Design method for simulation test environment of unmanned surface vehicle

Abstract

The invention discloses a method for designing a simulation test environment of an unmanned surface vehicle, and the method comprises the steps: constructing a digital boat, and simulating the behavior and performance of a real unmanned surface vehicle; a simulation test scheme is generated, and test schemes of typical tasks are created according to different task types; performing task scene fine-grained simulation, including simulation of typical obstacles and meteorological and hydrological environments; abstraction layering of the simulation system comprises four abstraction layers of infrastructure, business logic, service gateway and terminal application; a typical task test scheme generation process is described by using process construction and comprises a task scene generation main process step, a marine environment data generation process step and a typical target generation process step; and designing a simulation test platform UI. According to the method, diversified test scenes can be created according to task requirements, and the performance, the algorithm and the task module of the unmanned ship cluster are tested and evaluated. And a highly customizable and multi-machine cooperative solution is provided for simulation testing in the field of unmanned ships.

Description

Technical Field

[0001] The present invention relates to the field of simulation testing technology, and more specifically, to a method for designing a simulation testing environment for an unmanned surface boat. Background Art

[0002] With the rapid development of unmanned boat technology, the demand for unmanned boats is growing in areas including marine scientific research, seabed exploration, maritime rescue, and maritime transportation. However, in practical applications, comprehensive testing and evaluation of the performance, algorithms, and mission modules of unmanned boats is a complex and expensive task. In order to overcome this challenge, the unmanned boat simulation test environment design method came into being. It provides an efficient, safe, and economical solution for the testing and evaluation of unmanned boats by simulating real-world scenarios in a virtual environment.

[0003] At present, unmanned boat simulation testing usually relies on hardware models and actual testing. This method has the following problems and challenges:

[0004] High cost and time consumption: Building actual unmanned boat models and conducting field tests requires a lot of money and time, especially testing under different meteorological and hydrological conditions.

[0005] Complex and diverse test scenarios: Unmanned boats may need to perform tasks in a variety of complex marine environments, including different sea conditions, weather, hydrological conditions and targets. Therefore, the simulation environment must be highly flexible and able to simulate a variety of test scenarios.

[0006] Highly accurate simulation models: In order to accurately simulate the behavior and performance of unmanned boats, accurate physical simulation models and environmental models are required. These models need to take into account multiple factors such as water flow, waves, weather, sonar, etc. to ensure the credibility of the test results.

[0007] Large-scale multi-machine collaborative simulation: In practical applications, unmanned boats usually work in the form of clusters, so it is necessary to support large-scale multi-machine collaborative simulation. This means that the simulation environment must be able to simulate the collaborative behavior of multiple unmanned boats at the same time.

[0008] Limitations: Actual testing is limited by the environment, season, and geographical location and cannot fully cover various test scenarios.

[0009] Safety risks: In some cases, conducting realistic tests may involve risks, especially in simulated military combat or emergency rescue situations.

[0010] Test parameters are difficult to control: In actual testing, some parameters may be difficult to control, making it impossible to achieve an accurate evaluation of the system.

[0011] Complexity of data acquisition and analysis: For complex test scenarios, data acquisition and analysis become extremely complex, often requiring a lot of manpower and material resources.

[0012] Data generation and processing: Generating large amounts of ocean environment data, target trajectory data, and sensor data, and processing and analyzing them in real time is a complex task. Effective data management and processing are key to the simulation environment.

[0013] In the field of unmanned boat simulation test environment, there are some key technical challenges that need to be overcome:

[0014] Highly accurate simulation models: Develop highly accurate simulation models, including water current, wave, meteorological, and underwater sonar models, to simulate various situations in the real environment.

[0015] Real-time performance and scalability: Achieve real-time performance and scalability to support large-scale multi-machine collaborative simulation and ensure that the system can cope with complex testing requirements.

[0016] Data Generation and Management: Develop efficient data generation and management systems that can generate large-scale ocean environment data, target trajectory data, and sensor data, and provide real-time data processing and analysis.

[0017] User-friendly interface: An intuitive, easy-to-use user interface is designed to enable users to easily create mission plans, define test scenarios, submit tests, and analyze results.

[0018] Therefore, how to provide a method for designing a surface unmanned boat simulation test environment that can overcome the limitations and problems of traditional testing methods in the background technology has become a technical problem that needs to be solved urgently. Summary of the invention

[0019] The purpose of the present invention is to provide a method for designing a surface unmanned boat simulation test environment that allows creating a variety of test scenarios in a virtual environment to test and evaluate the performance, algorithms and mission modules of an unmanned boat cluster.

[0020] The present invention provides a method for designing a surface unmanned boat simulation test environment, comprising:

[0021] Digital prototype boat construction to simulate the behavior and performance of real unmanned boats;

[0022] Simulation test plan generation, creating test plans for typical tasks based on different task types;

[0023] Fine-grained simulation of mission scenarios, including simulation of typical obstacles and meteorological and hydrological environments;

[0024] The abstract layering of the simulation system includes four abstract layers: infrastructure, business logic, service gateway, and terminal application;

[0025] Using the construction of the process, describe the process of generating a typical mission test plan, including the main process steps of generating mission scenarios, the process steps of generating marine environment data, and the process steps of generating typical targets;

[0026] Simulation test platform UI design.

[0027] Optionally, the digital prototype build includes:

[0028] Application of virtualization technology: Use virtualization technology to create a logically independent computing environment to ensure that the digital prototype has the same computing resources and network environment as the actual unmanned boat;

[0029] Simulate basic load and mission load: The digital prototype simulates the basic load and mission load of the unmanned boat to facilitate the testing of the mission module;

[0030] Support multi-machine collaboration: The digital prototype can run on the same or different host servers, and establish virtual network connections between each other through software-defined networks, supporting multi-machine collaborative simulation;

[0031] Public services and application services on the boat side: Public services and application services are deployed on the digital prototype boat to realize environmental perception and target detection functions.

