CN116307835A - Performance evaluation index system and method for water surface unmanned aircraft detection system - Google Patents
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Abstract
The invention relates to a performance evaluation index system of a detection system of a water unmanned vehicle, which comprises the following components: 4 primary indicators and 12 secondary indicators. The invention also relates to a method for evaluating the efficiency of the detection system of the unmanned water surface aircraft under the index system framework, which comprises the following steps: step one, calculating index weights by using an analytic hierarchy process; step two, calculating an inherent energy vector Q of the detection system; step three, calculating an availability vector M and a feasibility matrix N of the detection system; and step four, calculating the efficiency value E of the detection system. The invention not only considers the detection precision, detection range, resolution, fault occurrence condition, state in executing task and the like of each detection device in the detection system, but also considers the influence of marine environment, and is especially suitable for the efficiency evaluation of the detection system of the unmanned water surface aircraft under the complex sea conditions of multiple detection devices.
Description
Technical Field
The invention relates to the field of performance evaluation of detection systems, in particular to a performance evaluation index system and method of a detection system of a water surface unmanned aircraft, which are particularly suitable for performance evaluation of the detection system of the water surface unmanned aircraft under complex sea conditions with multiple detection devices.
Background
Ocean reserves are rich in biological and mineral resources, and more attention is paid. The unmanned water surface vehicle can complete detection tasks on the sea surface by carrying various detection devices, so that exploration of ocean resources is realized. Various detection devices constitute a detection system of the unmanned water surface vessel, and the ability of the detection system to perform a detection task is referred to as the effectiveness of the detection system. According to the detection precision, detection range, resolution, fault condition, state in executing task and the like of each detection device in the detection system, the performance evaluation of the detection system can be carried out. However, since the unmanned water craft is much smaller than a general ship, the unmanned water craft is easily affected by complex marine environments during the task execution, such as wind, waves, currents, gushes, etc., which will interfere with the navigation posture of the unmanned water craft, resulting in excessive roll angle and pitch angle, and deviation from the preset course. In addition, the harsh marine environment can also affect the useful life of the detection equipment. Therefore, when performing tasks, the performance of the detection system needs to be evaluated by fully considering the marine environment at that time. The premise of performance evaluation is to establish a set of index system comprehensively considering the capability of the detection system and the influence of marine environmental factors.
Therefore, the invention provides a performance evaluation index system and a performance evaluation method for a water surface unmanned aircraft detection system, wherein the index system provides a general comprehensive standard for performance evaluation of the detection system, and the performance evaluation method can complete performance evaluation of the water surface unmanned aircraft detection system in the framework of the index system.
Disclosure of Invention
The invention aims to provide a performance evaluation index system and method for a water surface unmanned aircraft detection system, which are particularly suitable for performance evaluation of the water surface unmanned aircraft detection system under complex sea conditions with multiple detection devices.
The present invention will be described below with reference to the accompanying drawings.
The first aspect of the invention provides a performance evaluation index system of a detection system of a unmanned water surface vehicle, the composition structure of which is shown in fig. 1 and comprises 4 primary indexes and 12 secondary indexes. The first-level index of the index system comprises: submarine topography detection capability B 1 Submarine topography detection capability B 2 Device viability B is probed 3 Ship attitude anti-interference capability B 4 . Submarine topography detection capability B 1 Consists of 3 secondary indexes: detection accuracy C 1 Detection range C 2 Depth of detection C 3 . Submarine topography detection capability B 2 Consists of 3 secondary indexes: high resolution C 4 Horizontal resolution C 5 Time resolution C 6 . Detecting device viability B 3 Consists of 3 secondary indexes: endurance C 7 Communication anti-interference C 8 Work development speed C 9 . Ship attitude anti-interference capability B 4 Consists of 3 secondary indexes: roll angle influence C 10 Impact of pitch angle C 11 Course angle influence C 12 。
In a second aspect of the present invention, a method for evaluating the performance of a detection system of an unmanned surface vehicle is provided, and a block diagram of the steps is shown in fig. 2.
