Inflatable rescue boat stability analysis and subdivision optimization method and system

Abstract

The invention discloses a stability analysis and subdivision optimization method and system for an inflatable rescue boat, and belongs to the technical field of rescue boats. A plurality of carrying schemes are designed, the stability characteristics of the carrying schemes are correspondingly analyzed, and the uncertainty load statistical distribution rule of the rescue boat under complex working conditions is described; the stability characteristics of the inflatable rescue boat under different damage conditions are analyzed by combining a stability analysis theory, a large amount of simulation data is generated according to different damage conditions of an air chamber, three regression models for describing the carrying condition, the damage condition and the stability relation of the inflatable boat are established by using a BP neural network, and a static stability curve under a specific condition is predicted by using the three regression models; and finally, through subdivision optimization and verification of simulation data, the comprehensive stability of the rescue boat under the uncertain load condition is disclosed quantitatively, comprehensively and comprehensively. According to the stability analysis and subdivision optimization method and system for the inflatable rescue boat, a reference basis can be provided for design of the inflatable rescue boat, and a guiding effect is also achieved for practical application.

Description

Technical Field

[0001] The present invention relates to the technical field of rescue boats, and in particular to a stability analysis and subdivision optimization method and system for an inflatable rescue boat. Background Art

[0002] Inflatable rescue boats, with their exceptional operational capabilities, have become indispensable equipment in water rescue missions. However, the turbulent currents and volatile environments encountered during rescue operations pose a risk of capsizing and damage. The stability of the rescue boat is directly related to the safety of the crew and those being rescued, and also directly impacts the efficiency and success rate of rescue operations, enabling rescuers to more effectively carry out their mission. Stability studies and compartment optimization can help determine the rescue boat's carrying capacity and safe operating limits under different conditions, thereby reducing the risk of accidents.

[0003] Rescue boats are typically lightweight, so factors such as the increase or decrease in load on the boat, the horizontal and vertical movement of the load, and the air chamber subdivision have a significant impact on the buoyancy and stability of the rescue boat. Traditional stability analysis and subdivision optimization methods usually only focus on a single operating condition and fail to consider the impact of complex conditions such as the increase or decrease and distribution of the rescue boat's load, air chamber damage, and so on on the rescue boat's stability. Consequently, they are unable to comprehensively and accurately evaluate the comprehensive stability of the rescue boat. Therefore, it is urgent to propose a stability analysis and subdivision optimization method for inflatable rescue boats to study the impact of different load conditions on the rescue boat's stability, explore the changes in the rescue boat's stability under different damage conditions, and then establish a relevant regression model. This method can quantitatively describe the comprehensive stability of the rescue boat under complex conditions, enhance the stability and anti-sinking performance of the inflatable rescue boat, and provide a theoretical basis for the design, development, and stability verification of the rescue boat. Summary of the Invention

[0004] The purpose of the present invention is to provide a stability analysis and subdivision optimization method and system for an inflatable rescue boat, which can provide a certain reference basis for the design of inflatable rescue boats and also play a certain guiding role in their practical application.

[0005] To achieve the above objectives, the present invention provides a method for stability analysis and subdivision optimization of an inflatable rescue boat, comprising the following steps:

[0006] S1. Establish a model of the total load on the inflatable rescue boat and its spatial distribution based on the operating conditions of the boat, and identify the optimal parameters of the model;

[0007] S2. Divide the airbag of the inflatable rescue boat into different numbers of uniformly sized air chambers. Based on this, study the specific stability changes of the inflatable rescue boat when a certain air chamber is damaged, and solve the stability characteristic regression model;

[0008] S3. Optimize the compartments of the inflatable rescue boat based on the regression model of the relationship between the loading condition, damage condition and stability of the inflatable rescue boat.

