Unmanned ship task reliability distribution method, system and equipment

Abstract

The invention discloses an unmanned surface vehicle task reliability distribution method, system and device, and relates to the technical field of unmanned surface vehicles, the method comprises the following steps: acquiring task demand parameters and module basic information of an unmanned surface vehicle, the task demand parameters comprising task total reliability requirements; based on the task demand parameters and the module basic information, extracting a fault propagation rule of an algorithm and a hardware level to construct a fault propagation model; and based on the fault propagation model, in combination with a task total reliability requirement, distributing the task reliability to algorithm and hardware levels to obtain a reliability distribution value of each module in the unmanned ship. According to the invention, the reliability distribution precision and the distribution applicability can be improved.

Description

Technical Field

[0001] This application relates to the field of unmanned surface vessel (USV) technology, and in particular to a method, system and device for allocating the reliability of USV missions. Background Technology

[0002] When unmanned surface vessels (USVs) perform tasks such as patrol reconnaissance, coordinated encirclement, and close-in observation, mission reliability is a core indicator for measuring equipment performance, and reliability allocation is a crucial step in achieving mission reliability goals. Currently, USV mission reliability allocation suffers from three main limitations: First, the limitation of experience-based allocation: existing methods are mostly based on expert experience or simple proportional allocation, failing to consider the failure propagation patterns between USV modules; second, insufficient hierarchical refinement: existing allocations mostly remain at the system and subsystem levels, failing to extend mission reliability down to the core execution levels such as algorithms and hardware. Furthermore, complex failure propagation relationships exist between USV modules, and traditional allocation methods cannot withstand the impact of cascading failures on mission performance. Summary of the Invention

[0003] The purpose of this application is to provide a method, system, and device for unmanned surface vessel (USV) mission reliability allocation, which can improve the accuracy and applicability of reliability allocation.

[0004] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for allocating unmanned surface vessel (USV) mission reliability, including: Collect mission requirement parameters and basic module information for unmanned surface vessels; the mission requirement parameters include the overall mission reliability requirements; Based on the task requirement parameters and the basic information of the module, the fault propagation rules at the algorithm and hardware levels are extracted to construct a fault propagation model. Based on the fault propagation model and the overall mission reliability requirement, the mission reliability is allocated to the algorithm and hardware levels to obtain the reliability allocation value of each module in the unmanned surface vessel.

[0005] Secondly, this application provides an unmanned surface vessel (USV) mission reliability allocation system, including: The mission information input module is used to collect mission requirement parameters and basic module information of the unmanned surface vessel; the mission requirement parameters include the overall mission reliability requirements. The fault propagation analysis module is used to extract fault propagation patterns at the algorithm and hardware levels based on the task requirement parameters and the module's basic information, in order to construct a fault propagation model. The reliability allocation module is used to allocate the mission reliability to the algorithm and hardware levels based on the fault propagation model and the overall mission reliability requirements, so as to obtain the reliability allocation value of each module in the unmanned surface vessel.

[0006] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement an unmanned surface vessel mission reliability allocation method.

[0007] According to the specific embodiments provided in this application, the following technical effects are disclosed: Based on the mission requirement parameters and basic module information of the unmanned surface vessel (USV), this application extracts the fault propagation laws at the algorithm and hardware levels to construct a fault propagation model. Then, it combines these fault propagation laws to allocate mission reliability, achieving objective reliability allocation and improving the accuracy of reliability allocation. Furthermore, in the above processing, mission reliability is extended down to the algorithm and hardware levels. Compared with existing hierarchical allocation, this directly provides quantitative indicators for algorithm optimization and hardware selection, supporting the overall system and the reliability design of software and hardware. In addition, this application performs a series of subsequent processing based on the mission requirement parameters, enabling dynamic adjustment of the allocation model parameters according to the actual mission requirements. Compared with existing fixed allocation modes, this effectively reduces the difficulty of reusing reliability allocation. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is an application environment diagram of the unmanned surface vessel mission reliability allocation method in one embodiment of this application.

[0010] Figure 2 This is a flowchart illustrating the unmanned surface vessel (USV) mission reliability allocation method in one embodiment of this application.

