Remote sensing satellite system-level single-point fault mode identification method based on information fusion

Abstract

The invention provides a remote sensing satellite system level class I single point fault mode identification method based on information fusion, and the method comprises the steps: determining a remote sensing satellite main task according to user requirements, building a task profile, and determining a remote sensing satellite system level class I unexpected event in combination with a use environment and constraint conditions; the method comprises the following steps: establishing a fault tree by taking each system-level class-I unexpected event as a top event in sequence, carrying out fault tree analysis FTA from top to bottom, carrying out failure mode and influence analysis FMEA from bottom to top, carrying out redundancy three-element analysis aiming at all subsystems and important single machines as well as primary and secondary functions of a remote sensing satellite, carrying out effectiveness analysis on each redundancy design, and carrying out fault analysis on each redundancy design. And finally determining I-type single-point fault modes of the remote sensing satellite system, and forming an I-type single-point fault mode list. According to the method, the accuracy and comprehensiveness of remote sensing satellite system-level single-point identification can be practically improved, the risk of satellite models can be effectively reduced, the reliability is improved, the realizability is high, and the method has remarkable engineering value.

Description

Technical Field

[0001] The present invention relates to the field of satellite system-level risk identification technology, and more precisely, to the field of remote sensing satellite safety design. Specifically, it relates to a remote sensing satellite system-level single-point failure mode identification method and system based on information fusion, and in particular, to a remote sensing satellite system-level Class I single-point failure mode identification method based on information fusion. Background Art

[0002] A single-point failure mode refers to a product failure mode that can cause system failure and has no redundant or alternative operating procedures as a remedy. For remote sensing satellites, if a Class I or Class II single-point failure mode occurs at the entire satellite system level, it may cause the satellite system function to be degraded, and in severe cases, even lead to the failure of the satellite mission. Therefore, in order to ensure the success of the satellite mission, it is necessary to accurately and comprehensively identify the Class I and Class II single-point failure modes at the entire satellite system level during the development process, so that the single-point control measures can be effectively implemented and implemented in place, the risks can be minimized, and ultimately ensure the safe and reliable flight of the remote sensing satellite in orbit during its life cycle.

[0003] Currently, there are few effective methods for identifying single-point failure modes at the satellite system level. Many satellite models use FMEA (Failure Mode and Effect Analysis) to analyze the impact of failures of each component of the satellite on the overall operation of the system one by one, determine the Class I and Class II single-point failure modes based on the analysis results, and then identify the weak links in the satellite design. There are also models that use a combination of FMEA and Failure Tree Analysis (FTA) to identify single-machine-level single-point failures.

[0004] After literature search, no patents similar to the present invention for satellite system-level single point identification were found. The comparison results of similar papers are as follows:

[0005] Jing Quan, Wang Liwei, Yang Shuangjin and Li Jingyuan proposed a single-point failure mode identification and control technology combining FMEA and FTA in their paper “Single-point Failure Mode Identification and Control of Launch Vehicles” (see “China Quality”, Issue 385, July 2013). However, it did not involve redundant design and did not delve into the specific single-point links within the product.

[0006] In their paper “System-level Single-point Fault Identification Method and Application Based on Critical Events” (see “Spacecraft Environmental Engineering”, Vol. 31, No. 3, June 2014), Yang Haifeng, Zhao Xinpan, and Gou Zhongqiu proposed a critical event system-level single-point fault identification method based on a combination of time domain and spatial domain analysis, FTA, and FMEA. It did not involve redundant design, unified and standardized processes, and did not go deep into the specific single-point links within the product.

[0007] Zhao Shoujun, Shao Yanhang and He Jun proposed a single-point identification method for the launch vehicle servo mechanism in their paper “Single-point Failure Analysis Technology and Application of Launch Vehicle Servo Mechanism” (see “Manned Spaceflight”, Vol. 20, No. 5, 2014), but did not involve the satellite system level.

