A core performance matching management method and system of a multifunctional aquaculture auxiliary ship

Abstract

The application relates to the field of performance management, and discloses a core performance matching management method and system of a multifunctional aquaculture auxiliary ship, which comprises the following steps: performing parameter combination analysis on the multifunctional aquaculture auxiliary ship and a sailing water area by using an A* algorithm, generating a target route, and performing sailing stability testing and optimization on the multifunctional aquaculture auxiliary ship through a simulation model. The multifunctional aquaculture auxiliary ship controlled after stability optimization sails along the target route, and during the sailing, based on water quality parameters of the water area, the multifunctional aquaculture auxiliary ship is subjected to feeding function and power performance matching. The application can analyze and match various core performances of the multifunctional aquaculture auxiliary ship, and based on the analysis result, the various core performances are subjected to performance matching. After the core performance matching, the operation efficiency can be improved, the manual intervention can be reduced, the operation cost can be reduced, the working safety and stability of the multifunctional aquaculture auxiliary ship can be improved.

Description

Technical Field

[0001] This invention relates to the field of performance management, and in particular to a core performance matching management method and system for a multi-functional aquaculture auxiliary vessel. Background Technology

[0002] A multi-functional aquaculture support vessel is a vessel specifically designed to support marine or freshwater aquaculture activities. It can perform a range of tasks, including but not limited to feed transport and delivery, maintenance of aquaculture equipment and facilities, monitoring and management of fish and other marine life, and waste disposal. Its core performance characteristics include speed, seakeeping, and maneuverability. Matching these core performance characteristics involves optimizing design and comprehensive management to ensure the multi-functional aquaculture support vessel achieves the best balance between speed, seakeeping, and maneuverability. This matching includes adjusting the vessel's speed under different conditions, and adjusting its stability and resistance to turbulence in different sea states. Matching these core performance characteristics improves operational efficiency, reduces human intervention, lowers operating costs, ensures good stability and maneuverability under different sea states, ensures the safety of crew and equipment, and allows for the prevention and rapid response to emergencies, reducing the risk of accidents. Summary of the Invention

[0003] This invention overcomes the shortcomings of the prior art and provides a core performance matching management method and system for a multi-functional aquaculture auxiliary vessel.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0005] The first aspect of this invention provides a core performance matching and management method for a multi-functional aquaculture auxiliary vessel, comprising the following steps:

[0006] The specifications of the multi-functional aquaculture auxiliary vessel and the water parameters of the navigation area are combined and analyzed, and based on the results of the combined analysis, the A* algorithm is introduced to generate the target route.

[0007] The navigation stability of the target aquaculture support vessel was tested using a simulation model, and the navigation stability of the target aquaculture support vessel was optimized based on the navigation stability test results.

[0008] During the navigation of the qualified aquaculture auxiliary vessel, the water quality is monitored, and the feeding function and power performance of the qualified aquaculture auxiliary vessel are matched based on the water quality monitoring results.

[0009] Furthermore, in a preferred embodiment of the present invention, the step of combining and analyzing the specifications of the multi-functional aquaculture auxiliary vessel and the water parameters of the navigation area, and based on the combined analysis results, introducing the A* algorithm to generate the target route, specifically involves:

[0010] The multi-functional aquaculture auxiliary vessel that requires core performance matching management is identified as the target aquaculture auxiliary vessel, and the specification parameters of the target aquaculture auxiliary vessel are obtained. The specification parameters of the target aquaculture auxiliary vessel include the volume, empty mass, displacement and maximum cargo mass of the target aquaculture auxiliary vessel.

[0011] The waters in which the target aquaculture support vessel navigates are designated as the target waters, and the water parameters of the target waters are obtained. The water parameters of the target waters include the water depth at each point in the target waters and the distribution range of the target waters. At the same time, the meteorological parameters at each point in the target waters are also obtained.

[0012] A deep learning network is introduced, and based on the deep learning network, the specification parameters of the target aquaculture auxiliary vessel, the water parameters of the target water area, and the meteorological parameters of each point in the target water area are combined and analyzed to obtain the prohibited points of the target aquaculture auxiliary vessel in the target water area.

[0013] In the target waters, determine the starting point, the point where the target aquaculture support vessel needs to navigate, and the ending point. Introduce the A* algorithm, construct the basic model of the A* algorithm based on the A* algorithm, and construct the starting point node, navigation node, and ending point node within the basic model of the A* algorithm according to the starting point and the point where the target aquaculture support vessel needs to navigate.

[0014] In the A* algorithm training model, the prohibited points of the target aquaculture auxiliary vessels in the target waters are marked and defined as prohibited nodes;

[0015] The water parameters of the target water area are imported into the basic model of the A* algorithm for model training to obtain the A* algorithm training model. The A* algorithm training model contains multiple nodes, where each node represents a point within the target water area.

[0016] The A* algorithm is run to train the model, generating all node routes connecting the starting node, navigation node, and ending node. These routes are marked as node routes to be analyzed. Node routes with prohibited nodes are filtered out, resulting in node routes without prohibited nodes, which are marked as a type of node route.

[0017] Calculate the route length of all nodes of type I, select the node route of type I with the shortest route length as the output, obtain the target node route, and mark the route corresponding to the target node route in the target water area as the target route.

[0018] Furthermore, in a preferred embodiment of the present invention, the step of conducting a navigation stability test on the target aquaculture support vessel using a simulation model, and optimizing the navigation stability of the target aquaculture support vessel based on the navigation stability test results, specifically includes:

[0019] Obtain simulation modeling software, import the specification data of the target aquaculture auxiliary vessel into the simulation modeling software, construct a simulation model of the target aquaculture auxiliary vessel, and calibrate it as the target aquaculture auxiliary vessel simulation model;

[0020] Acquire a big data network, and within the big data network, based on the water parameters of the target water area, retrieve all sea states that may occur in the target water area and label them as a type of sea state;

[0021] A navigation simulation system is constructed, which can control the simulation model of the target aquaculture auxiliary vessel to conduct simulated navigation based on the target route and withstand different sea states during the simulated navigation.