[0009] Preferably, said S1 comprises the following steps:

[0010] S101. Build a complete empty inflatable rescue boat model and perform stability analysis;

[0011] S102. Build a complete inflatable rescue boat model under different load conditions and perform stability analysis;

[0012] S103. Determine optimal load condition parameters in the equivalent complete inflatable rescue boat model based on the stability analysis results;

[0013] The S2 comprises the following steps:

[0014] S201. Establish a model for dividing the different air chamber structures of an inflatable rescue boat, and evaluate the probability of damage to each air bag and the impact on the performance of the rescue boat if damaged;

[0015] S202, simulating the damage of each compartment and using BP neural network for training and prediction;

[0016] The S3 includes the following steps:

[0017] S301. Establish stability curves for the inflatable rescue boat under different compartmentalization methods, different load conditions, and different damage conditions for prediction, and perform simulation verification.

[0018] S302. Select a subdivision method with good overall stability from the results and perform further optimization.

[0019] Preferably, in said S101, the existing basic model of the inflatable rescue boat is simplified, additional structures other than the main structure of the hull are removed, and small-scale gaps and grooves are smoothed; and the stability calculation of the empty-load condition is performed using the relevant modules of the simulation software;

[0020] In said S102, different load conditions are set according to different weights, different center of gravity positions, and different gravity distributions, a corresponding basic model of the inflatable rescue boat is established, and stability calculations are performed for different load conditions using relevant modules of the simulation software;

[0021] In S103, the optimal load condition parameters are selected based on the statistical results of the stability of the complete inflatable rescue boat under different load conditions.

[0022] Preferably, in S201, a structural division model of different air chambers of an inflatable rescue boat is established, and the probability of damage of each airbag and the degree of impact on the performance of the rescue boat after damage are evaluated to establish a compartment optimization model of an inflatable rescue boat with 4 air chambers, 6 air chambers, or 8 air chambers. Since the damage probability of airbags at different positions is different, priority is given to the damage stability of the airbags with high damage probability in the front part and side airbags of the rescue boat;

[0023] The influence of different air chamber damage on the stability of the inflatable rescue boat in S202 is calculated by using the relevant modules of the simulation software to calculate the stability of each air chamber damage condition, with the roll range of 0 to 90° and the step length of 10°. The pitch angle is set to free pitch according to the load condition.

[0024] Preferably, the BP neural network consists of a three-layer topological structure consisting of an input layer, a hidden layer and an output layer, each layer consisting of a number of nodes, which are connected by weights and thresholds; the input layer is responsible for receiving and storing external signals and data; the hidden layer connects the input layer and the output layer, can automatically learn to extract key features and patterns in the input data, and provide the nonlinear modeling capability of the BP neural network; the output layer is used to save the network's response to the input signal.

[0025] Preferably, in said S301, under the condition of damage to each air chamber under different subdivision conditions, simulations are performed for a plurality of set load conditions, and after eliminating missing values ​​in the obtained results, the data are divided into training set data and test set data, and the training set data and the test set data are fitted using a neural network, and the predicted values ​​corresponding to the heel angle in the range of 0 to 90 degrees and a step size of 10 degrees under the predicted conditions are obtained by respectively predicting with the established regression model and software simulation, and the stability curve is obtained by polynomial fitting;

[0026] In the evaluation step S302 , the stability curves of the damage conditions of the air chambers under different subdivision conditions are obtained. First, a subdivision method with good stability is selected. Second, the economy and manufacturing difficulty are comprehensively considered and further optimized.

[0027] The present invention provides a stability analysis and subdivision optimization system used in a stability analysis and subdivision optimization method for an inflatable rescue boat, comprising the following modules:

[0028] Model building module: establishes a complete inflatable rescue boat model and its damage probability model based on the operating conditions, and identifies the model parameters;

[0029] Analysis module: divide the air chambers of the inflatable rescue boat, use the relevant modules of the simulation software to calculate the stability of each air chamber damage, and use the BP neural network for training and prediction;

[0030] Optimization module: Based on the stability curves of each air chamber damage condition under different subdivision conditions, the subdivision method with good stability is selected, and further optimization is performed while comprehensively considering the economy and manufacturing difficulty.