Step one: and calculating index weights by using a analytic hierarchy process.
(1) By using the analytic hierarchy process and utilizing the submarine topography detection capability B 1 Submarine topography detection capability B 2 Device viability B is probed 3 Ship attitude anti-interference capability B 4 Calculating the first-level index weight b 1 、b 2 、b 3 、b 4 ；
(2) By analytic hierarchy process, using detection accuracy C 1 Detection range C 2 Depth of detection C 3 Calculating a secondary index weight c 1 、c 2 、c 3 ；
(3) By analytic hierarchy process using high resolution C 4 Horizontal resolution C 5 Time resolution C 6 Calculating a secondary index weight c 4 、c 5 、c 6 ；
(4) By analytic hierarchy process and endurance C 7 Communication anti-interference C 8 Work development speed C 9 Calculating a secondary index weight c 7 、c 8 、c 9 ；
(5) Using analytic hierarchy process to influence C by roll angle 10 Impact of pitch angle C 11 Course angle influence C 12 Calculating a secondary index weight c 10 、c 11 、c 12 。
Step two: the detection system natural energy vector Q is calculated.
Defining an intrinsic capability vector:
Q＝[q 1 ,q 2 ]
wherein q 1 Indicating the ability of the system to be in a normal state, q 2 Ability to complete a given task in a failure state where there is no need to discuss the inherent capabilities of the system, so let q 2 ＝0。
Expert according to the second-level index weight c 1 、c 2 、c 3 、c 4 、c 5 、c 6 、c 7 、c 8 、c 9 、c 10 、c 11 、c 12 And the evaluation scoring is carried out by combining self experience to obtain a corresponding secondary index scoring value r 1 、r 2 、r 3 、r 4 、r 5 、r 6 、r 7 、r 8 、r 9 、r 10 、r 11 、r 12 。
Reusing the first-level index weight b 1 ，b 2 ，b 3 ，b 4 Second-level index weight c 1 、c 2 、c 3 、c 4 、c 5 、c 6 、c 7 、c 8 、c 9 、c 10 、c 11 、c 12 And a secondary index score value r 1 、r 2 、r 3 、r 4 、r 5 、r 6 、r 7 、r 8 、r 9 、r 10 、r 11 、r 12 Calculating R 1 ，R 2 ，R 3 ，R 4 . Here, R is 1 ，R 2 ，R 3 ，R 4 Is the first level index B 1 ，B 2 ，B 3 ，B 4 And a corresponding first-level index evaluation value.
R 1 ＝c 1 ·r 1 +c 2 ·r 2 +c 3 ·r 3
R 2 ＝c 4 ·r 4 +c 5 ·r 5 +c 6 ·r 6
R 3 ＝c 7 ·r 7 +c 8 ·r 8 +c 9 ·r 9
R 4 ＝c 10 ·r 10 +c 11 ·r 11 +c 12 ·r 12
Then obtaining the capacity q of the system in a normal state through weighted summation 1 ：
q 1 ＝R 1 ·B 1 +R 2 ·B 2 +R 3 ·B 3 +R 4 ·B 4
Step three: the availability vector M and the feasibility matrix N of the detection system are calculated.
(1) Computing availability vector M
Availability of detection devices in a detection system M k Describing, the expression is:
wherein MTBF represents average fault interval time of the equipment, MTTR represents average repair time of the equipment, MLDT represents average guarantee delay time, and m isThe number of the detection devices in the detection system is that k is the number of the detection devices and is a positive integer (k is more than or equal to 1 and less than or equal to m). Average availability of m probing devices
The method comprises the following steps:
the availability vector is further calculated and the availability vector,
wherein a is 1 、a 2 Respectively representing the probability that the detection system is in an available state and an unavailable state at the beginning of a task, and further obtaining an availability vector M= [ a ] 1 ,a 2 ]。
(2) Calculating a feasibility matrix N
The feasibility matrix N consists of 4 elements, denoted as:
in the method, in the process of the invention,
representing the average probability that each detection device in the detection system is in a working state in the process of executing a task; />
Representing the average probability that each detection device in the detection system is in a fault state in the process of executing a task; />
Representing the average probability that each detection device in the detection system is in a fault state when the task starts to be executed and is in an operable state when the task ends; />
The average probability that each detection device in the detection system is in an operable state when the task starts to be executed and is in a fault state when the task ends is shown.
