CN112896558B - Manual control rendezvous and docking human error identification method based on multi-dimensional data - Google Patents
Manual control rendezvous and docking human error identification method based on multi-dimensional data Download PDFInfo
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- CN112896558B CN112896558B CN202110294730.7A CN202110294730A CN112896558B CN 112896558 B CN112896558 B CN 112896558B CN 202110294730 A CN202110294730 A CN 202110294730A CN 112896558 B CN112896558 B CN 112896558B
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- 238000003032 molecular docking Methods 0.000 title claims abstract description 67
- 238000000034 method Methods 0.000 title claims abstract description 21
- 238000010606 normalization Methods 0.000 claims abstract description 5
- 238000005070 sampling Methods 0.000 claims description 7
- 230000007613 environmental effect Effects 0.000 claims description 4
- 239000008280 blood Substances 0.000 claims description 3
- 210000004369 blood Anatomy 0.000 claims description 3
- 230000005611 electricity Effects 0.000 claims description 3
- 238000005286 illumination Methods 0.000 claims description 3
- 230000036387 respiratory rate Effects 0.000 claims description 3
- 230000003542 behavioural effect Effects 0.000 claims 1
- 230000006399 behavior Effects 0.000 description 7
- 210000001503 joint Anatomy 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64G—COSMONAUTICS; VEHICLES OR EQUIPMENT THEREFOR
- B64G1/00—Cosmonautic vehicles
- B64G1/22—Parts of, or equipment specially adapted for fitting in or to, cosmonautic vehicles
- B64G1/64—Systems for coupling or separating cosmonautic vehicles or parts thereof, e.g. docking arrangements
- B64G1/646—Docking or rendezvous systems
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64G—COSMONAUTICS; VEHICLES OR EQUIPMENT THEREFOR
- B64G1/00—Cosmonautic vehicles
- B64G1/22—Parts of, or equipment specially adapted for fitting in or to, cosmonautic vehicles
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Abstract
The invention provides a manual control rendezvous and docking human error identification method based on multidimensional data, which comprises the following steps of: aiming at manual control meeting dockingSetting the rendezvous and docking data meeting the docking requirement as source sequence data
Aiming at the same rendezvous and docking task, various rendezvous and docking data are collected according to different time nodes to form target sequence data
The source sequence data
And the target sequence data
Description
Technical Field
The invention relates to the technical field of manual rendezvous and docking, in particular to a manual rendezvous and docking human error identification method based on multi-dimensional data.
Background
The control mode of the rendezvous and docking is mainly divided into an automatic control mode mainly based on human monitoring and a manual control mode mainly based on human operation according to different participation degrees of astronauts and spacecraft automatic control systems, wherein the automatic control does not exclude the manual control. The manual control rendezvous and docking task is one of the tasks which are most participated by people in manned space missions, the success or failure of the task is crucial to the safety and development of the space station, so whether the behavior fault in the manual control rendezvous and docking task can be identified or not can be identified, and huge positive influence is brought to the safety cause of the space flight.
The description of the space rendezvous and docking task is that an aircraft with known relative distance and attitude exists at a place where the space rendezvous and docking is carried out, the aircraft is to be docked smoothly under the manual operation of a spacecraft, in the process of the task, a series of system state parameters including relative position and attitude exist in a monitoring screen, when the docking is successfully carried out, the relative distance is 0 within a set time, and the deviation of the relative attitude is small; aiming at the identification of human error behaviors of astronauts in manual control rendezvous and docking tasks, the judgment of the human error behaviors of the astronauts is lacked at present.
Disclosure of Invention
Aiming at the technical problems to be solved, the invention provides a manual control rendezvous and docking human-induced error identification method based on multi-dimensional data, which is used for automatically identifying human-induced errors in controlled rendezvous and docking.
The invention provides a manual control rendezvous and docking human error identification method based on multidimensional data, which comprises the following steps of:
s1: aiming at the manual control rendezvous and docking task, rendezvous and docking data meeting the docking requirements are set as source sequence data
S2: aiming at the same rendezvous and docking task, various rendezvous and docking data are collected according to different time nodes to form target sequence data
S3: the source sequence data
And the target sequence data
After normalization processing, splicing by a first-order continuous Bezier curve splicing method to form a reference fitting curve and an actual fitting curve respectively;
s4: comparing the reference fitting curve with the actual fitting curve, and if the reference fitting curve and the actual fitting curve are superposed, no human error occurs; if the two are deviated, human error occurs.
Preferably, the rendezvous and docking data comprise system data, behavior data, environment data and physiological data, wherein the system data comprise aircraft rendezvous and docking distances and aircraft pose angular deviations; the behavior data is handle feedback data of an operator for adjusting the attitude of the aircraft; the environmental data comprises the temperature, the humidity and the illumination intensity of the environment around the operation cabin; the physiological data comprises pulse blood volume, heart rate, respiratory rate, skin electricity and skin temperature of an operator, and each intersection data represents one dimension.