[0022] Run the navigation simulation system to test the rolling amplitude of the target aquaculture auxiliary vessel simulation model under different sea states, and calibrate it as the navigation rolling amplitude.

[0023] The dangerous rolling amplitude is preset. During the operation of the navigation simulation system, if the navigation rolling amplitude of the target aquaculture auxiliary vessel simulation model under all sea states is less than the dangerous rolling amplitude, the target aquaculture auxiliary vessel will be evaluated as a qualified aquaculture auxiliary vessel in terms of navigation stability.

[0024] If the simulation model of the target aquaculture support vessel shows a sway amplitude greater than the dangerous sway amplitude under a certain sea state, the target aquaculture support vessel will be assessed as an aquaculture support vessel with unqualified navigation stability, and the corresponding sea state will be marked as a dangerous sea state.

[0025] Stress analysis of structural components was conducted on aquaculture auxiliary vessels with substandard navigation stability, and stability optimization was performed based on the results of the stress analysis.

[0026] Furthermore, in a preferred embodiment of the present invention, the step of performing structural component stress analysis on the aquaculture auxiliary vessel with substandard navigation stability, and optimizing the stability of the aquaculture auxiliary vessel based on the structural component stress analysis results, specifically includes:

[0027] Obtain the hull structural components of an aquaculture auxiliary vessel with substandard navigation stability, the hull structural components including deck, keel and bulkhead;

[0028] The simulation model of the aquaculture auxiliary vessel with unqualified navigation stability is calibrated as a Class I simulation model, and the hull structural components are marked in the Class I simulation model to obtain the hull structural component simulation model.

[0029] Run the navigation simulation system and test the stress values ​​of each region on the simulation model of the target aquaculture auxiliary vessel when the navigation roll amplitude of the simulation model is greater than the dangerous roll amplitude.

[0030] Based on the dangerous rolling amplitude, the maximum withstandable stress value of each region on the simulation model of the hull structural components is calculated and calibrated as the dangerous stress value. Regions on the simulation model of the hull structural components with stress values ​​greater than the dangerous stress value are calibrated as model stress anomaly regions.

[0031] On the simulation model of the ship's structural components, deformation analysis is performed on the stress anomaly region of the model to obtain the degree of deformation of the stress anomaly region under dangerous sea conditions, and the maximum degree of deformation of the stress anomaly region under dangerous sea conditions is preset.

[0032] If the deformation of the model stress anomaly region under dangerous sea conditions is greater than the maximum deformation, then the region corresponding to the model stress anomaly region is obtained on the hull structural component and marked as the component stress anomaly region.

[0033] On the structural components of the ship hull, the current material used in the stress anomaly area of ​​the component is obtained and labeled as a type of material. The material strength of the type of material is calculated, and materials that can act on the stress anomaly area of ​​the component and whose material strength is greater than that of the type of material are retrieved from the big data network and labeled as target materials.

[0034] In the stress anomaly region of the component, a type of material is replaced with the target material to obtain a material-replaced hull structure component. The design optimization scheme of the material-replaced hull structure component is retrieved from the big data network and output to obtain a qualified aquaculture auxiliary vessel with navigation stability. The design optimization scheme of the material-replaced hull structure component can make the navigation roll amplitude of the simulation model of the target aquaculture auxiliary vessel greater than the dangerous roll amplitude.

[0035] Furthermore, in a preferred embodiment of the present invention, during the navigation of the qualified aquaculture auxiliary vessel, the water quality is monitored, and the feeding function and power performance of the qualified aquaculture auxiliary vessel are matched based on the water quality monitoring results, specifically as follows:

[0036] A water quality sensor and an automatic feed dispensing system are installed in the aquaculture auxiliary vessel with qualified navigation stability. The aquaculture auxiliary vessel with qualified navigation stability is controlled to navigate in the target water area based on the target route. During the navigation of the aquaculture auxiliary vessel with qualified navigation stability, the water quality parameters of the target water area are monitored in real time through the water quality sensor and calibrated as the real-time water quality parameters of the target water area.

[0037] Identify all organisms in the target water area that require feeding, label them as target organisms, and retrieve the water quality parameter thresholds for the survival of target organisms from the big data network;

[0038] If the real-time water quality parameters of the target water area are within the threshold of the water quality parameters that allow the target organism to survive, the corresponding target water area will be marked as a water area that can be fed. If the real-time water quality parameters of the target water area are not within the threshold of the water quality parameters that allow the target organism to survive, the corresponding target water area will be marked as a water area that cannot be fed.

[0039] The rated output power, cruising output power and maximum output power of the propulsion system are obtained in the aquaculture support vessel with qualified navigation stability, and during the navigation of the aquaculture support vessel with qualified navigation stability, it is determined whether the water area in which the aquaculture support vessel with qualified navigation stability is navigating is a water area suitable for feeding.

[0040] If not, adjust the propulsion system output power to the maximum output power in aquaculture auxiliary vessels with qualified navigation stability;

[0041] If so, the quantity analysis of the target organisms will be conducted during the navigation of the aquaculture support vessel with qualified navigation stability, and the output power of the propulsion system will be adjusted based on the results of the quantity analysis in the aquaculture support vessel with qualified navigation stability.

[0042] Furthermore, in a preferred embodiment of the present invention, the step of conducting quantity analysis of the target organisms during navigation of the qualified aquaculture support vessel, and adjusting the output power of the propulsion system in the qualified aquaculture support vessel based on the quantity analysis results, specifically includes:

[0043] Obtain the rated frequency of the automatic feed feeding system for livestock and calibrate it as the rated feeding frequency.

