CN114916919A - System, method and application for predicting user alertness and fatigue risk index - Google Patents
System, method and application for predicting user alertness and fatigue risk index Download PDFInfo
- Publication number
- CN114916919A CN114916919A CN202210650393.5A CN202210650393A CN114916919A CN 114916919 A CN114916919 A CN 114916919A CN 202210650393 A CN202210650393 A CN 202210650393A CN 114916919 A CN114916919 A CN 114916919A
- Authority
- CN
- China
- Prior art keywords
- alertness
- sleep
- user
- consumption
- calculating
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000036626 alertness Effects 0.000 title claims abstract description 409
- 238000000034 method Methods 0.000 title claims abstract description 110
- 206010016256 fatigue Diseases 0.000 title claims abstract description 104
- 230000008859 change Effects 0.000 claims abstract description 69
- 230000003860 sleep quality Effects 0.000 claims abstract description 68
- 238000004088 simulation Methods 0.000 claims abstract description 51
- 230000027288 circadian rhythm Effects 0.000 claims abstract description 27
- 230000007958 sleep Effects 0.000 claims description 197
- 238000005381 potential energy Methods 0.000 claims description 50
- 230000008569 process Effects 0.000 claims description 42
- 150000001875 compounds Chemical class 0.000 claims description 36
- 230000014509 gene expression Effects 0.000 claims description 36
- 230000000694 effects Effects 0.000 claims description 33
- 238000012544 monitoring process Methods 0.000 claims description 33
- 230000033764 rhythmic process Effects 0.000 claims description 19
- 238000004590 computer program Methods 0.000 claims description 16
- 238000005265 energy consumption Methods 0.000 claims description 16
- 239000013589 supplement Substances 0.000 claims description 16
- 238000004364 calculation method Methods 0.000 claims description 14
- 230000037007 arousal Effects 0.000 claims description 13
- 206010062519 Poor quality sleep Diseases 0.000 claims description 11
- 206010041235 Snoring Diseases 0.000 claims description 9
- 230000009469 supplementation Effects 0.000 claims description 9
- 230000001502 supplementing effect Effects 0.000 claims description 6
- 238000012800 visualization Methods 0.000 claims description 6
- 230000010355 oscillation Effects 0.000 claims description 5
- 230000004622 sleep time Effects 0.000 claims description 5
- 230000000007 visual effect Effects 0.000 claims description 5
- 230000004617 sleep duration Effects 0.000 claims description 4
- 238000013277 forecasting method Methods 0.000 claims 2
- 238000013178 mathematical model Methods 0.000 abstract description 44
- 238000004458 analytical method Methods 0.000 abstract description 12
- 230000007547 defect Effects 0.000 abstract description 4
- 230000002618 waking effect Effects 0.000 description 15
- 230000006870 function Effects 0.000 description 13
- 206010041349 Somnolence Diseases 0.000 description 8
- 238000011156 evaluation Methods 0.000 description 8
- 238000011160 research Methods 0.000 description 7
- 208000010340 Sleep Deprivation Diseases 0.000 description 6
- 230000008901 benefit Effects 0.000 description 5
- 238000002474 experimental method Methods 0.000 description 5
- 208000032140 Sleepiness Diseases 0.000 description 4
- 230000001186 cumulative effect Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000003203 everyday effect Effects 0.000 description 4
- 238000012806 monitoring device Methods 0.000 description 4
- 230000006399 behavior Effects 0.000 description 3
- 230000002060 circadian Effects 0.000 description 3
- 230000006735 deficit Effects 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 210000000214 mouth Anatomy 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 239000000047 product Substances 0.000 description 3
- 230000037321 sleepiness Effects 0.000 description 3
- 230000003245 working effect Effects 0.000 description 3
- 208000017164 Chronobiology disease Diseases 0.000 description 2
- 206010039203 Road traffic accident Diseases 0.000 description 2
- 206010067493 Sleep inertia Diseases 0.000 description 2
- 230000006383 arousal process Effects 0.000 description 2
- 230000033228 biological regulation Effects 0.000 description 2
- 230000036760 body temperature Effects 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000005094 computer simulation Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 230000013632 homeostatic process Effects 0.000 description 2
- 238000003384 imaging method Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 230000003340 mental effect Effects 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000000737 periodic effect Effects 0.000 description 2
- 230000036578 sleeping time Effects 0.000 description 2
- 230000007704 transition Effects 0.000 description 2
- 206010024264 Lethargy Diseases 0.000 description 1
- 208000027418 Wounds and injury Diseases 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- FFBHFFJDDLITSX-UHFFFAOYSA-N benzyl N-[2-hydroxy-4-(3-oxomorpholin-4-yl)phenyl]carbamate Chemical compound OC1=C(NC(=O)OCC2=CC=CC=C2)C=CC(=C1)N1CCOCC1=O FFBHFFJDDLITSX-UHFFFAOYSA-N 0.000 description 1
- 230000019771 cognition Effects 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000006378 damage Effects 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000003292 diminished effect Effects 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000013210 evaluation model Methods 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 230000002650 habitual effect Effects 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 230000000977 initiatory effect Effects 0.000 description 1
- 208000014674 injury Diseases 0.000 description 1
- 238000009413 insulation Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000012067 mathematical method Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000003188 neurobehavioral effect Effects 0.000 description 1
- 230000000926 neurological effect Effects 0.000 description 1
- 230000004751 neurological system process Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 230000037081 physical activity Effects 0.000 description 1
- 230000035790 physiological processes and functions Effects 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 238000013441 quality evaluation Methods 0.000 description 1
- 230000035484 reaction time Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000012827 research and development Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 238000012502 risk assessment Methods 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 239000000758 substrate Substances 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 230000001550 time effect Effects 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 230000005428 wave function Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/48—Other medical applications
- A61B5/4806—Sleep evaluation
- A61B5/4815—Sleep quality
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Medical Informatics (AREA)
- Public Health (AREA)
- General Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Pathology (AREA)
- Primary Health Care (AREA)
- Epidemiology (AREA)
- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- Biophysics (AREA)
- Heart & Thoracic Surgery (AREA)
- Molecular Biology (AREA)
- Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- Veterinary Medicine (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
The invention belongs to the technical field of data identification, and discloses a system, a method and application for predicting user alertness and fatigue risk index. The simulation and prediction system for the user alertness and fatigue risk index is applied to the client and comprises a sleep quality data acquisition module, an alertness replenishment quantity data acquisition module, an inherent consumption data acquisition module, a workload consumption data acquisition module and an alertness consumption data acquisition module. The biological mathematical model provided by the invention also integrates scientific analysis related to alertness, such as human circadian rhythm, and the like, and can be used for predicting the change situation of alertness of a worker at any moment in a certain day, and the change trend of the alertness of the human body in a working period can be more intuitively found and potential risks in a period with lower alertness can be predicted no matter the certain day occurs or the certain day will occur. The model system and the method of the invention improve and solve the limitations and defects of the current alertness biological mathematical model.
Description
Technical Field
The invention belongs to the technical field of data identification, and particularly relates to a simulation and prediction system and method for user alertness and fatigue risk index, computer equipment, a computer readable storage medium and an activity recorder.
Background
Since the 20 th century, with the development of computer technology and the progress of scientific research of life processes, the biological mathematics scientists begin to analyze complex life processes and explore the development rules, in the process, many biological mathematics scientists begin to explain biological change rules and other phenomena by a mathematical method, and gradually combine mathematical model operation analysis with biological information processing research, establish a series of mathematical models in the form of equations by using physiological parameters related to organisms as input data, and draw up the related functional relationship of the organisms by calculating the related relationship between influencing factors and expression factors.
The biological mathematical model established aiming at the human alertness problem is a mathematical model which mainly takes the human sleep circadian rhythm, sleep steady state, sleep history, workload, environmental influence and other neurophysiological processes as independent variables, calculates through an equation formula for predicting risk measurement or correlation in a period with lower alertness and outputs dependent variables of evaluation values related to alertness expression. The ability of an alertness biological mathematical model is to incorporate scientific understanding from experimental observations into a general qualitative and quantitative prediction tool and to build a computer program for predicting the alertness degree/change trend of a human body based on the scientific understanding of alertness influencing factors.
