Unmanned cluster equivalent space-time capability field construction method oriented to task reliability

Abstract

The invention discloses a task reliability-oriented unmanned cluster equivalent space-time capability field construction method, which is used for effectively representing the correlation between the overall performance of an unmanned cluster and tasks, capturing the dynamic change of the state and capability of the unmanned cluster, quantitatively analyzing the space-time characteristics and emergence capability of the unmanned cluster in different scenes, and establishing an equivalent space-time capability field of the unmanned cluster. And accurate evaluation of unmanned cluster task reliability is supported. The method comprises the following steps: pertinently selecting system load action elements, including action range, action intensity and action time, for describing task execution capability of an unmanned cluster under different conditions; based on an actual application scene and a node operation state, measuring selected action elements; and constructing an unmanned cluster equivalent space-time capability field based on each measurement result.

Description

Technical Field

[0001] The invention provides a method for constructing an unmanned cluster equivalent space-time capability field oriented to task reliability, belonging to the technical field of reliability engineering. Background Art

[0002] At present, unmanned swarms are gradually becoming important carriers of automation and intelligent applications, and are widely used in military, logistics, agriculture, environmental monitoring and other fields. Through the coordination and cooperation of multiple unmanned devices, they demonstrate efficient information collection, task execution and environmental adaptability. However, with the increase in task complexity, the reliability and performance requirements of unmanned swarms are also increasing. In the process of executing tasks, they are often affected by multiple factors such as environmental changes, equipment failures, and information delays, which leads to a decrease in task execution efficiency or even failure. Therefore, in order to ensure that unmanned swarms can perform tasks reliably and stably in various environments, it is necessary to accurately evaluate the correlation between their performance and tasks.

[0003] However, existing evaluation methods mostly focus on specific performance indicators, and their application mainly focuses on the collection and analysis of single data, and the correlation discussion using linear models usually cannot fully reflect the actual working conditions of unmanned clusters. In addition, the existing evaluation process fails to effectively display the spatiotemporal dynamic characteristics of the system, resulting in insufficient evaluation of the adaptability and task execution capabilities of the unmanned cluster, increasing the risk of task failure. In the above context, how to establish a comprehensive and dynamic unmanned cluster task reliability evaluation system that can adapt to complex and changeable task scenarios has become a key issue that needs to be solved urgently. To this end, the present invention proposes a method for constructing an equivalent spatiotemporal capability field of unmanned clusters for task reliability, which aims to effectively characterize the overall performance of unmanned clusters in specific tasks, and then provide support for the accurate evaluation of system task reliability. Summary of the invention

[0004] The purpose of the present invention is to provide a method for constructing an equivalent spatiotemporal capability field of unmanned clusters for task reliability, so as to effectively characterize the correlation between the overall performance of the unmanned cluster and the task, quantitatively analyze its spatiotemporal characteristics and emergent capabilities in different scenarios, and thus support the accurate evaluation of the reliability of unmanned cluster tasks.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for constructing an equivalent spatiotemporal capability field of an unmanned swarm for mission reliability mainly includes the following steps:

[0007] S100: Targeted selection of system load action elements to describe the mission execution capabilities of unmanned swarms under different conditions;

[0008] S200: Based on the actual application scenario and node operation status, measure the selected action factors:

[0009] S201: Measurement scope;

[0010] S202: measure the intensity of action;

[0011] S203: measuring action time;

[0012] S300: Based on the measurement results, construct an equivalent spatiotemporal capability field of unmanned swarm.

[0013] In step S100, the system load action factors are selected in a targeted manner, including the action range, action intensity and action time, to accurately characterize the task execution capabilities 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, and ensure that the selected factors 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 where the system load can act, the action intensity focuses on the combined impact of multiple loads on the same area, and the action time involves the duration of the load's work or its effectiveness in a specific period of time;

[0014] In step S200, based on the actual application scenario and the node operation status, the selected action factors are measured:

[0015] In step S201, define the area factor F S (t) is used to measure the range of action of the unmanned swarm. The calculation process is as follows:

[0016]

[0017] Where t is the time, S is the set of coverage areas s of each single device, representing the overall area that the cluster load can cover, i is the index of the single device, and m is the total number of single devices contained in the unmanned cluster. s (t) is the current coverage area of ​​the unmanned cluster and the area of ​​the mission area The ratio is in the range of [0,1].

