A Shell State Recognition Method Based on Local Optimization and Support Vector Machine

Abstract

This invention relates to the field of hull state recognition technology, and discloses a hull state recognition method based on local optimization and support vector machines. The method includes: collecting hull state data of an underwater vehicle, including stress data, deformation data, and acoustic emission data; processing the state data and extracting key features based on a local optimization algorithm; training a support vector machine model based on the extracted key features to obtain a risk value for the hull state; acquiring the underwater vehicle's driving data, adjusting the risk value based on the driving data, and determining the final risk value of the hull; and generating a driving strategy for the underwater vehicle based on the final risk value. By analyzing and processing the hull state data, combined with the local optimization algorithm and the support vector machine model, the accuracy and efficiency of hull state recognition are improved, enabling real-time monitoring of the underwater vehicle's hull state and facilitating the timely detection of potential safety issues.

Description

Technical Field

[0001] This invention relates to the field of shell state recognition technology, and in particular to a shell state recognition method based on local optimization and support vector machines. Background Technology

[0002] With the development of marine resources and the deepening of marine scientific research, underwater vehicles (such as submarines and unmanned submersibles) are crucial exploration tools, and their safety and reliability are of paramount importance. The hull of an underwater vehicle is a key structural component protecting its internal instruments and equipment from seawater pressure and external environmental influences. During missions, they often face extreme marine environments, which can lead to damage or deterioration of the hull structure, affecting its performance and safety. Therefore, monitoring the health status of the hull is of great significance for ensuring the safe operation of underwater vehicles.

[0003] Traditional hull monitoring methods primarily rely on periodic inspections and manual visual checks. These methods are not only inefficient but also fail to provide real-time monitoring and early warning of the hull's condition. Furthermore, due to the complexity of the underwater environment, manual inspection often struggles to accurately assess the extent and location of hull damage. Therefore, there is an urgent need for a hull condition recognition method based on local optimization and support vector machines to achieve real-time monitoring and identification of underwater vehicle hulls. Summary of the Invention

[0004] The purpose of this invention is to provide a shell condition recognition method based on local optimization and support vector machines, which solves the problem that traditional shell monitoring mainly relies on periodic inspections and manual visual inspection, which is not only inefficient but also unable to achieve real-time monitoring and early warning of shell condition. Furthermore, due to the complexity of the underwater environment, manual inspection often struggles to accurately assess the degree and location of shell damage.

[0005] This invention provides a shell state recognition method based on local optimization and support vector machines, the method comprising:

[0006] The condition data of the underwater vehicle hull is collected, including stress data, deformation data, and acoustic emission data.

[0007] The state data is processed, and key features are extracted based on a local optimization algorithm;

[0008] A support vector machine model is trained based on the extracted key features to obtain the risk value of the shell state;

[0009] Acquire the driving data of the underwater vehicle, adjust the risk value based on the driving data, and determine the final risk value of the hull;

[0010] The underwater vehicle's driving strategy is generated based on the final risk value.

[0011] In some embodiments of this application, the state data is processed, and key features are extracted based on a local optimization algorithm, including:

[0012] The collected data is filtered, denoised, and normalized.

[0013] Features are extracted from the processed state data to obtain a feature set;

[0014] The feature set is optimized using a local optimization algorithm to find the best data combination and obtain the key features.

[0015] In some embodiments of this application, a support vector machine model is trained based on the extracted key features to obtain the risk value of the shell state, including:

[0016] The status data includes historical status data and real-time status data;

[0017] The key features are divided into a training set and a test set;

[0018] The support vector machine model is trained based on the training set, and the optimal parameters of the support vector machine model are found by cross-validation to establish the support vector machine model.

[0019] The support vector machine model is validated based on the test set to determine the accuracy of the support vector machine model;

[0020] When the accuracy rate reaches the preset accuracy rate, the risk value F of the shell state is determined based on the real-time status data.

[0021] In some embodiments of this application, the risk value is adjusted based on the driving data to obtain the final risk value of the housing, including:

[0022] The driving data includes driving speed and diving depth;

[0023] The driving speed is compared with a speed threshold. If the driving speed is less than or equal to the speed threshold, the risk value is not adjusted. If the driving speed is greater than the speed threshold, the risk value is adjusted according to the speed difference between the driving speed and the speed threshold.

