CN112370035A - Human-computer cooperation fatigue detection system based on digital twin platform - Google Patents

Human-computer cooperation fatigue detection system based on digital twin platform Download PDF

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CN112370035A
CN112370035A CN202011104707.9A CN202011104707A CN112370035A CN 112370035 A CN112370035 A CN 112370035A CN 202011104707 A CN202011104707 A CN 202011104707A CN 112370035 A CN112370035 A CN 112370035A
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何斌
徐彩月
李刚
朱忠攀
王志鹏
沈润杰
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Abstract

The invention discloses a human-computer cooperative personnel fatigue detection system based on a digital twin platform, which maps data of a physical entity space to the digital twin space in real time, realizes the evaluation of the working load and the human fatigue degree of the movement posture, the muscle load strength and the joint moment of an operator in human-computer cooperation based on a physical engine and a digital model of the digital twin space, and performs dynamic task distribution by integrating the working load analysis, the fatigue degree evaluation and the health state monitoring so as to realize fine automation and intelligent human-computer cooperation.

Description

Human-computer cooperation fatigue detection system based on digital twin platform
Technical Field
The invention relates to the field of man-machine cooperation production, in particular to a man-machine cooperation fatigue detection system based on a digital twin platform.
Background
With the advancement of collaborative automation technology, the industry goal is to replace the full manual process with a human robotic team, thereby increasing production at a lower cost while maintaining product diversity. Human-machine collaboration systems aim to achieve sophisticated automation by distributing tasks between people and robots according to the required complexity, repeatability and intelligence.
The fatigue degree of the operator is related to various human working states, including working posture, human body burden during loading, task frequency and the like. The synergistic effect of a plurality of muscles of the human body enables people to complete the action in a labor-saving way. Research shows that along with the increase of muscle load, discomfort is more obvious, the risk of muscle load injury and the possibility of safety accidents are increased, and the physical health and safe operation of operators are further influenced.
Digital Twin (Digital Twin) refers to a simulation process integrating multiple disciplines, multiple physical quantities, multiple scales and multiple probabilities by fully utilizing data such as physical models, sensor updates and operation histories and the like, and mapping is completed in a virtual space so as to reflect the full life cycle process of corresponding entity equipment. The digital twin can be regarded as one or more important digital mapping systems of equipment systems which depend on each other, and can be widely applied to multiple fields of industrial manufacturing, building operation and maintenance, smart cities and the like.
Disclosure of Invention
The invention aims to provide a human-computer cooperation fatigue detection system based on a digital twin platform, aiming at the defects of the prior art, and the human-computer cooperation fatigue detection system is used for evaluating the human fatigue degree of the human body in human-computer cooperation on the basis of a human body operation fatigue quantification model and performing dynamic task allocation.
In order to achieve the purpose, the invention adopts the following technical scheme:
a man-machine cooperation fatigue detection system based on a digital twin platform comprises a physical entity space and a digital twin space, wherein the physical entity space is communicated with the digital twin space through a communication interface;
the physical entity space comprises a master controller, a cooperative robot, a working environment and a state monitoring sensor device; the digital twin space comprises a controller module, a digital model module, a physical driving engine module and a working state analysis and evaluation module, and the mechanism mapping, real-time state data sharing, analysis and prediction of the physical space are realized through the modular digital twin space; the quantitative evaluation of the operation fatigue of the operators is based on real-time shared monitoring data, and the work fatigue degree of the operators is predicted and evaluated in a digital twin space.
Furthermore, the state monitoring sensor comprises an optical motion capture system which is installed on an operation site and used for capturing human body kinematic data in real time, a depth camera used for positioning and monitoring human-computer interaction relation, a biological myoelectric sensor which is used for monitoring health state, working strength and fatigue degree of workers and is carried on the surface of a key muscle group, wearable equipment of a heart rate signal sensor, and a motion data sensor and a moment sensor which are integrated on a robot joint and a tail end. The system and the equipment are the existing data acquisition equipment.
Furthermore, the digital model module comprises a human body three-dimensional kinematics model, a human body biomechanics model, a robot kinematics and dynamics model, a working environment model and a human-machine-environment ternary interaction coupling model.