At present, biological mathematical models related to human alertness can be divided into theoretical biological mathematical models and application biological mathematical models. The earliest of these models involved sleep regulation, such as the sleep regulation dual-process model
The model is composed of
In 1982, it was proposed that the model suggested that sleep and wake time are the steady state processes of sleep
And circadian rhythm process
Due to the interaction of (a) with (b),
the longer the awakening time is adjusted by the process, the easier the sleep is;
the process is affected by the time of day, while it also limits the ability of the person to sleep during abnormal times. The two-process model is described by
Process and apparatus
The duration of the human body alertness and the like are predicted by the mutual influence of the processes, the alertness and the sleepiness of the human body are predicted by the human body physiological data, and two basic factors of fatigue are explained: the relationship between sleep and arousal lays a foundation for a later fatigue prediction model, and a series of later fatigue biological mathematical models are supplemented and optimized by taking a double-process model as a basic theory. Another theoretical fatigue biological mathematical model is an alertness three-process model
The model is composed of
In 1987, the model is proposed to add a wake-up process, namely sleep inertia, on the basis of a double-process model
The influence of (a) on the performance of the device,
the process reflects a transient state of diminished alertness, perception, cognition, etc. immediately after arousal, which is a transition from sleep to awake. The three-process alertness model supplements the sleep and arousal process of two basic factors of fatigue into three basic factors of sleep-arousal, and predicts the fatigue and alertness change of the human body through the three process factors.
Applied biological mathematical models, some biological mathematical models related to fatigue research and commercialization appear abroad at present. Including being subsidized by the civil aviation administration of the uk,
unit fatigue evaluation system designed, developed and verified by company
The model is based on a double-process model, and sleep related factors and work related factors are added, such as the start and end time of the model input duty, the codes and time zones of the take-off and landing airports, the number of flight stations, the unit composition, the rest places and the like. And finally, the model outputs and displays the predicted alertness level and the sleep time in a graph form.
Professor developed sleep, activity, fatigue, and task efficiency models for the united states air force in 2001
It is supplemented and expanded based on three-process alertness model, and perfects fatigue biological mathematical model according to human sleep characteristicsThe sleep factors are added with factors such as sleep accumulation, sleep intensity, sleep quality and the like, and an imaging concept of a sleep storage pool is provided, the relationship between the whole sleep and arousal of a human body is simulated and expressed through the storage and consumption of the storage pool, the rest or sleep-in process is compared with the storage in the storage pool, the arousal process such as work is compared with the consumption of the storage pool, and the imaging concept has great revelation for the research of fatigue biological mathematical models. 2001 southern Australia university sleep research center researches and develops fatigue dynamic evaluation model
The model is based on a two-process model which assumes that the body alternates between operating and non-operating conditions, fatigue is developed during operation and regains fatigue during non-operation, and describes the operating schedule as a time-varying square wave function which oscillates continuously between operating and non-operating conditions. In addition, the
The model converts fatigue into a new concept of other forms for representation, and also provides a new idea for researching a fatigue biological mathematical model. By
Circadian rhythm simulator of mechanism development
Calculating an alertness curve based on a two-process model using sleep homeostasis and a sleep circadian rhythm, the sleep homeostasis being used to construct a level of sleepiness during the wake phase and a resolution of sleepiness during the sleep phase; circadian factors determine the time phase of the body's biological clock and corrections to time zone variations, and the outputs of the model include predicted sleep conditions (duration, quality, etc.), alertness levels, and the like. University of Onhver
Interactive neurobehavioral models developed by doctor et al
The model is based on an alertness three-process model that predicts neurological performance with sleep-wake history (preferably via an actigraph) and lighting as inputs, and is validated by experimental data as the only model to analyze the effect of ambient light on biological clocks. From Sweden
Research institute
At the university of Cartesian, doctor and Paris
Professor developed sleep/wake prediction model
The model is based on a three-process model and predicts the likelihood of sleep onset and cessation based on physiological parameters, and alertness through the degree of lethargy in relation to circadian variation, wake or time to fall asleep. Boeing alertness model operated by Boeing company
The model is based on an alertness three-process model and expands the functions of sleep prediction, task load, enhancement function and integration time sleep/wake part, and the output of the model is
，
General purpose scale 0-10000 police for representing drowsiness of somnolence scale and converting into prediction modelAnd (4) sensory scoring. Alertness model developed by German institute of aerospace medicine and aerospace center
The model is based on an alertness three-process model, models work task time effects, and provides different drowsiness thresholds as a fourth component of the model, wherein the different drowsiness thresholds represent the possible accident risk. England health and Security Bureau
And
index of fatigue risk in charge
The model is based on a two-process model and includes the effects of the length of the task shift, the rest time, the cumulative fatigue and the like, and the model output includes the fatigue index (in terms of the fatigue index)
The probability of occurrence of a value on the scale of 7 or more times 100) and a risk index (expressed in terms of making an estimate of the relative risk based on errors that may result in personal injury or accidents). In addition thereto
Doctor responsible sleep performance model based on dual process model
。
Limitations of current biomathematical models regarding alertness include: predicting the average alertness of the population instead of the instantaneous alertness of a specific individual, and neglecting the influence of individual differences, such as the difference of age, sex, work experience and the like; analyzing less external factors of the environment, such as sleeping forms, environment, workplace environment and the like; most models are laboratory products, and insufficient influences of real life and work, such as pressure, work positions, task categories and the like, are analyzed; some models seem to equate the time of getting-up with the time of work start, while others focus only on the time of work start without analyzing the time of getting-up, ignoring the transition time between the two, such as the period of waking time (non-sleep inertia time) between getting-up and putting into work; since the individual's rest-activity data and sleep-wake data are closer to the real work-life scenario than the work-rest data, while the human's activity patterns can also be measured with more objective detection tools (e.g., recorded with a wrist-type activity recorder), which may make predictions more accurate, most current models do not utilize the individual's activity patterns to predict specific individual's rest-activity behavior and sleep-wake behavior. Therefore, how to solve the defects of the alert biological mathematical model so that the model can better visualize the working mode, the sleep-wake behavior, the fatigue and the risks related to the fatigue is an important reason that alert researchers need to continuously develop, test and perfect the alert biological mathematical model.
Through the above analysis, the problems and defects of the prior art are as follows: (1) the prior art can not intuitively predict the change trend of alertness data in a working period and predict the risk of data information in a period with low potential alertness, and the accuracy of data identification and prediction is low. (2) The prior art does not use objective arousal-sleep data such as an activity recorder and the like for inputting sleep conditions and the like, and can not effectively and visually display the sleep conditions. (3) In the prior art, the distinction of two dimensions of people and work during work alertness consumption is not considered, and the change of human body alertness in the work process is influenced by the common influence factors of the people and the work.
Disclosure of Invention
In order to overcome the problems in the related art, the disclosed embodiments of the present invention provide a system and a method for simulating and predicting user alertness and fatigue risk index. In particular to a method for predicting human alertness and fatigue risk index based on an energy concept. The technical scheme is as follows:
a simulation and simulation prediction system for user alertness and fatigue risk index, applied to a client, comprises:
the sleep monitoring equipment is used for objectively recording the sleep quality of the user through an embedded sleep quality data expression;
the alertness supplementing quantity data acquisition module is used for calculating the alertness supplementing quantity of the user at each moment by combining the sleep intensity according to the sleep quality acquired by the sleep monitoring equipment;
the intrinsic consumption data acquisition module is used for calculating the intrinsic consumption of the user alertness during rest according to the active mode of the wakefulness state;
the workload consumption data acquisition module is used for calculating the workload consumption of the user alertness during work according to the workload influence factors and the workload influence factor weights in the activity mode of the wakefulness state;
and the alertness consumption data acquisition module is used for calculating the alertness consumption according to the inherent alertness consumption and the workload consumption when the user is awake.
