Self-adaptive water area obstacle avoidance route planning method and system and medium

Abstract

The invention discloses a self-adaptive water area obstacle avoidance route planning method and system and a medium. The method comprises the following steps that sonar parameter information is acquired, a sonar acquisition area is generated based on the sonar parameter information, and a corresponding sonar acquisition image is acquired in real time based on the sonar acquisition area; preprocessing the sonar acquisition image to obtain a preprocessed image, analyzing water area obstacle distribution information based on the preprocessed image, and inputting the obstacle distribution information into a path planning model to output initial path planning information; acquiring water area real-time change information and analyzing obstacle change information; generating correction information based on the obstacle change information, and adjusting the initial path planning information based on the correction information to obtain path planning information; by acquiring the sonar image in real time, the obstacle distribution in the water area can be accurately analyzed, so that the path planning can be accurately performed according to the path planning model, the planning path can be flexibly adjusted according to the dynamic change of the obstacles in the water area, and the obstacle avoidance effect is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of obstacle avoidance, and in particular to an adaptive water area obstacle avoidance route planning method, system and medium. Background Art

[0002] As we all know, more than 70% of the earth's surface is ocean, and the ocean contains extremely rich resources, such as minerals, organisms, etc. Therefore, in recent years, people have begun to continuously increase the development and utilization of marine resources and space. For example, my country is constantly laying a large number of submarine oil pipelines and natural gas pipelines in the South China Sea and other sea areas, which greatly reduces the cost and material resources of long-distance transportation of precious resources. This type of transportation has also become the most efficient, most secure and lowest cost form of transportation. The existing path planning method cannot analyze the obstacle distribution information based on the real-time sonar acquisition image, so it is difficult to accurately analyze the distribution of obstacles, affect the accuracy of route planning, and it is difficult to effectively avoid water obstacles. Summary of the invention

[0003] The purpose of the present invention is to provide an adaptive water obstacle avoidance route planning method, comprising the following steps:

[0004] Acquire sonar parameter information, generate a sonar acquisition area based on the sonar parameter information, and acquire corresponding sonar acquisition images in real time based on the sonar acquisition area;

[0005] Preprocessing the sonar collected images to obtain preprocessed images, analyzing the distribution information of obstacles in the water area based on the preprocessed images, inputting the obstacle distribution information into the path planning model to output initial path planning information;

[0006] Obtain real-time change information of water areas, and analyze obstacle change information based on the real-time change information of water areas;

[0007] Generate correction information based on obstacle change information, and adjust initial path planning information based on the correction information to obtain route planning information;

[0008] The route planning information is transmitted to the terminal in real time according to the set transmission method.

[0009] Further, sonar parameter information is obtained, a sonar acquisition area is generated based on the sonar parameter information, and a corresponding sonar acquisition image is acquired in real time based on the sonar acquisition area, specifically including:

[0010] Obtain sonar parameter information, including frequency, sound source level, detection threshold, directivity index and propagation loss;

[0011] Obtaining the sonar collection sector area based on sonar parameter information;

[0012] Generate sonar collection area based on collection sector area;

[0013] The detection image is collected in real time according to the sonar collection area to obtain the sonar collection image.

[0014] Further, the sonar collected image is preprocessed to obtain a preprocessed image, specifically including:

[0015] Acquire sonar images and analyze the resolution of sonar images;

[0016] Determine whether the resolution of the sonar-collected image meets the set resolution threshold;

[0017] If satisfied, the preprocessed image is obtained;

[0018] If it is not satisfied, an enhancement coefficient is generated, and the sonar collected image is enhanced based on the enhancement coefficient to obtain a preprocessed image.

[0019] Furthermore, the obstacle distribution information of the water area is analyzed based on the preprocessed image, and the obstacle distribution information is input into the path planning model to output the initial path planning information, which specifically includes:

[0020] Acquire a preprocessed image, and perform region segmentation on the preprocessed image to obtain a plurality of sub-region images;

[0021] Analyze the grayscale value of each sub-region image, compare the image grayscale value with the set grayscale value, and obtain the grayscale deviation rate;

[0022] Determining whether the grayscale deviation rate is greater than or equal to a set grayscale deviation rate;

[0023] If it is greater than or equal to the set grayscale deviation rate, it is determined that there is a water obstacle in the sub-region image;

[0024] If it is less than the set grayscale deviation rate, it is determined that there is no water obstacle in the sub-region image;

[0025] Based on the water obstacle analysis results of several sub-images, water obstacle distribution information is obtained, and the water obstacle distribution information is input into a path planning model to output initial path planning information.