[0011] Figure 3 This is a schematic diagram of an unmanned surface vessel mission reliability allocation system in one embodiment of this application.

[0012] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0014] This application proposes a scientific reliability allocation method by combining mission requirements and fault propagation patterns. It also identifies key parameters through quantitative parameters and sensitivity analysis, breaking through the technical bottleneck of unmanned surface vessel (USV) mission reliability allocation and providing precise support for the overall system, algorithm, and hardware reliability design.

[0015] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0016] The unmanned surface vessel (USV) mission reliability allocation method provided in this application embodiment can be applied to, for example, Figure 1 In the application environment shown, terminal 101 communicates with server 102 via a network. A data storage system can store the data that server 102 needs to process. The data storage system can be set up independently, integrated into server 102, or placed in the cloud or on another server. Terminal 101 can send task requirement parameters and basic module information to server 102. After receiving the information, server 102 extracts the fault propagation patterns at the algorithm and hardware levels to construct a fault propagation model. Then, based on the fault propagation model and the overall task reliability requirements, it allocates task reliability to the algorithm and hardware levels to obtain the reliability allocation values ​​for each module in the unmanned surface vessel (USV). Server 102 can feed back the obtained reliability allocation values ​​for each module in the USV to terminal 101. Furthermore, in some embodiments, the USV task reliability allocation method can also be implemented independently by server 102 or terminal 101.

[0017] The terminal 101 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. The server 102 can be implemented using a standalone server or a server cluster consisting of multiple servers, or it can be a cloud server.

[0018] In one exemplary embodiment, such as Figure 2 As shown, a method for allocating mission reliability for unmanned surface vessels (USVs) is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 102 as an example, the explanation includes the following steps 201 to 203.

[0019] Step 201: Collect the mission requirement parameters and basic module information of the unmanned surface vessel (USV). The mission requirement parameters include the overall mission reliability requirement. Specifically, the mission requirement parameters include the mission type, overall mission reliability requirement, mission duration, and mission environment. The basic module information includes the functional descriptions, hardware models / algorithm versions, and historical fault data of each module in the USV.

[0020] This process clarifies the mission objectives and basic module information, providing a data foundation for subsequent fault propagation analysis and reliability allocation, and avoiding deviations from the actual mission and module characteristics.

[0021] Step 202: Based on the task requirement parameters and the basic information of the modules, extract the fault propagation rules at the algorithm and hardware levels to construct a fault propagation model; through the constructed fault propagation model, reveal the fault propagation correlation between modules, provide a basis for fault impact weight for subsequent reliability allocation, and avoid the irrationality of empirical allocation.

[0022] In one specific application, step 202 includes: (21) Task requirement parameters and module basic information are the core basis for identifying fault propagation paths. Fault tree analysis is adopted, with the task requirement parameters as the path scenario boundary and the module basic information as the basis for clarifying the logical dependencies and fault characteristics between the modules in the unmanned surface vessel. The fault propagation path is decomposed layer by layer to form all possible fault propagation paths. The top event of the fault propagation path is task failure, and the bottom event is hardware failure or algorithm failure.

[0023] Specifically, taking task failure as the top event, the task execution logic, determined by the task type, the task duration, and the task environment, is broken down into intermediate events such as navigation failure and power interruption. Combining the module's functional description and historical fault data, the intermediate events are further decomposed into bottom events such as hardware failure and algorithm failure. The association links from each bottom event to the top event are traced, and after deduplication, all possible fault propagation paths are formed.

[0024] (22) Simulate the occurrence of each underlying event through a fault simulation platform, and record the fault propagation probability, fault impact range and fault severity; the fault impact range includes the module that has the fault and all its corresponding downstream modules.

[0025] (23) Determine the fault propagation relationship based on the fault impact range.

[0026] (24) Using the modules in the unmanned surface vessel as nodes, the fault propagation relationship as directed edges, the fault propagation probability as edge weights, and the fault severity as node weights, a directed weighted graph is constructed in the computer device and labeled as a fault propagation model.