[0024] The diving depth is compared with a depth threshold. If the diving depth is less than or equal to the depth threshold, the risk value is not adjusted. If the diving depth is greater than the depth threshold, the risk value is adjusted according to the depth difference between the diving depth and the depth threshold to obtain the final risk value of the shell.

[0025] In some embodiments of this application, adjusting the risk value based on the speed difference between the driving speed and the speed threshold includes:

[0026] Determine the speed difference V0 between the driving speed V and the speed threshold Vm, where V0 = V - Vm;

[0027] A first preset speed difference, a second preset speed difference, and a third preset speed difference are preset, and the first preset speed difference, the second preset speed difference, and the third preset speed difference increase sequentially;

[0028] The risk value F is corrected based on the relationship between the speed difference V0 and the first preset speed difference, the second preset speed difference, and the third preset speed difference;

[0029] If the speed difference V0 is less than the first preset speed difference, then the first speed correction coefficient m1 is selected to correct the risk value F, and the corrected value is F×m1.

[0030] If the speed difference V0 is greater than or equal to the first preset speed difference, and the speed difference V0 is less than the second preset speed difference, then the risk value F is corrected by the second speed correction coefficient m2, and the corrected value is F×m2.

[0031] If the speed difference V0 is greater than or equal to the second preset speed difference, and the speed difference V0 is less than the third preset speed difference, then the third speed correction coefficient m3 is selected to correct the risk value F, and the corrected value is F×m3.

[0032] If the speed difference V0 is greater than or equal to the third preset speed difference, then the fourth speed correction coefficient m4 is selected to correct the risk value F, and the corrected value is F×m4; where 1<m1<m2<m3<m4<1.2.

[0033] In some embodiments of this application, the risk value is adjusted based on the depth difference between the diving depth and the depth threshold to obtain the final risk value of the hull, including:

[0034] Determine the depth difference H0 between the diving depth H and the depth threshold Hm, where H0 = H - Hm;

[0035] If the driving speed is less than or equal to the speed threshold, the risk value F is adjusted according to the depth difference H0 to obtain the final risk value of the shell.

[0036] If the driving speed is greater than the speed threshold, the corrected F×mi is adjusted according to the depth difference H0, i = 1, 2, 3, 4, to obtain the final risk value of the shell.

[0037] In some embodiments of this application, the risk value F is adjusted based on the depth difference H0 to obtain the final risk value of the shell, including:

[0038] A first preset depth difference, a second preset depth difference, and a third preset depth difference are preset, and the first preset depth difference, the second preset depth difference, and the third preset depth difference are increased sequentially;

[0039] The risk value F is adjusted based on the relationship between the depth difference and the first preset depth difference, the second preset depth difference, and the third preset depth difference;

[0040] If the depth difference H0 is less than the first preset depth difference, then the first depth correction coefficient n1 is selected to correct the risk value F, and the final risk value is F×n1.

[0041] If the depth difference H0 is greater than or equal to the first preset depth difference, and the depth difference H0 is less than the second preset depth difference, then the second depth correction coefficient n2 is selected to correct the risk value F, and the final risk value is F×n2.

[0042] If the depth difference H0 is greater than or equal to the second preset depth difference, and the depth difference H0 is less than the third preset depth difference, then the third depth correction coefficient n3 is selected to correct the risk value F, and the final risk value is F×n3.

[0043] If the depth difference H0 is greater than or equal to the third preset depth difference, then the fourth depth correction coefficient n4 is selected to correct the risk value F, and the final risk value is F×n4; where 1<n1<n2<n3<n4<1.2.

[0044] In some embodiments of this application, the corrected F×mi is adjusted based on the depth difference H0 to obtain the final risk value of the shell, including:

[0045] The corrected F×mi is adjusted according to the relationship between the depth difference and the first preset depth difference, the second preset depth difference, and the third preset depth difference;

[0046] If the depth difference H0 is less than the first preset depth difference, then the fifth depth correction coefficient n5 is selected to correct the corrected F×mi, and the final risk value is F×mi×n5.