Further, the working state evaluation module comprises human work fatigue quantitative evaluation, working state evaluation, health index evaluation and the like.
Further, the control module comprises work efficiency and result prediction and dynamic task allocation.
Furthermore, in the man-machine cooperative operation process, the physical entity space shares the working state and man-machine interaction information of the operator to the digital twin space in real time through the motion capture system, the biological electromyographic signal monitoring system and the robot integrated sensor; establishing an operator digital human body model and an operation environment model in a digital twin space, carrying out engineering and biomechanical analysis and prediction on the operator based on real-time working state data, integrating key muscle group load strength and key joint moment, and carrying out working state evaluation through integrated operation fatigue quantification indexes.
Furthermore, the key muscle group load intensity is subjected to time-frequency domain analysis through the key muscle group biological electromyographic signals acquired in real time, and the activation degree and the load intensity of the key muscle group of the staff are analyzed by combining kinematic data and a digital human body model, and the method specifically comprises the following steps:
firstly, carrying out muscle grouping treatment on an operator based on biomechanics knowledge, and attaching a biological myoelectric sensor to a key muscle group of the operator;
and secondly, carrying out noise reduction and filtering processing on the original electromyographic signals, and obtaining characteristic parameter indexes, namely an integrated electromyographic value (iEMG), a mean square error (RMS), a Median Frequency (MF) and a Mean Power Frequency (MPF) of the fatigue degree quantization index through time-frequency domain analysis.
Further, the specific steps in the second step are as follows:
time domain analysis:
Figure BDA0002726560240000031
Figure BDA0002726560240000032
where n is the data length, xiIs a myoelectric signal sequence;
frequency domain analysis:
Figure BDA0002726560240000033
Figure BDA0002726560240000034
where f is the electromyographic signal frequency and P (f) is the power spectral density function.
Further, the key joint moment is based on kinematic data of a motion capture system to calculate a human joint jacobian matrix in real time, and then is based on biological electromyographic signals to carry out human key joint moment estimation, so as to carry out joint load evaluation, and the specific estimation steps are as follows:
step one, mapping the electromyographic signals into muscle activation degrees according to a muscle activation degree description function:
Figure BDA0002726560240000041
wherein u is a muscle surface electromyographic signal after low-pass filtering and denoising, R is a maximum value estimated value of the electromyographic signal u, A is a nonlinear curve factor of a muscle activity degree function, and the value range is-5 < A < 0;
step two, solving muscle force according to the neuromuscular skeletal model:
Figure BDA0002726560240000042
wherein, FtThe force of the tendon is the force of the tendon,
Figure BDA0002726560240000045
for optimal sub-muscular-fiber muscle force, fA(l) Is the relationship of muscle initiative force-length, fP(l) Is the muscle passive force-muscle length relation, f (v) is the muscle active force-velocity relation;
step three, solving the joint moment:
Figure BDA0002726560240000043
wherein M isj(theta, t) represents the joint moment r of the joint j at the sampling time t with the joint angle thetai(θ) is the i-th skeletal muscle at the joint angle to the joint jThe moment arm of the joint when the degree is theta,
Figure BDA0002726560240000046
the muscle force of the ith skeletal muscle to the joint j at a joint angle theta is obtained, wherein the solution to the moment ratio is:
Figure BDA0002726560240000044
furthermore, the analysis, prediction and evaluation model of the fatigue degree and the working state of the operator is based on the monitoring data of the load intensity of key muscle groups, the moment of key joints and the heart rate health state, and is simulated through the existing human body model and the human-computer cooperation task allocation of a digital twin space to predict the working intensity load of the operator in the whole working period, so that the physical and mental fatigue degree can be achieved.
By adopting the technical scheme of the invention, the invention has the beneficial effects that: the dynamic allocation method of the man-machine cooperation production task based on the operator fatigue prediction model performs dynamic allocation of the task based on the operation fatigue evaluation and prediction of operators and the factors of comprehensive skill characteristics, work efficiency and personnel fatigue degree, so as to reduce the physical load and mental load of the operators and maximize the production efficiency.