[0044] Sonar fish-finding equipment is installed on the aquaculture auxiliary vessel with qualified navigation stability. The aquaculture auxiliary vessel with qualified navigation stability is controlled to navigate along the target route in the feeding water area. During navigation, the sonar fish-finding equipment is controlled to transmit sonar signals to the feeding water area and receive sonar signals reflected in the feeding water area.

[0045] The sonar signals reflected from the feedable water area are preprocessed, and a sonar detection model is constructed based on the preprocessed sonar signals reflected from the feedable water area.

[0046] The sonar detection model is analyzed to construct a distribution map of target organisms in the feedable water area. The distribution map of target organisms in the feedable water area records the density of target organisms at each point on the target route within the feedable water area.

[0047] The target biological standard density is preset, and the distribution diagram of the target organisms in the feedable water area is imported into the propulsion system. When the aquaculture auxiliary vessel with qualified navigation stability is sailing along the target route in the feedable water area, if the target biological density at the point passed by the aquaculture auxiliary vessel with qualified navigation stability is greater than the target biological standard density, the aquaculture feed automatic feeding system is started, the feeding frequency of the aquaculture feed automatic feeding system is set to the rated feeding frequency, and the output power of the propulsion system in the aquaculture auxiliary vessel with qualified navigation stability is adjusted to the rated output power.

[0048] If the target biological density at the point the aquaculture support vessel passes through is less than the target standard biological density, the automatic aquaculture feed feeding system is activated, the feeding frequency of the automatic aquaculture feed feeding system is set to the rated feeding frequency, and the output power of the propulsion system in the aquaculture support vessel with qualified navigation stability is adjusted to the cruise output power.

[0049] A second aspect of the present invention also provides a core performance matching management system for a multifunctional aquaculture auxiliary vessel. The core performance matching management system includes a memory and a processor. The memory stores a core performance matching management method. When the processor executes the core performance matching management method, it performs the following steps:

[0050] The specifications of the multi-functional aquaculture auxiliary vessel and the water parameters of the navigation area are combined and analyzed, and based on the results of the combined analysis, the A* algorithm is introduced to generate the target route.

[0051] The navigation stability of the target aquaculture support vessel was tested using a simulation model, and the navigation stability of the target aquaculture support vessel was optimized based on the navigation stability test results.

[0052] During the navigation of the qualified aquaculture auxiliary vessel, the water quality is monitored, and the feeding function and power performance of the qualified aquaculture auxiliary vessel are matched based on the water quality monitoring results.

[0053] This invention addresses the technical deficiencies in the prior art and offers the following advantages: It utilizes the A* algorithm to analyze parameters of a multi-functional aquaculture support vessel and its navigation area, generating a target route. A simulation model is then used to test and optimize the vessel's navigation stability. The optimized vessel navigates along the target route, and during navigation, its feeding function and power performance are matched based on water quality parameters. This invention analyzes and matches various core performance characteristics of the multi-functional aquaculture support vessel, and performs performance matching based on the analysis results. This core performance matching improves operational efficiency, reduces manual intervention, lowers operating costs, and simultaneously enhances the vessel's safety and stability. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained from these drawings without creative effort.

[0055] Figure 1 A flowchart illustrating a core performance matching management method for a multi-functional aquaculture auxiliary vessel is shown.

[0056] Figure 2 A flowchart is shown to illustrate a method for testing and optimizing the navigation stability of a target aquaculture support vessel using a simulation model.

[0057] Figure 3 A program view of the core performance matching management system for a multi-functional aquaculture auxiliary vessel is shown. Detailed Implementation

[0058] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0059] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0060] Figure 1 A flowchart illustrating a core performance matching management method for a multi-functional aquaculture support vessel is shown, including the following steps:

[0061] S102: Combine and analyze the specifications of the multi-functional aquaculture auxiliary vessel with the water parameters of the navigation area, and based on the combined analysis results, introduce the A* algorithm to generate the target route;

[0062] S104: Conduct navigation stability tests on the target aquaculture support vessel using a simulation model, and optimize the navigation stability of the target aquaculture support vessel based on the navigation stability test results;

[0063] S106: During the navigation of an aquaculture auxiliary vessel with qualified navigation stability, the vessel shall be controlled to monitor water quality, and the feeding function and power performance of the vessel shall be matched based on the water quality monitoring results.

[0064] Furthermore, in a preferred embodiment of the present invention, the step of combining and analyzing the specifications of the multi-functional aquaculture auxiliary vessel and the water parameters of the navigation area, and based on the combined analysis results, introducing the A* algorithm to generate the target route, specifically involves:

[0065] The multi-functional aquaculture auxiliary vessel that requires core performance matching management is identified as the target aquaculture auxiliary vessel, and the specification parameters of the target aquaculture auxiliary vessel are obtained. The specification parameters of the target aquaculture auxiliary vessel include the volume, empty mass, displacement and maximum cargo mass of the target aquaculture auxiliary vessel.

[0066] The waters in which the target aquaculture support vessel navigates are designated as the target waters, and the water parameters of the target waters are obtained. The water parameters of the target waters include the water depth at each point in the target waters and the distribution range of the target waters. At the same time, the meteorological parameters at each point in the target waters are also obtained.

[0067] A deep learning network is introduced, and based on the deep learning network, the specification parameters of the target aquaculture auxiliary vessel, the water parameters of the target water area, and the meteorological parameters of each point in the target water area are combined and analyzed to obtain the prohibited points of the target aquaculture auxiliary vessel in the target water area.