In one embodiment, the sleep quality data expression is:
in the formula (I), the compound is shown in the specification,
for equipment sleep monitoring index factor, wherein equipment adopts the activity monitoring bracelet, sleep quality monitoring index factor includes: total sleep duration, number of awakenings in sleep, average awakening duration in sleep, sleep efficiency and mobility index;
for the corresponding sleep monitoring index factor weight monitored by the sleep monitoring equipment,
。
in one embodiment, the simulation and prediction system for user alertness and fatigue risk index further comprises:
the sleep quality data acquisition module is used for calculating the sleep quality of the user according to the sleep influence factor and the sleep influence factor weight;
the alertness potential energy rhythm simulation module is used for simulating the alertness potential energy rhythm of the recessive circadian rhythm oscillation change of the human body;
the sleep intensity acquisition module is used for simulating the sleep intensity according to the sleep tendency and the alertness loss;
and the alertness and fatigue risk index change simulation result visualization module is used for simulating and calculating the alertness and fatigue risk index change during task execution according to the alertness, the alertness potential energy and the alertness kinetic energy, and performing visualization display.
Another object of the present invention is to provide a simulation and prediction method for user alertness and fatigue risk index using the simulation and prediction system for user alertness and fatigue risk index, which is applied to a client, and which implements the following steps based on the obtained past and/or future shift or shift situation data:
objectively recording the sleep quality of the user through an embedded sleep quality data expression according to the sleep monitoring equipment; or calculating the sleep quality of the user according to the sleep influence factor and the sleep influence factor weight;
calculating alertness supplementary amount data of each moment when the user sleeps based on the acquired sleep quality data and the sleep intensity;
calculating intrinsic consumption for alertness at rest according to an activity mode of an awake state, calculating workload consumption of human alertness at work according to workload influence factors and weights thereof, and calculating alertness consumption of a user at each moment when the user is awake based on the acquired intrinsic consumption in combination with the workload consumption;
and calculating the alertness supplement amount and the alertness consumption amount at each moment when the user is awake according to the sleeping time, calculating the alertness change and the fatigue risk index change when the user is awake, and performing visual display.
In one embodiment, in calculating the sleep quality data of the user according to the sleep influence factor and the sleep influence factor weight, the sleep quality data expression when the user sleeps is calculated as follows:
in the formula (I), the compound is shown in the specification,
as the sleep quality influencing factor, the sleep quality influencing factor includes: sleep environment, sleep patterns, sleep self-assessment, and snoring frequency;
weights for the corresponding sleep impact factors;
；
the sleep environment includes: temperature, light-shielding and sound-insulating properties;
in the calculation of the intrinsic consumption amount for alertness at rest according to the activity pattern of wakefulness, the intrinsic consumption amount expression is:
wherein 100 is the maximum value of the alertness in the human body; 4 is the number of days; 24 is the number of hours of a day;
in the calculation of the workload consumption of the human body alertness in work according to the workload influence factors and the weights thereof, the workload consumption expression is as follows:
in the formula (I), the compound is shown in the specification,
alertness energy consumption rate;
the work load influence factors are human influence factors in the working process and comprise: age, experience, proficiency, human condition;
；
the influence factors of the work in the work process comprise: job strength, job nature and job responsibilities;
weight for the corresponding workload impact factor;
；
in the calculating of the alertness consumption per time when the user is awake based on the acquired intrinsic consumption amount in combination with the workload consumption amount, the calculating of the alertness consumption per time when the user is awake includes:
in the formula (I), the compound is shown in the specification,
alertness energy consumption rate;
is alert to the energy consumption.
In one embodiment, the calculating the alertness supplementation amount based on sleep time and the alertness consumption amount per time while awake comprises:
in the formula (I), the compound is shown in the specification,
in order to be a sleep tendency coefficient,
in order to alert the user to the loss factor,
in order to be prone to sleep, the sleep-oriented pillow has the advantages that,
in order to alert the user of the loss of energy,
in order to be the intensity of the sleep,
in order to be of a quality of sleep,
in order to be alert to the ability to supplement the amount,
the rate can be supplemented for alertness.
In one embodiment, in calculating the change of alertness when the user is awake and the change of fatigue risk index, the change of alertness is calculated as:
in the formula (I), the compound is shown in the specification,
is composed of
The alertness of the moment of time can be,
in order to initiate the point in time of arousal,
the intrinsic rate of consumption of alertness in the awake state,
to alert the energy workload consumption rate,
is alert energy;
calculating the change in the fatigue risk index includes:
in the formula (I), the compound is shown in the specification,
in order to be alert to the kinetic energy,
in order to be alert to the effects of alertness,
in order to alert the user of the potential energy,
is a fatigue risk index.
It is a further object of the invention to provide a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the method for user alertness and fatigue risk index simulation prediction.
It is a further object of the present invention to provide a computer readable storage medium storing a computer program which, when executed by a processor, causes the processor to execute the method for user alertness and fatigue risk index simulation prediction.
Another object of the present invention is to provide an activity recorder for the visual display of objective arousal-sleep data, which carries the simulation and prediction system for user alertness and fatigue risk index.
By combining all the technical schemes, the invention has the advantages and positive effects that:
first, aiming at the technical problems existing in the prior art and the difficulty in solving the problems, the technical problems to be solved by the technical scheme of the present invention are closely combined with results, data and the like in the research and development process, and how to solve the technical scheme of the present invention is deeply analyzed in detail, and some creative technical effects brought by the solution of the problems are specifically described as follows: the model system method of the invention predicts and evaluates the alertness change of the human body at any time (whether in working or non-working state) in a certain day, whether the 'certain day' has occurred or the 'certain day' planned to occur in the future, based on the historical and future predicted sleep conditions of the human body (analyzing the sleep intensity affected by the sleep tendency and the energy deficit size, and analyzing the sleep quality affected by the sleep self-evaluation, the sleep environment, the snoring frequency and the sleep form). In addition, the model system and the method of the invention improve and solve some limitations and defects existing in the current alert biological mathematical model. Neglecting the influence of individual difference, the model system and the method classify the crowd, distinguish the difference of people such as age, experience, proficiency, human body state and the like when alertness is consumed, and distinguish the difference of work influence such as working strength, working property, post responsibility and the like. When alertness can be supplemented, the influence of sleep quality of people with different snoring frequencies on supplementation is analyzed; the model system and the method analyze the environment, such as the analysis of the environment such as the temperature, the sound insulation, the light shielding and the like of sleep during sleep supplement, the analysis of the sleep forms of a habitual bed, a temporary bed and a seat during work consumption, and the analysis of the work environment and the determination of the sleep quality, and introduce application objective monitoring sleep equipment to be accessed into a model port, so that the parameters of the sleep quality in the model are more objective and accurate; confusion between work start time and wake up time, the model system and method of the present invention analyzes the awake non-work period and analyzes the inherent consumption of the part of the period while awake; meanwhile, the model system and the method can also be accessed into objective wake-sleep data such as an activity recorder and the like for inputting sleep conditions and the like.