[0031] Preferably, the model building module includes:

[0032] Model building unit: establish equivalent models of intact and damaged inflatable rescue boats respectively, and determine the probability of damage;

[0033] Identification of undetermined parameters unit: Determine the optimal load parameters based on load simulation of rescue boat models under different load conditions;

[0034] The analysis module includes:

[0035] Air chamber equalization unit: the air chamber of the inflatable rescue boat is divided according to the manufacturing and actual use conditions;

[0036] Stability solution unit: Use the relevant modules of the simulation software to solve the stability of each air chamber damage, and use the BP neural network for training and prediction.

[0037] Preferably, the working process of the optimization module is: through the established regression model prediction and software simulation, the predicted value corresponding to the heel angle in the range of 0 to 90° and the step size of 10° under the predicted working conditions is obtained, and the stability curve is obtained by polynomial fitting; the stability curves of each air chamber damage condition under different subdivision conditions are evaluated, and the subdivision method with good stability is first selected, and secondly, the economy and manufacturing difficulty are comprehensively considered for further optimization.

[0038] Preferably, the construction process of the model construction unit is as follows: simplifying the existing basic model of the inflatable rescue boat, removing the additional structure other than the main structure of the boat body, and smoothing the small-scale gaps and grooves;

[0039] The working process of the unit for identifying undetermined parameters is as follows: using the relevant modules of the simulation software to perform stability calculations for the empty load condition, using the relevant modules of the simulation software to perform stability calculations for different load conditions, and collecting the statistical results of the stability of the complete inflatable rescue boat under different load conditions to select the optimal load condition parameters;

[0040] The working process of the air chamber equalization unit is as follows: establishing a structural division model of different air chambers for the inflatable rescue boat, evaluating the probability of damage of each airbag and the degree of impact on the performance of the rescue boat after damage, and establishing a compartment optimization model for the inflatable rescue boat with 4 air chambers, 6 air chambers, or 8 air chambers. Since the damage probability of airbags in different positions is different, the damage stability of the airbags with high damage probability in the front part and side airbags of the rescue boat is given priority;

[0041] The solution process of the stability solution unit is as follows: distinguish the impact of different air chamber damage on the stability of the inflatable rescue boat; use the relevant modules of the simulation software to calculate the stability of each air chamber damage condition, with the roll range of 0 to 90 degrees, the step size of 10 degrees, and the pitch angle set to free pitch according to the load condition.

[0042] Therefore, the present invention adopts the above-mentioned inflatable rescue boat stability analysis and subdivision optimization method and system, which can provide a certain reference basis for the design of inflatable rescue boats and also play a certain guiding role in their practical application.

[0043] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a flow chart of an embodiment of a stability analysis and subdivision optimization method and system for an inflatable rescue boat of the present invention;

[0045] Figure 2 This is a flow chart of S1 of an embodiment of a method and system for stability analysis and subdivision optimization of an inflatable rescue boat according to the present invention;

[0046] Figure 3 This is a flow chart of S2 of an embodiment of a method and system for stability analysis and subdivision optimization of an inflatable rescue boat according to the present invention;

[0047] Figure 4 This is a flow chart of S3 of an embodiment of a method and system for stability analysis and subdivision optimization of an inflatable rescue boat of the present invention;

[0048] Figure 5 This is a flow chart of the BP neural network in S202 of an embodiment of a stability analysis and subdivision optimization method and system for an inflatable rescue boat of the present invention. DETAILED DESCRIPTION

[0049] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0050] Unless otherwise defined, technical or scientific terms used in the present invention shall have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs.