[0068] In the target waters, determine the starting point, the point where the target aquaculture support vessel needs to navigate, and the ending point. Introduce the A* algorithm, construct the basic model of the A* algorithm based on the A* algorithm, and construct the starting point node, navigation node, and ending point node within the basic model of the A* algorithm according to the starting point and the point where the target aquaculture support vessel needs to navigate.

[0069] In the A* algorithm training model, the prohibited points of the target aquaculture auxiliary vessels in the target waters are marked and defined as prohibited nodes;

[0070] The water parameters of the target water area are imported into the basic model of the A* algorithm for model training to obtain the A* algorithm training model. The A* algorithm training model contains multiple nodes, where each node represents a point within the target water area.

[0071] The A* algorithm is run to train the model, generating all node routes connecting the starting node, navigation node, and ending node. These routes are marked as node routes to be analyzed. Node routes with prohibited nodes are filtered out, resulting in node routes without prohibited nodes, which are marked as a type of node route.

[0072] Calculate the route length of all nodes of type I, select the node route of type I with the shortest route length as the output, obtain the target node route, and mark the route corresponding to the target node route in the target water area as the target route.

[0073] It should be noted that after acquiring the target aquaculture auxiliary vessel, its specifications are required, as vessels with different specifications have different navigable points and routes in the same body of water. For example, vessels with larger displacement and volume cannot navigate to narrow sections of the waterway, as this would cause navigation difficulties or even prevent passage. Therefore, it is also necessary to acquire the water parameters of the target waterway. The water depth can be combined with the vessel's specifications and analyzed, along with meteorological parameters during navigation, to determine the vessel's prohibited navigation points. The reason for acquiring meteorological parameters is that navigation is prohibited in areas with severe weather conditions, such as thunderstorms and strong winds. The deep learning network mentioned is a learning network that can be trained to combine and analyze various parameters. By combining and analyzing various parameters through the deep learning network, the vessel's prohibited navigation points can be obtained. The A* algorithm is a heuristic search algorithm for graph search and path planning, which can be used in this application to plan the vessel's route. In the A* algorithm, the points, starting points, and ending points of navigation are first determined and then converted into nodes for analysis. In the target waters, all points are converted into nodes, and the A* algorithm is used to obtain all routes passing through the points, starting points, and ending points of the required navigation. Since all nodes may contain prohibited points, routes with prohibited points need to be deleted, and the shortest route is selected from the remaining routes to obtain the target node route. The target route of the ship can then be generated based on the target node route.

[0074] Furthermore, in a preferred embodiment of the present invention, during the navigation of the qualified aquaculture auxiliary vessel, the water quality is monitored, and the feeding function and power performance of the qualified aquaculture auxiliary vessel are matched based on the water quality monitoring results, specifically as follows:

[0075] A water quality sensor and an automatic feed dispensing system are installed in the aquaculture auxiliary vessel with qualified navigation stability. The aquaculture auxiliary vessel with qualified navigation stability is controlled to navigate in the target water area based on the target route. During the navigation of the aquaculture auxiliary vessel with qualified navigation stability, the water quality parameters of the target water area are monitored in real time through the water quality sensor and calibrated as the real-time water quality parameters of the target water area.

[0076] Identify all organisms in the target water area that require feeding, label them as target organisms, and retrieve the water quality parameter thresholds for the survival of target organisms from the big data network;

[0077] If the real-time water quality parameters of the target water area are within the threshold of the water quality parameters that allow the target organism to survive, the corresponding target water area will be marked as a water area that can be fed. If the real-time water quality parameters of the target water area are not within the threshold of the water quality parameters that allow the target organism to survive, the corresponding target water area will be marked as a water area that cannot be fed.

[0078] The rated output power, cruising output power and maximum output power of the propulsion system are obtained in the aquaculture support vessel with qualified navigation stability, and during the navigation of the aquaculture support vessel with qualified navigation stability, it is determined whether the water area in which the aquaculture support vessel with qualified navigation stability is navigating is a water area suitable for feeding.

[0079] If not, adjust the propulsion system output power to the maximum output power in aquaculture auxiliary vessels with qualified navigation stability;

[0080] If so, the quantity analysis of the target organisms will be conducted during the navigation of the aquaculture support vessel with qualified navigation stability, and the output power of the propulsion system will be adjusted based on the results of the quantity analysis in the aquaculture support vessel with qualified navigation stability.

[0081] It should be noted that after the vessel's stability is optimized, a qualified aquaculture auxiliary vessel with stable navigation is obtained, and the vessel's navigation and operation need to be controlled. The vessel's operation involves controlling the feeding of aquatic organisms within the water area. Before feeding, the water quality parameters of the target water area need to be analyzed. This can be obtained through water quality sensors. The reason for analyzing the water quality is that if aquatic feed is placed in water with poor parameters, the organisms will swim to areas with poor water quality to forage. In this case, the poor water quality may affect the health of the organisms, even causing them to die. Therefore, aquatic feed should not be placed in water with poor parameters. The water area can be divided according to the survival threshold of the target organisms. If the water quality parameters of the target water area are within the survival threshold of the target organisms, it proves that the target organisms will not have problems ingesting feed in that water area. The target organisms include, but are not limited to, fish, shrimp, and crabs. The vessel's propulsion system controls the vessel's speed, corresponding to the vessel's core performance characteristic of speed. The higher the propulsion system power, the faster the vessel's speed. In areas where feeding is not permitted, since the automatic feeding system for aquaculture feed is not in operation, it is necessary to quickly pass through the areas where feeding is permitted in order to improve the efficiency of feed delivery. Therefore, after the vessel sails to the areas where feeding is not permitted, the propulsion system power needs to be adjusted to the maximum to quickly pass through the areas.