[0047] If the depth difference H0 is greater than or equal to the first preset depth difference, and the depth difference H0 is less than the second preset depth difference, then the sixth depth correction coefficient n6 is selected to correct the risk value F, and the final risk value is F×mi×n6.

[0048] If the depth difference H0 is greater than or equal to the second preset depth difference, and the depth difference H0 is less than the third preset depth difference, then the seventh depth correction coefficient n7 is selected to correct the risk value F, and the final risk value is F×mi×n7.

[0049] If the depth difference H0 is greater than or equal to the third preset depth difference, then the eighth depth correction coefficient n8 is selected to correct the risk value F, and the final risk value is F×mi×n8; where 1<n5<n6<n7<n8<1.2, and n1<n5, n2<n6, n3<n7, n4<n8.

[0050] In some embodiments of this application, generating a driving strategy for the underwater vehicle based on the final risk value includes:

[0051] A first preset final risk value and a second preset final risk value are preset, wherein the first preset final risk value is less than the second preset final risk value;

[0052] When the final risk value is less than the first preset final risk value, the underwater vehicle's driving strategy is to continue driving.

[0053] When the final risk value is greater than or equal to the first preset final risk value and the final risk value is less than the second preset final risk value, the underwater vehicle's driving strategy is generated as follows: it can continue to drive, but the driving time does not exceed the preset time.

[0054] When the final risk value is greater than or equal to the second preset final risk value, the underwater vehicle's driving strategy is to stop driving and return immediately.

[0055] Compared with the prior art, the beneficial effects of the present invention are that by analyzing and processing the shell state data, and combining local optimization algorithms and support vector machine models, the present invention improves the accuracy and efficiency of shell state identification, realizes real-time monitoring of the shell state of underwater vehicles, facilitates timely detection of potential safety issues, and reduces reliance on manual inspection by adopting online monitoring, thereby reducing maintenance costs. Attached Figure Description

[0056] 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 embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0057] Figure 1 This is a flowchart illustrating the shell state recognition method based on local optimization and support vector machine of the present invention. Detailed Implementation

[0058] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0059] like Figure 1 As shown, this invention provides a shell state recognition method based on local optimization and support vector machines, the method comprising:

[0060] Step S1: Collect state data of the underwater vehicle hull, including stress data, deformation data, and acoustic emission data.

[0061] Understandably, various sensors are installed on the hull of underwater vehicles to collect their state data. Stress is the internal force per unit area. When the hull of an underwater vehicle is subjected to external forces (such as water pressure, collisions, loads, etc.), internal forces resisting these forces are generated. The distribution of these internal forces within the structure is called stress, which can be directly measured using stress sensors. Deformation refers to the change in shape or volume of a structure under external forces, and it occurs alongside stress. When the hull is subjected to external forces and stress is generated, the material undergoes elastic or plastic deformation. Elastic deformation can return to its original shape after the external force is removed, while plastic deformation is permanent. Deformation can be measured using devices such as displacement sensors and strain gauges, and the data typically includes the amount of displacement and the magnitude of strain. Acoustic emission (AE) refers to transient elastic waves generated when a material undergoes localized changes (such as crack formation and propagation, plastic deformation, etc.). When a minute structural change occurs within a material, energy is rapidly released. This energy propagates to the material's surface in the form of waves and is captured by an AE sensor. AE data is typically a series of electrical signals that are sudden in time, and each signal contains information about the location, intensity, and nature of the event. AE data can then be used to detect and locate potential microscopic damage.

[0062] Step S2: Process the state data and extract key features based on a local optimization algorithm.

[0063] In some embodiments of this application, the state data is processed and key features are extracted based on a local optimization algorithm, including: filtering, denoising, and normalizing the collected data; extracting features from the processed state data to obtain a feature set; and optimizing the feature set using a local optimization algorithm to find the best data combination to obtain key features.