[0051] Example 1

[0052] like Figures 1 to 4 As shown, the present invention provides a method for stability analysis and subdivision optimization of an inflatable rescue boat, comprising the following steps:

[0053] S1. Establish a model of the total load on the inflatable rescue boat and its spatial distribution based on the operating conditions of the inflatable rescue boat, and identify the optimal parameters of the model; specifically, the following steps are included:

[0054] S101. Build a complete, unloaded inflatable rescue boat model and perform stability analysis. Simplify the existing basic model of the inflatable rescue boat, removing any additional structures beyond the main hull structure and smoothing small gaps, grooves, and other details. Use the relevant simulation software modules to perform stability calculations for the unloaded condition.

[0055] S102. Establish a complete inflatable rescue boat model for different load conditions and perform stability analysis. Different load conditions are set based on weight, center of gravity, and gravity distribution. A corresponding basic model of the inflatable rescue boat is established. Stability calculations are performed for each load condition using the relevant modules of the simulation software.

[0056] S103. Determine optimal load condition parameters in the equivalent complete inflatable rescue boat model based on the stability analysis results. Select the optimal load condition parameters based on the statistical results of the stability of the complete inflatable rescue boat under different load conditions.

[0057] S2. Divide the airbag of the inflatable rescue boat into different numbers of uniformly sized air chambers. Based on this, study the specific stability changes of the inflatable rescue boat when a certain air chamber is damaged, and solve the stability characteristic regression model. Specifically, the following steps are included:

[0058] S201. Establish a model for the division of different air chamber structures for inflatable rescue boats, and evaluate the probability of damage to each airbag and the impact on the rescue boat's performance if damaged. Establish a model for the division of different air chamber structures for inflatable rescue boats, and evaluate the probability of damage to each airbag and the impact on the rescue boat's performance if damaged. Establish a compartment optimization model for inflatable rescue boats with 4, 6, or 8 air chambers. Because the damage probability of airbags in different locations varies, prioritize the damage stability of airbags with high damage probability in the front and side of the rescue boat.

[0059] S202. Simulate the damage to each compartment using a BP neural network for training and prediction. The impact of different air chamber damage on the stability of the inflatable rescue boat is analyzed. Use the relevant modules of the simulation software to calculate the stability of each air chamber damage scenario. The heel range is 0 to 90° with a step size of 10°, and the trim angle is set to free trim based on the load condition.

[0060] like Figure 5As shown in the figure, a BP neural network is used for training and prediction. The network consists of a three-layer topology: input layer, hidden layer, and output layer. Each layer consists of several nodes connected by weights and thresholds. The input layer is responsible for receiving and storing external signals and data; the hidden layer connects the input and output layers, automatically learning to extract key features and patterns in the input data and providing the nonlinear modeling capabilities of the BP neural network; the output layer is used to store the network's response to the input signal.

[0061] The number of neurons in the input layer of the constructed BP neural network is 9, 11 and 13 in the order of the model, the number of neurons in the output layer is 1, the activation function is tansig and purelin, the training function is trainlm, the loss function is MSE function, the learning rate is 0.01, the maximum number of iterations is set to 1000, and the error accuracy is set to 10 -6 After the network construction is completed, 80% of the simulation data is randomly divided into a training set and 20% into a prediction set, and model fitting and testing are performed to evaluate the performance and generalization ability of the model.

[0062] S3. Optimize the compartments of the inflatable rescue boat based on the regression model of the relationship between the loading status, damage status and stability of the inflatable rescue boat. Specifically, the following steps are included:

[0063] S301. Establish stability curves for inflatable rescue boats under different subdivision modes, different load conditions, and different damage conditions for prediction, and perform simulation verification. Simulate various load conditions under different subdivision modes and damage conditions of each air chamber. After eliminating missing values ​​in the obtained results, divide the data into training set data and test set data, and use neural network to fit the training set data and test set data. Through the established regression model prediction and software simulation, respectively, the predicted values ​​corresponding to the heel angle in the range of 0 to 90 degrees and a step size of 10 degrees under the predicted conditions are obtained, and the stability curve is obtained by polynomial fitting.