Let t be the task duration, lambda k To detect the failure rate of device k, μ k For detecting the repair rate of device k, the feasibility matrix N is calculated by:
step four: the performance value E of the detection system is calculated.
Calculating a performance value E of the detection system by using the detection system inherent capability vector Q, the detection system availability vector M and the detection system feasibility matrix N:
E＝M·N·Q T
wherein the superscript T denotes a transpose.
Drawings
FIG. 1 is a system for evaluating performance metrics of a surface unmanned vehicle detection system;
FIG. 2 is a block diagram of a method for evaluating the performance of a surface unmanned vehicle detection system.
Detailed Description
The following is an example of an evaluation of the performance of a surface unmanned vehicle detection system.
The detection system of the unmanned water surface vehicle consists of four detection devices: multi-beam detection equipment, a temperature and salt depth detector, a Doppler surface acoustic velocity meter and a side scan sonar. The unmanned surface vehicle has a speed of 3Kn, a depth of-10 m, a wave level of 2, a wave direction angle of-90 degrees and a flow velocity of 0.5m/s. At this time, in the first-level index, the ship attitude anti-interference capability B 4 Specific detection device viability B 3 Slightly (slightly)Importantly, seafloor topography detection capability B 1 Ability to detect topography on the sea floor B 2 Equally important. In the second level index, the detection accuracy C 1 Ratio detection range C 2 Depth of detection C 3 Slightly important, detection Range C 2 And detection depth C 3 Pitch angle effects; time resolution C 6 And high resolution C 4 Equally important, than horizontal resolution C 5 Slightly important, high resolution C 4 Specific horizontal resolution C 5 Slightly important; work development speed C 9 Specific communication interference resistance C 8 Importantly, specific endurance C 7 Very important, communication anti-interference capability C 8 Specific endurance C 7 Important; c (C) 11 Specific roll angle C 10 Importantly, specific heading angle influences C 12 Very important, pitch angle influences C 11 Influence of specific heading angle C 12 Important.
Executing the first step: and calculating index weights by using a analytic hierarchy process.
(1) By using the analytic hierarchy process and utilizing the submarine topography detection capability B 1 Submarine topography detection capability B 2 Device viability B is probed 3 Ship attitude anti-interference capability B 4 Calculating the first-level index weight b 1 ＝0.24，b 2 ＝0.23，b 3 ＝0.21，b 4 ＝0.32；
(2) By analytic hierarchy process, using detection accuracy C 1 Detection range C 2 Depth of detection C 3 Calculating a secondary index weight c 1 ＝0.35，c 2 ＝0.33，c 3 ＝0.32；
(3) By analytic hierarchy process using high resolution C 4 Horizontal resolution C 5 Time resolution C 6 Calculating a secondary index weight c 4 ＝0.34，c 5 ＝0.27，c 6 ＝0.39；
(4) By analytic hierarchy process and endurance C 7 Communication anti-interference capability C 8 Work development speed C 9 Calculating a secondary index weight c 7 ＝0.12，c 8 ＝0.32，c 9 ＝0.56；
(5) Using analytic hierarchy process to influence C by roll angle 10 Impact of pitch angle C 11 Course angle influence C 12 Calculating a secondary index weight c 10 ＝0.32，c 11 ＝0.54，c 12 ＝0.14。
Step two: a detection system intrinsic ability vector Q is calculated.