[0082] Furthermore, in a preferred embodiment of the present invention, the step of conducting quantity analysis of the target organisms during navigation of the qualified aquaculture support vessel, and adjusting the output power of the propulsion system in the qualified aquaculture support vessel based on the quantity analysis results, specifically includes:

[0083] Obtain the rated frequency of the automatic feed feeding system for livestock and calibrate it as the rated feeding frequency.

[0084] Sonar fish-finding equipment is installed on the aquaculture auxiliary vessel with qualified navigation stability. The aquaculture auxiliary vessel with qualified navigation stability is controlled to navigate along the target route in the feeding water area. During navigation, the sonar fish-finding equipment is controlled to transmit sonar signals to the feeding water area and receive sonar signals reflected in the feeding water area.

[0085] The sonar signals reflected from the feedable water area are preprocessed, and a sonar detection model is constructed based on the preprocessed sonar signals reflected from the feedable water area.

[0086] The sonar detection model is analyzed to construct a distribution map of target organisms in the feedable water area. The distribution map of target organisms in the feedable water area records the density of target organisms at each point on the target route within the feedable water area.

[0087] The target biological standard density is preset, and the distribution diagram of the target organisms in the feedable water area is imported into the propulsion system. When the aquaculture auxiliary vessel with qualified navigation stability is sailing along the target route in the feedable water area, if the target biological density at the point passed by the aquaculture auxiliary vessel with qualified navigation stability is greater than the target biological standard density, the aquaculture feed automatic feeding system is started, the feeding frequency of the aquaculture feed automatic feeding system is set to the rated feeding frequency, and the output power of the propulsion system in the aquaculture auxiliary vessel with qualified navigation stability is adjusted to the rated output power.

[0088] If the target biological density at the point the aquaculture support vessel passes through is less than the target standard biological density, the automatic aquaculture feed feeding system is activated, the feeding frequency of the automatic aquaculture feed feeding system is set to the rated feeding frequency, and the output power of the propulsion system in the aquaculture support vessel with qualified navigation stability is adjusted to the cruise output power.

[0089] It should be noted that when navigating to a feedable area, feeding must be carried out within that area. The fish density varies at different points within the feedable area; a higher fish density necessitates slowing the vessel to allow for the distribution of more feed. The method for determining the fish density at specific points within the feedable area is sonar detection. After transmitting and receiving sonar signals, preprocessing is required. This preprocessing includes noise reduction and feature extraction to construct a sonar detection model. This model reveals the distribution and density of target organisms within the water area, allowing for the creation of a distribution map that clearly marks the target organism density at each point along the target route within the feedable area. Since the vessel travels along the target route, it only needs to determine the target organism density at each point along that route. If the target organism density at any point along the route exceeds a standard value, the vessel must slow down when passing that point to allow for the distribution of a larger quantity of feed. The automatic feed dispensing system maintains a constant feeding frequency at its rated frequency. When the vessel passes through a designated point, the propulsion system needs to adjust its output power to the rated power. If the target biological density at each point along the target route is less than the standard value, the automatic feed dispensing system's feeding frequency is also kept constant at the rated frequency. However, when the vessel passes through a designated point, the propulsion system needs to adjust its output power to the cruising power. The cruising power is the power set for the vessel to cruise at normal speed on the water.

[0090] Figure 2 The flowchart illustrates a method for testing and optimizing the navigation stability of a target aquaculture support vessel using a simulation model, including the following steps:

[0091] S202: Construct a simulation model and navigation simulation system for the target aquaculture support vessel, and determine a type of sea state;

[0092] S204: Run the navigation simulation system to test the sailing roll amplitude of the target aquaculture auxiliary vessel simulation model when navigating under a Class I sea state, and classify the target aquaculture auxiliary vessel based on the sailing roll amplitude;

[0093] S206: Conduct structural component stress analysis on aquaculture auxiliary vessels with substandard navigation stability, and optimize the stability of the aquaculture auxiliary vessels with substandard navigation stability based on the results of the structural component stress analysis.

[0094] Furthermore, in a preferred embodiment of the present invention, the construction of the target aquaculture auxiliary vessel simulation model and navigation simulation system, and the determination of a type of sea state, specifically includes:

[0095] Obtain simulation modeling software, import the specification data of the target aquaculture auxiliary vessel into the simulation modeling software, construct a simulation model of the target aquaculture auxiliary vessel, and calibrate it as the target aquaculture auxiliary vessel simulation model;

[0096] Acquire a big data network, and within the big data network, based on the water parameters of the target water area, retrieve all sea states that may occur in the target water area and label them as a type of sea state;

[0097] A navigation simulation system is constructed, which can control the simulation model of the target aquaculture auxiliary vessel to conduct simulated navigation based on the target route and withstand different sea states during the simulated navigation.

[0098] It should be noted that simulation modeling software is software capable of constructing simulation models. By inputting the specifications of the target aquaculture auxiliary vessel into the simulation modeling software, a simulation model of the target aquaculture auxiliary vessel can be constructed. The simulation model of the target aquaculture auxiliary vessel reproduces all its functions, including its seakeeping performance. Analyzing seakeeping performance is equivalent to analyzing stability. A big data network is a network that stores various data and solutions. By inputting the required conditions into the big data network, the required data and solutions can be obtained. Analyzing the water parameters of the target water area in the big data network can yield a type of sea state, which includes wave intensity, wind intensity, etc. The navigation simulation system is a system capable of controlling the simulation model of the target aquaculture auxiliary vessel to conduct simulated navigation based on a target route and to withstand different types of sea states during the simulated navigation. The simulated navigation refers to the behavior of controlling the simulation model of the target aquaculture auxiliary vessel to conduct simulated navigation and to withstand different types of sea states during the simulated navigation.

