Method and device for testing autonomous cognition and decision of unmanned ship

Abstract

The invention provides a test method and device for autonomous cognition and decision-making of an unmanned ship. The method comprises the following steps: acquiring a test sample based on a polymorphic sample and a multi-dimensional attack; identifying the test sample to obtain an identification result, and displaying the identification result through a visual system; and comparing the identification result with a preset identification result to obtain a comparison result which is used for evaluating the environmental perception ability of the target identification index system. Aiming at the requirements of water surface WR system autonomous cognition and decision behavior test and evaluation, the invention provides a water surface WR system autonomous cognition and decision behavior test and evaluation system which comprises a water surface WR system autonomous cognition and decision behavior test and evaluation method, index system construction, a test and evaluation process and the like. The autonomous cognition and decision behavior test and evaluation ability of the water surface WR system is formed, and the autonomous cognition and decision behavior test and evaluation efficiency of the unmanned ship is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned boat testing, and in particular relates to a testing method and device for autonomous cognition and decision-making of an unmanned boat. Background Art

[0002] The concept of autonomous perception was first proposed in the military field, covering three levels of perception, understanding and prediction. It is a way to improve the ability to discover, identify, understand, analyze, respond and deal with security threats from a global perspective based on the ability to understand security big data in an environmental, dynamic and holistic way. It aims to obtain, understand, display and predict the continuity of recent development trends of security factors that can cause changes in the situation in a large-scale environment, and then make decisions and actions. Decision control is the supervision and adjustment activities to ensure that the implementation activities of decision executors do not deviate from the track of the decision plan and the direction guided by the decision goal. In the process of decision implementation, both subjective and objective conditions are in constant development and change, and strict supervision should be used to control excessive fluctuations in conditions. If the fluctuation is too large, necessary adjustments should be made according to the specific situation. Therefore, how to provide a test method for autonomous cognition and decision-making of unmanned boats has become a technical problem that needs to be solved in this field. Summary of the invention

[0003] The purpose of the present invention is to provide a method and device for testing autonomous cognition and decision-making of an unmanned boat.

[0004] According to a first aspect of the present invention, a test of autonomous cognition and decision-making of an unmanned boat is provided, comprising:

[0005] Obtain test samples based on polymorphic samples and multi-dimensional attacks;

[0006] Identify the test sample to obtain an identification result, and display the identification result through a visualization system;

[0007] The recognition result is compared with a preset recognition result to obtain a comparison result, and the comparison result is used to evaluate the environmental perception capability of the target recognition index system.

[0008] Optionally, the method further comprises:

[0009] Build a test scenario map and configure obstacle attributes, set different threat radius for different obstacles, and mark virtual waypoints;

[0010] Conduct autonomous capability tests based on route planning results.

[0011] Optionally, the step of constructing a test scene map and configuring obstacle attributes includes:

[0012] Set the starting point, target point and static obstacles, and generate the test scenario map according to the test dimension configuration.

[0013] Optionally, the autonomous capability test is performed according to the route planning result, including:

[0014] Enter the test area in the test scenario map through the entry point according to the pre-calibrated standard entry path;

[0015] In the autonomous mission mode, obstacles are located and avoided according to the test scene map, and an obstacle avoidance path for route planning is generated;

[0016] The obstacle avoidance path of the route plan is evaluated.

[0017] Optionally, the method further comprises:

[0018] Set the starting point, target point, and static and dynamic obstacles, and generate a test scenario map based on the test dimension configuration;

[0019] After the WRT automatically leaves the berth, it enters the test area of ​​the test scene map via the entry point along the pre-calibrated standard entry path, searches and identifies different obstacles in the test area in the autonomous mission mode, and locates and avoids obstacles;

[0020] When the unmanned boat passes through the virtual waypoint and leaves the exit point, the autonomous obstacle avoidance results are evaluated through the autonomous capability test system.