[0026] Furthermore, a path planning model is constructed, which specifically includes:

[0027] Obtain water obstacle distribution information and matching path planning data based on big data, and establish training and test sets;

[0028] Iteratively train the initial model based on the training set to obtain the training results;

[0029] Determining whether the training result converges;

[0030] If converged, the initial model is tested based on the test set to obtain the test results;

[0031] Determine whether the test result meets the test condition information;

[0032] If the test condition information is met, a path planning model is generated;

[0033] If the test condition information is not met, optimization information is generated, and the hyperparameters of the initial model are optimized and adjusted based on the optimization information;

[0034] If it does not converge, adjust the number of iterations to perform secondary iterative training on the initial model.

[0035] Furthermore, the real-time change information of the water area is obtained, and the obstacle change information is analyzed based on the real-time change information of the water area, specifically including:

[0036] Obtain water area flow velocity information at different time nodes, analyze water area change information at different time nodes based on the water area flow velocity information at different time nodes, and obtain real-time water area change information;

[0037] Analyze the impact of water flow velocity information on water obstacles based on the size and shape of obstacles in the water area;

[0038] Analyze the position change information and shape change information of water obstacles based on the impact information of water obstacles;

[0039] The final obstacle change information is generated based on the position change information and shape change information of the water obstacles.

[0040] The present invention also provides an adaptive water obstacle avoidance route planning system, comprising a processor, a memory and at least one program, wherein the program is stored in the memory and is configured to be executed by the processor, and the program includes instructions for executing the adaptive water obstacle avoidance route planning method as described in any one of the above items.

[0041] The present invention also provides a computer-readable storage medium, which stores a computer program, and the computer program enables a computer to execute to implement any of the above-mentioned adaptive water area obstacle avoidance route planning methods.

[0042] Due to the application of the above technical solution, the present invention has the following advantages compared with the prior art:

[0043] The present invention can accurately analyze the distribution of obstacles in the water area by collecting sonar images in real time, so as to accurately plan the path according to the route planning model, and flexibly adjust the planned path according to the dynamic changes of obstacles in the water area to improve the obstacle avoidance effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A schematic diagram showing a flow chart of an adaptive water obstacle avoidance route planning method provided by an embodiment of the present invention;

[0045] Figure 2 A flow chart of a sonar image acquisition method for the adaptive water obstacle avoidance route planning method provided in this embodiment is shown.

[0046] Figure 3 A flow chart of a sonar image preprocessing method for the adaptive water obstacle avoidance route planning method provided in this embodiment is shown. DETAILED DESCRIPTION

[0047] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0048] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0049] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0050] like Figure 1-Figure 3 As shown, an embodiment of the present invention provides an adaptive water obstacle avoidance route planning method, comprising the following steps:

[0051] S101, obtaining sonar parameter information, generating a sonar acquisition area based on the sonar parameter information, and acquiring a corresponding sonar acquisition image in real time based on the sonar acquisition area;

[0052] S102, preprocessing the sonar collected image to obtain a preprocessed image, analyzing the obstacle distribution information in the water area based on the preprocessed image, inputting the obstacle distribution information into a path planning model to output initial path planning information;

[0053] S103, obtaining real-time change information of the water area, and analyzing obstacle change information based on the real-time change information of the water area;

[0054] S104, generating correction information based on the obstacle change information, and adjusting the initial path planning information based on the correction information to obtain route planning information;

[0055] S105, transmitting the route planning information to the terminal in real time according to the set transmission method.

[0056] It should be noted that by acquiring sonar images in real time, analyzing the distribution of water obstacles, and performing path planning based on the path planning model, and dynamically adjusting the path according to the changing status of the obstacles, water obstacles can be effectively avoided.

[0057] According to an embodiment of the present invention, obtaining sonar parameter information, generating a sonar acquisition area based on the sonar parameter information, and acquiring a corresponding sonar acquisition image in real time based on the sonar acquisition area specifically includes:

[0058] S201, obtaining sonar parameter information, where the sonar parameter information includes frequency, sound source level, detection threshold, directivity index and propagation loss;

[0059] S202, obtaining a sonar acquisition sector area based on the sonar parameter information;

[0060] S203, generating a sonar collection area based on the collection sector area;

[0061] S204, collecting detection images in real time according to the sonar collection area to obtain sonar collection images.