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FIG. 1 is a diagram of a human-computer cooperative personnel fatigue detection system based on a digital twin platform provided by the invention;
FIG. 2 is a flow chart of the muscle fatigue level detection provided by the present invention;
FIG. 3 is a flow chart of the joint moment load analysis provided by the present invention;
FIG. 4 is a flow chart of an application of a human-computer cooperative fatigue detection system based on a digital twin platform.
Detailed Description
Specific embodiments of the present invention will be further described with reference to the accompanying drawings.
To accomplish the assembly task, people and robots have some unique and versatile skill characteristics. The invention can identify these skills for task assignment problems, thereby forming a decision mechanism to assign tasks to robots and people for better performance. The invention estimates the task completion time, activity and idle time of the robot and the human through the simulation of the digital twin platform. The assembly sequence can be modified to minimize idle time and make better use of each resource. And the tasks are distributed in a manner that allows the workers enough recovery time to avoid work fatigue.
As shown in the figure, the man-machine cooperation digital twin production system is composed of a physical entity space, a digital twin space and a related communication interface.
The physical entity space comprises a master controller, professional operators, a cooperative robot, a working environment and a state monitoring sensor device. The state monitoring sensor comprises an optical motion capture system and a depth camera, wherein the optical motion capture system is arranged on a working site and used for capturing human body kinematic data in real time, and the depth camera is used for positioning and monitoring human-computer interaction relation; wearable equipment such as a biological myoelectric sensor and a heart rate signal sensor which are used for monitoring the health state, the working strength and the fatigue degree of workers and are provided with key muscle group surfaces, and a motion data sensor and a moment sensor which are integrated on a robot joint and a tail end.
The digital twin space comprises a controller module, a digital model module, a physical driving engine module and a working state analysis and evaluation module, and the mechanism mapping, real-time state data sharing, analysis and prediction of the physical space are realized through the modularized digital twin space. Specifically, the digital model module comprises a human body three-dimensional kinematics model, a human body biomechanics model, a robot kinematics and dynamics model, a working environment model, a human-machine-environment ternary interaction coupling model and the like; the working state evaluation module comprises human work fatigue quantitative evaluation, working state evaluation, health index evaluation and the like; the control module comprises work efficiency, result prediction, dynamic task allocation and the like.
The quantitative evaluation of the operation fatigue of the operators is based on real-time shared monitoring data, and the work fatigue degree of the operators is predicted and evaluated in a digital twin space. Specifically, in the man-machine cooperative operation process, the physical entity space shares the working state and man-machine interaction information of an operator to the digital twin space in real time through a motion capture system, a biological electromyographic signal monitoring system and a robot integrated sensor; and establishing an operator digital human body model and a working environment model in the digital twin space. And (3) carrying out engineering and biomechanical analysis and prediction on an operator based on the real-time working state data, synthesizing the load strength and the joint moment of key muscle groups, and carrying out working state evaluation through comprehensive operation fatigue quantification indexes.
The key muscle group load intensity is obtained by performing time-frequency domain analysis on the biological electromyographic signals of the key muscle groups acquired in real time and analyzing the activation degree and the load intensity of the key muscle groups of workers by combining kinematic data and a digital human body model. The method comprises the following specific steps:
firstly, carrying out muscle grouping treatment on an operator based on biomechanics knowledge, and attaching a biological myoelectric sensor to a key muscle group of the operator;
step two, the original electromyographic signals are subjected to noise reduction and filtering treatment, and characteristic parameter indexes, namely, fatigue degree quantization indexes, namely, an integral electromyographic value (iEMG), a mean square error (RMS), a Median Frequency (MF) and an average power frequency (MPF), are obtained through time-frequency domain analysis:
time domain analysis:
Figure BDA0002726560240000061
Figure BDA0002726560240000062
where n is the data length, xiIs a sequence of electromyographic signals.
Frequency domain analysis:
Figure BDA0002726560240000071
Figure BDA0002726560240000072
where f is the electromyographic signal frequency and P (f) is the power spectral density function.