[0032] Optionally, the simulation test plan generation includes:

[0033] Test case model library: According to different task types, establish a test case model library, including various test items and test scenarios;

[0034] Test requirements and test plans: Generate test requirements and test plans based on the test outline. Test requirements include test items and test scenarios, and test plans include test arrangements.

[0035] Test case instantiation: According to the test requirements, extract the test case template list corresponding to the test item from the test case template library, and specify the parameter threshold through human-computer interaction to realize the instantiation of the test case;

[0036] Mission scenario configuration: Provide input to the mission test guidance software based on the test scenario, and drive the test load equipment simulator to complete the test environment configuration;

[0037] Automated test execution: According to the test schedule, the test is automatically executed and the test data is recorded. The test results are given for quantifiable test content, and input is provided for data analysis and evaluation software.

[0038] Optionally, in the simulation environment, various types of typical obstacles may be set, including static targets and dynamic targets.

[0039] Optionally, the step of simulating a typical obstacle includes:

[0040] Obstacle deployment: deploy the initial position of static targets according to the constructed map, and batch set the properties of static targets, the initial position of dynamic targets, and the movement trend of dynamic targets;

[0041] Motion trajectory generation: The scenario guidance software generates the motion trajectory of typical obstacles during the task, and generates the position and speed information for each time period based on the time step.

[0042] Optionally, the simulation of the meteorological and hydrological environment includes generating meteorological and hydrological environment data, the meteorological and hydrological environment data including water flow velocity, water flow direction, wave height, and wavelength;

[0043] The steps to generate meteorological and hydrological environmental data include:

[0044] Convert longitude and latitude to tile coordinates: Calculate the tile coordinates of the sampling point and all tile coordinates within the test site for subsequent data generation;

[0045] Full time domain data of a single sampling point: Calculate the ocean environment information of multiple sampling points at each time step, use difference processing to generate data of all tiles at a single time;

[0046] Full-space and full-time data: Add the time dimension, perform linear difference processing on the data on the tiles between multiple sampling points, and generate full-time ocean environment data.

[0047] Optionally, the infrastructure layer: provides basic capabilities for continuous deployment integration, data storage management, and real-time status monitoring, using a cloud computing environment;

[0048] Service gateway layer: provides open Web programming interfaces and standardized authentication and authorization mechanisms, and supports load balancing and certificate encryption services;

[0049] Terminal application layer: build multiple graphical interface programs, including desktop, mobile and web terminals;

[0050] Business logic layer: Use containerized distributed microservice architecture design, including user services, integrated deployment, instance management, and data analysis.

[0051] Optionally, the main process of generating a task scenario includes:

[0052] Determine the test field range: Determine the test field data information by entering the test field range or selecting the test field range on the map;

[0053] Generate mission scenario: Generate mission scenario based on selected solution data, including target motion data;

[0054] Edit target motion trajectory: change the motion state of the target, including position and motion trajectory;

[0055] Parameter setting: Set task parameters to provide a basis for subsequent data generation.

[0056] Optionally, the marine environment data generation process includes:

[0057] Convert longitude and latitude to tile coordinates: Convert longitude and latitude coordinates to tile coordinates and calculate all tile coordinates contained in the test area;

[0058] Full time domain data of a single sampling point: Calculate the ocean environment information of multiple sampling points at each time step, perform bilinear difference processing, and generate data for all tiles at a single time;

[0059] Full-space and full-time data: linear difference processing is performed on the data on tiles between multiple sampling points within the spatial range to generate full-time ocean environment data;

[0060] Data storage: Data is stored in units of 5 minutes.

[0061] Optionally, a typical target generation process includes:

[0062] Basic parameter setting: set the basic parameters of the task, including the task area, task duration, data generation frequency, map tile level, and sea condition sampling points;

[0063] Target selection: select the target type, including dynamic targets and static targets;

[0064] Target location and attribute settings: change the location and default attributes of the target in the task area, and generate static targets in batches;

[0065] Generate motion trajectory: Generate the motion trajectory of the target based on the mission duration, input generation frequency, sea condition information and target attributes, combined with the motion trajectory generation algorithm.

[0066] According to the technical content disclosed in the present invention, the following beneficial effects are achieved:

[0067] The technical solution provided by the present invention describes a method for designing a simulation test environment for a surface unmanned boat, which can create a variety of test scenarios according to mission requirements, and test and evaluate the performance, algorithms, and mission modules of an unmanned boat cluster. The method covers multiple key modules such as digital prototype construction, simulation test plan generation, fine-grained simulation of mission scenarios, abstract layering, and usage processes, providing a highly customizable and multi-machine collaborative solution for simulation testing in the field of unmanned boats.

[0068] Further features and advantages of the present invention will become apparent from the following detailed description of exemplary embodiments of the present invention with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention.

[0070] Figure 1 This is a simulation system architecture diagram of the present invention.

[0071] Figure 2 It is a diagram of the simulation system implementation scheme of the present invention.

[0072] Figure 3 This is a design diagram of the simulation test platform architecture of the present invention.

[0073] Figure 4 This is a diagram of the digital sample boat and its associated components of the present invention.

[0074] Figure 5 A diagram of the simulation engine and its associated components of the present invention.

[0075] Figure 6 A diagram of the simulation service and its associated components of the present invention.

[0076] Figure 7 A diagram of the cloud computing infrastructure that the simulation test of the present invention relies on.

[0077] Figure 8 A data flow diagram for managing the scheme of the present invention.