[0062] It should be noted that different collection sector areas are formed according to different sonar parameters, so that accurate real-time detection of obstacles ahead can be performed to improve detection accuracy.

[0063] According to an embodiment of the present invention, the sonar collected image is preprocessed to obtain a preprocessed image, which specifically includes:

[0064] S301, acquiring a sonar-collected image, and analyzing the resolution of the sonar-collected image;

[0065] S302, determining whether the resolution of the sonar-collected image meets a set resolution threshold;

[0066] S303, if satisfied, obtaining a preprocessed image;

[0067] S304: If the conditions are not met, an enhancement coefficient is generated, and the sonar-collected image is enhanced based on the enhancement coefficient to obtain a preprocessed image.

[0068] It should be noted that by analyzing the resolution of the sonar-collected images, the sonar-collected images can be effectively enhanced and the clarity of the sonar-collected images can be improved.

[0069] According to an embodiment of the present invention, analyzing the distribution information of water obstacles based on the preprocessed image, inputting the obstacle distribution information into the path planning model to output the initial path planning information specifically includes:

[0070] Acquire a preprocessed image, and perform region segmentation on the preprocessed image to obtain a plurality of sub-region images;

[0071] Analyze the grayscale value of each sub-region image, compare the image grayscale value with the set grayscale value, and obtain the grayscale deviation rate;

[0072] Determining whether the grayscale deviation rate is greater than or equal to a set grayscale deviation rate;

[0073] If it is greater than or equal to the set grayscale deviation rate, it is determined that there is a water obstacle in the sub-region image; if it is less than the set grayscale deviation rate, it is determined that there is no water obstacle in the sub-region image;

[0074] Based on the water obstacle analysis results of several sub-images, water obstacle distribution information is obtained, and the water obstacle distribution information is input into a path planning model to output initial path planning information.

[0075] It should be noted that by segmenting the preprocessed image, each sub-region is analyzed and processed separately, and the processed images are fused to accurately reflect the distribution status of water obstacles.

[0076] According to an embodiment of the present invention, building a path planning model specifically includes:

[0077] Obtain water obstacle distribution information and matching path planning data based on big data, and establish training and test sets;

[0078] Iteratively train the initial model based on the training set to obtain the training results;

[0079] Determining whether the training result converges;

[0080] If converged, the initial model is tested based on the test set to obtain the test results;

[0081] Determine whether the test result meets the test condition information;

[0082] If the test condition information is met, a path planning model is generated;

[0083] If the test condition information is not met, optimization information is generated, and the hyperparameters of the initial model are optimized and adjusted based on the optimization information;

[0084] If it does not converge, adjust the number of iterations to perform secondary iterative training on the initial model.

[0085] It should be noted that by continuously training the model through big data, the output accuracy of the path planning model can be improved and the path planning model can have better learning ability.

[0086] According to an embodiment of the present invention, obtaining real-time change information of water areas and analyzing obstacle change information based on the real-time change information of water areas specifically includes:

[0087] Obtain water area flow velocity information at different time nodes, analyze water area change information at different time nodes based on the water area flow velocity information at different time nodes, and obtain real-time water area change information;

[0088] Analyze the impact of water flow velocity information on water obstacles based on the size and shape of obstacles in the water area;

[0089] Analyze the position change information and shape change information of water obstacles based on the impact information of water obstacles;

[0090] The final obstacle change information is generated based on the position change information and shape change information of the water obstacles.

[0091] It should be noted that the water flow at different time nodes will have a certain impact on the location of water obstacles. By analyzing the impact effect, the distribution of water obstacles can be dynamically adjusted, and then the planned path can be optimized to avoid obstacles with high precision.

[0092] According to an embodiment of the present invention, the detection image is collected in real time according to the sonar collection area to obtain the sonar collection image, which also includes:

[0093] Acquire the size information of the moving object, and analyze the area and shape in front of the moving object based on the size information of the moving object;

[0094] Set the minimum sonar collection area based on the area and shape directly in front of the moving object;

[0095] Analyze the current sonar acquisition sector area based on sonar parameter information;

[0096] Compare the current sonar collection sector area with the minimum sonar collection area to obtain the area difference;

[0097] Select different numbers of sonar linkage acquisition based on area difference.

[0098] It should be noted that when the size of the moving object is large, the image collected by one sonar cannot meet the requirements, thus forming a large blind spot. By selecting multiple sonars to operate in conjunction, it is ensured that there is no blind spot ahead.