[0021] According to a second aspect of the present invention, a test device for autonomous cognition and decision-making of an unmanned boat is provided, comprising:

[0022] An acquisition module, used to acquire test samples based on polymorphic samples and multi-dimensional attacks;

[0023] An identification module is used to identify the test sample, obtain an identification result, and display the identification result through a visualization system;

[0024] The evaluation module is used to compare the recognition result with a preset recognition result to obtain a comparison result, and the comparison result is used to evaluate the environmental perception capability of the target recognition index system.

[0025] Optionally, the evaluation module is used to:

[0026] Build a test scenario map and configure obstacle attributes, set different threat radius for different obstacles, and mark virtual waypoints;

[0027] Conduct autonomous capability tests based on route planning results.

[0028] Optionally, the evaluation module is used to:

[0029] Set the starting point, target point and static obstacles, and generate the test scenario map according to the test dimension configuration.

[0030] Optionally, the evaluation module is used to:

[0031] Enter the test area in the test scenario map through the entry point according to the pre-calibrated standard entry path;

[0032] In the autonomous mission mode, obstacles are located and avoided according to the test scene map, and an obstacle avoidance path for route planning is generated;

[0033] The obstacle avoidance path of the route plan is evaluated.

[0034] Optionally, the evaluation module is used to:

[0035] Set the starting point, target point, and static and dynamic obstacles, and generate a test scenario map based on the test dimension configuration;

[0036] After the WRT automatically leaves the berth, it enters the test area of ​​the test scene map via the entry point along the pre-calibrated standard entry path, searches and identifies different obstacles in the test area in the autonomous mission mode, and locates and avoids obstacles;

[0037] When the unmanned boat passes through the virtual waypoint and leaves the exit point, the autonomous obstacle avoidance results are evaluated through the autonomous capability test system.

[0038] In a third aspect, the present application shows an electronic device, which includes: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the method described in any of the above aspects.

[0039] In a fourth aspect, the present application illustrates a non-temporary computer-readable storage medium, which, when instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to execute a method as described in any of the above aspects.

[0040] In a fifth aspect, the present application illustrates a computer program product. When instructions in the computer program product are executed by a processor of an electronic device, the electronic device is enabled to execute the method described in any of the above aspects.

[0041] The beneficial effects brought by the present invention are as follows:

[0042] It can be seen from the above scheme that the embodiment of the present invention provides a test method and device for autonomous cognition and decision-making of unmanned boats, including: obtaining test samples based on polymorphic samples and multi-dimensional attacks; identifying the test samples to obtain recognition results, and displaying the recognition results through a visualization system; comparing the recognition results with preset recognition results to obtain comparison results, and the comparison results are used to evaluate the environmental perception ability of the target recognition index system. In response to the needs of testing and evaluating the autonomous cognition and decision-making behavior of the surface WR system, a test and evaluation system for the autonomous cognition and decision-making behavior of the surface WR system is proposed, including a test and evaluation method for the autonomous cognition and decision-making behavior of the surface WR system, an index system construction, a test and evaluation process, etc., to form a test and evaluation capability for the autonomous cognition and decision-making behavior of the surface WR system, and improve the test and evaluation efficiency of the autonomous cognition and decision-making behavior of the unmanned boat. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A flowchart of a method for testing autonomous cognition and decision-making of an unmanned boat provided according to an embodiment;

[0044] Figure 2 Constructing a technical roadmap for a test and evaluation system provided according to an embodiment;

[0045] Figure 3 A schematic diagram of a WR system test and evaluation accuracy solution provided according to an embodiment;

[0046] Figure 4 A target recognition capability testing technology route provided according to an embodiment;

[0047] Figure 5 This is a structural block diagram of a test device for autonomous cognition and decision-making of an unmanned boat in the present application.

[0048] Figure 6 It is a block diagram of an electronic device of the present application.