[0064] S302. Evaluate the stability curves of each air chamber damage condition obtained under different subdivision methods. First, select the subdivision method with good stability. Second, comprehensively consider the economy and manufacturing difficulty, and further optimize.

[0065] The present invention provides a stability analysis and subdivision optimization system used in a stability analysis and subdivision optimization method for an inflatable rescue boat, comprising the following modules:

[0066] Model construction module: establishes a complete inflatable rescue boat model and its damage probability model based on the operating conditions, and identifies the model parameters; specifically includes a model construction unit and an identification unit for undetermined parameters:

[0067] Model building unit: Equivalent models of intact and damaged inflatable rescue boats are established respectively to determine the probability of damage. The model building unit simplifies the existing basic model of the inflatable rescue boat, removes additional structures other than the main structure of the hull, and smoothes small-scale gaps, grooves and other details.

[0068] Identification unit of undetermined parameters: Based on the load simulation of the rescue boat model under different load conditions, the optimal load parameters are determined; the identification unit of undetermined parameters uses the relevant modules of the simulation software to perform stability calculations on the empty condition, and uses the relevant modules of the simulation software to perform stability calculations on different load conditions, and completes the statistical results of the stability of the inflatable rescue boat under different load conditions to select the optimal load condition parameters.

[0069] Analysis module: Divide the air chambers of the inflatable rescue boat, use the relevant modules of the simulation software to calculate the stability of each air chamber damage, and use the BP neural network for training and prediction; specifically includes the air chamber equalization unit and the stability solution unit:

[0070] Air chamber equalization unit: The air chambers of the inflatable rescue boat are divided according to the manufacturing and actual use conditions; the air chamber equalization unit establishes a structural division model of different air chambers for the inflatable rescue boat, evaluates the probability of each airbag being damaged and the degree of impact on the performance of the rescue boat after damage, and establishes a compartment optimization model for the inflatable rescue boat with 4 air chambers, 6 air chambers or 8 air chambers. Since the damage probability of airbags in different positions is different, priority is given to the damage stability of the airbags with high damage probability in the front part and side airbags of the rescue boat.

[0071] Stability solving unit: Use the relevant modules of the simulation software to solve the stability of each air chamber damage, and use the BP neural network for training and prediction; the impact of different air chamber damage on the stability of the inflatable rescue boat is calculated by the relevant modules of the simulation software for the stability of each air chamber damage. The roll range is 0~90°, the step size is 10°, and the pitch angle is set to free pitch according to the load condition.

[0072] Optimization module: Based on the stability curves of each air chamber damage condition under different subdivision conditions, a subdivision method with good stability is selected, and further optimization is performed while comprehensively considering the economy and manufacturing difficulty. Through the established regression model prediction and software simulation method, the predicted value corresponding to the heel angle in the range of 0 to 90 degrees and a step size of 10 degrees under the predicted working conditions is obtained, and the stability curve is obtained through polynomial fitting. The stability curves of each air chamber damage condition under different subdivision conditions are evaluated, and the subdivision method with good stability is first selected. Secondly, the economy and manufacturing difficulty are comprehensively considered and further optimization is performed.

[0073] The present invention may be applied in the following embodiments, and in the following embodiments, may be implemented in whole or in part by software, hardware, firmware, or any combination thereof:

[0074] A computer program for implementing the stability analysis and subdivision optimization method of the inflatable rescue boat in the preferred embodiment described above.

[0075] An information data processing terminal for implementing the inflatable rescue boat stability analysis and subdivision optimization method in the above preferred embodiment.

[0076] A computer-readable storage medium includes instructions, which, when executed on a computer, enable the computer to execute the inflatable rescue boat stability analysis and subdivision optimization method in the above preferred embodiment.