Preferably, the step S2 specifically includes the following steps:
s21: during the rendezvous task, sampling is carried out as long as the handle has feedback data, and the collected rendezvous and docking data C are collected aiming at a certain time node i Comprises the following steps:
wherein v is j,i J-dimension value of the ith sampling point, i is 1,2, … …, k; j is 1,2, … …, n;
s22: combining a plurality of the rendezvous and docking data according to the time sequence to form target sequence data
Preferably, the fitting principle of the rendezvous and docking data is as follows:
constructing a curve p by using a second-order Bessel curve to adjacent 3 n-dimensional vertexes in an intersection butt joint data set i (t) wherein p i (t) has a control vertex of
k n-dimensional vertexes can be divided into k-2 sections, a first-order geometrically continuous splicing algorithm is adopted to construct curves for all the n-dimensional vertexes, and the whole curve is represented by a way of P (t) ═ p ceil(t(k-2)) (t(k-2))。
Preferably, the step S4 specifically includes:
the total space spanned by the fitted curve generated by rendezvous and docking data in all dimensions is represented as:
the greater the difference between the source sequence data and the target sequence data, the greater the probability of human error occurring:
wherein omega s (t) is the total space spanned by the reference fitting curve; omega d (t) is the span total space of the actual fitting curve;
giving a varying function of the source sequence data and the target sequence data spanning a space in time series:
and solving the maximum value of Q '(t), wherein the position corresponding to the maximum value of Q' (t) is the position of the human error, and the time node corresponding to the position is the human error time.
Compared with the prior art, the manual control rendezvous and docking human-caused error identification method based on the multidimensional data can finish the identification of human-caused errors in rendezvous and docking tasks with high efficiency and high accuracy on the basis of automatic judgment.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a manual control rendezvous and docking human error identification method based on multidimensional data, which comprises the following steps of:
s1: aiming at the manual control rendezvous and docking task, rendezvous and docking data meeting docking requirements are set as source sequence data
The rendezvous data meeting the docking requirements can be set according to historical experience or expert opinions in the industry, and the rendezvous data must meet relevant standards in the industry, meet relevant specification requirements and can complete rendezvous and docking tasks in sequence. The rendezvous and docking data comprise system data, behavior data, environment data and physiological data, wherein the system data comprise aircraft rendezvous and docking distances and aircraft pose angle deviations; the behavior data is handle feedback data of an operator for adjusting the attitude of the aircraft; the environmental data comprises the temperature, the humidity and the illumination intensity of the environment around the operation cabin; the physiological data includes the pulse blood volume, heart rate, respiratory rate, skin electricity and skin temperature of the operator. Each of the above rendezvous and docking data represents a dimension, and the data of each dimension influences the smooth proceeding of the manual rendezvous and docking task. The setting of the meeting data meeting the docking requirements can provide a basis for the judgment of subsequent human errors.
It should be noted that the handle feedback data may be obtained through a center console of the aircraft, the "handle" is not limited to a structure that only can be a handle type, and all structures that can be used by an operator and used for sending an operation command, such as a key, a button, a push rod, and the like, belong to the protection scope of the present invention, and the "handle" may be a physical key or a virtual key, which is not limited in this embodiment.
S2: aiming at the same rendezvous and docking task, various rendezvous and docking data are collected according to different time nodes to form target sequence data
It should be noted that the same rendezvous and docking mission means that the initial relative distance and the initial relative angle of the aircraft are the same.
The data acquisition is performed by the relevant sensors or the corresponding detection devices, and the corresponding acquisition process and the acquisition principle belong to the conventional technical means in the field. For example: the aircraft rendezvous and docking distance can be measured by an infrared distance sensor, handle feedback data can be acquired from a console, the temperature in the environment can be measured by a thermometer, and the pulse of an operator can be measured by a pulse meter.
The step S2 specifically includes the following steps:
s21: during the rendezvous task, sampling is carried out as long as the handle has feedback data, and the collected rendezvous and docking data C are collected aiming at a certain time node i Comprises the following steps:
wherein v is j,i The j dimension value of the ith sampling point is 1,2, … …, k; j is 1,2, … …, n.
In the manual control rendezvous and docking task, the action command of the aircraft is sent out by an operator through the control of a handle, so that sampling can be carried out as long as the handle feeds back data, namely, the operator sends out the operation command.
S22: according toTime series, combining a plurality of the rendezvous and docking data to form target sequence data
S3: the source sequence data
And the target sequence data
After normalization processing, splicing is carried out through a first-order continuous Bezier curve splicing method to form a reference fitting curve and an actual fitting curve respectively.