[0008] Sun Yue, Han Feng, Tao Xiaoqiang, and Sun Zhaoniu proposed a single-point failure pattern identification method based on the traditional FMEA method in the paper "Research on Single-Point Fault Pattern Identification and Analysis Method of Launch Vehicle Control System" (see "Quality and Reliability", Issue 3, 2017), and took the launch vehicle control system as an example. FTA and redundant design were not involved, and the satellite system level was not involved.

[0009] In their paper “Research on Identification and Analysis Methods of Single-Point Failure Modes of Aerospace Products” (see “Quality and Reliability”, Issue 2, 2018), Jiao Shuyuan, Li Fuqiu, Zhang Rui, and Yang Jing summarized the problems existing in the process of identifying single-point failure modes of aerospace products and proposed identification precautions. However, they did not give specific identification methods and did not involve the satellite system level.

[0010] Xiao Xuedi, Yang Haifeng, Yin Xiaoyang, and Yu Jin gave a redundant effectiveness analysis in the paper "Spacecraft System-Level Single-Point Failure Pattern Identification Method and Application Based on Redundancy Effectiveness Analysis" (see "Manned Spaceflight", Vol. 29, No. 3, 2023), which helps to identify false redundant designs and fail to effectively integrate FMEA, FTA, redundant effectiveness analysis and other information to comprehensively identify single points.

[0011] In response to the above research, the present invention provides a remote sensing satellite system-level Class I single-point fault mode identification method based on information fusion. Satellite system-level single-point fault mode identification and control has always been one of the important tasks in satellite model risk identification and evaluation. This method integrates multi-dimensional output information such as FTA, FMEA, and redundant effectiveness analysis, and draws inferences from typical failure cases. It can effectively improve the accuracy and comprehensiveness of remote sensing satellite system-level Class I single-point fault mode identification and effectively reduce the risk of satellites. At present, no description or report similar to the present invention has been found, and similar information at home and abroad has not yet been collected. Summary of the invention

[0012] In view of the defects in the prior art, the purpose of the present invention is to provide a remote sensing satellite system-level single-point failure mode identification method based on information fusion.

[0013] According to the present invention, a remote sensing satellite system-level type I single-point failure mode identification method based on information fusion includes:

[0014] Step S1: Determine the main mission of the remote sensing satellite according to user requirements, establish the mission profile of the remote sensing satellite throughout its life cycle, and determine the remote sensing satellite system-level Class I undesirable events in combination with the use environment and constraints;

[0015] Step S2: Take each system-level Class I unexpected event as the top event, establish a fault tree, conduct a fault tree analysis FTA from top to bottom, and identify all first-order minimum cut sets of the bottom events of the fault tree, that is, the system-level Class I and Class II single-point failure modes;

[0016] Step S3: Combined with the system-level Class I unexpected events, conduct a bottom-up failure mode and effect analysis FMEA to identify the system-level Class I and Class II single-point failure modes;

[0017] Step S4: Carry out redundancy three-element analysis for all subsystems and important single machines, main and secondary functions of remote sensing satellites, conduct effectiveness analysis on each redundant design, and identify system-level Class I and Class II single-point failure modes;

[0018] Step S5: Integrate the output results of steps S2 to S4, finally determine the Class I single point failure mode of the remote sensing satellite system, and form a Class I single point failure mode list.

[0019] Preferably, the system-level Class I unexpected events include measurement and control link abnormalities, power supply and distribution function abnormalities, satellite-rocket separation abnormalities, measurement and control link abnormalities, solar array deployment abnormalities, power supply and distribution failures, power supply and distribution abnormalities, measurement and control link abnormalities, and attitude and orbit control abnormalities.

[0020] The FTA and FMEA analyses both include target function / product failure analysis, subsystem / key equipment failure impact analysis, and external environment impact analysis.

[0021] The three elements of redundancy include effective detection, sufficient isolation, and reliable switching.

[0022] The redundancy effectiveness includes analyzing the effectiveness of each redundant design of all satellite subsystems and important single machines, main and secondary functions, including: redundancy form, switching equipment, autonomous switching, fault isolation, common links, common modes and common causes, and measures for sharing / switching.