[0018] In step S202, the intensity factor F is defined I (t) is used to measure the strength of the unmanned swarm. The calculation process is as follows:

[0019] The scope of action of the unmanned cluster is divided according to the principle of regional closure:

[0020] Z(t)={z1,z2,…z j , …, z n(t)}

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

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

[0023] Among them, Z is the region set, z j represents any region j, n is the total number of regions. num, area respectively represent the number of service loads in each region and the corresponding area, and Num, Area are the corresponding sets.

[0024]

[0025] F I (t) The intensity of the unmanned swarm is quantified according to the degree of overlap of the areas covered by multiple single devices. It is the sum of the product of the number of service loads and the area of ​​each area and the ratio of the total effective area. It is normalized on this basis and the value range is [0,1].

[0026] In step S203, define the time factor F T To measure the action time of the unmanned swarm, the calculation process is as follows:

[0027]

[0028] 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 running time of each single load to the mission time, and its value range is [0,1].

[0029] In step S300, based on the measurement results, the unmanned cluster equivalent spatiotemporal capability field is constructed. The purpose of this process is to determine the correlation between the equivalent spatiotemporal capability field and the area factor, intensity factor and time factor according to the actual mission 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] Among them, K represents the equivalent space-time capability field of the unmanned cluster, and f is the corresponding functional relationship.

[0032] Compared with the existing task reliability evaluation process, the beneficial effect brought by the present invention is that with the help of the unmanned cluster equivalent spatiotemporal capability field construction method for task reliability developed by the present invention, the key factors affecting the successful execution of unmanned cluster tasks can be deeply identified and analyzed, and its overall performance in different task scenarios can be quantitatively analyzed. Compared with the existing methods, this method not only accurately captures the dynamic changes of the state and capabilities of the unmanned cluster, but also analyzes its spatiotemporal characteristics in different task environments, thereby providing effective support for the evaluation process of the reliability of unmanned cluster tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A flow chart of a method for constructing an equivalent spatiotemporal capability field of an unmanned swarm oriented to task reliability provided by the present invention;

[0034] Figure 2 The equivalent space-time capability field model of the drone cluster provided by the present invention. DETAILED DESCRIPTION

[0035] The following will refer to the attached Figure 1 With attached Figure 2 Specific embodiments of the present invention are described in detail. Although specific embodiments of the present invention are discussed, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, the embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the information of the present invention to those skilled in the relevant art.

[0036] The embodiment of the present invention provides a method for constructing an equivalent spatiotemporal capability field of a drone cluster oriented to mission reliability. The flow chart is as follows: Figure 1 As shown, including:

[0037] S100: Targeted selection of UAV cluster payload action factors, including action range, action intensity and action time, to accurately characterize the task execution capabilities of UAV clusters under different conditions. This process is used to determine the key features that the UAV cluster needs to embody in a specific task, ensuring that the selected factors can effectively reflect the correlation between the overall performance of the UAV cluster and the task. Among them, the action range is used to describe the area where the UAV payload can act, the action intensity focuses on the combined impact of multiple UAV payloads on the same area, and the action time involves the duration of the payload's work or its effectiveness in a specific period of time;

[0038] Example 1: Figure 2The drone cluster shown in the figure consists of 5 drones. The specific application scenario is forest fire monitoring. The selected action factors include action range, action intensity and action time. Among them: the action range represents the mission area that the drone cluster can effectively cover (a total area of ​​50 square kilometers); the action intensity represents the monitoring of the mission area by the infrared camera carried by the drone; the action time focuses on the effective working time of the drone payload within the 4-hour mission time.

[0039] S200: Based on the actual application scenario and node operation status, measure the selected action factors:

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

[0041]

[0042]

[0043] Where t is the time, S is the set of coverage areas s of each drone, representing the overall area that can be covered by the drone cluster load, i is the drone index, and m is the total number of drones in the cluster. s (t) is the current coverage area and mission area of ​​the drone cluster The ratio is in the range of [0,1].

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

[0045] The scope of action of drone clusters is divided according to the principle of regional closure:

[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] Among them, Z is the region set, z j represents any region j, n is the total number of regions. num, area represent the number of service loads in each region and the corresponding area, respectively, and Num, 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 product of the number of service loads and the area of ​​each area and the ratio of the total effective area. It is then normalized and takes the value range of [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 operating time of each UAV payload to the mission time, and its value range is [0,1].

