-performance impact data, the ship performance requirement data, and the wind and wave change characteristic data, specifically:

[0096] S302, obtaining wind and wave variation data of the target breeding site within a preset time period, performing feature extraction on the wind and wave variation data, determining the wind and wave variation law of the target breeding site within the preset time period, and obtaining wind and wave variation feature data;

[0097] S304, determining the influence of different wind and wave characteristics on ship performance according to the performance evaluation result, and obtaining wind and wave characteristics-performance influence data;

[0098] S306, constructing a ship design parameter analysis model based on the main control factor-performance impact data and the wind and wave characteristics-performance impact data, importing the ship performance requirement data into the ship design parameter analysis model for comparison with the main control factor-performance impact data, preliminarily determining the optional range of the main control factors that meet the ship performance requirement data, and obtaining the candidate main control factor range data;

[0099] S308, importing the wind and wave variation characteristic data into the ship design parameter analysis model, analyzing the wind and wave variation characteristic data and the candidate main control factor range data according to the wind and wave characteristic-performance impact data, determining the final main control factor range that meets the ship performance requirement data under the wind and wave variation characteristics in the target breeding site, selecting the minimum value of each main control factor in the final main control factor range as the design parameter of the breeding auxiliary ship in the target breeding site, and obtaining the ship design parameter combination.

[0100] It should be noted that, by extracting and analyzing the characteristic data of wind and wave changes, it is possible to accurately identify the specific wind and wave laws of the target breeding site, and match these laws with the performance requirements of the ship, ensuring that the designed ship can cope with the changing wind and wave environment in actual work, and has stronger adaptability and anti-interference ability; by constructing a ship design parameter analysis model, various data (such as wind and wave changes, the performance impact of the ship's main control factors, performance requirements, etc.) can be systematically integrated, and automatically compared and analyzed, so as to quickly determine the optional range of the main control factors that meet the ship's performance requirements. After comprehensively considering the impact of wind and wave characteristics on ship performance, the "final main control factor range" obtained by analysis can provide a stable parameter space for ship design, ensuring that the ship has sufficient stability and working performance in the wind and wave environment of the target breeding site; the selection of the minimum value of each main control factor in the final main control factor range as the design parameter of the breeding auxiliary ship in the target breeding site is that among multiple possible design options, selecting the minimum value of each main control factor can help minimize the size and weight of the ship without affecting the performance of the ship. This approach not only helps to reduce the construction cost of the ship, but also improves the energy efficiency of the ship. For example, reducing the length, width or draft of the ship can effectively reduce the resistance and energy consumption of the ship, while maintaining sufficient stability and wave resistance under wind and wave conditions. Since both wind and wave conditions and the ship's main control factors will affect the performance of the ship, the candidate main control factor range data is first screened out on the premise of meeting the ship's performance requirements, and then the final main control factor range that can meet the wind and wave change characteristics is screened out from the candidate main control factor range data, and finally the ship design parameter combination is obtained.

[0101] Figure 4 A block diagram of a ship design optimization system based on equipment collaborative operation data analysis of the present invention is shown.

[0102] The second aspect of the present invention further provides a ship design optimization system 4 based on equipment collaborative operation data analysis, the system comprising: a memory 41, a processor 42, the memory comprising a ship design optimization method program based on equipment collaborative operation data analysis, the ship design optimization method program based on equipment collaborative operation data analysis when executed by the processor, implements the following steps:

[0103] Obtaining historical operation data and historical ship sway coefficient data of aquaculture auxiliary ships with different main control factors under various wind and wave characteristics, and performing performance evaluation on aquaculture auxiliary ships with different main control factors according to the historical operation data and historical ship sway coefficient data to obtain performance evaluation results;

[0104] Comprehensively analyze the main control factors of the aquaculture auxiliary ship and the performance evaluation results to determine the impact of the main control factors of the ship on the ship performance, and obtain the main control factor-performance impact data;

[0105] Acquire the aquaculture scale data of the target aquaculture site, match the fishing techniques and equipment according to the aquaculture scale data, and determine the fishing techniques and equipment information of the target aquaculture site;

[0106] Determine the ship performance requirement data of the aquaculture auxiliary ship required for the operation of the fishery technology and fishery equipment according to the fishery technology and fishery equipment information;

[0107] The wind and wave variation characteristic data of the target aquaculture site are obtained, and the design parameters of the aquaculture auxiliary ship in the target aquaculture site are determined according to the main control factor-performance impact data, the ship performance requirement data, and the wind and wave variation characteristic data.