[0078] Fig. 9 The data flow chart of the test field is generated according to the present invention.

[0079] Fig.10 The target data of the present invention drives the data flow diagram.

[0080] Fig.11 The target data of the present invention drives the data flow diagram.

[0081] Fig.12 The present invention is a flow chart for generating sea state data.

[0082] Fig.13 The present invention is a flowchart for setting the task basic parameters.

[0083] Fig.14 A flow chart is selected for the target category of the present invention.

[0084] Fig.15 This is a flow chart of setting offshore target data of the present invention.

[0085] Fig.16 The present invention is a flow chart of setting underwater target data.

[0086] Fig.17 This is a data flow chart for setting water surface obstacles of the present invention.

[0087] Fig.18 The present invention is a flow chart for generating offshore target data.

[0088] Fig.19 The present invention is a flow chart for generating underwater target data.

[0089] Fig. 20 The present invention is a flow chart of generating water surface obstacle target data.

[0090] Fig.21 UI design for the present invention Figure 1 .

[0091] Fig. 22 UI design for the present invention Figure 2 . DETAILED DESCRIPTION

[0092] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless otherwise specifically stated.

[0093] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0094] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0095] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0096] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0097] The present invention relates to a method for designing a simulation test environment for unmanned boats, aiming to overcome the limitations and problems of traditional test methods. The main features of the present invention include the following aspects:

[0098] Digital prototype construction: The present invention provides a digital prototype construction method, which allows users to create virtual unmanned boat models, including external structures, internal components and sensors, etc. These digital prototypes can replace actual physical models in simulation tests, thereby greatly reducing costs and time consumption.

[0099] Simulation test scheme generation: The present invention also includes a simulation test scheme generation method, which allows users to automatically generate a variety of test schemes according to different test needs and scenario requirements. These schemes can include different meteorological, hydrological and obstacle conditions to ensure that the system can be fully tested in various situations.

[0100] Fine-grained simulation of mission scenarios: In order to more realistically simulate the performance of unmanned boats in different mission scenarios, the present invention provides a fine-grained simulation method, including simulation of typical obstacles and meteorological and hydrological environments. This helps to evaluate the performance of the system in complex environments.

[0101] System integration and monitoring: The present invention also includes test system software and command and control system software, which are used to realize the integration and monitoring of the test system. These software ensure the safety and controllability of the test process.

[0102] Supporting tool software: In order to facilitate users to manage the test process, the present invention provides supporting tool software such as R&D management software, scenario guidance software, ocean database and typical target database, providing comprehensive support.

[0103] The technical solution of the present invention aims to provide an innovative technology called "a method for designing a surface unmanned boat simulation test environment", which allows the creation of a variety of test scenarios in a virtual environment to test and evaluate the performance, algorithms and task modules of the unmanned boat cluster. The core construction of this method includes modules such as digital prototype construction, simulation test plan generation, fine-grained simulation of task scenarios, abstract layering, and use processes to achieve functions such as high customization and multi-machine collaborative simulation.

[0104] A method for designing a surface unmanned boat simulation test environment, comprising:

[0105] The digital prototype is constructed to simulate the behavior and performance of a real unmanned boat. The digital prototype is one of the core elements of the simulation environment, which simulates the behavior and performance of a real unmanned boat. The construction of the digital prototype includes the following key steps:

[0106] Step 1: Application of virtualization technology: Use virtualization technology to create a logically independent computing environment to ensure that the digital prototype boat has the same computing resources and network environment as the actual unmanned boat;

[0107] Simulating basic load and mission load: The digital prototype boat simulates the basic load and mission load of the unmanned boat in order to test the mission module; specifically, the digital prototype boat must simulate the basic load (such as navigation and optoelectronic equipment) and mission load (such as underwater sonar, etc.) of the unmanned boat in order to test the mission module.

[0108] Support multi-machine collaboration: The digital prototype can run on the same or different host servers, and establish virtual network connections between each other through software-defined networks, supporting multi-machine collaborative simulation;

[0109] Public services and application services on the boat side: Public services and application services are deployed on the digital prototype boat to realize environmental perception and target detection functions.

[0110] Step 2, simulation test plan generation, create a test plan for a typical task according to different task types; simulation test plan generation is to create a test plan for a typical task according to different task types. The following are the key steps to achieve this function:

[0111] Test case model library: According to different task types, establish a test case model library, including various test items and test scenarios;

[0112] Test requirements and test plans: Generate test requirements and test plans based on the test outline. Test requirements include test items and test scenarios, and test plans include test arrangements, etc.

[0113] Test case instantiation: According to the test requirements, extract the test case template list corresponding to the test item from the test case template library, and specify the parameter threshold through human-computer interaction to realize the instantiation of the test case;

[0114] Mission scenario configuration: Provide input to the mission test guidance software based on the test scenario, and drive the test load equipment simulator to complete the test environment configuration;

[0115] Automated test execution: According to the test schedule, the test is automatically executed and the test data is recorded. The test results are given for quantifiable test content, and input is provided for data analysis and evaluation software.

[0116] Step 3: Fine-grained simulation of mission scenarios, including simulation of typical obstacles and meteorological and hydrological environments;

[0117] (1) Typical disorders;

[0118] In the simulation environment, various types of typical obstacles can be set, including static targets and dynamic targets. Specifically, in the simulation environment, various types of typical obstacles can be set, including static targets (such as buoys, islands and reefs) and dynamic targets (such as fishing boats, ships, etc.). The key steps to simulate typical obstacles include:

[0119] Obstacle deployment: deploy the initial position of static targets according to the constructed map, and set the properties of static targets, the initial position of dynamic targets, and the movement trend of dynamic targets in batches;

[0120] Motion trajectory generation: The scenario guidance software generates the motion trajectory of typical obstacles during the task, and generates information such as position and speed for each time period based on the time step.