[0064] Understandably, the collected state data is raw data, requiring a series of processing steps to improve data quality. Since the collected data contains electrical signals, filtering is necessary. The purpose of filtering is to remove unwanted frequency components from the signal; common filtering methods include low-pass filtering, high-pass filtering, and band-pass filtering. In practical applications, appropriate filters can be selected for preprocessing based on signal characteristics and analytical needs. For example, moving average filters can be used to smooth data and reduce the impact of random noise. Denoising aims to eliminate noise from the signal while retaining useful information. Wavelet transform is a commonly used denoising method that decomposes the signal into sub-band coefficients of different frequencies, then removes noise through thresholding. For example, translation-invariant GHM multi-wavelet transform can effectively remove noise while preserving the data's characteristic information. Specific steps include pre-filtering, multi-wavelet decomposition, thresholding, multi-wavelet reconstruction, and post-filtering. Normalization scales the data proportionally to fit it into a small, specific interval, typically between 0 and 1. This eliminates the influence of data dimensions, making different features comparable, and also helps accelerate the convergence speed of machine learning algorithms. Commonly used normalization methods include min-max normalization and Z-score standardization.

[0065] Step S3: Train a support vector machine model based on the extracted key features to obtain the risk value of the shell state.

[0066] In some embodiments of this application, a support vector machine (SVM) model is trained based on extracted key features to obtain a risk value for the shell state, including: historical state data and real-time state data; the key features are divided into a training set and a test set; the SVM model is trained based on the training set, and the optimal parameters of the SVM model are found using cross-validation to establish the SVM model; the SVM model is validated based on the test set to determine the accuracy of the SVM model; when the accuracy reaches a preset accuracy, the risk value F of the shell state is determined based on the real-time state data.

[0067] Understandably, the state data includes historical state data and real-time state data. A support vector machine (SVM) model is constructed using historical state data, while real-time state data is used to obtain the shell state risk value. The optimal parameters of the SVM model are obtained using cross-validation to ensure accuracy in identifying risk values.

[0068] Step S4: Obtain the driving data of the underwater vehicle, adjust the risk value based on the driving data, and determine the final risk value of the hull.

[0069] Understandably, the driving data refers to the underwater vehicle's speed and diving depth, which can be measured using relevant sensors.

[0070] In some embodiments of this application, the risk value is adjusted based on the driving data to obtain the final risk value of the shell, including: the driving data includes driving speed and diving depth.

[0071] The driving speed is compared with a speed threshold. If the driving speed is less than or equal to the speed threshold, the risk value is not adjusted. If the driving speed is greater than the speed threshold, the risk value is adjusted according to the speed difference between the driving speed and the speed threshold.

[0072] The diving depth is compared with a depth threshold. If the diving depth is less than or equal to the depth threshold, the risk value is not adjusted. If the diving depth is greater than the depth threshold, the risk value is adjusted according to the depth difference between the diving depth and the depth threshold to obtain the final risk value of the shell.

[0073] In some embodiments of this application, adjusting the risk value based on the speed difference between the driving speed and the speed threshold includes: determining a speed difference V0 between the driving speed V and the speed threshold Vm, where V0 = V - Vm; pre-setting a first preset speed difference, a second preset speed difference, and a third preset speed difference, wherein the first preset speed difference, the second preset speed difference, and the third preset speed difference increase sequentially; correcting the risk value F based on the relationship between the speed difference V0 and the first preset speed difference, the second preset speed difference, and the third preset speed difference; if the speed difference V0 is less than the first preset speed difference, then selecting a first speed correction coefficient m1 to correct the risk value F. The result is F×m1; if the speed difference V0 is greater than or equal to the first preset speed difference and less than the second preset speed difference, then the second speed correction coefficient m2 is selected to correct the risk value F, resulting in F×m2; if the speed difference V0 is greater than or equal to the second preset speed difference and less than the third preset speed difference, then the third speed correction coefficient m3 is selected to correct the risk value F, resulting in F×m3; if the speed difference V0 is greater than or equal to the third preset speed difference, then the fourth speed correction coefficient m4 is selected to correct the risk value F, resulting in F×m4; where 1 < m1 < m2 < m3 < m4 < 1.2.

[0074] In this embodiment, the hull state is related to the underwater vehicle's speed. The higher the speed, the greater the pressure or stress on the underwater vehicle. Therefore, the risk value of the underwater vehicle's hull state is adjusted according to the speed. The higher the speed, the larger the selected speed correction coefficient and the larger the corrected value.