[0108] The present invention discloses a ship design optimization method and system based on equipment collaborative operation data analysis, aiming to optimize the design of aquaculture auxiliary ships. The method comprises the following steps: first, the historical operation data and sway coefficient data of aquaculture auxiliary ships under different main control factors are obtained, and performance evaluation is performed; then, the main control factors and performance evaluation results are analyzed to determine the influence of the main control factors on the ship performance; then, the fishing technology and fishing art equipment are matched according to the aquaculture scale data of the target aquaculture site to obtain equipment information; the required ship performance demand data is determined according to the equipment information; finally, the design parameters of the ship in the target aquaculture site are optimized and determined by combining the wind and wave change characteristic data, the main control factor-performance influence data and the ship performance demand data. This method improves the accuracy and adaptability of ship design through equipment collaborative analysis, meets the needs of different aquaculture environments, and improves work efficiency and stability.

[0109] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0110] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0111] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0112] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks or optical disks, and other media that can store program codes.

[0113] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0114] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A ship design optimization method based on equipment collaborative operation data analysis, characterized in that: The following steps are involved: Obtaining historical operation data and historical ship sway coefficient data of aquaculture auxiliary ships with different main control factors under various wind and wave characteristics, and performing performance evaluation on aquaculture auxiliary ships with different main control factors according to the historical operation data and historical ship sway coefficient data to obtain performance evaluation results; Comprehensively analyze the main control factors of the aquaculture auxiliary ship and the performance evaluation results to determine the impact of the main control factors of the ship on the ship performance, and obtain the main control factor-performance impact data; Acquire the aquaculture scale data of the target aquaculture site, match the fishing techniques and equipment according to the aquaculture scale data, and determine the fishing techniques and equipment information of the target aquaculture site; Determine the ship performance requirement data of the aquaculture auxiliary ship required for the operation of the fishery technology and fishery equipment according to the fishery technology and fishery equipment information; The wind and wave variation characteristic data of the target aquaculture site are obtained, and the design parameters of the aquaculture auxiliary ship in the target aquaculture site are determined according to the main control factor-performance impact data, the ship performance requirement data, and the wind and wave variation characteristic data.

2. A ship design optimization method based on equipment collaborative operation data analysis according to claim 1, characterized in that: The historical operation data and historical ship sway coefficient data of aquaculture auxiliary ships with different main control factors under various wind and wave characteristics are obtained, and the performance of aquaculture auxiliary ships with different main control factors is evaluated according to the historical operation data and historical ship sway coefficient data to obtain performance evaluation results, which are specifically: Obtaining historical operation data of a preset number of aquaculture auxiliary vessels in a preset working time period under various wind and wave characteristics, wherein the wind and wave characteristics include wind speed, wind direction, wave height, wave direction, water flow speed and water flow direction, and the historical operation data includes navigation speed and navigation resistance; Obtaining the main control factors of the aquaculture auxiliary ship, wherein the main control factors include ship length, ship width, draft, square coefficient, rhombus coefficient, cross-section coefficient and waterline area coefficient; Based on the sway sensor, the six-degree-of-freedom sway condition data of the aquaculture auxiliary ship at each time point in the preset working time period is obtained, and the ship sway coefficient of the aquaculture auxiliary ship in the preset working time period is calculated according to the six-degree-of-freedom sway condition data, wherein the ship sway coefficient includes a roll amplitude coefficient, a pitch period coefficient, and a heave acceleration coefficient, and the historical ship sway coefficient data is obtained; The acceleration performance and seakeeping performance of the aquaculture auxiliary ship are evaluated according to the historical operation data and the historical ship sway coefficient data to obtain a performance evaluation result.