The source sequence data is processed
And the target sequence data
After normalization processing, the situation that the decimal number is too small and the decimal number is too large can be avoided. Meanwhile, the rendezvous and docking data are discrete data and cannot be directly judged by a machine, so that the rendezvous and docking data can be spliced by a first-order continuous Bezier curve splicing method, the first-order parameter and geometric continuity of the rendezvous and docking data are ensured, and the data can be conveniently analyzed by a subsequent construction algorithm.
Specifically, the fitting principle of the rendezvous and docking data is as follows:
constructing a curve p by using a second-order Bezier curve to adjacent 3 n-dimensional vertexes in an intersecting butt joint data set i (t) wherein p i (t) has a control vertex of
The k n-dimensional vertexes can be divided into k-2 sections, a first-order geometrically continuous splicing algorithm is adopted to construct curves for all the n-dimensional vertexes, and the expression mode of the whole curve is P (t) ═ p ceil(t(k-2)) (t(k-2))。
The fitting principle described above applies to source sequence data as well
And target sequence data
And fitting the reference fitting curve and the actual fitting curve respectively to obtain the reference fitting curve and the actual fitting curve.
S4: comparing the reference fitting curve with the actual fitting curve, and if the reference fitting curve and the actual fitting curve are superposed, no human error occurs; if the two are deviated, human error occurs.
The comparison between the reference fitting curve and the actual fitting curve can be directly determined manually or by establishing a corresponding model, the determination principle belongs to the conventional technical means in the field, and the detailed description of the embodiment is omitted.
After the deviation occurs, the time node of the human factor task can be directly judged through the actual fitting curve, and the intersection butt joint data C of the time node is extracted i The specific operation of human error and the error reason of the influence can be obtained.
Specifically, the present embodiment provides a method for discriminating human error, comprising: the total space spanned by the fitted curve generated by rendezvous and docking data in all dimensions is represented as:
the greater the difference between the source sequence data and the target sequence data, the greater the probability of human error occurring, and the probability of human error occurring is:
wherein omega s (t) is the total space spanned by the reference fitted curves; omega d (t) is the span of the actual fitted curve.
Giving a varying function of the source sequence data and the target sequence data spanning a space in time series:
and solving the maximum value of Q '(t), wherein the position corresponding to the maximum value of Q' (t) is the position of the human error, and the time node corresponding to the position is the human error time.
Compared with the prior art, the manual control rendezvous and docking human-induced error identification method based on the multi-dimensional data can finish human-induced error identification in rendezvous and docking tasks with high efficiency and high accuracy on the basis of automatic judgment.
While the foregoing is directed to embodiments of the present invention, it will be understood by those skilled in the art that various changes may be made without departing from the spirit and scope of the invention.
Claims (3)
1. A manual control rendezvous and docking human error identification method based on multi-dimensional data is characterized by comprising the following steps:
s1: aiming at the manual control rendezvous and docking task, rendezvous and docking data meeting the docking requirements are set as source sequence data
S2: aiming at the same rendezvous and docking task, various rendezvous and docking data are collected according to different time nodes to form target sequence data
S3: the source sequence data is processed
And the target sequence data
After normalization processing, splicing by a first-order continuous Bezier curve splicing method to form a reference fitting curve and an actual fitting curve respectively;
s4: comparing the reference fitting curve with the actual fitting curve, and if the reference fitting curve and the actual fitting curve are superposed, no human error occurs; if the two are deviated, human error occurs.
2. The method of claim 1, wherein the rendezvous and docking human error recognition based on multidimensional data comprises system data, behavioral data, environmental data, and physiological data, wherein the system data comprises aircraft rendezvous and docking distances and aircraft pose angular deviations; the behavior data is handle feedback data of an operator for adjusting the attitude of the aircraft; the environmental data comprises the temperature, the humidity and the illumination intensity of the environment around the operation cabin; the physiological data comprises pulse blood volume, heart rate, respiratory rate, skin electricity and skin temperature of an operator, and each intersection data represents one dimension.
3. The manual rendezvous and human-caused-mistake recognition method based on multi-dimensional data as claimed in claim 2, wherein the step S2 specifically comprises the following steps:
s21: during the rendezvous task, sampling is carried out as long as the handle has feedback data, and the collected rendezvous and docking data C are collected aiming at a certain time node i Comprises the following steps:
wherein v is j,i J-dimension value of the ith sampling point, i is 1,2, … …, k; j is 1,2, … …, n;
s22: combining a plurality of the rendezvous and docking data according to the time sequence to form target sequence data
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Application publication date: 20210604 Assignee: Hunan Jiuzhe Information Technology Co.,Ltd. Assignor: HUNAN INSTITUTE OF TECHNOLOGY Contract record no.: X2023980053616 Denomination of invention: A method for identifying human error in manual rendezvous docking based on multi-dimensional data Granted publication date: 20220923 License type: Common License Record date: 20231222 |