Task-oriented reliability of unmanned cluster equivalent space-time capacity field construction method

Abstract

The application discloses a method for constructing an equivalent space-time capability field of an unmanned cluster for task reliability, so as to effectively represent the correlation between the overall performance of the unmanned cluster and a task, capture the dynamic change of the state and capability of the unmanned cluster, quantitatively analyze the space-time characteristics and emergence capability of the unmanned cluster in different scenes, and further support accurate evaluation of the task reliability of the unmanned cluster. The steps are as follows: the system load acting elements are selected in a targeted manner, including an acting range, an acting strength and an acting time, which are used for describing the task execution capability of the unmanned cluster under different conditions; the selected acting elements are measured based on actual application scenes and node running states; and the equivalent space-time capability field of the unmanned cluster is constructed based on the measurement results.

Description

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[0001] The application provides a task reliability-oriented unmanned cluster equivalent space-time capability field construction method, and belongs to the technical field of reliability engineering. BACKGROUND

[0002] At present, unmanned clusters are gradually becoming important automation and intelligent application carriers and are widely used in military, logistics, agriculture, environmental monitoring and other fields. Through the coordination and cooperation of multiple unmanned devices, the unmanned cluster shows high efficiency in information collection, task execution and environmental adaptation. However, with the increase of task complexity, the reliability and performance requirements of the unmanned cluster are also increasing. During the execution of the task, the unmanned cluster is often affected by environmental changes, device failures, information delays and other factors, thereby causing the decline or failure of the task execution efficiency. Therefore, in order to ensure that the unmanned cluster reliably and stably executes the task in various environments, the correlation between the performance and the task of the unmanned cluster must be accurately evaluated.

[0003] However, the existing evaluation methods mainly focus on specific performance indicators, and the application mainly focuses on the collection and analysis of single data. The correlation discussion using a linear model usually cannot fully reflect the actual working conditions of the unmanned cluster. In addition, the existing evaluation process cannot effectively show the space-time dynamic characteristics of the system, resulting in insufficient evaluation of the adaptability and task execution capability of the unmanned cluster, thereby increasing the risk of task failure. Under the above background, how to establish a comprehensive and dynamic unmanned cluster task reliability evaluation system to adapt to complex and variable task scenarios has become a key problem to be solved. Therefore, the application provides a task reliability-oriented unmanned cluster equivalent space-time capability field construction method, which aims to effectively represent the overall performance of the unmanned cluster in a specific task and thereby provide support for accurate evaluation of the task reliability of the system. SUMMARY

[0004] The application aims to provide a task reliability-oriented unmanned cluster equivalent space-time capability field construction method to effectively represent the correlation between the overall performance of the unmanned cluster and the task, quantitatively analyze the space-time characteristics and emergence capability of the unmanned cluster in different scenarios, and thereby support accurate evaluation of the task reliability of the unmanned cluster.

[0005] To achieve the above-mentioned purpose, the application provides the following technical solutions.

[0006] A task reliability-oriented unmanned cluster equivalent space-time capability field construction method mainly includes the following steps:

[0007] S100: Selecting a system load acting element for describing the task execution capability of the unmanned cluster under different conditions;

[0008] S200: Measure the selected action elements based on the actual application scenario and node running state:

[0009] S201: Measure the action range;

[0010] S202: Measure the action intensity;

[0011] S203: Measure the action time;

[0012] S300: Based on the measurement results, construct the equivalent space-time capability field of the unmanned cluster.

[0013] In step S100, the system load action elements are selected, including the action range, the action intensity, and the action time, to accurately represent the task execution capability of the unmanned cluster under different conditions. This process is used to determine the key features that the unmanned cluster needs to exhibit in a specific task, ensuring that the selected elements can effectively reflect the correlation between the overall performance of the unmanned cluster and the task. Among them, the action range is used to describe the area that the system load can act on, the action intensity focuses on the comprehensive influence of multiple loads on the same area, and the action time involves the duration of the load work or its effectiveness within a specific period;

[0014] In step S200, the selected action elements are measured based on the actual application scenario and node running state:

[0015] In step S201, the area factor F S (t) is defined to measure the action range of the unmanned cluster, and the calculation process is as follows:

[0016]

[0017] Where t is time, S is the set of each single coverage area s, representing the overall area covered by the cluster load, i is the single index, and m is the total number of single loads in the unmanned cluster. F s (t) is the ratio of the current coverage area of the unmanned cluster to the task area , with a value range of [0, 1].

[0018] In step S202, the intensity factor F I (t) is defined to measure the action intensity of the unmanned cluster, and the calculation process is as follows:

[0019] According to the area closure principle, the action range of the unmanned cluster is divided:

[0020] Z(t) = {z1, z2, … z j , …, z n(t)}

[0021] Num(t) = {num1, num2, … num jnum n(t)}

[0022] Area(t)={area1,area2,…area j ,…,area n(t)}

[0023] where Z is the set of areas, z j represents any area j, and n is the total number of areas. num, area represent the service load quantity and the corresponding area of each area, respectively, and Num, Area are the corresponding sets.