3. A ship design optimization method based on equipment collaborative operation data analysis according to claim 1, characterized in that: The main control factors of the aquaculture auxiliary ship and the performance evaluation results are comprehensively analyzed to determine the impact of the main control factors of the ship on the ship performance, and obtain the main control factor-performance impact data, specifically: Constructing a ship performance evaluation model based on a neural network, constructing an input layer, a hidden layer and an output layer of the ship performance evaluation model, wherein the number of neurons in the input layer corresponds to the number of main control factors, the number of neurons in the output layer corresponds to the number of ship performance indicators to be evaluated, the number of hidden layers and the number of neurons in each layer are preset values, initializing the weights and bias parameters of the ship performance evaluation model, and importing the main control factors as the input layer and the performance evaluation results as the output layer into the ship performance evaluation model for pre-training operation; A genetic algorithm is introduced, a fitness function of the genetic algorithm is constructed based on a mean square error, a prediction error of a ship performance evaluation model on ship performance is used as an evaluation index of the fitness function, a population of the genetic algorithm is randomly generated, each individual in the population represents a set of weights and bias parameters of the ship performance evaluation model, and the number of iterations, crossover probability and mutation probability of the genetic algorithm are set; For each iteration process, the ship performance evaluation model is forward calculated based on the main control factors and the performance evaluation results to obtain the predicted value of the ship performance, the fitness of each individual in the population is calculated according to the fitness function and the predicted value, individuals are selected according to the fitness, and crossover and mutation operations are performed on the selected individuals until the iteration is completed to obtain a set of optimal weights and bias parameters; The ship performance evaluation model is trained according to the optimal weight and bias parameters, partial derivatives of each main control factor on the ship performance index are calculated during the training process, and the degree and direction of influence of each main control factor on the ship performance are determined according to the partial derivatives; The main control factor-performance impact data is generated according to the degree and direction of the impact of each main control factor on the ship performance.

4. A ship design optimization method based on equipment collaborative operation data analysis according to claim 1, characterized in that: The obtaining of the aquaculture scale data and wind and wave condition data of the target aquaculture site, matching of fishing techniques and equipment according to the aquaculture scale data, and determining the fishing techniques and equipment information of the target aquaculture site are specifically as follows: Obtaining the breeding scale data of the target breeding site, wherein the breeding scale data includes breeding area, breeding species and quantity, and breeding density; Obtaining working parameter information of various fishing equipment, including technical parameters, applicable scope, working principle, and production capacity; Constructing a fishing technique and fishing equipment database, and importing the working parameter information of the various types of fishing technique and fishing equipment into the fishing technique and fishing equipment database; Obtain the type of fishing technology and fishing art equipment required to be carried by the breeding auxiliary ship during the breeding process of the target breeding site, match the breeding scale data of the target breeding site and the type of fishing technology and fishing art equipment required to be carried by the breeding auxiliary ship with the fishing technology and fishing art equipment database, and determine the equipment parameters of the fishing technology and fishing art equipment, wherein the equipment parameters include equipment power or size; The fishery technology and fishery equipment information of the target breeding site is determined according to the type and equipment parameters of the fishery technology and fishery equipment required to be carried by the breeding auxiliary vessel.