[0121] (2) meteorological and hydrological environment;

[0122] The simulation environment supports the generation of meteorological and hydrological environmental data. The simulation of the meteorological and hydrological environment includes the generation of meteorological and hydrological environmental data. The meteorological and hydrological environmental data includes water flow velocity, water flow direction, wave height, and wavelength. The steps of generating meteorological and hydrological environmental data include:

[0123] Convert longitude and latitude to tile coordinates: Calculate the tile coordinates of the sampling point and all tile coordinates within the test site for subsequent data generation;

[0124] Full time domain data of a single sampling point: Calculate the ocean environment information of multiple sampling points at each time step, use difference processing to generate data of all tiles at a single time;

[0125] Full-space and full-time data: Add the time dimension, perform linear difference processing on the data on the tiles between multiple sampling points, and generate full-time ocean environment data.

[0126] Step 4: Abstract layering. The abstract layering of the simulation system includes four abstract layers: infrastructure, business logic, service gateway, and terminal application. The functions of each layer are as follows:

[0127] Infrastructure layer: Provides basic capabilities of continuous deployment integration, data storage management, and real-time status monitoring, using cloud computing environments;

[0128] Service gateway layer: provides an open Web programming interface (API) and standardized authentication and authorization mechanisms, and supports load balancing and certificate encryption services;

[0129] Terminal application layer: build multiple graphical interface programs, including desktop, mobile and web terminals;

[0130] Business logic layer: Use containerized distributed microservice architecture design, including user services, integrated deployment, instance management, data analysis, etc.

[0131] Step 5, usage process;

[0132] The construction of the usage process describes the process of generating a typical task test plan, including the main process steps of task scenario generation, the process steps of marine environment data generation, the process steps of typical target generation, etc. The specific process is as follows:

[0133] (1) The main process of task scenario generation includes:

[0134] Determine the test field range: Determine the test field data information by entering the test field range or selecting the test field range on the map;

[0135] Generate mission scenario: Generate mission scenario based on selected solution data, including target motion data;

[0136] Edit target motion trajectory: change the motion state of the target, including position and motion trajectory;

[0137] Parameter setting: Set task parameters to provide a basis for subsequent data generation.

[0138] (2) The process of generating marine environmental data includes:

[0139] Convert longitude and latitude to tile coordinates: Convert longitude and latitude coordinates to tile coordinates and calculate all tile coordinates contained in the test area;

[0140] Full time domain data of a single sampling point: Calculate the ocean environment information of multiple sampling points at each time step, perform bilinear difference processing, and generate data for all tiles at a single time;

[0141] Full-space and full-time data: linear difference processing is performed on the data on tiles between multiple sampling points within the spatial range to generate full-time ocean environment data;

[0142] Data storage: Data is stored in units of 5 minutes.

[0143] (3) The typical target generation process includes:

[0144] Basic parameter setting: set the basic parameters of the task, including the task area, task duration, data generation frequency, map tile level, and sea condition sampling points;

[0145] Target selection: select the target type, including dynamic targets and static targets;

[0146] Target location and attribute settings: change the location and default attributes of the target in the task area, and generate static targets in batches;

[0147] Generate motion trajectory: Generate the motion trajectory of the target based on the mission duration, input generation frequency, sea condition information and target attributes, combined with the motion trajectory generation algorithm.

[0148] Step 6: Simulation test platform UI design.

[0149] The user interface (UI) of the simulation test platform should be intuitive and easy to use to meet the needs of users. The interface includes the control center console and the simulation console, which are used for task planning and test submission and result analysis respectively. The UI should provide the following functions:

[0150] Mission planning interface: used to set mission parameters, select mission scenarios, edit target trajectories, etc., and intuitively display mission scenarios and target status.

[0151] Task submission interface: Users submit test requests through this interface, including task scenarios, target data, marine environment data, etc.

[0152] Test result analysis interface: displays test results, including data records, performance evaluation, etc., to help users perform data analysis and evaluation.

[0153] User account and permission management: ensure security and permission control, and different users have different permissions.

[0154] User-friendly interface: The interface should be intuitive, customizable, and responsive.

[0155] In summary, the technical solution provided by the present invention describes a method for designing a surface unmanned boat simulation test environment, which can create a variety of test scenarios according to mission requirements, and test and evaluate the performance, algorithms and mission modules of the unmanned boat cluster. The method covers multiple key modules such as digital prototype construction, simulation test plan generation, fine-grained simulation of mission scenarios, abstract layering and usage processes, providing a highly customizable and multi-machine collaborative solution for simulation testing in the field of unmanned boats.

[0156] The design and implementation of a simulated test environment for unmanned surface vehicles will bring multiple benefits to unmanned surface vehicle technology and applications. These benefits are explored in more detail below:

[0157] (1) Safety and reliability

[0158] Risk reduction: The simulated test environment can reduce the risks and unexpected events that may occur in actual testing. Testing unmanned boats in complex marine environments often involves risks, such as equipment damage, equipment loss, or personnel safety issues. The simulated environment can avoid these risks and ensure the safety of the test.

[0159] Safety measures verification: In a simulation environment, the safety measures of the unmanned boat and the performance of the autonomous navigation system can be verified to ensure reliability in actual missions. This includes obstacle avoidance capabilities, emergency shutdown procedures, etc.

[0160] (2) Cost-effectiveness

[0161] Reduced testing costs: Actual testing requires the purchase and maintenance of expensive unmanned boat equipment, while the simulated test environment eliminates these costs. In addition, actual testing may need to be conducted in remote or dangerous locations, resulting in higher transportation and personnel expenses.