[0049] Figure 7 It is a block diagram of a computer-readable storage medium of the present application. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] Reference Figure 1, shows a flow chart of the steps of a test method for autonomous cognition and decision-making of an unmanned boat of the present application, which can be applied to electronic equipment, wherein the method can specifically include the following steps:

[0052] S101, obtaining a test sample based on a polymorphic sample and a multi-dimensional attack;

[0053] S102, identifying the test sample, obtaining an identification result, and displaying the identification result through a visualization system;

[0054] S103: Compare the recognition result with the preset recognition result to obtain a comparison result, and the comparison result is used to evaluate the environmental perception capability of the target recognition index system.

[0055] The target recognition test process is as follows:

[0056] a) Polymorphic samples and multi-dimensional attacks generate a series of test samples and send them to the module under test;

[0057] b) The module under test identifies the test sample and feeds the identification result back to the test system, while the visualization system displays the identification result;

[0058] c) The autonomous capability testing system compares the recognition results fed back by the tested object with the actual results, and statistically calculates the target recognition index system for environmental perception capability assessment.

[0059] The route planning capability test of WRT mainly detects the planning reliability and planning accuracy. It is intended to examine the planning reliability and planning behavior accuracy of WRT by monitoring the planning results of WRT in the entire planning process. The planning results are solved by the module under test. The process is shown in the figure below. It is necessary to build a test map, configure obstacle attributes, and conduct autonomous capability testing based on the route planning results.

[0060] In response to the needs of testing and evaluation of the autonomous cognition and decision-making behavior of the surface WR system, a testing and evaluation system for the autonomous cognition and decision-making behavior of the surface WR system was proposed. The focus was on studying the testing and evaluation methods, indicator system construction, testing and evaluation process, etc. of the autonomous cognition and decision-making behavior of the surface WR system, so as to form the testing and evaluation capabilities of the autonomous cognition and decision-making behavior of the surface WR system.

[0061] The technical roadmap for constructing the test and evaluation index system for autonomous cognition and decision-making behavior of surface WR system is as follows: Figure 2 As shown, it is mainly divided into test evaluation process, technical indicator system construction, test profile design and test evaluation method.

[0062] The technical indicator system for testing and evaluating the autonomous capability and decision-making behavior of unmanned systems is the foundation of the evaluation system and also the difficulty. For three typical tasks, namely target recognition, path planning, and autonomous obstacle avoidance, the evaluation system is based on the difficulty of the task and the degree of task completion: for the evaluation of the difficulty of target recognition, the evaluation system including the mean square error, peak signal-to-noise ratio, structural similarity, quality factor, and natural scene statistics, which contain absolute and relative indicators, is selected to evaluate the difficulty of data sample recognition; for the completion of target recognition, the confusion matrix and its derived indicators are selected to measure the target recognition performance; for the evaluation of the difficulty of route planning, the evaluation system including the map area, the number of obstacles, the total area of ​​obstacles, the average area of ​​obstacles, and the proportion of the area of ​​obstacles is selected to evaluate the complexity of the planning task; for the completion of route planning, the time consumption and the best path ratio are selected as performance indicators; for the evaluation of the difficulty of autonomous obstacle avoidance, the dynamic description indicator system including the map area, the number of obstacles, the total area of ​​obstacles, the average area of ​​obstacles, the proportion of the area of ​​obstacles, the maximum speed of the WR system, and the average speed of obstacles is selected to evaluate the difficulty of the autonomous obstacle avoidance task; for the completion of autonomous obstacle avoidance, the delay factor, trajectory safety, and driving cost are selected as performance indicators.

[0063] Another embodiment of the present application further supplements the test method for autonomous cognition and decision-making of the unmanned boat provided in the above embodiment.

[0064] Through the surface WR system autonomous cognition ability and decision-making behavior test and evaluation framework, the WR system's autonomous cognition and decision-making behavior test capabilities have been acquired. In order to support the development of various typical task tests, a surface WR system autonomous cognition and decision-making behavior test method library has been built.