5. The ship design optimization method based on equipment collaborative operation data analysis according to claim 1 is characterized in that: The ship performance requirement data of the aquaculture auxiliary ship required for the operation of the fishery technology and fishery equipment is determined according to the fishery technology and fishery equipment information, specifically: According to the fishing technique and fishing technology equipment information, equipment operation data of the fishing technique and fishing technology equipment required to be carried by the aquaculture auxiliary ship in the target aquaculture site under the performance characteristics of each ship are obtained, wherein the equipment operation data includes operation efficiency, equipment damage degree, and equipment shaking degree; Performing a working stability evaluation on each of the carried fishing technique and fishing technology equipment according to the equipment operation data to obtain a stability evaluation result; Acquire the collaborative operation relationship data between each carried fishery technology and fishery art equipment, construct the equipment collaborative operation relationship topology structure according to the collaborative operation relationship data, determine the influence degree of the working stability of each fishery technology and fishery art equipment on the stability of other fishery technology and fishery art equipment carried in the aquaculture auxiliary vessel according to the equipment collaborative operation relationship topology structure, and obtain the equipment collaborative operation stability influence data; Obtaining work efficiency demand data of the target breeding site, and setting a comprehensive stability threshold for the coordinated operation of fishing technology and fishing equipment according to the work efficiency demand data; A simulated annealing algorithm is introduced, the comprehensive stability threshold is used as the optimization target of the simulated annealing algorithm, and the data affecting the coordinated operation stability of the equipment is analyzed based on the simulated annealing algorithm to determine the minimum stability threshold required for each fishing technology and fishery equipment carried by the aquaculture auxiliary vessel under the condition of meeting the comprehensive stability threshold; The minimum ship performance characteristics of the aquaculture auxiliary ship are determined according to the minimum stability threshold, and the ship performance requirement data of the aquaculture auxiliary ship required for the operation of the fishing technology and fishing equipment are obtained.

6. A ship design optimization method based on equipment collaborative operation data analysis according to claim 1, characterized in that: The wind and wave variation characteristic data of the target aquaculture site are obtained, and the design parameters of the aquaculture auxiliary ship in the target aquaculture site are determined according to the main control factor-performance impact data, the ship performance requirement data, and the wind and wave variation characteristic data, specifically: Acquire wind and wave change data of the target breeding site within a preset time period, perform feature extraction on the wind and wave change data, determine the wind and wave change law of the target breeding site within the preset time period, and obtain wind and wave change feature data; Determine the influence of different wind and wave characteristics on ship performance according to the performance evaluation results, and obtain wind and wave characteristics-performance influence data; A ship design parameter analysis model is constructed based on the main control factor-performance impact data and the wind and wave characteristics-performance impact data, the ship performance requirement data is imported into the ship design parameter analysis model for comparison with the main control factor-performance impact data, and the optional range of the main control factors that meet the ship performance requirement data is preliminarily determined to obtain the range data of the candidate main control factors; The wind and wave variation characteristic data are imported into the ship design parameter analysis model, and the wind and wave variation characteristic data and the candidate main control factor range data are analyzed according to the wind and wave characteristic-performance impact data to determine the final main control factor range that meets the ship performance requirement data under the wind and wave variation characteristics in the target breeding site, and the minimum value of each main control factor in the final main control factor range is selected as the design parameter of the breeding auxiliary ship in the target breeding site to obtain the ship design parameter combination.

7. A ship design optimization system based on equipment collaborative operation data analysis, characterized in that: The ship design optimization system based on equipment collaborative operation data analysis includes a storage device and a processor, wherein the storage device includes a ship design optimization method program based on equipment collaborative operation data analysis, and when the ship design optimization method program based on equipment collaborative operation data analysis is executed by the processor, the following steps are implemented: Obtaining historical operation data and historical ship sway coefficient data of aquaculture auxiliary ships with different main control factors under various wind and wave characteristics, and performing performance evaluation on aquaculture auxiliary ships with different main control factors according to the historical operation data and historical ship sway coefficient data to obtain performance evaluation results; Comprehensively analyze the main control factors of the aquaculture auxiliary ship and the performance evaluation results to determine the impact of the main control factors of the ship on the ship performance, and obtain the main control factor-performance impact data; Acquire the aquaculture scale data of the target aquaculture site, match the fishing techniques and equipment according to the aquaculture scale data, and determine the fishing techniques and equipment information of the target aquaculture site; Determine the ship performance requirement data of the aquaculture auxiliary ship required for the operation of the fishery technology and fishery equipment according to the fishery technology and fishery equipment information; The wind and wave variation characteristic data of the target aquaculture site are obtained, and the design parameters of the aquaculture auxiliary ship in the target aquaculture site are determined according to the main control factor-performance impact data, the ship performance requirement data, and the wind and wave variation characteristic data.

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