[0023] According to the present invention, a remote sensing satellite system-level type I single-point failure mode identification system based on information fusion is provided, comprising:

[0024] Module M1: Determine the main mission of the remote sensing satellite according to user requirements, establish the mission profile of the remote sensing satellite throughout its life cycle, and determine the remote sensing satellite system-level Class I unexpected events in combination with the use environment and constraints;

[0025] Module M2: Take each system-level Class I unexpected event as the top event, establish a fault tree, conduct fault tree analysis FTA from top to bottom, and identify all first-order minimum cut sets of the bottom events of the fault tree, that is, system-level Class I and Class II single-point failure modes;

[0026] Module M3: Combined with the system-level Class I unexpected events, conduct failure mode and effect analysis FMEA from bottom to top to identify the system-level Class I and Class II single-point failure modes;

[0027] Module M4: Carry out redundancy three-element analysis for all subsystems and important single machines, main and secondary functions of remote sensing satellites, conduct effectiveness analysis on each redundant design, and identify system-level Class I and Class II single-point failure modes;

[0028] Module M5: Integrate the output results of modules M2 to M4, finally determine the Class I single-point failure mode of the remote sensing satellite system, and form a list of Class I single-point failure modes.

[0029] The system-level Class I unexpected events include measurement and control link abnormalities, power supply and distribution function abnormalities, satellite-rocket separation abnormalities, measurement and control link abnormalities, solar array deployment abnormalities, power supply and distribution failures, power supply and distribution abnormalities, measurement and control link abnormalities, and attitude and orbit control abnormalities.

[0030] The FTA and FMEA analyses both include target function / product failure analysis, subsystem / key equipment failure impact analysis, and external environment impact analysis.

[0031] The three elements of redundancy include effective detection, sufficient isolation, and reliable switching.

[0032] The redundancy effectiveness includes analyzing the effectiveness of each redundant design of all satellite subsystems and important single machines, main and secondary functions, including: redundancy form, switching equipment, autonomous switching, fault isolation, common links, common modes and common causes, and measures for sharing / switching.

[0033] According to a computer-readable storage medium storing a computer program provided by the present invention, when the computer program is executed by a processor, the steps of the remote sensing satellite system-level Type I single-point failure mode identification method based on information fusion are implemented.

[0034] An intelligent mobile terminal provided according to the present invention includes the computer-readable storage medium storing a computer program, or includes the remote sensing satellite system-level type I single-point failure mode recognition system based on information fusion.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The present invention is based on the actual situation of satellite engineering and adopts a method that combines top-down and bottom-up iterations, forward identification and reverse elimination, which can effectively improve the accuracy and comprehensiveness of single-point identification at the system level of remote sensing satellites, effectively help satellite models reduce risks and improve reliability, has strong feasibility, and has significant engineering value. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0038] Figure 1 This is a flow chart of the method for identifying type I single-point fault patterns at the remote sensing satellite system level;

[0039] Figure 2 Example of a fault tree for the T&C downlink. DETAILED DESCRIPTION

[0040] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0041] The present invention aims to ensure the normal basic functions of remote sensing satellites and the safety of major flight events on orbit, sort out and determine system-level Class I unexpected events that may affect the success or failure of the mission; take them as the top event, carry out FTA from top to bottom, identify all first-order minimum cut sets; and carry out system FMEA from bottom to top, analyze all possible failure modes, causes and impacts; conduct "effective detection, sufficient isolation, reliable switching" three-element analysis on all redundant designs; and finally determine the remote sensing satellite system-level Class I single-point failure mode.