[0065] The test and evaluation method of the autonomous capability of the surface WR system is the research focus of this project. The basic idea of ​​this topic is: take the field test evaluation results as the true value, use the same tasks, the same test items, the same test process, the same capability evaluation model, and construct a test environment similar to the field through simulation to give the simulation evaluation results. Generally, the relevant capabilities are quantified through test questions, and the real and simulated evaluation results are compared to ensure that under the same test process, the method of testing the WRT autonomy level through the simulation construction environment, and the test coverage of the WRT cognitive ability is not less than 85%, and the deviation between the decision-making ability simulation evaluation results and the test data is no more than 20%, thereby improving the efficiency of the WRT capability evaluation and promoting the relevant scientific research process. The schematic diagram for determining the test evaluation accuracy is shown in the figure below. Figure 3 shown.

[0066] The autonomous behavior and capabilities of the surface WR system are the key characteristics of its intelligence, and compared with the characteristics of traditional equipment, it is open and subjective. In order to ensure that the research is highly feasible, this project will conduct research on testing and evaluation technologies around the autonomous control capabilities required by three typical tasks of the surface WR system. In the evaluation accuracy schematic, simulation environment construction and motion models are the key to conducting autonomous capability testing and evaluation. Among them, the target simulation problem in environmental changes related to autonomous performance and the position prediction problem based on motion models under simulated static conditions are the key points and difficulties in the above work. At the same time, the field data collection and related capability testing under low sea conditions are also the data source for evaluating the true value in this work. Therefore, the focus of the research work on autonomous capability testing and evaluation technology of the surface WR system is as follows:

[0067] The indoor field environmental perception capability test technology based on virtual visual targets completes the construction of the test environment through indoor visual simulation and motion environment loading; the indoor field virtual GPS real-time position obstacle avoidance and tracking performance test technology realizes position simulation and track recording in the indoor field environment through virtual GPS equipment; the outdoor field offshore real boat obstacle avoidance performance test technology records the test process through the boat platform test system.

[0068] The environmental perception capability test mainly solves the WRT's ability to identify targets. In order to efficiently discover the failure boundary samples of the object under test, it is necessary to construct polymorphic samples and conduct multi-dimensional algorithm attacks. The technical roadmap for the target recognition capability test technology is as follows: Figure 4 shown.

[0069] The environmental perception performance test system mainly includes: test sample generation system, key system under test, and test visualization system. The test sample generation system includes background and simulated targets, which can simulate a variety of natural conditions, such as rotation, blur, illumination, etc., and has the function of counterattack simulation. The simulated targets on the sea surface include civil JC, etc. The test visualization system mainly realizes the setting of the test profile, displays the perception results of WRT on the target and the test results.

[0070] Optionally, the method further comprises:

[0071] Build a test scenario map and configure obstacle attributes, set different threat radius for different obstacles, and mark virtual waypoints;

[0072] Conduct autonomous capability tests based on route planning results.

[0073] Optionally, build a test scenario map and configure obstacle properties, including:

[0074] Set the starting point, target point and static obstacles, and generate the test scenario map according to the test dimension configuration.

[0075] A certain measurement map is used as the offshore test site, and a red rectangular test area is set. There is a typical test scenario composed of static obstacles. Different obstacles are set with different threat radius, such as 200m for red, 150m for blue, and 100m for green. WRT is not allowed to enter within its threat radius. At the same time, virtual waypoints are marked, and WRT must pass through the waypoints, marking the standard entry path, entry point (small green rectangular box), exit point (small red rectangular box), and exit path. Through the setting of the basic test environment, the dock berthing and unberthing scenes are formed, and by setting up accompanying test facilities, the task autonomy capability test scene is formed.

[0076] The overall testing process is as follows:

[0077] a) Set the starting point, target point, and static obstacles, and generate a test scenario map according to the test dimension configuration

[0078] b) After the WRT automatically leaves the berth, it enters the test area via the entry point along the pre-calibrated standard entry path. In the autonomous mission mode, it completes the positioning and obstacle avoidance of obstacles and path generation according to the electronic nautical chart, and records and saves the system information.