[0162] Reduced maintenance costs: Actual unmanned boats require regular maintenance and repairs, while digital prototypes in a simulated test environment do not require physical maintenance, which can significantly reduce maintenance costs.

[0163] (3) Diverse test scenarios

[0164] Multi-environment testing: The simulation test environment can simulate various marine environments, including different meteorological and hydrological conditions, such as wind speed, wave height, tide, etc. This enables testers to evaluate the performance of the unmanned boat in a variety of environments.

[0165] Multi-mission type testing: Different types of mission scenarios can be created, such as maritime patrol, underwater exploration, rescue missions, etc., to evaluate the performance and applicability of unmanned boats under various missions.

[0166] (4) Algorithm optimization and performance evaluation

[0167] Performance evaluation: The simulation test environment can provide detailed performance data, including navigation accuracy, speed, stability, etc. This helps to evaluate the performance of the unmanned boat and make improvements.

[0168] Algorithm optimization: In a simulation environment, the navigation and control algorithms of the unmanned boat can be tested and optimized to ensure that they can operate effectively in different situations. This can greatly improve the autonomy and intelligence of the unmanned boat.

[0169] (5) Multi-machine collaborative simulation

[0170] Cluster performance test: The simulation environment allows the simulation of scenarios where multiple unmanned boats work together to evaluate the effectiveness of cluster performance and collaborative strategies.

[0171] Multi-machine interaction simulation: It can simulate the communication and collaboration between multiple unmanned boats to test the information exchange and collaborative behavior between unmanned boats, including path planning, task allocation, etc.

[0172] (6) Data analysis and decision support

[0173] Data analysis drives decision making: The large amount of data generated by the simulation test environment can be used for data-driven decision making. This helps improve mission planning and UAV operations, making them smarter and more efficient.

[0174] Mission module optimization: Based on data analysis of the simulation test environment, the mission modules of the unmanned boat can be optimized to make them more suitable for specific mission requirements.

[0175] Decision support system: A decision support system can be built to provide intelligent suggestions and decision support based on data from the simulation test environment to optimize task execution, including obstacle avoidance decisions, path planning, etc.

[0176] (7) Research and training

[0177] Research Platform: The simulation test environment provides an ideal platform for researchers to study unmanned watercraft technology, algorithms, and applications. Researchers can test and validate new ideas and technologies in a simulation environment.

[0178] Training tool: For UAV operators and maintainers, the simulation test environment can be used as a training tool to improve their skills and response capabilities. Training is conducted in a virtual environment, which can be safer, more economical and more efficient.

[0179] In short, the surface unmanned boat simulation test environment not only provides safety and cost-effectiveness, but also supports a variety of test scenarios, algorithm optimization and performance evaluation, multi-machine collaborative simulation, data analysis and decision support, as well as research and training. This will effectively promote the development and application of unmanned boat technology and bring huge impetus to the field of surface unmanned boats. Specific embodiment:

[0181] The following is a specific implementation plan, which aims to implement a method for designing a surface unmanned boat simulation test environment. This implementation plan converts the theoretical content of the invention into practical and executable planning steps to ensure its successful implementation.

[0182] (1) Hardware and infrastructure preparation

[0183] Cloud computing environment selection: Choose a suitable cloud computing service provider, such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP). Ensure that the computing, storage, and network resources can meet the needs of the simulation environment.

[0184] Server setup: Establish server architecture in cloud computing environment, including computing servers, storage servers and network servers. Select server configuration with high performance and scalability.

[0185] Network settings: Deploy a virtual private network (VPC) to isolate the simulation environment and ensure data security and isolation. Configure software-defined networking (SDN) to support communication within the virtual environment.

[0186] (2) Software Development and Integration

[0187] Simulation engine development: Develop the simulation engine, including calculation, motion and physical models, etc. Ensure that the simulation engine can simulate the motion and environment of the surface unmanned boat.

[0188] Simulation service development: Develop simulation services, including user interface, test case management, data analysis, etc. Use cloud native technologies to build a scalable microservice architecture.

[0189] User terminal development: Develop user terminal applications for control center operations and test task submission. Ensure that the application supports multiple platforms, including desktop, mobile, and web.

[0190] (3) Data model and scenario construction

[0191] Digital prototype model: Create a digital prototype model, including surface vessels, underwater equipment, and surface obstacles. Ensure that the model can simulate various mission loads and motion conditions.

[0192] Test scenario design: Based on mission requirements, design various test scenarios, including different ocean environments and mission types. The scenarios need to include elements such as typical obstacles, meteorological and hydrological environments, etc.

[0193] (4) System integration and testing

[0194] Hardware and software integration: Integrate hardware and software components to ensure that the simulation environment can run on the physical server and communicate with the user terminal and the simulation engine.

[0195] Performance testing: Perform performance testing on the entire system, including server performance, simulation engine performance, multi-machine coordination performance, etc. Ensure that the system runs stably under various loads.

[0196] Regression testing: Perform regression testing regularly to ensure the stability and reliability of the system and to detect and fix problems in a timely manner.

[0197] (5) Data management and security

[0198] Data management: Establish a data management system, including data storage, backup and recovery mechanisms, to ensure that the data generated by the simulation environment is safe and reliable.

[0199] Security measures: Deploy security measures, including access control, authentication, and data encryption, to protect the system from potential threats and attacks.

[0200] (6) Training and maintenance

[0201] Training plan: Develop training plans for operators and maintenance personnel to train them on how to use the simulation test environment, including mission planning, data analysis, etc.

[0202] Maintenance plan: Develop a regular maintenance plan, including hardware and software maintenance, to ensure system stability and performance.