[0042] According to a remote sensing satellite system-level type I single-point failure mode recognition method based on information fusion provided by the present invention, Figure 1 As shown, the following steps are included:

[0043] Step S1: According to user requirements, determine the main mission of the remote sensing satellite, establish the mission profile of the remote sensing satellite throughout its life cycle, and combine the use environment and constraints to ensure the normal basic functions of the satellite and the safety of the main flight events on orbit, sort out and determine the remote sensing satellite system-level Class I unexpected events that will affect the success or failure of the mission. The system-level Class I unexpected events include measurement and control link abnormalities, power supply and distribution function abnormalities, satellite-rocket separation abnormalities, measurement and control link abnormalities, solar array deployment abnormalities, power supply and distribution failures, power supply and distribution abnormalities, measurement and control link abnormalities, and attitude and orbit control abnormalities. The safety of the main flight events on orbit is the primary condition to ensure that the remote sensing satellite completes its mission, including at least five aspects: energy security, measurement and control safety, control safety, structure and mechanism safety, and payload safety.

[0044] Step S2: Take each system-level Class I unexpected event as the top event, establish a fault tree, conduct FTA (Fault Tree Analysis) from top to bottom, and identify all first-order minimum cut sets of bottom events of the fault tree. Among them, the products / functions involved in the first-order minimum cut set are confirmed as system-level Class I and Class II single-point failure modes. The FTA includes failure analysis of target functions / products, failure impact analysis of other subsystems / key equipment, and external environment impact analysis. Among them, the failure impact analysis of other subsystems / key equipment focuses on strengthening the failure correlation analysis between subsystems, including design interfaces, motion interference, energy transfer, and material exchange.

[0045] Step S3: Combined with the system-level Class I unexpected events, conduct FMEA (Failure Mode and Effect Analysis) from bottom to top, analyze all possible failure modes, causes and impacts, and determine whether it is a system-level Class I or Class II single-point failure mode. The FMEA analysis includes the failure discovery of target functions / products, the failure impact discovery of other subsystems / key equipment, and the analysis of external environmental impacts. Among them, the product failure mode should include all mission stages or working modes of remote sensing satellites, covering failure cases or quality problems of similar or related products in recent years.

[0046] Step S4: For all subsystems and important single machines, main and secondary functions of remote sensing satellites, carry out redundancy three-element analysis, analyze the switching scheme, signal interface, and common links of each redundant design, analyze the effectiveness of each redundant design, and identify system-level Class I and Class II single-point failure modes. The three elements of redundancy include effective detection, full isolation, and reliable switching. The redundancy effectiveness analysis includes all subsystems and important single machines, main and secondary functions of the satellite, and analyzes the effectiveness of each redundant design in detail: redundancy form, switching equipment (including relays, switching switches, etc.), autonomous switching, fault isolation, common links (including the same power module, the same connector, the same cable, etc.), common mode and common cause, measures for sharing / switching, etc.

[0047] Step S5: Integrate the output results of steps S2 to S4 to finally determine the Class I single-point failure mode of the remote sensing satellite system and form a list of Class I single-point failure modes. For the Class I single-point failure modes identified as remote sensing satellite system-level, the lowest level of analysis and identification is a single machine or important functional module, and the important interfaces and switching links should be analyzed to specific devices and functions. This also applies to the lowest agreed levels such as FMEA and FTA at the remote sensing satellite system level. For each Class I single-point failure mode at the system level on the list, first take measures from the design to eliminate the single-point failure mode; for the single-point failure mode that cannot be eliminated, take control measures from the entire process of design, production, testing, and launch to reduce its probability of occurrence and minimize its impact and harm. Specifically, after determining the list of Type I single-point failure modes, for each single point, if backup (i.e. redundancy, such as adding a cable or a power board) and relay switching are taken, the "single point" can be turned into a "non-single point"; if there are insufficient resources and no more backup (i.e. redundancy, such as weight, power, etc. are limited, and no more cable or power board can be added), the single point will be isolated, and even if the single point fails in the future, it will not affect the upper level; then, on the ground, refined control measures will be taken to minimize the probability of on-orbit failure of the single point.

[0048] The present invention targets system-level Class I unexpected events, focuses on the energy flow, information flow, control flow and other main threads of the entire satellite, conducts FTA from top to bottom and system FMEA from bottom to top, and strengthens the fault correlation analysis and common cause failure link analysis between subsystems to ensure that the system-level Class I single point identification is comprehensive and accurate. The present invention can identify "pseudo-redundancy, true single point", effectively improve the accuracy and comprehensiveness of satellite single point identification, and effectively reduce the risk of satellites.