[0079] c) Finally, the autonomous capability test system evaluates the path results of route planning.

[0080] The autonomous obstacle avoidance test of WRT mainly detects the obstacle avoidance reliability and accuracy. It is intended to examine the obstacle avoidance reliability and accuracy of WRT by monitoring the track and heading angle of WRT during the local obstacle avoidance process. The track and heading are obtained based on the virtual GPS initial value and the WRT hydrodynamic model. The WRT control system matches the speed and heading according to the received motion instructions and sea condition data to simulate the actual navigation process of WRT. This process is as follows Figure 4 As shown, it is necessary to build a test map, configure obstacle properties, and calculate the track information based on motion instructions and sea condition data for testing.

[0081] Optionally, conduct autonomous capability tests based on the route planning results, including:

[0082] Enter the test area in the test scenario map through the entry point according to the pre-calibrated standard entry path;

[0083] In autonomous mission mode, obstacles are located and avoided based on the test scene map, and obstacle avoidance paths for route planning are generated;

[0084] Evaluate the obstacle avoidance path for route planning.

[0085] Optionally, the method further comprises:

[0086] Set the starting point, target point, and static and dynamic obstacles, and generate a test scenario map based on the test dimension configuration;

[0087] After the WRT automatically leaves the berth, it enters the test area of ​​the test scene map via the entry point along the pre-calibrated standard entry path, searches and identifies different obstacles in the test area in the autonomous mission mode, and locates and avoids obstacles;

[0088] When the unmanned boat passes through the virtual waypoint and leaves the exit point, the autonomous obstacle avoidance results are evaluated through the autonomous capability test system.

[0089] The overall scenario design implementation plan is as follows:

[0090] A certain measurement map is used as the offshore test site, and a red rectangular test area is set with static obstacles and dynamic obstacles to form a typical test scenario. Different obstacles are set with different threat radius, such as 200m for red, 150m for blue, and 100m for green. WRT is not allowed to enter within its threat radius. At the same time, virtual waypoints are marked, and WRT must pass through the waypoints, and the standard entry path, entry point (green small rectangular box), exit point (red small rectangular box), and exit path are marked. Through the setting of the basic test environment, the dock berthing and unberthing scenes are formed, and by setting up accompanying test facilities, the task autonomy capability test scene is formed.

[0091] The overall testing process is as follows:

[0092] 1) Set the starting point, target point, static and dynamic obstacles, and generate the test scenario map according to the test dimension configuration

[0093] 2) After the WRT automatically leaves the berth, it enters the test area via the entry point along the pre-calibrated standard entry path, searches and identifies different obstacles in the test area in the autonomous mission mode, locates and avoids obstacles, and records and saves system information.

[0094] 3) Finally, the WRT drives out from the exit point via the virtual waypoint, and the autonomous capability test system evaluates the autonomous obstacle avoidance results.

[0095] In terms of establishing the evaluation system, this study will conduct research on autonomous control behavior test evaluation technology from the task top layer, functional performance layer and autonomous capability layer. The main work includes three aspects:

[0096] 1) Determine requirements and goals. Construct a comprehensive evaluation index system for the autonomous behavior of the surface WR system, analyze the various factors that affect the autonomous capability based on the goals to be achieved and considering the constraints.

[0097] 2) Evaluate functional performance. Based on the comprehensive evaluation index system, adopt reasonable methods to evaluate the functional performance of the autonomous completion of a single task. On the premise that the single index meets the system requirements, further evaluate the autonomous capability.

[0098] 3) Evaluate autonomous capabilities. Study the failure patterns of autonomous capabilities, related influencing factors and evaluation models, use effective methods to analyze, and judge whether the stability and credibility of autonomous capabilities can meet the task requirements.

[0099] First, determine the evaluation objectives and evaluation conditions of the autonomous capability of the surface WR system, define the system boundary, conduct system analysis, conduct structural analysis, functional analysis, work description, and performance understanding of the system, so as to clarify the main tasks of the evaluation system. From the aspects of tasks, environment, equipment, etc., according to the purpose and requirements of the autonomous capability evaluation, select variables and parameters to describe the task profile, comprehensively analyze the relevant influencing factors, and establish scientific and reasonable effectiveness evaluation indicators.