Secondly, regarding the technical solution as a whole or from the perspective of products, the technical effects and advantages of the technical solution to be protected by the present invention are specifically described as follows: the invention performs calculations based on the concept of energy, analyzing the characteristics of circadian rhythm/time of day, sleep intensity (sleep tendency and energy deficit magnitude), sleep quality (self-assessment of sleep quality, sleep environment, frequency of snoring and sleep patterns and introduction of objective equipment for monitoring), intrinsic consumption of wake-up quiescence, workload consumption (human factors and work factors). The invention quantifies human alertness from the energy point of view and provides an alertness concept leading the human alertness based thereon. Human body alertness is composed of two parts, one part is the dominant alertness kinetic energy (like the dominant kinetic energy of an object) which dominates alertness, and the other part is the recessive dominant alertness potential energy of a human body circadian rhythm, which is interconverted with alertness kinetic energy and is related to the circadian rhythm or the time of day only (like the recessive potential energy of an object, which is related to the position height only). The biological mathematical model provided by the invention analyzes the strength and quality of sleep. Wherein the sleep intensity analyzes the influence of sleep tendency caused by circadian rhythm or time and the influence of energy loss; the sleep quality analyzes the evaluation of the self-feeling of the sleep quality, the sleep environment, the frequency of snoring, and the influence of the sleep form. The present invention provides a biological mathematical model that distinguishes between intrinsic consumption in the awake state and consumption in the workload state. The workload consumption analyzes the influence of factors such as the work type and the work experience. The biological mathematical model provided by the invention also integrates scientific analysis related to alertness, such as human circadian rhythm, and the like, and can be used for predicting the change situation of alertness of workers (particularly mental workers) at any time in a certain day (no matter the workers have occurred or will occur), more intuitively finding out the change trend of the alertness of the human body in a working period and predicting potential lower-period alertness risks.
Third, as an inventive supplementary proof of the claims of the present invention, there are also presented several important aspects: the expected income and commercial value after the technical scheme of the invention is converted are as follows: the method can be applied to all industries related to human work, such as traffic industries of civil aviation, railways and the like, and other shift system industries and the like. The technical scheme of the invention fills the technical blank in the industry at home and abroad: provides a tool for predicting, monitoring and investigating alertness and fatigue risk of a human body.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
FIG. 1 is a schematic diagram of a method for predicting a person alertness and a fatigue risk index according to an embodiment of the present invention;
drawing (A)
The situation of comparison between the alertness potential energy dominated by the circadian rhythm and the oral cavity temperature change at any moment provided by the embodiment of the invention is shown;
drawing (A)
The situation of comparison between the alertness potential energy dominated by the circadian rhythm and the alertness experimental parameter data provided by the embodiment of the invention changes at any moment;
drawing (A)
The situation that the alertness potential energy dominated by the circadian rhythm and the traffic accident rate caused by sleeping change at any moment is compared;
drawing (A)
The invention provides the alertness potential energy and the alertness potential energy which are dominated by the circadian rhythm
The rhythm oscillator in the model changes and contrasts the situation at any moment;
drawing (A)
According to the embodiment of the invention, the alert energy biological mathematical model predicts three energies of alert kinetic energy, alert potential energy and alert energy of a person sleeping for 8 hours and waking for 16 hours every day from 12 o 'clock to 8 o' clock at midnight;
drawing (A)
According to the embodiment of the invention, the alertness energy biological mathematical model predicts the alertness kinetic energy/alertness of a person sleeping for 8 hours and waking for 16 hours every day from 12 o 'clock to 8 o' clock at midnight;
drawing (A)
The energy prediction method is used for predicting three energies of alertness kinetic energy, alertness potential energy and alertness energy of a person sleeping for 4 hours and waking for 20 hours every day from 12 o 'clock to 4 o' clock in the midnight according to the biomathematic model of alertness.
Drawing (A)
According to the embodiment of the invention, the alertness biological mathematical model predicts the alertness kinetic energy/alertness of a person sleeping for 4 hours and waking for 20 hours every day from 12 o 'clock to 4 o' clock at midnight;
drawing (A)
According to the embodiment of the invention, the alert energy biological mathematical model predicts three energies of alert kinetic energy, alert potential energy and alert energy of a person sleeping for 0 hour and waking for 24 hours;
drawing (A)
The alertness biological mathematical model predicts the alertness kinetic energy/alertness of a person sleeping for 0 hour and waking for 24 hours;
FIG. 6 is a diagram of the calculation of alertness kinetic energy/alertness and fatigue risk index of a person in a 7-8-12-13-18-23-sleep by an energy-of-mind biological mathematical model simulation according to an embodiment of the present invention;
drawing (A)
Is an alert energy according to an embodiment of the present inventionMany people undertake (such as a certain flight task) flight unit together for certain responsibility through biological mathematical model analog simulation calculation
Alertness kinetic energy/alertness while on duty;
drawing (A)
According to the embodiment of the invention, the alertness biological mathematical model simulates and simulates a certain responsibility and multiple persons to undertake (such as a certain flight task) the flight unit
Alertness kinetic energy/alertness while on duty;
drawing (A)
According to the embodiment of the invention, the alertness kinetic energy/alertness of a cockpit which is assumed by a plurality of persons in a certain responsibility (such as a certain flight task) is calculated by analog simulation of the alertness energy biological mathematical model;
FIG. 8 is a potential application function of an alertness biometrical model according to an embodiment of the invention;
fig. 9 is a theoretical conceptual block diagram of the application of an alertness biological mathematical model according to an embodiment of the invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, but rather should be construed as broadly as the present invention is capable of modification in various respects, all without departing from the spirit and scope of the present invention.
First, illustrative embodiments:
example 1
The embodiment of the invention provides a simulation and prediction system for user alertness and fatigue risk index, which is applied to a client, and comprises:
the sleep quality data acquisition module is used for calculating the sleep quality of the user according to the sleep influence factor and the sleep influence factor weight;
the alertness supplement amount data acquisition module is used for calculating alertness supplement amount of the user at each moment by combining the sleep quality acquired by the sleep quality data acquisition module with the sleep intensity;
the intrinsic consumption data acquisition module is used for calculating the intrinsic consumption of the user alertness when the user does not work and has a rest according to the active mode of the wakefulness state;
the workload consumption data acquisition module is used for calculating the workload consumption of the user alertness during work according to the workload influence factors and the workload influence factor weights in the active mode of the wakefulness state;
and the alertness consumption data acquisition module is used for calculating alertness consumption according to the inherent alertness consumption and workload consumption of the user when the user is awake.
In a preferred embodiment, the simulation and prediction system for user alertness and fatigue risk index further comprises: the sleep monitoring device is used for recording the sleep quality of the user;
the alertness potential energy rhythm simulation module is used for simulating the alertness potential energy rhythm of the recessive circadian rhythm oscillation change of the human body;
the sleep intensity acquisition module is used for simulating the sleep intensity according to the sleep tendency and the alertness loss;
and the alertness and fatigue risk index change simulation result visualization module is used for simulating and simulating to calculate the alertness and fatigue risk index change during task execution according to the alertness energy, the alertness potential energy and the alertness kinetic energy, and performing visualization display.
The embodiment of the invention also provides a simulation and prediction method for the user alertness and the fatigue risk index, which is applied to the client side, and the simulation and prediction method for the user alertness and the fatigue risk index implements the following steps based on the acquired past and/or future shift or shift situation data:
calculating sleep quality data of a user during sleeping according to the sleep influence factor and the sleep influence factor weight, or accessing the sleep quality data recorded by sleep monitoring equipment, and calculating alertness supplementation data of the user at each moment during sleeping based on the acquired sleep quality data and the sleep intensity;
calculating the intrinsic consumption for alertness at rest according to the activity mode of the wakefulness state, calculating the workload consumption of human body alertness at work according to the workload influence factors and the weights thereof, and calculating the alertness consumption of the user at each moment when the user is wakefulness based on the acquired intrinsic consumption and the workload consumption;
and calculating the alertness supplement amount and the alertness consumption amount at each moment when the user is awake according to the sleeping time, calculating the alertness change and the fatigue risk index change when the user is awake, and performing visual display.