[0055] Continuing from the previous example: To measure the range of action of a drone cluster, the coverage area of ​​each drone at t = 4h 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 strength of the drone cluster, the coverage area is divided 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] Then the calculation result of the intensity factor is:

[0067]

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

[0069]

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

[0071]

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

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

[0074] Among them, K represents the equivalent space-time capability field of the drone cluster, and f is the corresponding functional relationship.

[0075] Continuing from the previous example: Based on the above measurement results of area factor, intensity factor and time factor, the equivalent space-time capability field of unmanned swarm 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] Where 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 spatiotemporal capability field K of the drone cluster is 0.642, which indicates the actual capability of the drone cluster in performing forest fire monitoring. On this basis, the mission success threshold can be agreed upon to carry out the mission reliability evaluation of the drone cluster.

[0080] Although the embodiments of the present invention are described above in conjunction with the accompanying drawings, the present invention is not limited to the above specific embodiments and application fields, and the above specific embodiments are only illustrative and instructive, rather than restrictive. A person of ordinary skill in the art can also make many forms under the guidance of this specification and without departing from the scope of protection of the claims of the present invention, all of which belong to the protection of the present invention.

Claims

1. A method for constructing an equivalent spatiotemporal capability field of an unmanned swarm for mission reliability, characterized in that: include: S100: Targeted selection of system payload action factors, including action range, action intensity and action time, to accurately characterize the mission execution capabilities of unmanned swarms under different conditions; This process is used to determine the key features that the unmanned swarm needs to embody in a specific task, ensuring that the selected elements can effectively reflect the correlation between the overall performance of the unmanned swarm and the task; the scope of action is used to describe the area where the system load can act, the intensity of action focuses on the combined impact of multiple loads on the same area, and the action time involves the duration of the load's work or its effectiveness in a specific period of time; S200: Based on the actual application scenario and node operation status, measure the selected action factors: S201: Measurement scope; S202: measure the intensity of action; S203: measuring action time; S300: Based on the measurement results, an equivalent spatiotemporal capability field of the unmanned cluster is constructed; this process is to determine the correlation between the equivalent spatiotemporal capability field K and the area factor, intensity factor and time factor according to the actual mission scenario of the unmanned cluster, and then quantify the overall performance of the unmanned cluster.

2. According to the method for constructing an unmanned cluster equivalent spatiotemporal capability field for task reliability according to claim 1, it is characterized by: In the "measurement scope" described in S201, the area factor F is defined S (t) is used to measure the range of action of the unmanned swarm. The calculation process is as follows: Where t is the time, S is the set of coverage areas s of each single device, which represents the overall area that the cluster load can cover, i is the index of the single device, m is the total number of single devices contained in the unmanned cluster, and F s (t) is the current coverage area of ​​the unmanned cluster and the area of ​​the mission area The ratio is in the range of [0,1].

3. The method for constructing an unmanned cluster equivalent spatiotemporal capability field for task reliability according to claim 1, characterized in that: In the “measurement of action intensity” in S202, an intensity factor t(t) is defined to measure the action intensity of the unmanned cluster, and the calculation process is as follows: The scope of action of the unmanned cluster is divided according to the principle of regional closure: Z(t)={z1,z2,…z j ,…,With n(t) } Num(t) = {num1,num2,...num j ...number n(t) } Area(t)={area1,area2,…area j ,…,area n(t) } Among them, Z is the region set, z j represents any region j, n is the total number of regions, num,area respectively represent the number of service loads and the corresponding area of ​​each region, and Num,Area are the corresponding sets; Qiao(t) quantifies the intensity of the unmanned cluster according to the degree of overlap of the areas covered by multiple single devices. It is the sum of the ratio of the product of the number of service loads and the area in each area to the total effective area, and is normalized on this basis. The value range is [0, 1].

4. The method for constructing an unmanned cluster equivalent spatiotemporal capability field for task reliability according to claim 1, characterized in that: In the "measurement action time" in S203, the time factor F is defined T To measure the action time of the unmanned swarm, the calculation process is as follows: 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 running time of each single load to the mission time, and its value range is [0, 1].

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