[0077] When the use is implemented in whole or in part in the form of a computer program product, the computer program product includes one or more computer instructions. When the computer program instructions are loaded or executed on a computer, the process or function according to the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrations. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0078] Therefore, the present invention adopts the above-mentioned inflatable rescue boat stability analysis and subdivision optimization method and system, which can provide a certain reference basis for the design of inflatable rescue boats and also play a certain guiding role in their practical application.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for stability analysis and subdivision optimization of an inflatable rescue boat, characterized by: The following steps are involved: S1. Establish a model of the total load on the inflatable rescue boat and its spatial distribution based on the operating conditions of the boat, and identify the optimal parameters of the model; S2. Divide the airbag of the inflatable rescue boat into different numbers of uniformly sized air chambers. Based on this, study the specific stability changes of the inflatable rescue boat when a certain air chamber is damaged, and solve the stability characteristic regression model; S3. Optimize the compartments of the inflatable rescue boat based on the regression model of the relationship between the loading condition, damage condition and stability of the inflatable rescue boat.

2. The method for stability analysis and subdivision optimization of an inflatable rescue boat according to claim 1, characterized in that: Said S1 comprises the following steps: S101. Build a complete empty inflatable rescue boat model and perform stability analysis; S102. Build a complete inflatable rescue boat model under different load conditions and perform stability analysis; S103. Determine optimal load condition parameters in the equivalent complete inflatable rescue boat model based on the stability analysis results; The S2 comprises the following steps: S201. Establish a model for dividing the different air chamber structures of an inflatable rescue boat, and evaluate the probability of damage to each air bag and the impact on the performance of the rescue boat if damaged; S202, simulating the damage of each compartment and using BP neural network for training and prediction; The S3 includes the following steps: S301. Establish stability curves for the inflatable rescue boat under different compartmentalization methods, different load conditions, and different damage conditions for prediction, and perform simulation verification. S302. Select a subdivision method with good overall stability from the results and perform further optimization.

3. The method for stability analysis and subdivision optimization of an inflatable rescue boat according to claim 2, characterized in that: In the step S101, the existing basic model of the inflatable rescue boat is simplified, additional structures other than the main structure of the boat body are removed, and small-scale gaps and grooves are smoothed; and the stability calculation of the empty-load condition is performed using the relevant modules of the simulation software; In said S102, different load conditions are set according to different weights, different center of gravity positions, and different gravity distributions, a corresponding basic model of the inflatable rescue boat is established, and stability calculations are performed for different load conditions using relevant modules of the simulation software; In S103, the optimal load condition parameters are selected based on the statistical results of the stability of the complete inflatable rescue boat under different load conditions.

4. The method for stability analysis and subdivision optimization of an inflatable rescue boat according to claim 2, characterized in that: In S201, a structural partitioning model for different air chambers of an inflatable rescue boat is established, and the probability of damage of each airbag and the degree of impact on the performance of the rescue boat after damage are evaluated. A compartment optimization model for an inflatable rescue boat with 4 air chambers, 6 air chambers, or 8 air chambers is established. Since the damage probability of airbags in different positions is different, priority is given to the damage stability of the airbags in the front part and side of the rescue boat with a high damage probability; The influence of different air chamber damage on the stability of the inflatable rescue boat in S202 is calculated by using the relevant modules of the simulation software to calculate the stability of each air chamber damage condition, with the roll range of 0 to 90° and the step length of 10°. The pitch angle is set to free pitch according to the load condition.

5. The method for stability analysis and subdivision optimization of an inflatable rescue boat according to claim 2, characterized in that: The BP neural network consists of a three-layer topological structure: input layer, hidden layer and output layer. Each layer consists of several nodes, which are connected by weights and thresholds. Among them, the input layer is responsible for receiving and storing external signals and data; the hidden layer connects the input layer and the output layer, and can automatically learn to extract key features and patterns in the input data, and provide the nonlinear modeling capability of the BP neural network; the output layer is used to save the network's response to the input signal.