Defining an intrinsic capability vector:
Q＝[q 1 ,q 2 ]
wherein q 1 Indicating the ability of the system to be in a normal state, q 2 Ability to complete a given task in a failure state where there is no need to discuss the inherent capabilities of the system, so let q 2 ＝0。
Expert according to the second-level index weight c 1 、c 2 、c 3 、c 4 、c 5 、c 6 、c 7 、c 8 、c 9 、c 10 、c 11 、c 12 And the evaluation scoring is carried out by combining self experience to obtain a corresponding secondary index scoring value r 1 ＝0.78、r 2 ＝0.85、r 3 ＝0.83、r 4 ＝0.93、r 5 ＝0.76、r 6 ＝0.86、r 7 ＝0.94、r 8 ＝0.92、r 9 ＝0.87、r 10 ＝0.75、r 11 ＝0.77、r 12 ＝0.88。
Reusing the first-level index weight b 1 ，b 2 ，b 3 ，b 4 Second-level index weight c 1 、c 2 、c 3 、c 4 、c 5 、c 6 、c 7 、c 8 、c 9 、c 10 、c 11 、c 12 And a secondary index score value r 1 、r 2 、r 3 、r 4 、r 5 、r 6 、r 7 、r 8 、r 9 、r 10 、r 11 、r 12 Calculating R 1 ，R 2 ，R 3 ，R 4 . Here, R is 1 ，R 2 ，R 3 ，R 4 Is the first level index B 1 ，B 2 ，B 3 ，B 4 And a corresponding first-level index evaluation value.
R 1 ＝c 1 ·r 1 +c 2 ·r 2 +c 3 ·r 3 ＝0.35×0.78+0.33×0.85+0.32×0.83＝0.8191
R 2 ＝c 4 ·r 4 +c 5 ·r 5 +c 6 ·r 6 ＝0.34×0.93+0.27×0.76+0.39×0.86＝0.8568
R 3 ＝c 7 ·r 7 +c 8 ·r 8 +c 9 ·r 9 ＝0.12×0.94+0.32×0.92+0.56×0.87＝0.8944
R 4 ＝c 10 ·r 10 +c 11 ·r 11 +c 12 ·r 12 ＝0.32×0.75+0.54×0.77+0.14×0.88＝0.779
Then obtaining the capacity q of the system in a normal state through weighted summation 1 ：
q 1 ＝R 1 ·B 1 +R 2 ·B 2 +R 3 ·B 3 +R 4 ·B 4 ＝0.8191×0.24+0.8568×0.23+0.8944×0.21+0.779×0.32＝0.8307
Finally, Q= [0.8307,0] is obtained.
Executing the third step: the availability vector M and the feasibility matrix N of the detection system are calculated.
(1) Computing availability vector M
Availability of detection devices in a detection system M k Describing, the expression is:
wherein MTBF represents the average fault interval time of the equipment, MTTR represents the average repair time of the equipment, MLDT represents the average guarantee delay time, m is the number of the detection equipment in the detection system, and k is the positive integer (k is more than or equal to 1 and less than or equal to m) of the sequence number of the detection equipment. Average availability of m probing devices
The method comprises the following steps:
the detection system of the unmanned surface vehicle consists of 4 detection devices, and then m=4, and the availability parameters of the four detection devices are shown in table 1.
Table 1 availability parameters for four detection devices in a detection system
Calculated using the availability parameters in table 1:
the availability vector M is further calculated and,
wherein a is 1 、a 2 Respectively representing the probability that the detection system is in an available state and an unavailable state at the beginning of a task, and further obtaining an availability vector M= [ a ] 1 ,a 2 ]＝[0.9667,0.0333]。
(2) Calculating a feasibility matrix N
The feasibility matrix N consists of 4 elements, denoted as:
in the method, in the process of the invention,
representing the average probability that each detection device in the detection system is in a working state in the process of executing a task; />
Representing the average probability that each detection device in the detection system is in a fault state in the process of executing a task; />
Representing the average probability that each detection device in the detection system is in a fault state when the task starts to be executed and is in an operable state when the task ends; />
The average probability that each detection device in the detection system is in an operable state when the task starts to be executed and is in a fault state when the task ends is shown.
Let t be the task duration, lambda k To detect the failure rate of device k, μ k For detecting the repair rate of device k, the feasibility matrix N is calculated by:
n is the number n due to the inability of the unmanned surface vehicle to repair equipment during the performance of the probing mission 21 ＝0，n 22 The failure rate of each detection device in the detection system is shown in table 2.