[0024]

[0025] F I (t) quantifies the strength of the unmanned cluster according to the degree of overlap of the areas covered by multiple single loads, and is the sum of the product of the service load quantity and the area of each area and the total action area, and is normalized on this basis, with a value range of [0, 1].

[0026] In step S203, a time factor F T is defined to measure the action time of the unmanned cluster, and the calculation process is as follows:

[0027]

[0028] where w represents the load normal operation time, T represents the total task time, and F T is the sum of the ratio of the operation time of each single load to the task time, with a value range of [0, 1].

[0029] In step S300, based on the measurement results, an equivalent space-time capability field of the unmanned cluster is constructed. The purpose of this process is to determine the correlation between the equivalent space-time capability field and the area factor, the strength factor, and the time factor according to the actual task scenario of the unmanned cluster, and then quantify the overall performance of the unmanned cluster. The general formula can be expressed as follows:

[0030] K=f(F S (t), F I (t), F T )

[0031] where K represents the equivalent space-time capability field of the unmanned cluster, and f is the corresponding function relationship.

[0032] Compared with the existing task reliability evaluation process, the beneficial effects brought by the present application are that: by means of the task reliability oriented unmanned cluster equivalent space-time capability field construction method developed by the present application, the key factors influencing the success of unmanned cluster task execution can be deeply identified and analyzed, and the overall performance thereof under different task scenarios can be quantitatively analyzed. Compared with the prior art, the present method not only accurately captures the dynamic changes of the unmanned cluster state and capability, but also analyzes the space-time characteristics thereof under different task environments, thereby providing effective support for the task reliability evaluation process of the unmanned cluster. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 A task reliability oriented unmanned cluster equivalent space-time capability field construction method flowchart is provided for the present application.

[0034] Figure 2 An unmanned cluster equivalent space-time capability field model is provided for the present application. DETAILED DESCRIPTION

[0035] The specific embodiments of the present application will be described below with reference to the accompanying drawings. Figure 1 The accompanying drawings are included to provide a further understanding of the present application, and are incorporated herein and constitute a part of the detailed description. The drawings illustrate embodiments of the present application and, together with the description, serve to explain the principles of the present application. Figure 2 The specific embodiments of the present application will be described below with reference to the accompanying drawings.

[0036] The present application provides a task reliability oriented unmanned cluster equivalent space-time capability field construction method, and the flowchart is shown in Figure 1 The present application provides a task reliability oriented unmanned cluster equivalent space-time capability field construction method, and the flowchart is shown in

[0037] S100: Selecting the unmanned cluster load action elements including the action range, the action strength and the action time to accurately represent the task execution capability of the unmanned cluster under different conditions. This process is used to determine the key features required by the unmanned cluster in a specific task, and to ensure that the selected elements can effectively reflect the correlation between the overall performance of the unmanned cluster and the task. Among them, the action range is used to describe the area that can be acted on by the unmanned load, the action strength focuses on the comprehensive influence of multiple unmanned load on the same area, and the action time involves the duration of the load work or its effectiveness in a specific period of time;

[0038] Example 1: As shown in Figure 2The certain UAV cluster shown is composed of 5 UAVs, and the specific application scenario is forest fire monitoring. The selected action elements include action range, action intensity, and action time. Among them: the action range represents the task area that the UAV cluster can effectively cover (the total area is 50 square kilometers); the action intensity represents the monitoring situation of the task area by the infrared camera carried by the UAV; and the action time concerns the effective working time of the UAV load within 4 hours of task time.

[0039] S200: Based on the actual application scenario and the node running state, the selected action elements are measured:

[0040] S201: Define the area factor F S (t) to measure the action range of the UAV cluster, the calculation process is as follows:

[0041]

[0042]

[0043] Where t is time, S is a set of coverage areas s of each UAV, represents the overall area that can be covered by the load of the UAV cluster, i is the UAV index, and m is the total number of UAVs in the cluster. F s (t) is the ratio of the current coverage area of the UAV cluster to the task area , with a value range of [0, 1].

[0044] S202: Define the intensity factor F I (t) to measure the action intensity of the UAV cluster, the calculation process is as follows:

[0045] According to the area closure principle, the action range of the UAV cluster is divided:

[0046] Z(t) = {z1, z2, … z j , …, z n(t)}

[0047] Num(t) = {num1, num2, … num j , …, num n(t)}

[0048] Area(t) = {area1, area2, … area j , …, area n(t)}

[0049] Where Z is a set of regions, z j represents any region j, and n is the total number of regions. num and area represent the number of service loads and the corresponding area of each region, respectively, and Num and Area are the corresponding sets.

[0050]

[0051] F I (t) The intensity of the UAV cluster is quantified according to the degree of overlap of the areas covered by multiple UAVs. It is the sum of the ratio of the product of the number of service loads and the area of ​​each area to the total effective area, and is normalized on this basis. The value range is [0,1].