[0099] Furthermore, in a preferred embodiment of the present invention, the navigation simulation system tests the sway amplitude of the target aquaculture auxiliary vessel simulation model during simulated navigation in a Class I sea state, and classifies the target aquaculture auxiliary vessel based on the sway amplitude, specifically as follows:

[0100] Run the navigation simulation system to test the rolling amplitude of the target aquaculture auxiliary vessel simulation model under different sea states, and calibrate it as the navigation rolling amplitude.

[0101] The dangerous rolling amplitude is preset. During the operation of the navigation simulation system, if the navigation rolling amplitude of the target aquaculture auxiliary vessel simulation model under all sea states is less than the dangerous rolling amplitude, the target aquaculture auxiliary vessel will be evaluated as a qualified aquaculture auxiliary vessel in terms of navigation stability.

[0102] If the simulation model of the target aquaculture support vessel exhibits a sway amplitude greater than the dangerous sway amplitude under a certain sea state, the target aquaculture support vessel will be assessed as an aquaculture support vessel with unqualified navigation stability, and the corresponding sea state will be marked as a dangerous sea state.

[0103] It should be noted that after running the navigation simulation system, the rolling amplitude of the simulated model of the target aquaculture support vessel can be tested. This represents the rolling amplitude of the target aquaculture support vessel under a certain sea state in real-world conditions. If the rolling amplitude is too large, it may have serious consequences for the vessel, such as capsizing or causing casualties. Therefore, a dangerous rolling amplitude is simulated. If the rolling amplitude of the simulated model of the target aquaculture support vessel under a certain sea state exceeds the dangerous rolling amplitude, the target aquaculture support vessel will be assessed as having unqualified navigation stability, and optimizations will be required in aspects such as vessel structural design. Simultaneously, the sea state that causes the rolling amplitude to exceed the dangerous rolling amplitude will be designated as a dangerous sea state.

[0104] Furthermore, in a preferred embodiment of the present invention, the step of performing structural component stress analysis on the aquaculture auxiliary vessel with substandard navigation stability, and optimizing the stability of the aquaculture auxiliary vessel based on the structural component stress analysis results, specifically includes:

[0105] Obtain the hull structural components of an aquaculture auxiliary vessel with substandard navigation stability, the hull structural components including deck, keel and bulkhead;

[0106] The simulation model of the aquaculture auxiliary vessel with unqualified navigation stability is calibrated as a Class I simulation model, and the hull structural components are marked in the Class I simulation model to obtain the hull structural component simulation model.

[0107] Run the navigation simulation system and test the stress values ​​of each region on the simulation model of the target aquaculture auxiliary vessel when the navigation roll amplitude of the simulation model is greater than the dangerous roll amplitude.

[0108] Based on the dangerous rolling amplitude, the maximum withstandable stress value of each region on the simulation model of the hull structural components is calculated and calibrated as the dangerous stress value. Regions on the simulation model of the hull structural components with stress values ​​greater than the dangerous stress value are calibrated as model stress anomaly regions.

[0109] On the simulation model of the ship's structural components, deformation analysis is performed on the stress anomaly region of the model to obtain the degree of deformation of the stress anomaly region under dangerous sea conditions, and the maximum degree of deformation of the stress anomaly region under dangerous sea conditions is preset.

[0110] If the deformation of the model stress anomaly region under dangerous sea conditions is greater than the maximum deformation, then the region corresponding to the model stress anomaly region is obtained on the hull structural component and marked as the component stress anomaly region.

[0111] On the structural components of the ship hull, the current material used in the stress anomaly area of ​​the component is obtained and labeled as a type of material. The material strength of the type of material is calculated, and materials that can act on the stress anomaly area of ​​the component and whose material strength is greater than that of the type of material are retrieved from the big data network and labeled as target materials.

[0112] In the stress anomaly region of the component, a type of material is replaced with the target material to obtain a material-replaced hull structure component. The design optimization scheme of the material-replaced hull structure component is retrieved from the big data network and output to obtain a qualified aquaculture auxiliary vessel with navigation stability. The design optimization scheme of the material-replaced hull structure component can make the navigation roll amplitude of the simulation model of the target aquaculture auxiliary vessel greater than the dangerous roll amplitude.

[0113] It should be noted that the significant rolling amplitude of a vessel under dangerous sea conditions is due to potential uneven stress distribution among its hull structural components, which can lead to deformation and subsequent rolling. Therefore, a simulation model of the hull structural components is necessary for stress value simulation analysis. In this simulation model, if a region experiences stress values ​​exceeding the dangerous stress value, this region is designated as a model stress anomaly region. The corresponding area on the hull structural component is then designated as the component stress anomaly region. The degree of deformation at the model stress anomaly region reflects the degree of deformation within that component. If the deformation of the model stress anomaly region under dangerous sea conditions exceeds the maximum deformation, the material in the stress anomaly region must be replaced. Otherwise, the hull structural components may break under the influence of dangerous sea conditions, potentially causing a serious accident. A material capable of acting on the component stress anomaly region, with a strength exceeding that of a primary material category, should be selected as the replacement material—the target material. The aim is to ensure that the deformation of the hull structural components is less than a preset value after encountering dangerous sea conditions. Even after replacing the materials, structural design optimization of the vessel is still required. This is because even if the deformation is less than the preset value in the stress abnormality area of ​​the component, the high stress value will still aggravate the swaying of the vessel. After the vessel's structural design is optimized, the stress in the stress abnormality area of ​​the component will be dispersed, resulting in a reduction in stress value, thereby reducing the swaying amplitude of the vessel and obtaining a qualified aquaculture auxiliary vessel with good navigation stability.

[0114] like Figure 3 As shown, a second aspect of the present invention also provides a core performance matching management system for a multi-functional aquaculture auxiliary vessel. The core performance matching management system includes a memory 31 and a processor 32. The memory 31 stores a core performance matching management method. When the core performance matching management method is executed by the processor 32, it performs the following steps:

[0115] The specifications of the multi-functional aquaculture auxiliary vessel and the water parameters of the navigation area are combined and analyzed, and based on the results of the combined analysis, the A* algorithm is introduced to generate the target route.