[0027] After a fault simulation event occurs, all downstream modules affected by the fault are identified as the fault's influence range. The starting point of a directed edge corresponds to the fault source module, and the ending point corresponds to a downstream module within the fault's influence range. A directed edge from module A to module B exists only if the fault's influence range includes module B. If the fault in module A does not affect module B, there is no directed edge corresponding to the fault propagation relationship between them. The fault propagation model based on a directed weighted graph is expressed as: G = (V, E, W1, W2), where V is the set of module nodes, E is the set of fault propagation edges, W1 is the edge weight (propagation probability), and W2 is the node weight (fault severity).

[0028] Step 203: Based on the fault propagation model and the overall mission reliability requirement, the mission reliability is allocated to the algorithm and hardware levels to obtain the reliability allocation value for each module in the unmanned surface vessel. This process scientifically extends mission reliability down to the algorithm and hardware levels, and by allocating based on fault impact weights, it ensures that modules with a high fault impact receive higher reliability, thus improving the scientific rigor and rationality of the allocation.

[0029] In one specific application, step 203 includes: (31) Based on the fault propagation model, calculate the fault impact weight of each module. Wherein, the fault impact weight ω of module i... i The formula for calculating ω is: i = (Fault severity W) 2i ×Σ(fault propagation probability W) 1ij × Number of downstream modules)) / Σ((Fault severity × Σ Fault propagation probability × Number of downstream modules) of all modules), ω i The larger the value, the greater the impact of a module failure on the task.

[0030] (32) Based on the fault impact weight of each module and the overall reliability requirement of the task, a weighted allocation algorithm is used to allocate the algorithm / hardware reliability to obtain the initial allocation result; the weighted allocation algorithm uses the following formula: R algn =1-(1-R total ×(ω algn / Σω all R hwm =1-(1-R tota ×(ω hwm / Σω all )).

[0031] Among them, R algn Assign a reliability value ω to algorithm module n. algn The fault impact weights of algorithm module n are Σω all R is the sum of the failure impact weights of all modules;hwm Assign a reliability value ω to hardware module m. hwm The weight of the impact of hardware module m failure; R total The overall reliability requirement for the task is m+n=i.

[0032] (33) Input the initial allocation result into the unmanned surface vessel fault simulation platform and simulate the mission process to obtain the simulation mission reliability.

[0033] (34) If the reliability of the simulation task is greater than or equal to the total reliability requirement of the task, and the reliability of each module is achieved, then the allocation is valid, and the initial allocation result is determined as the reliability allocation value of each module in the unmanned surface vessel.

[0034] (35) If the reliability of the simulation task is less than the total reliability requirement of the task, or if the reliability of any module cannot be achieved, then the failure impact weights of each module are updated, and the reliability is redistributed. Specifically, the failure impact weights ω need to be adjusted. i The calculation involves key parameters, including the severity of the fault and the probability of fault propagation W. 1ij The calculation results of fault impact weights are optimized by modifying parameters such as the number of downstream modules and the screening conditions for fault propagation paths, thereby reallocating reliability.

[0035] In step 203 (32) of this application, a reliability allocation model can be constructed, the input of which is the total reliability R of the task. total The output is the algorithm level reliability assignment value (Ri). alg1 ,R alg2 ,...R algn Hardware-level reliability allocation value (R) hw1 ,R hw2 ,...R hwm ).

[0036] In a specific application example, the method further includes: performing parameter sensitivity analysis to accurately identify and rank key reliability parameters, providing a clear direction for reliability optimization. The specific steps are as follows: (41) Define the reliability parameters of each module in the unmanned surface vessel.

[0037] (42) Set sensitivity analysis: Select the reliability parameter of any module as the parameter to be analyzed, change only the value of the parameter to be analyzed, fix the value of other reliability parameters, and simulate the reliability of the task under different values ​​under the condition of the reliability allocation value of each module.

[0038] (43) Calculate the influence of parameters: Based on the task reliability obtained from the simulation under different values, calculate the influence of parameters. The calculation formula is: η = (task reliability corresponding to the maximum value of the parameter - task reliability corresponding to the minimum value of the parameter) / total task reliability requirement × 100%.