[0203] (7) Continuous Improvement

[0204] User feedback: Collect user feedback and requirements, continuously improve the system, and add new features and scenarios.

[0205] Technology update: Continue to track the development of unmanned boat technology and cloud computing technology, and adopt new technologies in a timely manner to improve system performance and functions.

[0206] (8) Deployment and promotion

[0207] Deployment plan: Develop a system deployment plan to ensure that the system can be promoted and applied in actual tasks.

[0208] Promotion strategy: Develop a promotion strategy to publicize the benefits and application scenarios of the simulation test environment to potential users and research institutions.

[0209] (9) Data analysis and application

[0210] Data Analysis Tools: Develop data analysis tools to process the large amounts of data generated by simulation environments. These tools can help users optimize mission planning and decision making.

[0211] Application cases: Develop application cases to demonstrate the specific application of simulation test environment in the field of surface unmanned boats and attract more users and partners.

[0212] This implementation plan will help transform the surface unmanned boat simulation test environment design method into a practical system, and realize the practical application and implementation of the plan through hardware and software development and integration, data model and scenario construction, system integration and testing, security and data management, training and maintenance, continuous improvement, deployment and promotion, as well as data analysis and application.

[0213] The scheme of the present invention is specifically described below in conjunction with the accompanying drawings:

[0214] (1) Overall structure

[0215] See also Figure 1 ,The simulation environment is mainly implemented in a virtual environment, which can build test scenarios according to the mission requirements, simulate the main payload equipment, and finally test and evaluate the typical algorithms and mission modules of the unmanned boat cluster.,Based on the above analysis, the simulation environment is mainly composed of test system related software, supporting tool software, etc., and interacts with the unmanned boat cluster control system.

[0216] (2) Abstract layering

[0217] See also Figure 2 and Figure 3 ,The simulation system can be horizontally divided into four abstract layers according to the ,abstraction level: “Infrastructure”, “Business Logic”, “Service Gateway” and “Terminal ,Application”.

[0218] The infrastructure layer makes full use of the basic capabilities provided by the cloud computing environment, such as "continuous deployment integration", "massive data storage management" and real-time status monitoring based on log data streams.

[0219] The service gateway layer adopts an open Web programming interface (API) and a standardized authentication and authorization mechanism (OAuth2.0), and supports terminal applications through the "load balancing" and "certificate encryption" services provided by the cloud computing environment.

[0220] The terminal application layer is a number of graphical interface programs such as desktop, mobile and Web terminals built based on Web technology.

[0221] The business logic layer adopts a containerized distributed microservice architecture design, and vertically divides the system according to business dimensions such as "user service", "integrated deployment", "instance management", and "data analysis".

[0222] The core modules of the simulation test platform include: "digital equipment platform", "simulation engine", "simulation service" and "infrastructure"; the peripheral modules include "control center background" and "user terminal".

[0223] The digital equipment platform of simulation test platform architecture design is a "digital model" of the real equipment platform, which can provide calculations consistent with the real equipment.

[0224] The communication environment supports the running of real (binary) "equipment platform public services" and "equipment platform application services", and can obtain "environmental perception" and "target detection" capabilities consistent with real equipment by docking with "test payload equipment".

[0225] See also Figure 4 The lower layer of the digital prototype is a variety of virtual sensor payloads supported by the simulation engine. They are mapped with the target objects and environmental variables in the virtual physical world created by the simulation engine, so that the "digital prototype" can obtain the same "environmental perception" and "target detection" capabilities as the real boat by mounting "test payload equipment".

[0226] The simulation test engine is the core system module for creating and driving the digital prototype, and providing virtual computing environment, virtual physical environment, virtual target objects, etc. for the digital prototype. It mainly includes the principle models, historical data and algorithm modules that support the simulation of the "computing", "motion" and "physics" dimensions of the virtual digital environment, as well as the "state calculation engine" that drives these models and algorithms for simulation deduction.

[0227] See also Figure 5 The upper layer of the "simulation engine" is the "simulation service", which is responsible for creating "simulation engine" instances according to the test requirements submitted by users and managing the execution process of the simulation. The lower layer of the "simulation engine" is the cloud computing infrastructure, which mainly includes (1) software-defined network infrastructure that assists the simulation engine in "computing communication environment simulation", (2) data storage management that provides data input and output support for the state calculation process, and (3) log collection and status monitoring as a guarantee for the reliable operation of the simulation engine.

[0228] The simulation engine is driven by discrete time state prediction, and the time slice of each simulation iteration cycle is determined by the test plan. In each iteration cycle, the state calculation engine will add the system state variables based on the previous moment, apply the principle model to predict the system state variables at the next moment, and update the corresponding states of the digital boat and the virtual environment according to the calculation results.

[0229] See also Figure 6 The "Simulation Service" is a service-oriented and platform-based implementation of the core functions of the "Simulation Engine". It provides a multi-tenant platform application that runs and manages multiple simulation engine instances, allowing multiple simulation test scenarios to run on a shared cloud computing infrastructure without interfering with each other.

[0230] The "Simulation Service" is a typical cloud-native application. It provides upper-level users and applications with (1) a Web-based graphical user interface and (2) a REST-based application programming interface. The user interface and programming interface together encapsulate core business functions including (3) test case and test plan management, (4) user account and permission management, (5) simulation engine instance creation, operation and lifecycle management, and (6) simulation result data analysis support.

[0231] The underlying layer of the simulation service is general cloud computing infrastructure such as (1) “automated testing and continuous integration”, (2) “distributed data storage management”, (3) “unified log collection and analysis”, and (4) “microservice operation status monitoring”.