[0099] The invention also includes providing impactors at the front and rear of the moving body, the impactors being used to impact the water flow;

[0100] Obtain sonar images in front of the moving object and analyze the dynamic changes of obstacles in front of the moving object;

[0101] The movement of the mobile body is controlled based on the path planning information. When an obstacle suddenly appears in front of the mobile body and cannot be avoided, the front impactor and the rear impactor are controlled to emit airflow at the same time to impact the obstacle in the water flow in front to avoid it.

[0102] It should be noted that by synchronously emitting airflow from the front impactor and the rear impactor, the front airflow can impact the obstacle while the rear impactor is used to stabilize the position of the moving body, ensuring that the moving body impacts the front obstacle without changing its position. During the process of impacting the obstacle, the pressure of the front impactor and the rear impactor can be dynamically adjusted to improve the impact effect and stabilize the moving body.

[0103] In summary, the present invention can accurately analyze the distribution of obstacles in the water area by collecting sonar images in real time, so as to accurately plan the path according to the route planning model, and flexibly adjust the planned path according to the dynamic changes of obstacles in the water area to improve the obstacle avoidance effect.

[0104] This embodiment also provides an adaptive water obstacle avoidance route planning system, including a processor, a memory, and at least one program, wherein the program is stored in the memory and is configured to be executed by the processor, and the program includes instructions for executing any of the above-mentioned adaptive water obstacle avoidance route planning methods.

[0105] The present invention also provides a computer-readable storage medium, which stores a computer program, and the computer program enables a computer to execute to implement any of the above-mentioned adaptive water area obstacle avoidance route planning methods.

[0106] Those skilled in the art will appreciate that, for ease of description, the example in which both the memory and the processor are provided with one is used for description. In an actual terminal or server, there may be multiple processors and memories. The memory may also be referred to as a storage medium or a storage device, etc., which is not limited in the embodiments of the present application.

[0107] It should be understood that in the embodiments of the present application, the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor may also be a general-purpose microprocessor, graphics processing unit (GPU), or one or more integrated circuits for executing related programs to implement the functions required to be executed in the embodiments of the present application.

[0108] The processor can also be an integrated circuit chip with signal processing capabilities. In the implementation process, the various steps of the present application can be completed by the integrated logic circuit of the hardware in the processor or the instructions in the form of software. The above-mentioned processor can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor to be executed, or a combination of hardware and software modules in the decoding processor to be executed. The software module can be located in a random access memory, a flash memory and a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register and other mature storage media in the art. The storage medium is located in the memory, and the processor reads the information in the memory, and combines its hardware to complete the functions required to be performed by the unit included in the method, device and storage medium of the embodiment of the present application.

[0109] It should also be understood that the memory mentioned in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache.

[0110] By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM) and direct memory bus random access memory (Direct Rambus RAM, DR RAM).

[0111] The memory may also be a read-only optical disc (Compact Disc Read-Only Memory, CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory may be independent and connected to the processor via a bus. The memory may also be integrated with the processor, and the memory may store a program. When the program stored in the memory is executed by the processor, the processor is used to execute the various steps of the determination method in the above-mentioned embodiment of the present application.

[0112] It should be noted that when the processor is a general-purpose processor, DSP, ASIC, FPGA or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, the memory (storage module) is integrated into the processor. It should be noted that the memory described herein is intended to include but is not limited to these and any other suitable types of memory.

[0113] It should be understood that the term "and / or" in this article is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0114] In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in a processor or an instruction in the form of software. The steps of the method disclosed in conjunction with the embodiment of the present application can be directly embodied as a hardware processor for execution, or a combination of hardware and software modules in a processor for execution. The software module can be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory, and completes the steps of the above method in conjunction with its hardware. To avoid repetition, it is not described in detail here.

[0115] Those skilled in the art will appreciate that the various illustrative logical blocks (ILBs) and steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0116] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a program product of computer programming. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a processor, all or part of the processes or functions according to the embodiments of the present application are generated. The computer may be a general-purpose computer, a computer network, or other programmable device.

[0117] This embodiment also provides a computer-readable storage medium, which stores a computer program. The computer program enables a computer to execute to implement the above-mentioned adaptive water obstacle avoidance route planning method.

[0118] It should be noted that 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, computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., coaxial cable, optical fiber) or wireless (e.g., infrared, wireless, microwave, etc.) means, or can be transmitted from one website, computer, server or data center to a mobile phone processor by wired means. Computer-readable storage media can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. Available media can be magnetic media (e.g., floppy disk, hard disk), optical media (e.g., DVD), or semiconductor media (e.g., solid-state hard disk), etc.