[0039] (44) Prioritization of key parameters: The reliability parameters of different modules are sorted based on the influence of the parameters to determine the key parameters. This process of prioritizing key parameters based on their influence provides a basis for prioritization in reliability optimization.

[0040] Through the above processing, key parameters that play a decisive role in task reliability can be accurately identified, the core direction of reliability optimization can be clarified, resource waste caused by indiscriminate optimization can be avoided, and the efficiency of reliability improvement can be improved. Some applications of reliability parameter sensitivity analysis are shown in Table 1 below.

[0041] Table 1

[0042] Based on the same inventive concept, this application also provides a system. The solution provided by this system is similar to the solution described in the above method. Therefore, the specific limitations of one or more system embodiments provided below can be found in the limitations of the method above, and will not be repeated here.

[0043] In one exemplary embodiment, such as Figure 3 As shown, an unmanned surface vessel (USV) mission reliability allocation system is provided, which can interact with USV fault simulation platforms and reliability testing platforms to achieve USV mission reliability allocation and key parameter identification based on fault propagation laws. The system specifically includes: a mission information input module, a fault propagation analysis module, a reliability allocation module, and a sensitivity analysis module. These modules are connected via a high-speed data bus to achieve data interaction and collaborative operation.

[0044] The task information input module is used to collect the task requirement parameters and basic module information of the unmanned surface vessel. The task requirement parameters include the overall reliability requirement of the task. This enables efficient collection and storage of task requirements and module information, providing basic data for subsequent analysis and allocation.

[0045] The fault propagation analysis module is used to extract fault propagation patterns at the algorithm and hardware levels based on the task requirement parameters and the module's basic information, in order to construct a fault propagation model; thus, it provides the core basis for reliability allocation.

[0046] The reliability allocation module is used to allocate the task reliability to the algorithm and hardware levels based on the fault propagation model and the overall task reliability requirements, so as to obtain the reliability allocation value of each module in the unmanned surface vessel; thus, the task reliability is allocated down to the algorithm and hardware levels, the validity of the allocation result is verified, and the reliability allocation value of each module is output.

[0047] In addition, the sensitivity analysis module is used to perform parameter sensitivity analysis, accurately identify and rank key reliability parameters, and provide a clear direction for reliability optimization.

[0048] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 4 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and databases. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for allocating unmanned surface vessel (USV) mission reliability.

[0049] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0050] In one exemplary embodiment, a computer device is also provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps in the above-described method embodiments.

[0051] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0052] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0053] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0054] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0055] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0056] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0057] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for allocating mission reliability for unmanned surface vessels, characterized in that, The method includes: Collect mission requirement parameters and basic module information for unmanned surface vessels; the mission requirement parameters include the overall mission reliability requirements; Based on the task requirement parameters and the basic information of the module, the fault propagation rules at the algorithm and hardware levels are extracted to construct a fault propagation model. Based on the fault propagation model and the overall mission reliability requirement, the mission reliability is allocated to the algorithm and hardware levels to obtain the reliability allocation value of each module in the unmanned surface vessel.

2. The unmanned surface vessel mission reliability allocation method according to claim 1, characterized in that, Based on the task requirement parameters and the basic information of the modules, fault propagation patterns at the algorithm and hardware levels are extracted to construct a fault propagation model, including: The fault tree analysis method is adopted, with the task requirement parameters as the path scenario boundary, and the basic information of the modules to clarify the logical dependencies and fault characteristics between the modules in the unmanned surface vessel. The fault propagation path is decomposed layer by layer. The top event of the fault propagation path is task failure, and the bottom event is hardware failure or algorithm failure. Simulate the occurrence of each basic event and record the fault propagation probability, fault impact range, and fault severity; the fault impact range includes the module where the fault occurred and all corresponding downstream modules; The fault propagation relationship is determined based on the fault's impact range. Using the modules in the unmanned surface vessel as nodes, the fault propagation relationship as directed edges, the fault propagation probability as edge weights, and the fault severity as node weights, a directed weighted graph is constructed and labeled as a fault propagation model.