[0232] The simulation service is based on a microservice architecture, which vertically divides modules into microservices according to business functions. Each microservice can be independently deployed. Microservices adopt a stateless design, and drive and exchange data through distributed message queues and distributed data storage. The system adopts a standard DevOps operation and maintenance model and supports public and private cloud computing environments.

[0233] See also Figure 7 The infrastructure of the simulation test platform is hosted by a (public or private) cloud computing platform. 一Generally, an independent network domain is divided by virtual private cloud (VPC) technology. The general cloud computing infrastructure that simulation testing relies on includes (1) automated testing and deployment, (2) distributed data storage management, (3) distributed message queues, (4) software-defined networks, (5) log collection and status monitoring, and (6) virtual cloud computing hosts.

[0234] The functions of automated test deployment include two aspects: (1) providing continuous integration for each microservice included in the "simulation service" itself, and (2) using automated methods to deploy test loads and prepare virtual operating environments for the "simulation engine". Distributed data storage management is responsible for storing (1) historical data dependent on the principle model, (2) business data related to simulation plans, test plans and test results, (3) state data and log data accumulated during the simulation process, and (4) auxiliary data such as user account system, permission management, and load application software packages. These data exist in the form of relationships, binary objects and semi-structured text, and rely on relational databases, object storage and log storage index services provided by the cloud. Distributed message queues provide asynchronous calls and data distribution channels for each microservice of the "simulation service", helping to decouple each microservice. Software-defined networks and virtual cloud computing hosts are the carriers of computing resources and network resources of the simulation test platform, and are provided by standard components of cloud virtual servers and their operating systems.

[0235] (3) Usage process

[0236] Take the typical task test plan generation process as an example.

[0237] See also Fig. 9 ,The simulation system supports the management of test plans.,By collecting the names of plans and the data assumed in the plans,,these data are classified and stored in the database for management.

[0238] See also Fig. 9 By entering the test field range or selecting the test field range on the map, you can determine the test field data information for subsequent operations. You can generate three different mission scenarios: patrol and alert, reconnaissance and surveillance, and underwater small target detection.

[0239] See also Fig.10 , select a plan in the plan import interface, import the selected plan data, and the target motion data can display the target's motion status on the interface.

[0240] See also Fig.11 , enter the target editing interface, and preview the target's motion state by changing the time axis. The target's motion trajectory is derived through the software's built-in motion simulation model.

[0241] See also Fig.12 , parameter settings are provided on the interface, and users set the task parameters according to the prompts, providing a basis for subsequent data generation.

[0242] (4) Data generation process

[0243] According to the data flow of generating sea condition data, it can be seen that the user generates sea condition information within the mission range through the software's built-in environmental model based on the parameters set in advance, the test site range, and sampling point data.

[0244] The specific steps to generate marine environment data are as follows:

[0245] 1) Convert longitude and latitude to tile coordinates

[0246] Calculate the tile coordinates of the sampling point and all tile coordinates within the test field. Because the input sampling point coordinates and test field range coordinates are both longitude and latitude coordinates, the longitude and latitude coordinates need to be converted into tile coordinates to calculate all tile numbers contained in the test field area.

[0247] 2) Full time domain data of a single sampling point

[0248] Calculate the ocean environment information of multiple sampling points at each time step, use dual (X, Y two-dimensional) linear difference method to perform difference processing on each data (wind speed, wave height, etc.) in the ocean environment, and generate data for all tiles in a single time.

[0249] 3) Full airspace and full time domain data

[0250] The time dimension is added, and linear difference processing is performed on the data on tiles between multiple sampling points within the spatial range to generate full-time ocean environment data in sequence.

[0251] 4) Data preservation

[0252] The data is saved in units of 5 minutes.

[0253] (5) Typical target generation process

[0254] See also Fig.13 , set the basic parameters of the task as the basis for subsequent data generation. The design interface is used to input data such as the task area, task duration, data generation frequency, map tile level, and sea state sampling points.

[0255] See also Fig.14 , select the target, the selected target type is preset in the database, and is divided into two types: dynamic target and static target.

[0256] See also Fig.15, change the position and default attributes of sea targets in the mission area, according to different types. If it is a static target, static targets can be generated in batches according to the different attribute items of each target.

[0257] See also Fig.16 , change the position and default attributes of underwater targets in the mission area, according to different types, if it is a static target, you can batch generate static targets according to the different attribute items of each target. If it is a dynamic target, you can also set the navigation point information of each target to generate subsequent path planning.

[0258] See also Fig.17 , change the position and default attributes of surface obstacles in the mission area, and generate static targets in batches according to different types.

[0259] See also Fig.18 ,According to the mission duration, input generation frequency, sea condition information and target attributes, combined with the sea target trajectory generation algorithm, the motion trajectory of the sea target is generated.

[0260] See also Fig.19 ,According to the mission duration, input generation frequency, sea condition information and target attributes, the motion trajectory of the underwater target is generated by combining the underwater target trajectory generation algorithm.

[0261] See also Fig. 20 ,According to the mission duration, input generation frequency, sea condition information and target attributes, combined with the surface obstacle target trajectory generation algorithm, the motion trajectory of the surface obstacle target is generated.

[0262] (6) Simulation test platform UI design

[0263] Platform UI design includes data display interface such as Fig.21 and Fig. 22 shown.

[0264] Please note that the technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification. The above embodiments only express several implementation methods of the present application, and their descriptions are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention. It should be pointed out that for ordinary technicians in this field, without departing from the concept of the present application, several variations and improvements can be made, which all belong to the scope of protection of the present application. Therefore, the scope of protection of the invention of the present application shall be based on the attached claims.