[0049] Furthermore, combined with Figure 1 to Figure 2Taking a low-orbit remote sensing satellite operating in a sun-synchronous orbit as an example, the remote sensing satellite system-level Class I single-point fault pattern recognition method based on information fusion of the present invention is specifically described as follows: microwave and optical imaging remote sensing are combined to monitor natural disasters, ecology and environment, provide global and regional remote sensing information, and improve the ability to respond to weather and climate disasters and emergencies.

[0050] First, according to user requirements, the main mission of the remote sensing satellite is determined. The satellite mission profile can be divided into active phase, state establishment phase, and on-orbit operation phase. With the goal of ensuring the satellite's stable flight safety on orbit and the safety of important flight events, 23 system-level Class I unexpected events are sorted out.

[0051] Table 1 List of undesirable events for the whole star rating

[0052]

[0053] Then, the 23 system-level Class I unexpected events in Table 1 are used as top events to build a fault tree and conduct FTA. Take the fault tree of the measurement and control downlink as an example, such as Figure 2 As shown. After analysis, it can be seen that: the measurement and control link is equipped with two transponders. If a single transponder fails, the backup measurement and control channel can be used directly to establish a downlink link, and both transponders have ground measurement and control channels and relay measurement and control channels. If an abnormality occurs in the ground channel, it can be switched to the relay channel to establish a return link. Therefore, there is no single-point link in the measurement and control downlink. However, it can be seen from the bottom event X4 that the two transponders are powered by the power supply and distribution subsystem, which is a common cause failure link, and there is a system-level single-point failure mode.

[0054] Next, combined with the 23 Class I unexpected events in Table 1, a system-level FMEA was conducted: the loss or partial loss of the main and secondary functions of the system was taken as the failure mode of the system, and the cause of the failure was the loss or partial loss of the main and secondary functions of the modules, single machines and interfaces between subsystems to which the subsystem belongs. Through the iteration of system-level FMEA, subsystem-level FMEA and single-machine-level FMEA, a total of 25 system-level single-point failure modes were identified.

[0055] Next, for all subsystems, important single machines, main and secondary functions of the remote sensing satellite, we conducted a redundancy three-element analysis from the perspective of "effective detection, sufficient isolation, and reliable switching", and analyzed in detail the switching scheme, signal interface, and common links of each redundant design to identify Class I and Class II single-point failure modes at the satellite system level.

[0056] Then, for all subsystems and important single machines, main and secondary functions of remote sensing satellites, the redundancy three-element analysis is carried out from the perspective of "effective detection, sufficient isolation, and reliable switching", and each redundant design is analyzed in detail: redundancy form, switching equipment (including relays, switches, etc.), autonomous switching, fault isolation, shared links (including the same power module, the same connector, the same cable, etc.), common mode and common cause, and measures for sharing / switching, etc. The redundant effectiveness analysis is explained by taking the measurement and control link as an example. As shown in Table 2, taking the measurement and control link as the object, the measurement and control link is analyzed from the above 7 aspects, including whether the measurement and control link has redundant forms, and an analysis result is obtained, and a total of 22 system-level single-point failure modes are identified.

[0057] Table 2 Example of analysis of the effectiveness of redundant measurement and control links

[0058]

[0059] Finally, based on the above output results, a total of 29 satellite system-level Class I and Class II single-point failure modes were determined. There were 14 Class I single-point failure modes, mainly concentrated in abnormal satellite-rocket separation, failure of the solar array to deploy, power supply short circuit, etc. There were 15 Class II single-point failure modes, mainly concentrated in abnormal rotation of the solar array drive mechanism, failure of the pressure relief valve of the propulsion subsystem, etc.