[0116] The navigation stability of the target aquaculture support vessel was tested using a simulation model, and the navigation stability of the target aquaculture support vessel was optimized based on the navigation stability test results.

[0117] During the navigation of the qualified aquaculture auxiliary vessel, the water quality is monitored, and the feeding function and power performance of the qualified aquaculture auxiliary vessel are matched based on the water quality monitoring results.

[0118] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A core performance matching and management method for a multi-functional aquaculture auxiliary vessel, characterized in that, Includes the following steps: The specifications of the multi-functional aquaculture auxiliary vessel and the water parameters of the navigation area are combined and analyzed, and based on the results of the combined analysis, the A* algorithm is introduced to generate the target route. The navigation stability of the target aquaculture support vessel was tested using a simulation model, and the navigation stability of the target aquaculture support vessel was optimized based on the navigation stability test results. During the navigation of the aquaculture auxiliary vessel with qualified navigation stability, the vessel is controlled to monitor water quality, and the feeding function and power performance of the vessel are matched based on the water quality monitoring results. Specifically, the process involves combining and analyzing the specifications of the multi-functional aquaculture support vessel with the water parameters of the navigation area, and then using the A* algorithm to generate the target route based on the results of this combined analysis. The multi-functional aquaculture auxiliary vessel that requires core performance matching management is identified as the target aquaculture auxiliary vessel, and the specification parameters of the target aquaculture auxiliary vessel are obtained. The specification parameters of the target aquaculture auxiliary vessel include the volume, empty mass, displacement and maximum cargo mass of the target aquaculture auxiliary vessel. The waters in which the target aquaculture support vessel navigates are designated as the target waters, and the water parameters of the target waters are obtained. The water parameters of the target waters include the water depth at each point in the target waters and the distribution range of the target waters. At the same time, the meteorological parameters at each point in the target waters are also obtained. A deep learning network is introduced, and based on the deep learning network, the specification parameters of the target aquaculture auxiliary vessel, the water parameters of the target water area, and the meteorological parameters of each point in the target water area are combined and analyzed to obtain the prohibited points of the target aquaculture auxiliary vessel in the target water area. In the target waters, determine the starting point, the point where the target aquaculture support vessel needs to navigate, and the ending point. Introduce the A* algorithm, construct the basic model of the A* algorithm based on the A* algorithm, and construct the starting point node, navigation node, and ending point node within the basic model of the A* algorithm according to the starting point and the point where the target aquaculture support vessel needs to navigate. In the A* algorithm training model, the prohibited points of the target aquaculture auxiliary vessels in the target waters are marked and defined as prohibited nodes; The water parameters of the target water area are imported into the basic model of the A* algorithm for model training to obtain the A* algorithm training model. The A* algorithm training model contains multiple nodes, where each node represents a point within the target water area. The A* algorithm is run to train the model, generating all node routes connecting the starting node, navigation node, and ending node. These routes are marked as node routes to be analyzed. Node routes with prohibited nodes are filtered out, resulting in node routes without prohibited nodes, which are marked as a type of node route. Calculate the route length of all nodes of type I, select the node route of type I with the shortest route length as the output, obtain the target node route, and mark the route corresponding to the target node route in the target water area as the target route.

2. The core performance matching and management method for a multi-functional aquaculture auxiliary vessel according to claim 1, characterized in that, The process involves conducting navigation stability tests on the target aquaculture support vessel using a simulation model, and then optimizing the navigation stability of the target aquaculture support vessel based on the test results. Specifically: Obtain simulation modeling software, import the specification data of the target aquaculture auxiliary vessel into the simulation modeling software, construct a simulation model of the target aquaculture auxiliary vessel, and calibrate it as the target aquaculture auxiliary vessel simulation model; Acquire a big data network, and within the big data network, based on the water parameters of the target water area, retrieve all sea states that may occur in the target water area and label them as a type of sea state; A navigation simulation system is constructed, which can control the simulation model of the target aquaculture auxiliary vessel to conduct simulated navigation based on the target route and withstand different sea states during the simulated navigation. Run the navigation simulation system to test the rolling amplitude of the target aquaculture auxiliary vessel simulation model under different sea states, and calibrate it as the navigation rolling amplitude. The dangerous rolling amplitude is preset. During the operation of the navigation simulation system, if the navigation rolling amplitude of the target aquaculture auxiliary vessel simulation model under all sea states is less than the dangerous rolling amplitude, the target aquaculture auxiliary vessel will be evaluated as a qualified aquaculture auxiliary vessel in terms of navigation stability. If the simulation model of the target aquaculture support vessel shows a sway amplitude greater than the dangerous sway amplitude under a certain sea state, the target aquaculture support vessel will be assessed as an aquaculture support vessel with unqualified navigation stability, and the corresponding sea state will be marked as a dangerous sea state. Stress analysis of structural components was conducted on aquaculture auxiliary vessels with substandard navigation stability, and stability optimization was performed based on the results of the stress analysis.