In a preferred embodiment, in calculating the sleep quality data of the user during sleep according to the sleep influence factor and the sleep influence factor weight, the sleep quality data expression of the user during sleep is calculated as follows:
in the formula (I), the compound is shown in the specification,
the sleep quality influence factors include: sleep environment, sleep patterns, sleep self-assessment, and snoring frequency;
weights for the corresponding sleep impact factors;
；
the sleep environment includes: temperature, light-shielding and sound-insulating properties;
in the sleep quality data recorded by the access sleep monitoring device, calculating the sleep quality data expression of the user during sleep as follows:
in the formula (I), the compound is shown in the specification,
for equipment sleep monitoring index factor, wherein equipment adopts the activity monitoring bracelet, sleep quality monitoring index factor includes: total sleep duration, number of awakenings in sleep, average awakening duration in sleep, sleep efficiency and mobility index;
for the corresponding sleep monitoring index factor weight monitored by the sleep monitoring equipment,
。
in a preferred embodiment, in calculating an intrinsic consumption amount for alertness at rest from an activity pattern of an awake state, and calculating a workload consumption amount of human alertness at work from a workload influence factor and a weight thereof, in calculating an alertness consumption amount per time when a user is awake based on the acquired intrinsic consumption amount in combination with the workload consumption amount,
the intrinsic consumption expression is:
in the formula, 100 is the maximum value of the alertness in the human body; 4 is the number of days; 24 is the number of hours of a day;
the workload consumption expression is:
in the formula (I), the compound is shown in the specification,
alertness energy consumption rate;
the work load influence factors are human influence factors in the working process and comprise: age, experience, proficiency, human condition;
；
the influence factors of the work in the work process comprise: job strength, job nature and job responsibilities;
weighting the corresponding workload impact factors;
；
the calculation of the alertness consumption of the user at each moment when the user is awake comprises the following steps:
in the formula (I), the compound is shown in the specification,
alertness energy consumption rate;
energy consumption is alert.
In a preferred embodiment, said calculating an alertness supplementation amount based on sleep time and said alertness consumption amount per time of arousal comprises:
in the formula (I), the compound is shown in the specification,
in order to be a sleep tendency coefficient,
in order to alert the user to the loss factor,
in order to be prone to sleep, the sleep-oriented pillow has the advantages that,
in order to alert the person of the loss of energy,
in order to be the intensity of the sleep,
in order to achieve the quality of sleep,
in order to be alert to the ability to supplement the amount,
the rate can be supplemented for alertness.
In a preferred embodiment, in calculating the change of alertness when the user is awake and the change of fatigue risk index, the change of alertness is calculated as:
in the formula (I), the compound is shown in the specification,
is composed of
The alertness of the moment of time can be,
in order to initiate the point in time of arousal,
the intrinsic rate of consumption of alertness in the awake state,
to alert the rate of consumption of the workload,
is alert energy;
calculating the change in the fatigue risk index includes:
in the formula (I), the compound is shown in the specification,
in order to alert the user of the kinetic energy,
in order to be alert to the effects of alertness,
in order to alert the user of the potential energy,
is a fatigue risk index.
Example 2
As shown in fig. 1, there is illustrated an analog simulation prediction system for user alertness and fatigue risk index (an alertness biological mathematical model system for evaluating alertness of a person) provided according to an embodiment of the present invention, which can be used by a user to predict changes in alertness from past and/or future shift or shift situations.
The human body alertness potential energy is dominated by the human body circadian rhythm and is influenced by alertness energy (as alertness energy is gradually reduced when the human body is awake, the sum of alertness kinetic energy and alertness potential energy is also gradually reduced, so that the fluctuation range of alertness potential energy rhythm is also reduced along with the reduction of alertness energy, so that alertness energy is adopted
As a function of the alert potential rhythm amplitude).
The alertness is divided into alertness during sleep and alertness during wakefulness.
The change of the alertness during sleeping is a process of supplementing the alertness of the human body, namely the alertness of the human body during sleeping can be supplemented (because the sleeping is the most effective measure for relieving fatigue, the related alertness supplementation in the system and the method of the invention of the biological mathematical model of the alertness is only related to the sleeping), the alertness supplementation is related to the sleeping intensity and the sleeping quality, the sleeping intensity is related to the sleeping tendency and the loss of the alertness, and the sleeping quality influence factors are more (the invention provides the sleeping quality influence factors which comprise four types of sleeping environment, sleeping form, sleeping self-evaluation and snoring frequency).
The change of the alertness during waking is the process of the expenditure of the alertness of the human body, and the invention divides the alertness expenditure into the inherent expenditure during the waking state (as long as the alertness is gradually reduced in the waking state) and the workload expenditure during the working state.
The invention discloses a method for obtaining alertness by subtracting alertness potential energy from alertness potential energy, which indicates that alertness potential energy is the alertness degree expressed by human body activities (including wakefulness states, working activities and the like), is expressed by alertness degree, and can be converted into fatigue risk index through functional relation.
An application example of the biological mathematical model of alertness of the present invention includes using circadian rhythm process, alertness potential energy change process, alertness energy supplement process (circadian rhythm sleep tendency process, alertness energy loss process, sleep intensity process, sleep quality evaluation process), alertness energy consumption process (arousal state alertness energy inherent consumption process, working state alertness energy workload consumption process), alertness kinetic energy-alertness change process, and fatigue risk index change process. The processes can be realized on a sub-general digital computer, and through the realization of mathematical modeling, the alertness biological mathematical model system and the alertness biological mathematical model method can predict the change of alertness and fatigue risk index.
To some extent, alertness and desire to sleep in the awake state are controlled by circadian process 1. Many studies on performance, reaction time, alertness, body temperature, etc. have shown that the circadian process is not a simple sine wave. Generally, human performance and alertness reach a minimum at about 04:00 early in the morning and a maximum at about 20:00 early in the morning, during which time drowsiness occurs between 12:00 and 14:00 hours. The presence of these conditions indicates that at least two oscillators are involved in the circadian process, and these multiple oscillators are taken into account in the method of assessing alertness according to the invention.
The alertness potential of the invention combines a day consisting of the sum of two cosine wavesThe night rhythm process, the two cosine waves are one for a 24 hour period and the other for a 12 hour period, and are out of phase. Thereby generating the predicted change of the alertness potential rhythm which is very similar to the known body temperature circadian rhythm change mode, and referring to the figure
The mathematical simulation prediction result of the change of the alertness potential energy along with the moment is compared with the experimental result of the change of the oral cavity temperature along with the moment, the general change trend of the alertness potential energy is found to be consistent with the change trend of the oral cavity temperature, and the consistency of the simulation prediction result of the alertness potential energy and the change of the biological rhythm of the human body is shown. Reference to the drawings
Comparing the mathematical simulation prediction result of the change of the alertness potential energy at any moment with the experimental result of the change of the subjective alertness at any moment, wherein the condition that the rising trend of the alertness potential energy is slowed down between 12:00 and 14:00 noon corresponds to the experimental result that the subjective alertness rating result of the human body begins to fall after 12 o' clock at noon, and the graph corresponds to
The invention shows that the alertness potential energy of the invention can explain the change of alertness at noon, and the same conclusion is also shown in the figure
Is verified. Reference to the drawings
Comparing the mathematical simulation prediction result of the change of the alertness potential energy with time with the research result of a traffic accident related to sleep between 1984 and 1989 in Israel, two accident-high-occurrence time periods appear in the sleep-related accidents, the highest accident-occurrence time period is 03:00-06:00 in the early morning, the higher accident-occurrence time period is about 15:00 in the afternoon, and in addition, a graph is drawn
The results of the sleep-related accidents and the alertness potential energy predicted and simulated by the method are shown as follows on the whole: the time period with lower alertness potential energy is a sleep-related accident high-occurrence period; the time period when the alertness potential is high is the sleep-related accident low-frequency period. Reference to the drawings
The mathematical simulation prediction result of the change of the alertness potential energy at any moment in the invention is compared with that of the Hursh professor and the like
The performance/performance of the biological mathematical model and the mathematical simulation prediction result of the circadian rhythm model are found to be very similar to each other in terms of the overall change trend along with time and the wave form change condition. In conclusion, the alertness potential function of the present invention can be used as a function reflecting the circadian rhythm in a biological mathematical model, and the alertness potential expression is as follows:
in the formula (I), the compound is shown in the specification,
is alert potential energy;
is alert energy;
is an alert potential energy rhythm.