The key joint moment is obtained by calculating a human body joint Jacobian matrix in real time based on kinematic data of a motion capture system and then estimating the human body key joint moment based on a biological electromyographic signal so as to evaluate the joint load. The specific estimation steps are as follows:
step one, mapping the electromyographic signals into muscle activation degrees according to a muscle activation degree description function:
Figure BDA0002726560240000073
wherein u is a muscle surface electromyographic signal after low-pass filtering and denoising, R is a maximum value estimated value of the electromyographic signal u, A is a nonlinear curve factor of a muscle activity degree function, and the value range is-5 < A < 0.
Step two, solving muscle force according to the neuromuscular skeletal model:
Figure BDA0002726560240000074
wherein, FtThe force of the tendon is the force of the tendon,
Figure BDA0002726560240000077
for optimal sub-muscular-fiber muscle force, fA(l) Is the relationship of muscle initiative force-length, fP(l) Is the muscle passive force-muscle length relationship, and f (v) is the muscle active force-velocity relationship.
Step three, solving the joint moment:
Figure BDA0002726560240000075
wherein M isj(theta, t) represents the joint moment r of the joint j at the sampling time t with the joint angle thetai(theta) is the moment arm of the joint when the ith skeletal muscle is coupled to the joint j at the joint angle theta,
Figure BDA0002726560240000078
the muscle force of the ith skeletal muscle to the joint j at a joint angle theta is obtained, wherein the solution to the moment ratio is:
Figure BDA0002726560240000076
the analysis, prediction and evaluation model of the fatigue degree and the working state of the operator is based on the monitoring data of the load intensity of the key muscle group, the moment of the key joint and the heart rate health state, and is used for simulating through the existing human body model and the human-computer cooperation task distribution of the digital twin space to predict the working intensity load of the operator in the whole working period, so as to achieve the physical and mental fatigue degree possibly.
The dynamic allocation method of the man-machine cooperation production task based on the operator fatigue prediction model is based on the evaluation and prediction of the operation fatigue of operators and integrates the factors of skill characteristics, work efficiency and personnel fatigue degree to dynamically allocate the task, so that the physical load and mental load of the operators are reduced, and the production efficiency is maximized.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims (10)

1. A man-machine cooperation fatigue detection system based on a digital twin platform is characterized by comprising a physical entity space and a digital twin space, wherein the physical entity space is communicated with the digital twin space through a communication interface;
the physical entity space comprises a master controller, a cooperative robot, a working environment and a state monitoring sensor device; the digital twin space comprises a controller module, a digital model module, a physical driving engine module and a working state analysis and evaluation module, and the mechanism mapping, real-time state data sharing, analysis and prediction of the physical space are realized through the modular digital twin space; the quantitative evaluation of the operation fatigue of the operators is based on real-time shared monitoring data, and the work fatigue degree of the operators is predicted and evaluated in a digital twin space.
2. The human-computer cooperative fatigue detection system based on the digital twin platform is characterized in that the state monitoring sensors comprise an optical motion capture system which is installed on a working site and used for capturing human body kinematic data in real time, a depth camera used for positioning and monitoring human-computer interaction relation, a biological electromyographic sensor which is used for monitoring the health state, working intensity and fatigue degree of workers and is provided with a key muscle group surface, a wearable device of a heart rate signal sensor, and a motion data sensor and a moment sensor which are integrated on joints and tail ends of a robot.
3. The system as claimed in claim 1, wherein the digital twin platform-based human-computer cooperative fatigue detection system is characterized in that the digital model module comprises a human three-dimensional kinematics model, a human biomechanics model, a robot kinematics and dynamics model, a working environment model and a human-machine-environment ternary interactive coupling model.
4. The human-computer cooperative fatigue detection system based on the digital twin platform as claimed in claim 1, wherein the working state evaluation module comprises human work fatigue quantitative evaluation, working state evaluation and health index evaluation.
5. The system of claim 1, wherein the control module comprises work efficiency and outcome prediction and dynamic task allocation.
6. The human-computer cooperative fatigue detection system based on the digital twin platform as claimed in claim 1, wherein in the human-computer cooperative operation process, the physical entity space shares the working state and human-computer interaction information of the operator to the digital twin space in real time through the motion capture system, the biological electromyographic signal monitoring system and the robot integrated sensor; establishing an operator digital human body model and an operation environment model in a digital twin space, carrying out engineering and biomechanical analysis and prediction on the operator based on real-time working state data, integrating key muscle group load strength and key joint moment, and carrying out working state evaluation through integrated operation fatigue quantification indexes.