Table 2 feasibility parameters of four detection devices in a detection system
Feasibility matrix
Executing the fourth step: the performance value E of the detection system is calculated.
According to the formula
The performance values range between 0 and 1 and are close to 1. Therefore, the detection system can obtain a good detection effect.
A third aspect of the invention provides a computer program comprising software instructions which when executed by a computer implement the performance assessment method, including but not limited to a personal computer, mini-computer, mainframe, workstation, network or distributed computing environment, a separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, or the like. Aspects of the invention may be implemented in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optical read and/or write storage medium, RAM, ROM, etc., such that it is readable by a programmable computer, which when read by a computer, is operable to configure and operate the computer to perform the processes described herein. Further, the machine readable code, or portions thereof, may be transmitted over a wired or wireless network. When such media includes instructions or programs that, in conjunction with a microprocessor or other data processor, implement the steps described above, the invention described herein includes these and other different types of non-transitory computer-readable storage media. The invention also includes the computer itself when programmed according to the methods and techniques of the present invention.
The present invention is not limited to the above embodiments, but is merely preferred embodiments of the present invention, and the present invention should be construed as being limited to the above embodiments as long as the technical effects of the present invention are achieved by the same means. Various modifications and variations are possible in the technical solution and/or in the embodiments within the scope of the invention.
Claims (8)
1. The performance evaluation index system of the unmanned water surface vehicle detection system is characterized by comprising: a plurality of primary indicators and a plurality of secondary indicators; the plurality of first-level indexes comprise: submarine topography detection capability, detection equipment viability and ship attitude anti-interference capability; the plurality of secondary indexes comprise: detection precision, detection range, detection depth, height resolution, horizontal resolution, time resolution, cruising ability, communication anti-interference, work development speed, roll angle influence, pitch angle influence and course angle influence; the submarine topography detection capability comprises: detection precision, detection range and detection depth; the submarine topography detection capability comprises: high resolution, horizontal resolution, and temporal resolution; the viability of the detection device comprises: endurance capacity, communication anti-interference and work development speed; the ship attitude anti-interference capability comprises: roll angle effects, pitch angle effects, and heading angle effects.
2. The method for evaluating the efficiency of the detection system of the unmanned water vehicle is characterized by comprising the following steps of:
step one: calculating index weight by using an analytic hierarchy process;
step two: calculating an inherent energy vector Q of the detection system;
step three: calculating an availability vector M and a feasibility matrix N of the detection system;
step four: the performance value E of the detection system is calculated.
3. The method for evaluating the performance of a water unmanned vehicle detection system according to claim 2, wherein the first step is: calculating the index weight by using an analytic hierarchy process comprises:
(1) By using the analytic hierarchy process and utilizing the submarine topography detection capability B 1 Submarine topography detection capability B 2 Device viability B is probed 3 Ship attitude anti-interference capability B 4 Calculating the first-level index weight b 1 、b 2 、b 3 、b 4 ；
(2) By analytic hierarchy process, using detection accuracy C 1 Detection range C 2 Depth of detection C 3 Calculating a secondary index weight c 1 、c 2 、c 3 ；
(3) By analytic hierarchy process using high resolution C 4 Horizontal resolution C 5 Time resolution C 6 Calculating a secondary index weight c 4 、c 5 、c 6 ；
(4) By analytic hierarchy process and endurance C 7 Communication anti-interference C 8 Work development speed C 9 Calculating a secondary index weight c 7 、c 8 、c 9 ；