[0060] The present invention also provides a remote sensing satellite system-level Class I single-point fault pattern recognition system based on information fusion. The remote sensing satellite system-level Class I single-point fault pattern recognition system based on information fusion can be realized by executing the process steps of the remote sensing satellite system-level Class I single-point fault pattern recognition method based on information fusion, that is, those skilled in the art can understand the remote sensing satellite system-level Class I single-point fault pattern recognition method based on information fusion as a preferred implementation of the remote sensing satellite system-level Class I single-point fault pattern recognition system based on information fusion.

[0061] According to the present invention, a remote sensing satellite system-level type I single-point failure mode identification system based on information fusion is provided, comprising:

[0062] Module M1: According to user requirements, determine the main mission of the remote sensing satellite, establish the mission profile of the remote sensing satellite throughout its life cycle, and determine the remote sensing satellite system-level Class I unexpected events in combination with the use environment and constraints. The system-level Class I unexpected events include measurement and control link abnormalities, power supply and distribution function abnormalities, satellite-rocket separation abnormalities, measurement and control link abnormalities, solar array deployment abnormalities, power supply and distribution failures, power supply and distribution abnormalities, measurement and control link abnormalities, and attitude and orbit control abnormalities.

[0063] Module M2: Take each system-level Class I unexpected event as the top event, establish a fault tree, conduct fault tree analysis FTA from top to bottom, and identify all first-order minimum cut sets of the bottom events of the fault tree, that is, system-level Class I and Class II single-point failure modes.

[0064] Module M3: Combined with the system-level Class I unexpected events, the failure mode and effect analysis FMEA is carried out from bottom to top to identify the system-level Class I and Class II single-point failure modes. The FTA and FMEA analysis include the failure analysis of the target function / product, the failure impact analysis of the subsystem / key equipment, and the external environment impact analysis.

[0065] Module M4: For all subsystems and important single machines, main and secondary functions of remote sensing satellites, carry out redundancy three-element analysis, and conduct effectiveness analysis on each redundant design to identify system-level Class I and Class II single-point failure modes. The three redundancy elements include effective detection, sufficient isolation, and reliable switching. The redundancy effectiveness includes the effectiveness of each redundant design for all subsystems and important single machines, main and secondary functions of the satellite, including: redundancy form, switching equipment, autonomous switching, fault isolation, common links, common modes and common causes, and measures for sharing / switching.

[0066] Module M5: Integrate the output results of modules M2 to M4, finally determine the Class I single-point failure mode of the remote sensing satellite system, and form a list of Class I single-point failure modes.

[0067] According to a computer-readable storage medium storing a computer program provided by the present invention, when the computer program is executed by a processor, the steps of the remote sensing satellite system-level Type I single-point failure mode identification method based on information fusion are implemented.

[0068] An intelligent mobile terminal provided according to the present invention includes the computer-readable storage medium storing a computer program, or includes the remote sensing satellite system-level type I single-point failure mode recognition system based on information fusion.

[0069] Those skilled in the art know that, in addition to realizing the system and its various devices, modules, and units provided by the present invention in a purely computer-readable program code, it is entirely possible to realize the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered as a hardware component, and the devices, modules, and units included therein for realizing various functions can also be regarded as structures within the hardware component; the devices, modules, and units for realizing various functions can also be regarded as both software modules for realizing the method and structures within the hardware component.

[0070] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A remote sensing satellite system-level type I single-point failure mode identification method based on information fusion, characterized in that: include: Step S1: Determine the main mission of the remote sensing satellite according to user requirements, establish the mission profile of the remote sensing satellite throughout its life cycle, and determine the remote sensing satellite system-level Class I undesirable events in combination with the use environment and constraints; Step S2: Take each system-level Class I unexpected event as the top event, establish a fault tree, conduct a fault tree analysis FTA from top to bottom, and identify all first-order minimum cut sets of the bottom events of the fault tree, that is, the system-level Class I and Class II single-point failure modes; Step S3: Combined with the system-level Class I unexpected events, conduct a bottom-up failure mode and effect analysis FMEA to identify the system-level Class I and Class II single-point failure modes; Step S4: Carry out redundancy three-element analysis for all subsystems and important single machines, main and secondary functions of remote sensing satellites, conduct effectiveness analysis on each redundant design, and identify system-level Class I and Class II single-point failure modes; Step S5: Integrate the output results of steps S2 to S4, finally determine the Class I single point failure mode of the remote sensing satellite system, and form a Class I single point failure mode list.

