Multi-task scene-oriented star catalogue intelligent autonomous system reliability index optimization method

Abstract

The invention relates to a multi-task scene-oriented star catalogue intelligent autonomous system reliability index optimization method, which comprises the following steps of: firstly, decomposing a star catalogue intelligent autonomous system into subsystems, secondly, identifying task profile essential characteristics of a multi-task scene, and finally, carrying out system reliability index optimization weighing. The method can effectively solve the problem of optimizing and balancing the reliability index of the star catalogue intelligent autonomous system in a multi-task scene, ensures that the index distribution is reasonable and effective to the maximum extent, and provides a complete and effective solution for the reliability index optimization problem of the star catalogue intelligent autonomous system facing the multi-task scene.

Description

Technical Field

[0001] The present invention belongs to the field of reliability index optimization methods for star chart intelligent autonomous systems, and specifically relates to a reliability index optimization method for star chart intelligent autonomous systems that takes into account different profile features of multi-task scenarios. Background Art

[0002] In recent years, my country's aerospace industry has flourished, and the exploration field has been continuously extended from the near-Earth to the wider space field. Compared with traditional near-Earth exploration aerospace products, the satellite intelligent autonomous system faces the difficulty of longer earth communication delay, so the exploration mission requires more significant intelligence and autonomy. However, the mission scenarios faced by the satellite intelligent autonomous system are often complex and changeable, and the operating environment is harsh. Therefore, in the design and development process, the optimization of reliability indicators has always been an important issue faced by designers.

[0003] From the perspective of design indicators, the satellite intelligent autonomous system must focus on scientific research benefits. This means that the system design should be suitable for multi-task scenarios, and try to ensure that the system maintains high reliability under multiple environmental stresses and artificial intelligence. This requires that difficult problems such as optimization and verification of reliability indicators of satellite intelligent autonomous systems must be solved in the design. In order to meet these new challenges, we cannot rely entirely on the experience of optimizing reliability indicators of traditional aerospace products. We need to adopt a new design concept, decompose tasks into specific actions at the subsystem level, and optimize reliability indicators layer by layer from the bottom up. In summary, there is an urgent need for a reliability indicator optimization method for satellite intelligent autonomous systems for multi-task scenarios to cope with the increasingly complex equipment design and improve the accuracy and economy of reliability optimization. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a reliability index optimization method for a star-gallery intelligent autonomous system for multi-task scenarios, which effectively solves the problem of optimizing the reliability index of the star-gallery intelligent autonomous system in multi-task scenarios, maximizes the guarantee that the index optimization is reasonable and effective, and improves the economic benefits of aerospace products of the star-gallery intelligent autonomous system.

[0005] The technical solution adopted by the present invention is: a reliability index optimization method for a star-table intelligent autonomous system for multi-task scenarios, and the specific steps are as follows:

[0006] Step 1: Decompose the star chart intelligent autonomous system into subsystems according to their functions;

[0007] Step 2: Identify the essential characteristics of the task profile of the multi-task scenario;

[0008] Step 3: Optimize and balance system reliability indicators.

[0009] Furthermore, the step 1 is specifically as follows:

[0010] Step 1.1, decompose the star catalog intelligent autonomous system into various subsystems;

[0011] Step 1.2: Determine the actions that each subsystem can perform.

[0012] Furthermore, the step 2 is specifically as follows:

[0013] Step 2.1, clarify the mission scenario, identify the essential characteristics of the mission profile, and extract commonalities and distinguishing features;

[0014] Step 2.2: Based on the results of step 1.2, decompose the task profiles of different task scenarios into subtasks, and then transform the subtasks into different action sequences according to their characteristics.

[0015] Furthermore, the step 3 is specifically as follows:

[0016] Step 3.1, according to step 2.2, the transformed action sequence is disassembled and the importance of subsystem actions is calculated;

[0017] Step 3.2: Optimize the system reliability index based on the importance ranking results.

[0018] Beneficial effects of the invention: The method of the present invention first identifies the essential task profile characteristics of the multi-task scenario, then decomposes the star-table intelligent autonomous system into various subsystems, and finally optimizes and balances the system reliability index to obtain a reliability index optimization method for the star-table intelligent autonomous system for multi-task scenarios. The method of the present invention can effectively solve the problem of optimizing and balancing the reliability index of the star-table intelligent autonomous system in multi-task scenarios, maximize the reasonableness and effectiveness of the index allocation, and provide a complete and effective solution for the problem of optimizing the reliability index of the star-table intelligent autonomous system for multi-task scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The present invention is a flow chart of a reliability index optimization method for a star chart intelligent autonomous system for multi-task scenarios.

[0020] Figure 2 Schematic diagram of a case in an embodiment of the present invention. DETAILED DESCRIPTION

[0021] Taking a planetary probe as an example, the technical solution of the invention is further explained in conjunction with the accompanying drawings.

[0022] like Figure 1 As shown, a flow chart of a reliability index optimization method of a star-table intelligent autonomous system for multi-task scenarios of the present invention is shown, and the specific steps are as follows:

[0023] Step 1: Decompose the star chart intelligent autonomous system into subsystems according to their functions;

[0024] Step 2: Identify the essential characteristics of the task profile of the multi-task scenario;

[0025] Step 3: Optimize and balance system reliability indicators.