[0119] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An adaptive water obstacle avoidance route planning method, characterized in that: The following steps are involved: Acquire sonar parameter information, generate a sonar acquisition area based on the sonar parameter information, and acquire corresponding sonar acquisition images in real time based on the sonar acquisition area; Preprocessing the sonar collected images to obtain preprocessed images, analyzing the distribution information of obstacles in the water area based on the preprocessed images, inputting the obstacle distribution information into the path planning model to output initial path planning information; Obtain real-time change information of water areas, and analyze obstacle change information based on the real-time change information of water areas; Generate correction information based on obstacle change information, and adjust initial path planning information based on the correction information to obtain route planning information; The route planning information is transmitted to the terminal in real time according to the set transmission method.

2. The adaptive water obstacle avoidance route planning method according to claim 1, characterized in that: Acquire sonar parameter information, generate a sonar acquisition area based on the sonar parameter information, and acquire corresponding sonar acquisition images in real time based on the sonar acquisition area, specifically including: Obtain sonar parameter information, including frequency, sound source level, detection threshold, directivity index and propagation loss; Obtaining the sonar collection sector area based on sonar parameter information; Generate sonar collection area based on collection sector area; The detection image is collected in real time according to the sonar collection area to obtain the sonar collection image.

3. The adaptive water obstacle avoidance route planning method according to claim 2, characterized in that: The sonar collected image is preprocessed to obtain a preprocessed image, specifically including: Acquire sonar images and analyze the resolution of sonar images; Determine whether the resolution of the sonar-collected image meets the set resolution threshold; If satisfied, the preprocessed image is obtained; If it is not satisfied, an enhancement coefficient is generated, and the sonar collected image is enhanced based on the enhancement coefficient to obtain a preprocessed image.

4. The adaptive water obstacle avoidance route planning method according to claim 3, characterized in that: Based on the pre-processed image, the obstacle distribution information of the water area is analyzed, and the obstacle distribution information is input into the path planning model to output the initial path planning information, which specifically includes: Acquire a preprocessed image, and perform region segmentation on the preprocessed image to obtain a plurality of sub-region images; Analyze the grayscale value of each sub-region image, compare the image grayscale value with the set grayscale value, and obtain the grayscale deviation rate; Determining whether the grayscale deviation rate is greater than or equal to a set grayscale deviation rate; If it is greater than or equal to the set grayscale deviation rate, it is determined that there is a water obstacle in the sub-region image; If it is less than the set grayscale deviation rate, it is determined that there is no water obstacle in the sub-region image; Based on the water obstacle analysis results of several sub-images, water obstacle distribution information is obtained, and the water obstacle distribution information is input into a path planning model to output initial path planning information.

5. The adaptive water obstacle avoidance route planning method according to claim 4, characterized in that: Construct a path planning model, including: Obtain water obstacle distribution information and matching path planning data based on big data, and establish training and test sets; Iteratively train the initial model based on the training set to obtain the training results; Determining whether the training result converges; If converged, the initial model is tested based on the test set to obtain the test results; Determine whether the test result meets the test condition information; If the test condition information is met, a path planning model is generated; If the test condition information is not met, optimization information is generated, and the hyperparameters of the initial model are optimized and adjusted based on the optimization information; If it does not converge, adjust the number of iterations to perform secondary iterative training on the initial model.

6. The adaptive water obstacle avoidance route planning method according to claim 5, characterized in that: Obtain real-time water area change information, and analyze obstacle change information based on the real-time water area change information, including: Obtain water area flow velocity information at different time nodes, analyze water area change information at different time nodes based on the water area flow velocity information at different time nodes, and obtain real-time water area change information; Analyze the impact of water flow velocity information on water obstacles based on the size and shape of obstacles in the water area; Analyze the position change information and shape change information of water obstacles based on the impact information of water obstacles; The final obstacle change information is generated based on the position change information and shape change information of the water obstacles.

7. An adaptive water obstacle avoidance route planning system, characterized in that: It includes a processor, a memory and at least one program, wherein the program is stored in the memory and is configured to be executed by the processor, and the program includes instructions for executing the adaptive water obstacle avoidance route planning method as described in any one of claims 1-6.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which enables a computer to execute to implement the adaptive water area obstacle avoidance route planning method described in any one of claims 1-6.

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