2. The method for identifying type I single-point failure patterns at the remote sensing satellite system level based on information fusion according to claim 1 is characterized in that: The system-level Class I unexpected events include measurement and control link abnormalities, power supply and distribution function abnormalities, satellite-rocket separation abnormalities, measurement and control link abnormalities, solar array deployment abnormalities, power supply and distribution failures, power supply and distribution abnormalities, measurement and control link abnormalities, and attitude and orbit control abnormalities.

3. The method for identifying type I single-point failure patterns at the remote sensing satellite system level based on information fusion according to claim 1 is characterized in that: The FTA and FMEA analyses both include target function / product failure analysis, subsystem / key equipment failure impact analysis, and external environment impact analysis.

4. The method for identifying type I single-point failure patterns at the remote sensing satellite system level based on information fusion according to claim 1 is characterized in that: The three elements of redundancy include effective detection, adequate isolation, and reliable switching; The redundancy effectiveness includes analyzing the effectiveness of each redundant design of all satellite subsystems and important single machines, main and secondary functions, including: redundancy form, switching equipment, autonomous switching, fault isolation, common links, common modes and common causes, and measures for sharing / switching.

5. A remote sensing satellite system-level type I single-point failure mode identification system based on information fusion, characterized in that: include: Module M1: Determine the main mission of the remote sensing satellite according to user requirements, establish the mission profile of the remote sensing satellite throughout its life cycle, and determine the remote sensing satellite system-level Class I unexpected events in combination with the use environment and constraints; Module M2: Take each system-level Class I unexpected event as the top event, establish a fault tree, conduct fault tree analysis FTA from top to bottom, and identify all first-order minimum cut sets of the bottom events of the fault tree, that is, system-level Class I and Class II single-point failure modes; Module M3: Combined with the system-level Class I unexpected events, conduct failure mode and effect analysis FMEA from bottom to top to identify the system-level Class I and Class II single-point failure modes; Module M4: Carry out redundancy three-element analysis for all subsystems and important single machines, main and secondary functions of remote sensing satellites, conduct effectiveness analysis on each redundant design, and identify system-level Class I and Class II single-point failure modes; Module M5: Integrate the output results of modules M2 to M4, finally determine the Class I single-point failure mode of the remote sensing satellite system, and form a list of Class I single-point failure modes.

6. The remote sensing satellite system-level type I single-point failure mode identification system based on information fusion according to claim 5 is characterized in that: The system-level Class I unexpected events include measurement and control link abnormalities, power supply and distribution function abnormalities, satellite-rocket separation abnormalities, measurement and control link abnormalities, solar array deployment abnormalities, power supply and distribution failures, power supply and distribution abnormalities, measurement and control link abnormalities, and attitude and orbit control abnormalities.

7. The remote sensing satellite system-level type I single-point failure mode identification system based on information fusion according to claim 5 is characterized in that: The FTA and FMEA analyses both include target function / product failure analysis, subsystem / key equipment failure impact analysis, and external environment impact analysis.

8. The remote sensing satellite system-level type I single-point failure mode identification system based on information fusion according to claim 5 is characterized in that: The three elements of redundancy include effective detection, adequate isolation, and reliable switching; The redundancy effectiveness includes analyzing the effectiveness of each redundant design of all satellite subsystems and important single machines, main and secondary functions, including: redundancy form, switching equipment, autonomous switching, fault isolation, common links, common modes and common causes, and measures for sharing / switching.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the remote sensing satellite system-level type I single-point failure mode identification method based on information fusion described in any one of claims 1 to 4 are implemented.

10. An intelligent mobile terminal, characterized in that: It includes the computer-readable storage medium storing the computer program as described in claim 9, or includes the remote sensing satellite system-level Type I single-point failure mode identification system based on information fusion as described in any one of claims 5 to 8.

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