[0026] In this embodiment, Figure 2 This is a schematic diagram of a case in an embodiment of the present invention, and step 1 is specifically as follows:

[0027] Step 1.1, decompose the planetary probe into various subsystems, including: propulsion subsystem, power supply subsystem, communication subsystem, sampling subsystem, patrol detection subsystem;

[0028] Step 1.2, determine the executable actions of each subsystem: the main actions of the propulsion subsystem include orbit transfer, orbit capture, braking and deceleration, satellite descent, satellite landing, hover control, flyby control, and satellite takeoff; the main actions of the power supply subsystem include driving the propulsion subsystem, driving the communication subsystem, driving the sampling subsystem, driving the observation camera, driving the detection radar, driving the magnetometer, and driving the mineral spectrum analysis; the main actions of the communication subsystem include information storage, information transmission, and relay communication support; the main actions of the sampling subsystem include drilling, excavation, blasting, particle sample capture, loose sample collection, solid sample collection, sample packaging, and sample transfer; the main actions of the patrol detection subsystem include camera observation, radar detection, magnetic intensity detection, and mineral spectrum analysis.

[0029] In this embodiment, step 2 is specifically as follows:

[0030] Step 2.1, planetary probe mission scenarios are wide-ranging. Currently, the planets that can be explored include Mars, Jupiter, Saturn, Uranus, and Neptune. The essential characteristics of the mission profiles for exploring different planets are that the probes break away from the Earth's gravity, achieve orbital transfer, and approach the planets for braking, etc. The commonalities include surface charge accumulation, space debris impact, single particle effect and other space environments and surface environments with large temperature gradients. The characteristics include different planetary morphologies, different gravity fields, different extreme temperatures, different radiation intensities, and different rays for different planets to be explored, as well as different ridges, gullies, slopes, cliffs, craters and other landforms for different exploration areas of the same planet.

[0031] Step 2.2, further taking a certain type of planetary probe as an example, according to the results of step 1.2, the task profiles of different mission scenarios are disassembled into subtasks, including: ascent from the earth's surface, entry into low-Earth orbit, orbit transfer, entry into planetary orbit, patrol, braking descent, landing, detection, surface sample acquisition and transfer, surface ascent, entry into planetary orbit, orbit transfer, entry into Earth orbit, Earth braking descent, Earth re-entry and landing. The surface sample acquisition and transfer subtask uses different sampling methods due to different surface characteristics of the sampling area or different target sample characteristics or different sampling depths, such as drilling, digging, impact, explosion, flyby, etc. Among them, the action sequence of the digging sampling subtask is star surface descent, star surface landing, radar detection, digging, solid sample collection, sample packaging, sample transfer, the flyby sampling subtask sequence is star surface descent, flyby control, particle sample capture, sample packaging, sample transfer, and the explosive sampling subtask sequence is star surface descent, star surface landing, mineral spectrum analysis, blasting, loose sample collection, sample packaging, sample transfer.

[0032] In this embodiment, step 3 is specifically as follows:

[0033] Step 3.1: Disassemble the transformed subsystem action sequence according to step 2.2, and use BM (Birnbaum) importance to sort the subsystem action importance;

[0034] BM is the rate of change of the expected risk R caused by the change in the probability of a single event. i The BM importance of is:

[0035]

[0036] When the risk has a linear form, BM can be calculated as follows:

[0037]

[0038] Among them, RPr(x i )=1 means when action x i The conditional expected risk when the probability of success is set to 1, RPr(x i )=0 means when action x i The conditional expected risk when the probability of success is set to 0.

[0039] Step 3.2: Sort the results according to the importance of subsystem actions, that is, those with higher Action x i When failure occurs, the negative impact is the greatest. Combined with the system reliability model, the system reliability indicators are optimized from bottom to top.

[0040] In summary, the method of the present invention can effectively solve the problem of optimizing the reliability index of the star chart intelligent autonomous system in a multi-task scenario, and maximize the guarantee of reasonable and effective index allocation, providing a complete and effective solution to the problem of optimizing the reliability index of the star chart intelligent autonomous system for multi-task scenarios.

[0041] Those skilled in the art will appreciate that the embodiments herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A reliability index optimization method for a star-level intelligent autonomous system for multi-task scenarios, the specific steps are as follows: Step 1: Decompose the star table intelligent autonomous system into various subsystems; Step 2: Identify the essential characteristics of the task profile of the multi-task scenario; Step 3: Optimize and balance system reliability indicators.

2. The reliability index optimization method of a star-table intelligent autonomous system for multi-task scenarios according to claim 1 is characterized in that: The step 1 is specifically as follows: Step 1.1, decomposing the star table intelligent autonomous system into various subsystems according to their functions; Step 1.2: Determine the actions that can be performed by each subsystem.

3. The reliability index optimization method of a star-table intelligent autonomous system for multi-task scenarios according to claim 1 is characterized in that: The step 2 is specifically as follows: Step 2.1, clarify the task scenario, identify the essential characteristics of the task profile, and extract commonalities and distinguishing features; Step 2.2: According to the result of step 1.2, the task profiles of the different task scenarios are decomposed into subtasks, and then the subtasks are converted into different action sequences according to their characteristics.

4. The reliability index optimization method of a star-table intelligent autonomous system for multi-task scenarios according to claim 1 is characterized in that: The step 3 is as follows: Step 3.1, according to the action sequence converted by step 2.2, calculate the importance of the subsystem action; Step 3.2: Optimize the system reliability index according to the importance ranking result.

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