[0100] Second, in order to more clearly describe the functional performance requirements of the autonomous capability of the surface WR system, the indicators are further refined into sub-indicators. Each major indicator can be divided into several sub-indicators according to the nature of the problem represented, and the sub-indicators can be further divided into secondary sub-indicators according to the breadth of their connotations. For a specific problem and system operating environment assumption, if there is actual data, the relevant indicator data is directly collected and the actual data is sorted. At the same time, based on the actual data of the field, a virtual test field is constructed to generate virtual test sample data to obtain sufficient evaluation test data, which is the key to the evaluation of autonomous capability. On this basis, the hierarchical analysis method, expert survey method, index method and other methods will be adopted to evaluate the single performance related to the autonomous control capability of WRT.

[0101] Third, the evaluation of the autonomous capability of the surface WR system is essentially a task failure probability evaluation problem. However, in the process of failure probability evaluation of different tasks, the standardized processing of sub-indicators with different dimensions and representing different physical meanings needs to reflect the true level of the evaluated object to the greatest extent; determine the weight of each indicator in the comprehensive evaluation index system, and take into account the influence of random factors such as human factors as much as possible; determine the correlation between each indicator, etc. This technical solution will adopt statistical methods, multi-attribute evaluation and fuzzy comprehensive evaluation methods to conduct a comprehensive evaluation of the autonomous control capability of WRT. Among these methods, it is necessary to select an evaluation method suitable for the goal of autonomous capability evaluation, which is mainly determined by analyzing the advantages and disadvantages of various methods.

[0102] Finally, based on the results of the task difficulty assessment and task capability assessment, quantitative / qualitative comprehensive computational analysis is performed through field tests and simulated virtual simulation environments to ultimately evaluate the overall autonomous capability, ultimately achieving the ability to test and evaluate the autonomous cognition of the surface WR system and its decision-making behavior test.

[0103] The embodiment of the present invention provides a test method for autonomous cognition and decision-making of an unmanned boat, including: obtaining a test sample based on polymorphic samples and multi-dimensional attacks; identifying the test sample to obtain a recognition result, and displaying the recognition result through a visualization system; comparing the recognition result with a preset recognition result to obtain a comparison result, and the comparison result is used to evaluate the environmental perception capability of the target recognition index system. In response to the demand for testing and evaluating the autonomous cognition and decision-making behavior of the surface WR system, a test and evaluation system for the autonomous cognition and decision-making behavior of the surface WR system is proposed, including a test and evaluation method for the autonomous cognition and decision-making behavior of the surface WR system, an index system construction, a test and evaluation process, etc., to form a test and evaluation capability for the autonomous cognition and decision-making behavior of the surface WR system, and improve the test and evaluation efficiency of the autonomous cognition and decision-making behavior of the unmanned boat.

[0104] It should be noted that each implementable method in this embodiment may be implemented separately, or may be implemented in combination in any manner without conflict, and this application is not limited thereto.

[0105] Another embodiment of the present application provides a testing device for autonomous cognition and decision-making of an unmanned boat, which is used to execute the testing method for autonomous cognition and decision-making of an unmanned boat provided in the above embodiment.

[0106] like Figure 5 , which is a schematic diagram of the structure of the test device for autonomous cognition and decision-making of an unmanned boat provided in an embodiment of the present application. The test device for autonomous cognition and decision-making of an unmanned boat comprises an acquisition module 501, an identification module 502 and an evaluation module 503, wherein:

[0107] The acquisition module 501 is used to acquire test samples based on polymorphic samples and multi-dimensional attacks;

[0108] The recognition module 502 is used to recognize the test sample, obtain the recognition result, and display the recognition result through the visualization system;

[0109] The evaluation module 503 is used to compare the recognition result with the preset recognition result to obtain a comparison result, and the comparison result is used to evaluate the environmental perception capability of the target recognition index system.