[0075] In some embodiments of this application, adjusting the risk value based on the depth difference between the diving depth and the depth threshold to obtain the final risk value of the hull includes: determining the depth difference H0 between the diving depth H and the depth threshold Hm, where H0 = H - Hm; if the travel speed is less than or equal to the speed threshold, adjusting the risk value F based on the depth difference H0 to obtain the final risk value of the hull; if the travel speed is greater than the speed threshold, adjusting the corrected F × mi based on the depth difference H0, where i = 1, 2, 3, 4, to obtain the final risk value of the hull.

[0076] In this embodiment, when adjusting the risk value based on the diving depth, it is necessary to determine whether the risk value has been adjusted once in the previous step. If the driving speed is less than or equal to the speed threshold, the risk value has not been adjusted once. If the driving speed is greater than the speed threshold, it means that the risk value has been adjusted once. When adjusting the risk value based on the diving depth again, the adjustment is based on the corrected F×mi.

[0077] In some embodiments of this application, adjusting the risk value F based on the depth difference H0 to obtain the final risk value of the shell includes: pre-setting a first preset depth difference, a second preset depth difference, and a third preset depth difference, wherein the first preset depth difference, the second preset depth difference, and the third preset depth difference increase sequentially; adjusting the risk value F according to the relationship between the depth difference and the first preset depth difference, the second preset depth difference, and the third preset depth difference; if the depth difference H0 is less than the first preset depth difference, then selecting a first depth correction coefficient n1 to correct the risk value F, obtaining a final risk value of F×n1; if the depth difference H0 is greater than or equal to... If the depth difference H0 is less than the second preset depth difference, then a second depth correction coefficient n2 is selected to correct the risk value F, resulting in a final risk value of F×n2; if the depth difference H0 is greater than or equal to the second preset depth difference, and the depth difference H0 is less than the third preset depth difference, then a third depth correction coefficient n3 is selected to correct the risk value F, resulting in a final risk value of F×n3; if the depth difference H0 is greater than or equal to the third preset depth difference, then a fourth depth correction coefficient n4 is selected to correct the risk value F, resulting in a final risk value of F×n4; where 1 < n1 < n2 < n3 < n4 < 1.2.

[0078] In some embodiments of this application, adjusting the corrected F×mi based on the depth difference H0 to obtain the final risk value of the shell includes: adjusting the corrected F×mi according to the relationship between the depth difference and the first preset depth difference, the second preset depth difference, and the third preset depth difference; if the depth difference H0 is less than the first preset depth difference, then a fifth depth correction coefficient n5 is selected to correct the corrected F×mi to obtain the final risk value F×mi×n5; if the depth difference H0 is greater than or equal to the first preset depth difference, and the depth difference H0 is less than the second preset depth difference, then a sixth depth correction coefficient is selected. n6 corrects the risk value F to obtain a final risk value of F×mi×n6; if the depth difference H0 is greater than or equal to the second preset depth difference and the depth difference H0 is less than the third preset depth difference, then the seventh depth correction coefficient n7 is selected to correct the risk value F to obtain a final risk value of F×mi×n7; if the depth difference H0 is greater than or equal to the third preset depth difference, then the eighth depth correction coefficient n8 is selected to correct the risk value F to obtain a final risk value of F×mi×n8; where 1 < n5 < n6 < n7 < n8 < 1.2, and n1 < n5, n2 < n6, n3 < n7, n4 < n8.

[0079] Step S5: Generate the underwater vehicle's driving strategy based on the final risk value.

[0080] In some embodiments of this application, generating a driving strategy for an underwater vehicle based on the final risk value includes: pre-setting a first preset final risk value and a second preset final risk value, wherein the first preset final risk value is less than the second preset final risk value; when the final risk value is less than the first preset final risk value, the driving strategy for the underwater vehicle is to continue driving; when the final risk value is greater than or equal to the first preset final risk value and less than the second preset final risk value, the driving strategy for the underwater vehicle is to continue driving, but the driving time shall not exceed a preset time; when the final risk value is greater than or equal to the second preset final risk value, the driving strategy for the underwater vehicle is to stop driving and return immediately.