3. The unmanned surface vessel mission reliability allocation method according to claim 2, characterized in that, The task requirements parameters include task type, total reliability requirement, task duration, and task environment; The basic information of the modules includes the functional descriptions of each module in the unmanned surface vessel, the hardware model / algorithm version, and historical fault data.

4. The unmanned surface vessel mission reliability allocation method according to claim 3, characterized in that, Decompose the fault propagation path layer by layer, including: Taking task failure as the top event, the task execution logic, determined by the task type, the task duration, and the task environment, is broken down into intermediate events. Based on the module's functional description and historical fault data, the intermediate events are decomposed into basic events such as hardware failure and algorithm failure. Tracing the association link from each bottom event to the top event, and forming a fault propagation path after deduplication.

5. The unmanned surface vessel mission reliability allocation method according to claim 1, characterized in that, Based on the aforementioned fault propagation model and considering the overall mission reliability requirement, mission reliability is allocated to the algorithm and hardware levels to obtain the reliability allocation values ​​for each module in the unmanned surface vessel, including: Based on the fault propagation model, the fault impact weight of each module is calculated; Based on the failure impact weight of each module and the overall reliability requirement of the task, a weighted allocation algorithm is used to allocate algorithm / hardware reliability to obtain the initial allocation result. The initial allocation results are input into the unmanned surface vessel (USV) fault simulation platform and the mission process is simulated to obtain the reliability of the simulated mission. If the reliability of the simulation task is greater than or equal to the total reliability requirement of the task, and the reliability of each module is achieved, then the initial allocation result is determined as the reliability allocation value of each module in the unmanned surface vessel. If the reliability of the simulation task is less than the total reliability requirement of the task, or if the reliability of any module cannot be achieved, then the failure impact weight of each module is updated and the reliability is redistributed.

6. The unmanned surface vessel mission reliability allocation method according to claim 5, characterized in that, The impact weight ω of module i's failure i The calculation formula is: ω i = (Fault severity W) 2i ×Σ(fault propagation probability W) 1ij × Number of downstream modules)) / Σ (Fault severity × Σ Fault propagation probability × Number of downstream modules for all modules)); The weighted allocation algorithm uses the following formula: R algn =1-(1-R) total ×(ω) algn / Sow all )); R hwm =1-(1-R) tota ×(ω hwm / Sow all )); Among them, R algn Assign a reliability value ω to algorithm module n. algn The fault impact weights of algorithm module n are Σω all R is the sum of the failure impact weights of all modules; hwm Assign a reliability value ω to hardware module m. hwm The weight of the impact of hardware module m failure; R total The overall reliability requirement for the task is m + n = i.

7. The unmanned surface vessel mission reliability allocation method according to claim 1, characterized in that, The method further includes: Define the reliability parameters of each module in the unmanned surface vessel; Choose any module's reliability parameter as the parameter to be analyzed, change only the value of the parameter to be analyzed, keep the values ​​of other reliability parameters fixed, and simulate the task reliability under different values ​​under the condition of the reliability allocation values ​​of each module; Based on the task reliability obtained from simulations under different values, the influence of parameters is calculated. The reliability parameters of different modules are sorted based on the influence of the parameters to determine the key parameters.

8. The unmanned surface vessel mission reliability allocation method according to claim 7, characterized in that, The formula for calculating the influence of the parameter is: η = (task reliability corresponding to the maximum parameter value - task reliability corresponding to the minimum parameter value) / total task reliability requirement × 100%.

9. An unmanned surface vessel (USV) mission reliability allocation system, characterized in that, The system includes: The mission information input module is used to collect mission requirement parameters and basic module information of the unmanned surface vessel; the mission requirement parameters include the overall mission reliability requirements. The fault propagation analysis module is used to extract fault propagation patterns at the algorithm and hardware levels based on the task requirement parameters and the module's basic information, in order to construct a fault propagation model. The reliability allocation module is used to allocate the mission reliability to the algorithm and hardware levels based on the fault propagation model and the overall mission reliability requirements, so as to obtain the reliability allocation value of each module in the unmanned surface vessel.

10. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the unmanned surface vessel mission reliability allocation method according to any one of claims 1-8.

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