[0110] Regarding the device in this embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0111] Another embodiment of the present application further supplements the description of the test device for autonomous cognition and decision-making of unmanned boats provided in the above embodiments.

[0112] Optionally, an evaluation module for:

[0113] Build a test scenario map and configure obstacle attributes, set different threat radius for different obstacles, and mark virtual waypoints;

[0114] Conduct autonomous capability tests based on route planning results.

[0115] Optionally, an evaluation module for:

[0116] Set the starting point, target point and static obstacles, and generate the test scenario map according to the test dimension configuration.

[0117] Optionally, an evaluation module for:

[0118] Enter the test area in the test scenario map through the entry point according to the pre-calibrated standard entry path;

[0119] In autonomous mission mode, obstacles are located and avoided based on the test scene map, and obstacle avoidance paths for route planning are generated;

[0120] Evaluate the obstacle avoidance path for route planning.

[0121] Optionally, an evaluation module for:

[0122] Set the starting point, target point, and static and dynamic obstacles, and generate a test scenario map based on the test dimension configuration;

[0123] After the WRT automatically leaves the berth, it enters the test area of ​​the test scene map via the entry point along the pre-calibrated standard entry path, searches and identifies different obstacles in the test area in the autonomous mission mode, and locates and avoids obstacles;

[0124] When the unmanned boat passes through the virtual waypoint and leaves the exit point, the autonomous obstacle avoidance results are evaluated through the autonomous capability test system.

[0125] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0126] The embodiment of the present invention provides a test device for autonomous cognition and decision-making of an unmanned boat, including: obtaining test samples based on polymorphic samples and multi-dimensional attacks; identifying the test samples to obtain identification results, and displaying the identification results through a visualization system; comparing the identification results with preset identification results to obtain comparison results, and the comparison results are used to evaluate the environmental perception ability of the target identification index system. In response to the needs of testing and evaluating the autonomous cognition and decision-making behavior of the surface WR system, a test and evaluation system for the autonomous cognition and decision-making behavior of the surface WR system is proposed, including a test and evaluation method for the autonomous cognition and decision-making behavior of the surface WR system, an index system construction, a test and evaluation process, etc., to form the test and evaluation capability of the autonomous cognition and decision-making behavior of the surface WR system, and improve the test and evaluation efficiency of the autonomous cognition and decision-making behavior of the unmanned boat.

[0127] Optionally, an embodiment of the present application further provides an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the various processes of the above-mentioned method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0128] The embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, each process of the above method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. The computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0129] Figure 6 800 is a block diagram of an electronic device 800 shown in the present application. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0130] Reference Figure 6 , the electronic device 800 may include one or more of the following components: a processing component 802 , a memory 804 , a power component 806 , a multimedia component 808 , an audio component 810 , an input / output (I / O) interface 812 , a sensor component 814 , and a communication component 816 .

[0131] The processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the above method. In addition, the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.

[0132] The memory 804 is configured to store various types of data to support operations on the device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, images, videos, etc. The memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0133] The power supply component 806 provides power to the various components of the electronic device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 800.

[0134] The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and the rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0135] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), and when the electronic device 800 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 804 or sent via the communication component 816. In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.

[0136] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: home button, volume button, start button, and lock button.

[0137] The sensor assembly 814 includes one or more sensors for providing various aspects of status assessment for the electronic device 800. For example, the sensor assembly 814 can detect the open / closed state of the device 800, the relative positioning of components, such as the display and keypad of the electronic device 800, and the sensor assembly 814 can also detect the position change of the electronic device 800 or a component of the electronic device 800, the presence or absence of contact between the user and the electronic device 800, the orientation or acceleration / deceleration of the electronic device 800, and the temperature change of the electronic device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 814 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0138] The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 can access a wireless network based on a communication standard, such as WiFi, a carrier network (such as 2G, 3G, 4G or 5G), or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast operation information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0139] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above methods.