(5) Using analytic hierarchy process to influence C by roll angle 10 Impact of pitch angle C 11 Course angle influence C 12 Calculating a secondary index weight c 10 、c 11 、c 12 。
4. The method for evaluating the performance of a water surface unmanned vehicle detection system according to claim 2, wherein the step two: calculating a detection system natural energy vector Q, comprising:
defining an intrinsic capability vector:
Q＝[q 1 ,q 2 ]
wherein q 1 Indicating the ability of the system to be in a normal state, q 2 Ability to complete a given task in a failure state where there is no need to discuss the inherent capabilities of the system, so let q 2 ＝0；
Expert according to the second-level index weight c 1 、c 2 、c 3 、c 4 、c 5 、c 6 、c 7 、c 8 、c 9 、c 10 、c 11 、c 12 And the evaluation scoring is carried out by combining self experience to obtain a corresponding secondary index scoring value r 1 、r 2 、r 3 、r 4 、r 5 、r 6 、r 7 、r 8 、r 9 、r 10 、r 11 、r 12 ；
Reusing the first-level index weight b 1 ，b 2 ，b 3 ，b 4 Second-level index weight c 1 、c 2 、c 3 、c 4 、c 5 、c 6 、c 7 、c 8 、c 9 、c 10 、c 11 、c 12 And a secondary index score value r 1 、r 2 、r 3 、r 4 、r 5 、r 6 、r 7 、r 8 、r 9 、r 10 、r 11 、r 12 Calculating R 1 ，R 2 ，R 3 ，R 4 . Here, R is 1 ，R 2 ，R 3 ，R 4 Is the first level index B 1 ，B 2 ，B 3 ，B 4 Corresponding primary index evaluation value:
R 1 ＝c 1 ·r 1 +c 2 ·r 2 +c 3 ·r 3
R 2 ＝c 4 ·r 4 +c 5 ·r 5 +c 6 ·r 6
R 3 ＝c 7 ·r 7 +c 8 ·r 8 +c 9 ·r 9
R 4 ＝c 10 ·r 10 +c 11 ·r 11 +c 12 ·r 12
then obtaining the capacity q of the system in a normal state through weighted summation 1 ：
q 1 ＝R 1 ·B 1 +R 2 ·B 2 +R 3 ·B 3 +R 4 ·B 4 。
5. The method for evaluating the performance of a water surface unmanned vehicle detection system according to claim 2, wherein the step three: calculating an availability vector M and a feasibility matrix N of the detection system, comprising:
(1) Computing availability vector M
Availability of detection devices in a detection system M k Describing, the expression is:
wherein MTBF represents the average fault interval time of the equipment, MTTR represents the average repair time of the equipment, MLDT represents the average guarantee delay time, m is the number of the detection equipment in the detection system, and k is the positive integer (k is more than or equal to 1 and less than or equal to m) of the sequence number of the detection equipment; average availability of m probing devices
The method comprises the following steps:
the availability vector is further calculated and the availability vector,
wherein a is 1 、a 2 Respectively representing the probability that the detection system is in an available state and an unavailable state at the beginning of a task, and further obtaining an availability vector M= [ a ] 1 ,a 2 ]；
(2) Calculating a feasibility matrix N
The feasibility matrix N consists of 4 elements, denoted as:
in the method, in the process of the invention,
representing the average probability that each detection device in the detection system is in a working state in the process of executing a task;
representing the average probability that each detection device in the detection system is in a fault state in the process of executing a task; />
Representing the average probability that each detection device in the detection system is in a fault state when the task starts to be executed and is in an operable state when the task ends; />
Representing the average probability that each detection device in the detection system is in an operable state when the task starts to be executed and is in a fault state when the task ends;
let t be the task duration, lambda k To detect the failure rate of device k, μ k To detectThe repair rate of device k, the feasibility matrix N is calculated by:
6. the method for evaluating the performance of a water surface unmanned vehicle detection system according to claim 2, wherein the step four: calculating a performance value E of the detection system, comprising:
calculating a performance value E of the detection system by using the detection system inherent capability vector Q, the detection system availability vector M and the detection system feasibility matrix N:
E＝M·N·Q T
wherein the superscript T denotes a transpose.
7. A computer device, comprising: a memory for storing a computer program; a processor for implementing the method according to any of claims 2 to 6 when executing said computer program.
8. A readable storage medium, characterized in that the readable storage medium has stored thereon a computer program which, when executed by a processor, implements the method according to any of claims 2 to 6.
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