[0081] In this embodiment, the underwater vehicle's driving strategy is generated based on the final risk value. When the final risk value is too small, i.e., less than the first preset final risk value, the underwater vehicle can continue to drive. When the final risk value is greater than or equal to the first preset final risk value and less than the second preset final risk value, the underwater vehicle's hull has not yet reached an unbearable state and it can continue to drive, but the driving time cannot be too long. When the final risk value is too large, i.e., greater than or equal to the second preset final risk value, the underwater vehicle is not suitable to continue driving and needs to return to port immediately for maintenance.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

[0083] The system provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be further decomposed or combined. For example, the modules in the above embodiments can be merged into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the various modules or steps and are not considered as an improper limitation of the present invention.

[0084] Those skilled in the art will recognize that the modules and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. The programs corresponding to the software modules and method steps can be placed in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the art. To clearly illustrate the interchangeability of electronic hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the invention.

Claims

1. A shell state recognition method based on local optimization and support vector machine, characterized in that, The method includes: The condition data of the underwater vehicle hull is collected, including stress data, deformation data, and acoustic emission data. The state data is processed, and key features are extracted based on a local optimization algorithm; A support vector machine model is trained based on the extracted key features to obtain the risk value of the shell state; Acquire the driving data of the underwater vehicle, adjust the risk value based on the driving data, and determine the final risk value of the hull; A driving strategy for the underwater vehicle is generated based on the final risk value; The risk value is adjusted based on the driving data to obtain the final risk value of the housing, including: The driving data includes driving speed and diving depth; The driving speed is compared with a speed threshold. If the driving speed is less than or equal to the speed threshold, the risk value is not adjusted. If the driving speed is greater than the speed threshold, the risk value is adjusted according to the speed difference between the driving speed and the speed threshold. The diving depth is compared with a depth threshold. If the diving depth is less than or equal to the depth threshold, the risk value is not adjusted. If the diving depth is greater than the depth threshold, the risk value is adjusted according to the depth difference between the diving depth and the depth threshold to obtain the final risk value of the shell.

2. The shell state recognition method based on local optimization and support vector machine according to claim 1, characterized in that, The state data is processed, and key features are extracted based on a local optimization algorithm, including: The collected data is filtered, denoised, and normalized. Features are extracted from the processed state data to obtain a feature set; The feature set is optimized using a local optimization algorithm to find the best data combination and obtain the key features.

3. The shell state recognition method based on local optimization and support vector machine according to claim 2, characterized in that, A support vector machine model is trained based on the extracted key features to obtain the risk value of the shell state, including: The status data includes historical status data and real-time status data; The key features are divided into a training set and a test set; The support vector machine model is trained based on the training set, and the optimal parameters of the support vector machine model are found by cross-validation to establish the support vector machine model. The support vector machine model is validated based on the test set to determine the accuracy of the support vector machine model; When the accuracy rate reaches the preset accuracy rate, the risk value F of the shell state is determined based on the real-time status data.

4. The shell state recognition method based on local optimization and support vector machine according to claim 3, characterized in that, Adjusting the risk value based on the speed difference between the driving speed and the speed threshold includes: Determine the speed difference V0 between the driving speed V and the speed threshold Vm, where V0 = V - Vm; A first preset speed difference, a second preset speed difference, and a third preset speed difference are preset, and the first preset speed difference, the second preset speed difference, and the third preset speed difference increase sequentially; The risk value F is corrected based on the relationship between the speed difference V0 and the first preset speed difference, the second preset speed difference, and the third preset speed difference; If the speed difference V0 is less than the first preset speed difference, then the first speed correction coefficient m1 is selected to correct the risk value F, and the corrected value is F×m1. If the speed difference V0 is greater than or equal to the first preset speed difference, and the speed difference V0 is less than the second preset speed difference, then the risk value F is corrected by the second speed correction coefficient m2, and the corrected value is F×m2. If the speed difference V0 is greater than or equal to the second preset speed difference, and the speed difference V0 is less than the third preset speed difference, then the third speed correction coefficient m3 is selected to correct the risk value F, and the corrected value is F×m3. If the speed difference V0 is greater than or equal to the third preset speed difference, then the fourth speed correction coefficient m4 is selected to correct the risk value F, and the corrected value is F×m4; where 1<m1<m2<m3<m4<1.