[0140] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, and the instructions can be executed by a processor 820 of an electronic device 800 to perform the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0141] Figure 7 19 is a block diagram of a computer-readable storage medium 1900 shown in the present application. For example, the computer-readable storage medium 1900 may be provided as a server.

[0142] Reference Figure 7 , the computer-readable storage medium 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions, such as an application, that can be executed by the processing component 1922. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above method.

[0143] The computer readable storage medium 1900 may also include a power supply component 1926 configured to perform power management of the computer readable storage medium 1900, a wired or wireless network interface 1950 configured to connect the computer readable storage medium 1900 to a network, and an input / output (I / O) interface 1958. The computer readable storage medium 1900 may operate based on an operating system stored in the memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™ or the like.

[0144] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0145] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0146] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

[0147] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present application can be implemented in electronic hardware, or 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 can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0148] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0149] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0150] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0151] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0152] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard drives, ROM, RAM, magnetic disks, or optical disks.

[0153] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0154] 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 test method for autonomous cognition and decision-making of an unmanned boat, characterized in that: include: Obtain test samples based on polymorphic samples and multi-dimensional attacks; Identify the test sample to obtain an identification result, and display the identification result through a visualization system; The recognition result is compared with a preset recognition result to obtain a comparison result, and the comparison result is used to evaluate the environmental perception capability of the target recognition index system.

2. The method for testing autonomous cognition and decision-making of unmanned boats according to claim 1, characterized in that: The method further comprises: Build a test scenario map and configure obstacle attributes, set different threat radius for different obstacles, and mark virtual waypoints; Conduct autonomous capability tests based on route planning results.

3. The method for testing autonomous cognition and decision-making of unmanned boats according to claim 2, characterized in that: The construction of the test scene map and configuration of obstacle properties include: Set the starting point, target point and static obstacles, and generate the test scenario map according to the test dimension configuration.

4. The method for testing autonomous cognition and decision-making of unmanned boats according to claim 3, characterized in that: The autonomous capability test according to the route planning result includes: Enter the test area in the test scenario map through the entry point according to the pre-calibrated standard entry path; In the autonomous mission mode, obstacles are located and avoided according to the test scene map, and an obstacle avoidance path for route planning is generated; The obstacle avoidance path of the route plan is evaluated.

5. The method for testing autonomous cognition and decision-making of unmanned boats according to claim 1, characterized in that: The method further comprises: Set the starting point, target point, and static and dynamic obstacles, and generate a test scenario map based on the test dimension configuration; After the WRT automatically leaves the berth, it enters the test area of ​​the test scene map via the entry point along the pre-calibrated standard entry path, searches and identifies different obstacles in the test area in the autonomous mission mode, and locates and avoids obstacles; When the unmanned boat passes through the virtual waypoint and leaves the exit point, the autonomous obstacle avoidance results are evaluated through the autonomous capability test system.

6. A test device for autonomous cognition and decision-making of unmanned boats, characterized in that: include: An acquisition module, used to acquire test samples based on polymorphic samples and multi-dimensional attacks; An identification module is used to identify the test sample, obtain an identification result, and display the identification result through a visualization system; The evaluation module is used to compare the recognition result with a preset recognition result to obtain a comparison result, and the comparison result is used to evaluate the environmental perception capability of the target recognition index system.

7. The test device for autonomous cognition and decision-making of an unmanned boat according to claim 6, characterized in that: The evaluation module is used to: Build a test scenario map and configure obstacle attributes, set different threat radius for different obstacles, and mark virtual waypoints; Conduct autonomous capability tests based on route planning results.

8. The test device for autonomous cognition and decision-making of unmanned boats according to claim 7, characterized in that: The evaluation module is used to: Set the starting point, target point and static obstacles, and generate the test scenario map according to the test dimension configuration.

9. An electronic device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the method according to any one of claims 1 to 5 when executed by the processor.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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