6. The method for stability analysis and subdivision optimization of an inflatable rescue boat according to claim 2, characterized in that: In S301, simulations are performed for various load conditions under different compartmentalization conditions and under the condition that each air chamber is damaged. After eliminating missing values ​​in the obtained results, the data are divided into training set data and test set data. The training set data and the test set data are fitted using a neural network. The predicted values ​​corresponding to the heel angle in the range of 0 to 90 degrees with a step size of 10 degrees under the predicted conditions are obtained by respectively using the established regression model prediction and software simulation methods, and the stability curve is obtained by polynomial fitting. In the evaluation step S302 , the stability curves of the damage conditions of the air chambers under different subdivision conditions are obtained. First, a subdivision method with good stability is selected. Second, the economy and manufacturing difficulty are comprehensively considered and further optimized.

7. The stability analysis and subdivision optimization system used in the stability analysis and subdivision optimization method for an inflatable rescue boat according to any one of claims 1 to 6, characterized in that: Includes the following modules: Model building module: establishes a complete inflatable rescue boat model and its damage probability model based on the operating conditions, and identifies the model parameters; Analysis module: divide the air chambers of the inflatable rescue boat, use the relevant modules of the simulation software to calculate the stability of each air chamber damage, and use the BP neural network for training and prediction; Optimization module: Based on the stability curves of each air chamber damage condition under different subdivision conditions, the subdivision method with good stability is selected, and further optimization is performed while comprehensively considering the economy and manufacturing difficulty.

8. The stability analysis and subdivision optimization system for an inflatable rescue boat according to claim 7, characterized in that: The model building module includes: Model building unit: establish equivalent models of intact and damaged inflatable rescue boats respectively, and determine the probability of damage; Identification of undetermined parameters unit: Determine the optimal load parameters based on load simulation of rescue boat models under different load conditions; The analysis module includes: Air chamber equalization unit: the air chamber of the inflatable rescue boat is divided according to the manufacturing and actual use conditions; Stability solution unit: Use the relevant modules of the simulation software to solve the stability of each air chamber damage, and use the BP neural network for training and prediction.

9. The stability analysis and subdivision optimization system for an inflatable rescue boat according to claim 7, characterized in that: The working process of the optimization module is as follows: through the established regression model prediction and software simulation, the predicted value corresponding to the heel angle in the range of 0 to 90 degrees with a step size of 10 degrees under the predicted working conditions is obtained, and the stability curve is obtained through polynomial fitting; the stability curves of each air chamber damage condition under different subdivision conditions are evaluated, and the subdivision method with good stability is first selected. Secondly, the economy and manufacturing difficulty are comprehensively considered and further optimization is carried out.

10. The stability analysis and subdivision optimization system for an inflatable rescue boat according to claim 8, characterized in that: The construction process of the model construction unit is as follows: simplifying the existing basic model of the inflatable rescue boat, removing the additional structure other than the main structure of the boat body, and smoothing the small-scale gaps and grooves; The working process of the unit for identifying undetermined parameters is as follows: using the relevant modules of the simulation software to perform stability calculations for the empty load condition, using the relevant modules of the simulation software to perform stability calculations for different load conditions, and collecting the statistical results of the stability of the complete inflatable rescue boat under different load conditions to select the optimal load condition parameters; The working process of the air chamber equalization unit is as follows: establishing a structural division model of different air chambers for the inflatable rescue boat, evaluating the probability of damage of each airbag and the degree of impact on the performance of the rescue boat after damage, and establishing a compartment optimization model for the inflatable rescue boat with 4 air chambers, 6 air chambers, or 8 air chambers. Since the damage probability of airbags in different positions is different, the damage stability of the airbags with high damage probability in the front part and side airbags of the rescue boat is given priority; The solution process of the stability solution unit is as follows: distinguish the impact of different air chamber damage on the stability of the inflatable rescue boat; use the relevant modules of the simulation software to calculate the stability of each air chamber damage condition, with the roll range of 0 to 90 degrees, the step size of 10 degrees, and the pitch angle set to free pitch according to the load condition.

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