2.

5. The shell state recognition method based on local optimization and support vector machine according to claim 4, characterized in that, The risk value is adjusted based on the depth difference between the diving depth and the depth threshold to obtain the final risk value of the hull, including: Determine the depth difference H0 between the diving depth H and the depth threshold Hm, where H0 = H - Hm; If the driving speed is less than or equal to the speed threshold, the risk value F is adjusted according to the depth difference H0 to obtain the final risk value of the shell. If the driving speed is greater than the speed threshold, the corrected F×mi is adjusted according to the depth difference H0, i=1, 2, 3, 4, to obtain the final risk value of the shell.

6. The shell state recognition method based on local optimization and support vector machine according to claim 5, characterized in that, The risk value F is adjusted based on the depth difference H0 to obtain the final risk value of the shell, including: A first preset depth difference, a second preset depth difference, and a third preset depth difference are preset, and the first preset depth difference, the second preset depth difference, and the third preset depth difference are increased sequentially; The risk value F is adjusted based on the relationship between the depth difference and the first preset depth difference, the second preset depth difference, and the third preset depth difference; If the depth difference H0 is less than the first preset depth difference, then the first depth correction coefficient n1 is selected to correct the risk value F, and the final risk value is F×n1. If the depth difference H0 is greater than or equal to the first preset depth difference, and the depth difference H0 is less than the second preset depth difference, then the second depth correction coefficient n2 is selected to correct the risk value F, and the final risk value is F×n2. If the depth difference H0 is greater than or equal to the second preset depth difference, and the depth difference H0 is less than the third preset depth difference, then the third depth correction coefficient n3 is selected to correct the risk value F, and the final risk value is F×n3. If the depth difference H0 is greater than or equal to the third preset depth difference, then the fourth depth correction coefficient n4 is selected to correct the risk value F, and the final risk value is F×n4; where 1<n1<n2<n3<n4<1.

2.

7. The shell state recognition method based on local optimization and support vector machine according to claim 6, characterized in that, The corrected F×mi is adjusted based on the depth difference H0 to obtain the final risk value of the shell, including: The corrected F×mi is adjusted according to the relationship between the depth difference and the first preset depth difference, the second preset depth difference, and the third preset depth difference; If the depth difference H0 is less than the first preset depth difference, then the fifth depth correction coefficient n5 is selected to correct the corrected F×mi, and the final risk value is F×mi×n5. If the depth difference H0 is greater than or equal to the first preset depth difference, and the depth difference H0 is less than the second preset depth difference, then the sixth depth correction coefficient n6 is selected to correct the risk value F, and the final risk value is F×mi×n6. If the depth difference H0 is greater than or equal to the second preset depth difference, and the depth difference H0 is less than the third preset depth difference, then the seventh depth correction coefficient n7 is selected to correct the risk value F, and the final risk value is F×mi×n7. If the depth difference H0 is greater than or equal to the third preset depth difference, then the eighth depth correction coefficient n8 is selected to correct the risk value F, and the final risk value is F×mi×n8; where 1<n5<n6<n7<n8<1.2, and n1<n5, n2<n6, n3<n7, n4<n8.

8. The shell state recognition method based on local optimization and support vector machine according to claim 1, characterized in that, Based on the final risk value, a driving strategy for the underwater vehicle is generated, including: A first preset final risk value and a second preset final risk value are preset, wherein the first preset final risk value is less than the second preset final risk value; When the final risk value is less than the first preset final risk value, the underwater vehicle's driving strategy is to continue driving. When the final risk value is greater than or equal to the first preset final risk value and the final risk value is less than the second preset final risk value, the underwater vehicle's driving strategy is generated as follows: it can continue to drive, but the driving time does not exceed the preset time. When the final risk value is greater than or equal to the second preset final risk value, the underwater vehicle's driving strategy is to stop driving and return immediately.

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