The expression for circadian rhythms (also alert potential rhythms) is as follows:
in the formula (I), the compound is shown in the specification,
is an alert potential energy rhythm;
and
is the amplitude of the cosine periodic function;
is the time;
is a period;
and
is the phase of the cosine periodic function.
One case of the alertness of the present invention is the alertness during sleep, which is equal to the residual alertness plus the alertness complement at the beginning of sleep, since the alertness during sleep is only complemented. The expressions that alert can supplement are as follows:
in the formula (I), the compound is shown in the specification,
can be used for supplementing alertness;
for alertness replenishment, the expression is as follows:
in the formula (I), the compound is shown in the specification,
sleep intensity 4;
sleep quality 6.
in the formula (I), the compound is shown in the specification,
sleep propensity 2, which is a function of the alertness potential rhythm;
in order to alert the user to the loss of energy 3,
the loss of alertness at a moment is the maximum sum of alertness
A function of the difference in temporal alertness. Thus, it is possible to provide
Sleep tendency 2 and
the expression for alertness deficit 3 is as follows:
in the formula (I), the compound is shown in the specification,
is a sleep tendency coefficient;
the coefficient is lost for alertness.
in the formula (I), the compound is shown in the specification,
for the sleep quality influence factors, the invention provides four sleep quality influence factors (sleep environment, sleep form, sleep self-evaluation and snoring frequency);
weights for the corresponding sleep impact factors;
。
meanwhile, the determination of the model about the sleep quality data also allows objective sleep monitoring equipment (such as an activity monitoring bracelet) to record the sleep condition input so as to replace subjective sleep quality self-evaluation and enhance the objectivity. When the sleep monitoring device is accessed to record sleep data, the sleep quality formula at the moment is as follows:
in the formula (I), the compound is shown in the specification,
for equipment sleep monitoring index factor, wherein equipment adopts the activity monitoring bracelet, sleep quality monitoring index factor includes: total sleep duration, number of awakenings in sleep, average awakening duration in sleep, sleep efficiency and mobility index;
for the corresponding sleep monitoring index factor weight monitored by the sleep monitoring equipment,
。
another instance of the alert energy of the present invention is the alert energy upon arousal, since the alert energy is consumed as long as it is aroused, and thus the alert energy upon arousal is equal to the remaining alert energy at the moment of initiation of arousal minus the alert energy consumption. The expression for alertness consumption is as follows:
in the formula (I), the compound is shown in the specification,
energy consumption for alertness;
for alertness consumption, the expression is as follows:
in the formula (I), the compound is shown in the specification,
in order to obtain the intrinsic consumption rate in an awake state, it was found from the past sleep deprivation experiments that it takes about 4 days to consume to 0, and assuming that the maximum value of the alertness in the human body is 100, the alertness in an awake state of no operation is decreased at a rate of about 25% per day, so that the intrinsic consumption rate in the awake state of the human body per hour is expressed as follows:
reference to the drawings
The mental alertness fatigue biological mathematical model of the invention is used for predicting and simulating that a person is always in an aroused state, and finds that the energy of the human body can be maintained for 4 days at most.
In expression (10)
The workload consumption rate of the working alertness is expressed as follows:
in the formula (I), the compound is shown in the specification,
alertness energy consumption rate;
the invention provides four influencing factors (age, experience, proficiency and human body state) for the influencing factors of people in the working process
；
The invention provides three influence factors (working strength, working property and post responsibility) for the influence factors of the work in the working process;
weighting the corresponding workload impact factors;
。
in the formula (I), the compound is shown in the specification,
is composed of
A first derivative of alertness;
for quality of sleep
；
Is a sleep tendency coefficient;
the energy loss coefficient is alert;
alert potential energy rhythm;
solving the differential equation (13) can obtain
General solution expression of alertness:
the human body only consumes the alert energy when being aroused, so the human body only consumes the alert energy when being aroused
Alert energy expression of (a):
in the formula (I), the compound is shown in the specification,
is composed of
Alertness at the moment;
the point at which arousal begins;
the intrinsic consumption rate of alertness in the awake state;
alert energy workload consumptionAnd (4) the ratio.
In summary,
the expression for alertness is:
in the formula, when the human body is sleeping
When awake
. In addition, the first and second substrates are,
expressed as:
in the formula (I), the compound is shown in the specification,
is alert kinetic energy;
is alert energy;
is alert potential energy.
If the mechanical kinetic energy shows the motion capability of the substance, the alertness kinetic energy can be used for showing the alertness capability of the person and is expressed in the alertness degree.
in the formula (I), the compound is shown in the specification,
is a fatigue risk index;
is alert kinetic energy (alertness).
Fig. 1 is a flowchart illustrating an analog simulation prediction method (a method of an alertness biological mathematical model) for user alertness and fatigue risk index provided by an embodiment of the present invention. In step 31, a schedule (past and/or future) is input into the model anddetermining a start model simulation time
The human state (sleep or wake) at that moment and the corresponding computational simulation is started.
During sleep, the step 34 of calculating alertness supplement in simulation requires the step 32 of calculating sleep quality and the step 33 of calculating sleep intensity, while the step 33 of calculating sleep intensity must determine the step 37 of calculating sleep tendency and the step 38 of losing alertness, the step 37 of calculating sleep tendency is determined by the step 35 of oscillating circadian rhythm and the step 36 of alertness potential, the step 38 of calculating sleep tendency is determined by the step 43 of alertness supplement of the t0 of the step 34 of calculating alertness and the step 44 of alertness of sleep and the step 45 of alertness of the step 36 of calculating alertness of the step 43 of alertness supplement of the step 34 of alertness supplement of the step 36 of alertness supplement, and finally the step 46 of fatigue risk index is calculated by the conversion formula.
Upon waking, the analog computation step 42 is alert to energy consumption, and prior to computation, step 39 is performed to determine whether to operate, step 42 is determined only by step 40 intrinsic consumption when "no", and step 42 is determined by both step 40 intrinsic consumption and step 41 workload consumption when "yes". Then the calculation of the alertness energy in the awake state is determined by the alertness energy step 43 and the alertness energy step 42 at time t0, the alertness energy and the alertness energy are determined in combination with the alertness energy of steps 35 and 36, the alertness energy and the alertness 45 of step 44, and finally the fatigue risk index of step 46 is determined by the conversion formula.
Fig. 9 shows the theoretical concept of the application of the alert energy biological mathematical model of the present invention, first collecting the data information in step 20, inputting the data set into the model through step 21, and analyzing 23 "alert kinetic energy/alert/fatigue risk possibility", 26 "risk of task error due to fatigue" and 29 "risk of accident due to fatigue" through the computational simulation of the model in step 22. The personal data collected in step 20 determines 24 "personal fatigue" and the "personal fatigue" is verified 24 by 23 calculated by model simulation. The "personal task error" is then contributed 27 by both aspects of the "personal fatigue" in combination with the 25 work-related impact factors, while the "personal task error" is verified 27 by the model simulation calculation 26. The 30 "incidents" are contributed by the 27 "personal task errors" in combination with the 28 incident impact factors in the human-machine-ring-tube organizational system, while the 30 "incidents" are validated by 29 calculated by model simulation.
In the above embodiments, the description of each embodiment has its own emphasis, and reference may be made to the related description of other embodiments for parts that are not described or recited in any embodiment.
For the information interaction, execution process and other contents between the above-mentioned devices/units, because the embodiments of the method of the present invention are based on the same concept, the specific functions and technical effects thereof can be referred to the method embodiments specifically, and are not described herein again.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
II, application embodiment:
application example 1
Based on the above-mentioned embodiments, the method for predicting the alertness and fatigue risk index of a person according to the embodiments of the present invention is applicable to a computer device, the computer device including: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, the processor implementing the steps of any of the various method embodiments described above when executing the computer program.
Application example 2
Based on the above-mentioned embodiments, the method for predicting the alertness and the fatigue risk index of a person according to the embodiments of the present invention is applicable to a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method embodiments can be implemented.