[0052] Step S203: Define the time factor F T To measure the action time of the drone cluster, the calculation process is as follows:

[0053]

[0054] Where w represents the normal operation time of the load, T represents the total mission time, and F T It is the sum of the ratios of the payload running time of each UAV to the mission time, and its value range is [0,1].

[0055] Continuing with the previous example: measuring the range of a drone swarm, the coverage area of ​​each drone at t = 4 hours is:

[0056]

[0057] like Figure 2 As shown, the total coverage area is:

[0058]

[0059] Calculate the area factor and the result is:

[0060]

[0061] To measure the impact strength of drone swarms, we divided the coverage area using the regional closure principle. The results are as follows:

[0062] Z(4)={z1,z2,z3,z4,z5}

[0063] The service load and corresponding area of ​​each area are:

[0064] Num(4)={1,1,2,1,1}

[0065] Area(4)={10,12,8,2,9}

[0066] The calculation result of the intensity factor is:

[0067]

[0068] Measuring the operational time of the drone cluster, the normal operating time of each drone payload is as follows:

[0069]

[0070] The calculation result of the time factor is:

[0071]

[0072] S300: Based on the measurement results, the equivalent space-time capability field of the UAV cluster is constructed. The purpose of this process is to determine the correlation between the equivalent space-time capability field and the area factor, the intensity factor and the time factor according to the actual task scene of the UAV cluster, and then to quantify the overall performance of the UAV cluster. The general formula can be expressed as follows:

[0073] K = f (F S (t), F I (t), F T )

[0074] Wherein, K represents the equivalent space-time capability field of the UAV cluster, and f is the corresponding function relationship.

[0075] Continue the example: based on the measurement results of the area factor, the intensity factor and the time factor, the equivalent space-time capability field of the UAV cluster is constructed:

[0076] K = f (F S (t), F I (t), F T ) = a·F S (t) + b·F I (t) + c·F T

[0077] Wherein a = 0.5, b = 0.3, c = 0.2, then:

[0078] K = a·F S (t) + b·F I (t) + c·F T = 0.5·0.82 + 0.3·0.24 + 0.2·0.8 = 0.642

[0079] Finally, the equivalent space-time capability field K of the UAV cluster is 0.642, which represents the actual capability of the UAV cluster in executing forest fire monitoring. On this basis, the task success threshold can be agreed to carry out the task reliability evaluation of the UAV cluster.

[0080] Although the embodiments of the present application have been described above with reference to the accompanying drawings, the present application is not limited to the above-described specific embodiments and areas of application, and the above-described specific embodiments are merely illustrative and instructive, but are not restrictive. Many modifications can be made by those skilled in the art under the teachings of the present specification and without departing from the scope of the present application as defined by the claims.

Claims

1.A method for constructing a task-reliability-oriented spatiotemporal capability field of a swarm of unmanned vehicles, characterized in that, Comprising: S100: Selecting system load action elements including action range, action strength and action time to accurately characterize the mission execution capability of the unmanned cluster under different conditions; This process is used to determine the key features that the unmanned cluster needs to embody in a specific task, ensuring that the selected elements can effectively reflect the correlation between the overall performance of the unmanned cluster and the task; wherein the action range is used to describe the area that the system load can act on, the action strength represents the comprehensive influence of multiple loads on the same area, and the action time represents the duration of the load work or its effectiveness within a certain period; S200: Based on the actual application scene and the node running state, the selected action elements are measured: S201: define the area factor F S (t) To measure the effective range of the unmanned cluster, the calculation process is as follows: Wherein, t is time, S is the set of each single-coverage area s, represents the overall area covered by the cluster load, i is the single index, m is the total number of single-loads contained in the unmanned cluster, F S (t) is the ratio of the current coverage area of the unmanned cluster to the task area , and the value range is [0, 1]. S202: define the intensity factor F I (t) To measure the strength of the role of the swarm, the calculation process is as follows: according to the principle of regional closure, the action range of the swarm is divided: Z(t) = {z1, z2,... z j ,…,z n(t)} Num(t) = { num1, num2,... num j ,...,num n(t)} Area(t) = {area1, area2,... area j ,...,area n(t)} where Z is a set of areas, z j represents an arbitrary area j, n is the total number of areas, num, area represents the number of service loads and the corresponding area of each area, respectively, and Num, Area is the corresponding set; F I (t) The strength of the role of the unmanned cluster is quantified according to the degree of overlap of the areas covered by multiple single loads, and the sum of the product of the service load quantity and the area of each area and the ratio of the total action area is normalized on this basis, with a value range of [0, 1]; S203: define time factor F T To measure the action time of the unmanned cluster, the calculation process is as follows: wherein w denotes the load uptime, T denotes the total task time, F T is the sum of the ratio of the individual load uptime to the task time, and has a value in the range [0, 1]; S300: Based on the measurement results, an equivalent space-time capability field of the unmanned cluster is constructed; this process is to determine the correlation between the equivalent space-time capability field K and the area factor, the strength factor and the time factor according to the actual task scene of the unmanned cluster, and then to quantify the overall performance of the unmanned cluster.

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