3. The core performance matching and management method for a multi-functional aquaculture auxiliary vessel according to claim 2, characterized in that, The process involves performing structural component stress analysis on aquaculture support vessels with substandard navigation stability, and then optimizing the stability of these vessels based on the analysis results. Specifically: Obtain the hull structural components of an aquaculture auxiliary vessel with substandard navigation stability, the hull structural components including deck, keel and bulkhead; The simulation model of the aquaculture auxiliary vessel with unqualified navigation stability is calibrated as a Class I simulation model, and the hull structural components are marked in the Class I simulation model to obtain the hull structural component simulation model. Run the navigation simulation system and test the stress values ​​of each region on the simulation model of the target aquaculture auxiliary vessel when the navigation roll amplitude of the simulation model is greater than the dangerous roll amplitude. Based on the dangerous rolling amplitude, the maximum withstandable stress value of each region on the simulation model of the hull structural components is calculated and calibrated as the dangerous stress value. Regions on the simulation model of the hull structural components with stress values ​​greater than the dangerous stress value are calibrated as model stress anomaly regions. On the simulation model of the ship's structural components, deformation analysis is performed on the stress anomaly region of the model to obtain the degree of deformation of the stress anomaly region under dangerous sea conditions, and the maximum degree of deformation of the stress anomaly region under dangerous sea conditions is preset. If the deformation of the model stress anomaly region under dangerous sea conditions is greater than the maximum deformation, then the region corresponding to the model stress anomaly region is obtained on the hull structural component and marked as the component stress anomaly region. On the structural components of the ship hull, the current material used in the stress anomaly area of ​​the component is obtained and labeled as a type of material. The material strength of the type of material is calculated, and materials that can act on the stress anomaly area of ​​the component and whose material strength is greater than that of the type of material are retrieved from the big data network and labeled as target materials. In the stress anomaly region of the component, a type of material is replaced with the target material to obtain a material-replaced hull structure component. The design optimization scheme of the material-replaced hull structure component is retrieved from the big data network and output to obtain a qualified aquaculture auxiliary vessel with navigation stability. The design optimization scheme of the material-replaced hull structure component can make the navigation roll amplitude of the simulation model of the target aquaculture auxiliary vessel greater than the dangerous roll amplitude.

4. The core performance matching and management method for a multi-functional aquaculture auxiliary vessel according to claim 1, characterized in that, During the navigation of the qualified aquaculture auxiliary vessel, the vessel is controlled to monitor water quality, and the feeding function and power performance of the vessel are matched based on the water quality monitoring results. Specifically: A water quality sensor and an automatic feed dispensing system are installed in the aquaculture auxiliary vessel with qualified navigation stability. The aquaculture auxiliary vessel with qualified navigation stability is controlled to navigate in the target water area based on the target route. During the navigation of the aquaculture auxiliary vessel with qualified navigation stability, the water quality parameters of the target water area are monitored in real time through the water quality sensor and calibrated as the real-time water quality parameters of the target water area. Identify all organisms in the target water area that require feeding, label them as target organisms, and retrieve the water quality parameter thresholds for the survival of target organisms from the big data network; If the real-time water quality parameters of the target water area are within the threshold of the water quality parameters that allow the target organism to survive, the corresponding target water area will be marked as a water area that can be fed. If the real-time water quality parameters of the target water area are not within the threshold of the water quality parameters that allow the target organism to survive, the corresponding target water area will be marked as a water area that cannot be fed. The rated output power, cruising output power and maximum output power of the propulsion system are obtained in the aquaculture support vessel with qualified navigation stability, and during the navigation of the aquaculture support vessel with qualified navigation stability, it is determined whether the water area in which the aquaculture support vessel with qualified navigation stability is navigating is a water area suitable for feeding. If not, adjust the propulsion system output power to the maximum output power in aquaculture auxiliary vessels with qualified navigation stability; If so, the quantity analysis of the target organisms will be conducted during the navigation of the aquaculture support vessel with qualified navigation stability, and the output power of the propulsion system will be adjusted based on the results of the quantity analysis in the aquaculture support vessel with qualified navigation stability.

5. The core performance matching and management method for a multi-functional aquaculture auxiliary vessel according to claim 4, characterized in that, The process of conducting quantity analysis of target organisms during navigation of a qualified aquaculture support vessel, and adjusting the output power of the propulsion system based on the quantity analysis results, specifically involves: Obtain the rated frequency of the automatic feed feeding system for livestock and calibrate it as the rated feeding frequency. Sonar fish-finding equipment is installed on the aquaculture auxiliary vessel with qualified navigation stability. The aquaculture auxiliary vessel with qualified navigation stability is controlled to navigate along the target route in the feeding water area. During navigation, the sonar fish-finding equipment is controlled to transmit sonar signals to the feeding water area and receive sonar signals reflected in the feeding water area. The sonar signals reflected from the feedable water area are preprocessed, and a sonar detection model is constructed based on the preprocessed sonar signals reflected from the feedable water area. The sonar detection model is analyzed to construct a distribution map of target organisms in the feedable water area. The distribution map of target organisms in the feedable water area records the density of target organisms at each point on the target route within the feedable water area. The target biological standard density is preset, and the distribution diagram of the target organisms in the feedable water area is imported into the propulsion system. When the aquaculture auxiliary vessel with qualified navigation stability is sailing along the target route in the feedable water area, if the target biological density at the point passed by the aquaculture auxiliary vessel with qualified navigation stability is greater than the target biological standard density, the aquaculture feed automatic feeding system is started, the feeding frequency of the aquaculture feed automatic feeding system is set to the rated feeding frequency, and the output power of the propulsion system in the aquaculture auxiliary vessel with qualified navigation stability is adjusted to the rated output power. If the target biological density at the point the aquaculture support vessel passes through is less than the target standard biological density, the automatic aquaculture feed feeding system is activated, the feeding frequency of the automatic aquaculture feed feeding system is set to the rated feeding frequency, and the output power of the propulsion system in the aquaculture support vessel with qualified navigation stability is adjusted to the cruise output power.

6. A core performance matching management system for a multi-functional aquaculture auxiliary vessel, characterized in that, The core performance matching management system includes a memory and a processor. The memory stores a core performance matching management method program. When the core performance matching management method program is executed by the processor, it implements the core performance matching management method steps as described in any one of claims 1-5.

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