[0265] The above are preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for designing a surface unmanned boat simulation test environment, characterized in that: include: Digital prototype boat construction to simulate the behavior and performance of real unmanned boats; Simulation test plan generation, creating test plans for typical tasks based on different task types; Fine-grained simulation of mission scenarios, including simulation of typical obstacles and meteorological and hydrological environments; The abstract layering of the simulation system includes four abstract layers: infrastructure, business logic, service gateway, and terminal application; Using the construction of the process, describe the process of generating a typical mission test plan, including the main process steps of generating mission scenarios, the process steps of generating marine environment data, and the process steps of generating typical targets; Simulation test platform UI design.

2. The method for designing a surface unmanned boat simulation test environment according to claim 1, characterized in that: The digital prototype boat construction includes: Application of virtualization technology: Use virtualization technology to create a logically independent computing environment to ensure that the digital prototype has the same computing resources and network environment as the actual unmanned boat; Simulate basic load and mission load: The digital prototype simulates the basic load and mission load of the unmanned boat to facilitate the testing of the mission module; Support multi-machine collaboration: The digital prototype can run on the same or different host servers, and establish virtual network connections between each other through software-defined networks, supporting multi-machine collaborative simulation; Public services and application services on the boat side: Public services and application services are deployed on the digital prototype boat to realize environmental perception and target detection functions.

3. The method for designing a surface unmanned boat simulation test environment according to claim 1 or 2, characterized in that: The simulation test scheme generation includes: Test case model library: According to different task types, establish a test case model library, including various test items and test scenarios; Test requirements and test plans: Generate test requirements and test plans based on the test outline, where test requirements include test items and test scenarios, and test plans include test arrangements; Test case instantiation: According to the test requirements, extract the test case template list corresponding to the test item from the test case template library, and specify the parameter threshold through human-computer interaction to realize the instantiation of the test case; Mission scenario configuration: Provide input to the mission test guidance software based on the test scenario, and drive the test load equipment simulator to complete the test environment configuration; Automated test execution: According to the test schedule, the test is automatically executed and the test data is recorded. The test results are given for quantifiable test content, and input is provided for data analysis and evaluation software.

4. The method for designing a surface unmanned boat simulation test environment according to claim 1 or 2, characterized in that: In the simulation environment, various types of typical obstacles can be set, including static targets and dynamic targets.

5. The method for designing a simulated test environment for an unmanned surface vehicle according to claim 4, characterized in that: The steps to simulate a typical obstacle include: Obstacle deployment: deploy the initial position of the static target according to the constructed map, and batch set the attributes of the static target, the initial position of the dynamic target, and the movement trend of the dynamic target; Motion trajectory generation: The scenario guidance software generates the motion trajectory of typical obstacles during the task, and generates the position and speed information of each time period based on the time step.

6. The method for designing a simulated test environment for an unmanned surface vehicle according to claim 1 or 2, characterized in that: The simulation of the meteorological and hydrological environment includes generating meteorological and hydrological environmental data, wherein the meteorological and hydrological environmental data includes water flow velocity, water flow direction, wave height, and wavelength; The steps of generating the meteorological and hydrological environmental data include: Convert longitude and latitude to tile coordinates: Calculate the tile coordinates of the sampling point and all tile coordinates within the test site for subsequent data generation; Full time domain data of a single sampling point: Calculate the ocean environment information of multiple sampling points at each time step, use difference processing to generate data of all tiles at a single time; Full-space and full-time data: Add the time dimension, perform linear difference processing on the data on the tiles between multiple sampling points, and generate full-time ocean environment data.

7. The method for designing a simulated test environment for an unmanned surface vehicle according to claim 1 or 2, characterized in that: The infrastructure layer: provides basic capabilities of continuous deployment integration, data storage management, and real-time status monitoring, using a cloud computing environment; The service gateway layer: provides an open Web programming interface and standardized authentication and authorization mechanisms, and supports load balancing and certificate encryption services; The terminal application layer: constructs multiple graphical interface programs, including desktop, mobile and web terminals; The business logic layer: uses a containerized distributed microservice architecture design, including user services, integrated deployment, instance management, and data analysis.

8. The method for designing a simulated test environment for an unmanned surface vehicle according to claim 1 or 2, characterized in that: The main process of generating the task scenario includes: Determine the test field range: Determine the test field data information by entering the test field range or selecting the test field range on the map; Generate mission scenario: Generate mission scenario based on selected solution data, including target motion data; Edit target motion trajectory: change the motion state of the target, including position and motion trajectory; Parameter setting: Set task parameters to provide a basis for subsequent data generation.

9. The method for designing a simulated test environment for an unmanned surface vehicle according to claim 8, characterized in that: The marine environment data generation process includes: Convert longitude and latitude to tile coordinates: Convert longitude and latitude coordinates to tile coordinates and calculate all tile coordinates contained in the test area; Full time domain data of a single sampling point: Calculate the ocean environment information of multiple sampling points at each time step, perform bilinear difference processing, and generate data for all tiles at a single time; Full-space and full-time data: linear difference processing is performed on the data on tiles between multiple sampling points within the spatial range to generate full-time ocean environment data; Data storage: Data is stored in units of 5 minutes.

10. The method for designing a simulated test environment for an unmanned surface vehicle according to claim 9, characterized in that: The typical target generation process includes: Basic parameter setting: set the basic parameters of the task, including the task area, task duration, data generation frequency, map tile level, and sea condition sampling points; Target selection: select the target type, including dynamic targets and static targets; Target location and attribute settings: change the location and default attributes of the target in the task area, and generate static targets in batches; Generate motion trajectory: Generate the motion trajectory of the target based on the mission duration, input generation frequency, sea condition information and target attributes, combined with the motion trajectory generation algorithm.

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