Application example 3
Based on the above-mentioned embodiments, the method for predicting the alertness and the fatigue risk index of the person according to the embodiments of the present invention is applicable to an information data processing terminal, which is configured to provide a user input interface to implement the steps in the above-mentioned embodiments when implemented on an electronic device, and the information data processing terminal is not limited to a mobile phone, a computer, or a switch.
Application example 4
Based on the embodiments described above, the method for predicting the alertness and the fatigue risk index of a person according to the embodiments of the present invention is applicable to a server, and the server is configured to provide a user input interface to implement the steps in the above embodiments of the method when implemented on an electronic device.
Application example 5
Based on the above-mentioned embodiments, the method for predicting alertness of a person and fatigue risk index provided by the embodiments of the present invention may be applied to a computer program product, and when the computer program product runs on an electronic device, the electronic device may be enabled to implement the steps in the above-mentioned method embodiments when executed.
Application example 6
Based on the above-mentioned embodiments, the method for predicting alertness and fatigue risk index of a person according to the embodiments of the present invention is applicable to a computer-readable medium having computer-executable instructions for inputting past and/or future shift or shift situations to perform a program for assessing alertness of a person performing a work, the program comprising the steps of:
simulating the alert potential energy rhythm of the recessive circadian rhythm oscillation change of the human body;
simulating sleep intensity according to the sleep tendency and the alertness loss;
calculating sleep quality according to the sleep quality influence factor, or accessing an objective sleep monitoring device to record the sleep quality;
calculating alertness supplementation according to sleep intensity and sleep quality;
calculating the alertness workload consumption according to the workload influence factor;
calculating the consumption of alertness according to the inherent consumption of alertness and workload consumption when a human body is awake;
and (4) simulating and calculating the alertness and fatigue risk index change during the task execution according to the alertness energy, the alertness potential energy and the alertness kinetic energy.
Application example 7
As shown in fig. 8, the potential application of the alert energy biological mathematical model provided by the embodiment of the present invention is demonstrated, and according to the analysis, the method of the present invention can be applied to "predictive applications" such as personal alertness and fatigue risk, "auxiliary applications" such as work task shift table design, and "passive survey applications" such as accident sign and accident survey.
The 'forecast application' comprises forecast of alertness/fatigue risk of personnel, advance risk assessment of shift schedule/shift planning mode. The auxiliary application comprises a scheduling list/shift plan design, scheduling/shift control and emergency adjustment management. The "passive survey class application" includes the evaluation of alertness and impact to emergencies of shift schedules/shift plans that actually occurred in the past, providing proof verification for fatigue/safety reporting/safety incidents/accident investigation analysis. Meanwhile, a feedback loop is formed together, and the risk is reduced through continuous adjustment measures. Based on the analysis, the model system method can process and analyze data of large-scale scheduling.
The above-mentioned integrated unit ifWhen implemented in the form of software functional units and sold or used as a stand-alone product, can be stored in a computer-readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments described above may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include at least: any entity or device, record medium, computer memory, read-only memory capable of carrying computer program code to a photographing device/terminal apparatus
Random access memory
Electrical carrier wave signals, telecommunications signals, and software distribution media. Such as a usb-disk, a removable hard disk, a magnetic or optical disk, etc.
Third, evidence of relevant effects of the embodiment:
the first experimental case: it is generally believed that people need 8 hours of sleep per day to be fully effective, avoiding cumulative sleep debt. Drawing (A)
Based on the alertness type of the present invention, it can be found that there are two peaks near 1000 and 2000, a valley at 1200 and 1400, and a minimum at about 4 am each day, for the alertness kinetic energy/alertness prediction of a person sleeping 8 hours and waking 16 hours. Drawing (A)
Based on the model, the alertness (with a triangular curve) of 8 hours of sleep and 16 hours of waking,The change of the alertness kinetic energy (with a full circle curve) and the alertness potential energy (with a black circle curve) can find that the change amplitude of the three energies in one day is changed in an oscillation way by about 20 percent and is more stable after the mode is adapted. Therefore, in response to such a work and rest mode of 8-16-hour sleep, cumulative sleep debt, i.e., cumulative fatigue risk, does not occur.
Experiment case two: the alertness of a person sleeping for 4 hours and waking for 20 hours is simulated and calculated by applying the alertness model of the invention
Shows the change of alertness and kinetic energy in the work and rest mode, and can find the change situation and graph in the previous 2 days
The change situation of the alertness kinetic energy/alertness in the sleep 8-waking 16-hour work and rest mode is similar, but from the third day, the alertness kinetic energy/alertness of the human body in the awake state is continuously reduced (but a small alertness peak is still formed at about 1000 points), until the alertness kinetic energy/alertness of the human body is below 50% after a period of time, stronger accumulative sleep debt is generated, and accumulative fatigue is caused. Fig. 4A shows changes of human body alertness (curve with triangle), alertness (curve with circle) and alertness (curve with black dots) in sleep 4 hours-wake 20 hours mode, which shows that the whole of alertness (curve with triangle) and alertness (curve with circle) is continuously reduced with time and is always in loss state, and meanwhile, the phenomenon of circadian rhythm disorder appears after a period of time when alertness (curve with black dots) is in sleep 4 hours-wake 20 hours mode.
Experiment case three: the alertness of a person sleeping for 0 hour and waking for 24 hours (continuous sleep deprivation) is simulated and calculated by applying the alertness model of the invention, and the diagram
Shows the mode of work and restThe change of the lower alertness kinetic energy/alertness can be found out from the change situation and the graph of the previous 1 day
The change conditions of the alertness kinetic energy/alertness in the sleep mode from 8 hours to 16 hours are similar, but from the second day, the alertness kinetic energy/alertness of the human body is continuously reduced in the awake state, and linearly reduced on the fourth day until the alertness kinetic energy/alertness of the human body is reduced to 0 percent after a period of time, namely, the state without alertness (namely, the state without consciousness) is generated. Same drawing
The changes of human body alertness energy (with a triangular curve), alertness kinetic energy (with a circular curve) and alertness potential energy (with a black circular curve) in a continuous sleep deprivation mode are shown, so that the fact that the whole of alertness energy (with a triangular curve) and alertness kinetic energy (with a circular curve) can be continuously reduced along with time can be found, the energy is 0 after about four days, namely, normal physiological function can not be maintained, and meanwhile, circadian rhythm disorder continuously occurs in alertness potential energy (with a black circular curve) until no physiological characteristics exist.
Experiment case four: the alertness model of the invention is applied to carry out analog simulation calculation on a person in normal waking-working-resting-sleeping, and fig. 6 shows that the alertness kinetic energy/alertness and fatigue risk index analog simulation calculation on a person in 7-8-12-13-18-23-point sleeping based on the model system and method of the invention can find out the alertness and fatigue condition of the person at each moment in one day intuitively from fig. 6.
Experiment case five: the alertness model of the invention can also be used for each person (flight unit) in a certain duty (such as a certain flight task) of multi-person shift duty
) And the alertness of the duty (cockpit) for the simulation calculations. Drawing (A)
And the drawings
Respectively show a flight mission flight unit
And
and (3) simulating and calculating the condition of alertness kinetic energy/alertness during the duty flight task. Drawing (A)
The simulation calculation condition of the alertness kinetic energy/alertness of the personnel in the flight mission cockpit is shown.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, and any modification, equivalent replacement, and improvement made by those skilled in the art within the technical scope of the present invention disclosed herein, which is within the spirit and principle of the present invention, should be covered by the present invention.
Claims (10)
1. An analog simulation prediction system for user alertness and fatigue risk index, applied to a client, comprises:
the sleep monitoring equipment is used for objectively recording the sleep quality of the user through an embedded sleep quality data expression;
the alertness supplementing quantity data acquisition module is used for calculating the alertness supplementing quantity of the user at each moment according to the sleep quality acquired by the sleep monitoring equipment and by combining the sleep intensity;
the intrinsic consumption data acquisition module is used for calculating the intrinsic consumption of user alertness during rest in the active mode of an awakening state;
the workload consumption data acquisition module is used for calculating the workload consumption of the user alertness during work according to the workload influence factors and the workload influence factor weights in the activity mode of the wakefulness state;
and the alertness energy consumption data acquisition module is used for calculating alertness energy consumption according to alertness energy inherent consumption and workload consumption when the user is awake.