7. The human-computer cooperative fatigue detection system based on the digital twin platform as claimed in claim 6, wherein the key muscle group load intensity is analyzed in time-frequency domain through the real-time collected biological myoelectric signals of the key muscle group, and the activation degree and the load intensity of the key muscle group of the staff are analyzed by combining the kinematic data and the digital human body model, and the specific steps are as follows:
firstly, carrying out muscle grouping treatment on an operator based on biomechanics knowledge, and attaching a biological myoelectric sensor to a key muscle group of the operator;
and secondly, carrying out noise reduction and filtering processing on the original electromyographic signals, and obtaining characteristic parameter indexes, namely an integrated electromyographic value (iEMG), a mean square error (RMS), a Median Frequency (MF) and a Mean Power Frequency (MPF) of the fatigue degree quantization index through time-frequency domain analysis.
8. The human-computer cooperation fatigue detection system based on the digital twin platform as claimed in claim 7, wherein the specific steps in the second step are as follows:
time domain analysis:
Figure FDA0002726560230000021
Figure FDA0002726560230000022
where n is the data length, xiIs a myoelectric signal sequence;
frequency domain analysis:
Figure FDA0002726560230000031
Figure FDA0002726560230000032
where f is the electromyographic signal frequency and P (f) is the power spectral density function.
9. The human-computer cooperative fatigue detection system based on the digital twin platform as claimed in claim 6, wherein the key joint moment is based on kinematic data of a motion capture system to calculate a human joint jacobian matrix in real time, and then based on a biological electromyographic signal to perform human key joint moment estimation, so as to perform joint load evaluation, the specific estimation steps are as follows:
step one, mapping the electromyographic signals into muscle activation degrees according to a muscle activation degree description function:
Figure FDA0002726560230000033
wherein u is a muscle surface electromyographic signal after low-pass filtering and denoising, R is a maximum value estimated value of the electromyographic signal u, A is a nonlinear curve factor of a muscle activity degree function, and the value range is-5 < A < 0;
step two, solving muscle force according to the neuromuscular skeletal model:
Figure FDA0002726560230000034
wherein, FtThe force of the tendon is the force of the tendon,
Figure FDA0002726560230000035
for optimal sub-muscular-fiber muscle force, fA(l) Is the relationship of muscle initiative force-length, fp(l) Is the muscle passive force-muscle length relation, f (v) is the muscle active force-velocity relation;
step three, solving the joint moment:
Figure FDA0002726560230000036
wherein M isj(theta, t) represents the joint moment r of the joint j at the sampling time t with the joint angle thetai(theta) is the moment arm of the joint when the ith skeletal muscle is coupled to the joint j at the joint angle theta,
Figure FDA0002726560230000037
the muscle force of the ith skeletal muscle to the joint j at a joint angle theta is obtained, wherein the solution to the moment ratio is:
Figure FDA0002726560230000041
10. the human-computer cooperative fatigue detection system based on the digital twin platform as claimed in claim 1, wherein the analysis, prediction and evaluation model of the fatigue degree and the working state of the operator is based on the monitoring data of the load intensity of key muscle groups, the moment of key joints and the heart rate health state, and the physical and mental fatigue degree which can be achieved by predicting the working intensity load of the operator in the whole working period through simulation by the human body model and the human-computer cooperative task allocation existing in the digital twin space.
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CN114587387B (en) * 2022-02-18 2024-05-28 金华送变电工程有限公司三为金东电力分公司 Live working insulating operation rod use fatigue evaluation method and device
CN114723886A (en) * 2022-04-08 2022-07-08 贵州大学 Human body digital twinning thought-based method for constructing high-fidelity digital twins of scapula levator
CN115543094A (en) * 2022-11-28 2022-12-30 杭州轻宇宙科技有限公司 Interaction method, system and electronic device of digital twin virtual human and human body
CN115543094B (en) * 2022-11-28 2023-05-30 杭州轻宇宙科技有限公司 Interaction method, system and electronic equipment of digital twin virtual person and human body

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Application publication date: 20210219