2. The system for user alertness and fatigue risk index simulation prediction according to claim 1 wherein the sleep quality data expression is:
in the formula (I), the compound is shown in the specification,
for equipment sleep monitoring index factor, wherein equipment adopts the activity monitoring bracelet, sleep quality monitoring index factor includes: total sleep duration, number of awakenings in sleep, average awakening duration in sleep, sleep efficiency and mobility index;
weighting the sleep monitoring index factors for monitoring by corresponding sleep monitoring equipment;
。
3. the system for simulative simulation prediction of user alertness and fatigue risk index of claim 1, further comprising:
the sleep quality data acquisition module is used for calculating the sleep quality of the user according to the sleep influence factor and the sleep influence factor weight;
the alertness potential energy rhythm simulation module is used for simulating the alertness potential energy rhythm of the recessive circadian rhythm oscillation change of the human body;
the sleep intensity acquisition module is used for simulating the sleep intensity according to the sleep tendency and the alertness loss;
and the alertness and fatigue risk index change simulation result visualization module is used for simulating and calculating the alertness and fatigue risk index change during task execution according to the alertness, the alertness potential energy and the alertness kinetic energy, and performing visualization display.
4. An analog simulation forecasting method for user alertness and fatigue risk index for a user simulation forecasting system according to any one of claims 1 to 3, applied to a client, wherein the analog simulation forecasting method for user alertness and fatigue risk index for a user based on past and/or future shift or shift situation data acquired implements the following steps:
objectively recording the sleep quality of the user through an embedded sleep quality data expression according to the sleep monitoring equipment; or calculating the sleep quality of the user according to the sleep influence factor and the sleep influence factor weight;
calculating alertness supplementary amount data of each moment when the user sleeps based on the acquired sleep quality data and the sleep intensity;
calculating intrinsic consumption for alertness at rest according to an activity mode of an awake state, calculating workload consumption of human alertness at work according to workload influence factors and weights thereof, and calculating alertness consumption of a user at each moment when the user is awake based on the acquired intrinsic consumption in combination with the workload consumption;
and calculating the alertness supplementation amount and the alertness consumption amount at each moment when the user is awake according to the sleep time, calculating the alertness change and the fatigue risk index change when the user is awake, and performing visual display.
5. The method of claim 4, wherein in calculating the sleep quality of the user according to the sleep influencing factor and the sleep influencing factor weight, the sleep quality data expression when the user sleeps is calculated as follows:
in the formula (I), the compound is shown in the specification,
as the sleep quality influencing factor, the sleep quality influencing factor includes: sleep environment, sleep patterns, sleep self-assessment, and snoring frequency;
weights for the corresponding sleep impact factors;
；
the sleep environment includes: temperature, light-shielding property and sound-insulating property;
in the calculating of the intrinsic consumption amount for alertness at rest from the activity pattern of the wakefulness state, the intrinsic consumption amount expression is:
in the formula, 100 is the maximum value of the alertness in the human body; 4 is the number of days; 24 is the number of hours of a day;
in the calculation of the workload consumption of human body alertness in work according to the workload influence factors and the weights thereof, the workload consumption expression is as follows:
in the formula (I), the compound is shown in the specification,
rate of energy consumption for alertness;
the work load influence factors are human influence factors in the working process and comprise: age, experience, proficiency, human condition;
； 
the influence factors of the work in the work process comprise: job strength, job nature and job responsibilities;
weighting the corresponding workload impact factors;
；
in the calculating of the alertness consumption per time when the user is awake based on the acquired intrinsic consumption in combination with the workload consumption, the calculating of the alertness consumption per time when the user is awake includes:
in the formula (I), the compound is shown in the specification,
rate of energy consumption for alertness;
is alert to the energy consumption.
6. The method for user alertness and fatigue risk index simulation prediction according to claim 4, wherein the calculating of the amount of alertness supplementation from sleep time and the amount of alertness consumption per moment of arousal comprises:
in the formula (I), the compound is shown in the specification,
in order to be a sleep tendency coefficient,
in order to alert the user to the loss factor,
in order to have a tendency to sleep,
in order to alert the user of the loss of energy,
in order to be the intensity of the sleep,
in order to be of a quality of sleep,
in order to be alert to the ability to supplement the amount,
the rate can be supplemented for alertness.
7. The simulation and prediction method for user alertness and fatigue risk index according to claim 4, wherein in calculating the alertness change and the fatigue risk index change when the user is awake, the calculation of the alertness change is:
in the formula (I), the compound is shown in the specification,
is composed of
The alertness of the moment of time can be,
in order to be the point at which arousal begins,
the intrinsic rate of consumption of alertness in the awake state,
to alert the energy workload consumption rate,
is alert energy;
calculating the change in the fatigue risk index includes:
in the formula (I), the compound is shown in the specification,
in order to be alert to the kinetic energy,
in order to be alert to the effects of the alarm,
in order to alert the user of the potential energy,
is a fatigue risk index.
8. A computer arrangement, characterized in that the computer arrangement comprises a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to carry out the method for simulation prediction of user alertness and fatigue risk index according to any one of claims 5 to 7.
9. A computer-readable storage medium, storing a computer program which, when executed by a processor, causes the processor to carry out the method for user alertness and fatigue risk index simulation prediction according to any one of claims 5 to 7.
10. An activity recorder for the visual display of objective arousal-sleep data, characterized in that it carries a system for the analogue simulation prediction of user alertness and fatigue risk index according to any one of claims 1 to 3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210650393.5A CN114916919A (en) | 2022-06-10 | 2022-06-10 | System, method and application for predicting user alertness and fatigue risk index |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210650393.5A CN114916919A (en) | 2022-06-10 | 2022-06-10 | System, method and application for predicting user alertness and fatigue risk index |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114916919A true CN114916919A (en) | 2022-08-19 |
Family
ID=82813358
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210650393.5A Pending CN114916919A (en) | 2022-06-10 | 2022-06-10 | System, method and application for predicting user alertness and fatigue risk index |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114916919A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB2611427A (en) * | 2022-11-04 | 2023-04-05 | Polar Electro Oy | Predicting awake-time alertness |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110071873A1 (en) * | 2009-09-01 | 2011-03-24 | Edward Vaughan | Method and apparatus for mitigating aviation risk by analyzing and modeling air crew fatigue |
| CN113520328A (en) * | 2021-08-16 | 2021-10-22 | 成都云卫康医疗科技有限公司 | Sleep quality evaluation method |
| CN113907757A (en) * | 2021-10-18 | 2022-01-11 | 中国民航大学 | Alertness testing method based on attention system theory |
| US20220079512A1 (en) * | 2018-12-18 | 2022-03-17 | Koninklijke Philips N.V. | System and method for determining a level of alertness |
-
2022
- 2022-06-10 CN CN202210650393.5A patent/CN114916919A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110071873A1 (en) * | 2009-09-01 | 2011-03-24 | Edward Vaughan | Method and apparatus for mitigating aviation risk by analyzing and modeling air crew fatigue |
| US20220079512A1 (en) * | 2018-12-18 | 2022-03-17 | Koninklijke Philips N.V. | System and method for determining a level of alertness |
| CN113520328A (en) * | 2021-08-16 | 2021-10-22 | 成都云卫康医疗科技有限公司 | Sleep quality evaluation method |
| CN113907757A (en) * | 2021-10-18 | 2022-01-11 | 中国民航大学 | Alertness testing method based on attention system theory |
Non-Patent Citations (2)
| Title |
|---|
| 孙瑞山 等: "《警觉能心理疲劳风险模型》", 《中国安全科学学报》 * |
| 宋歌: "《飞行疲劳模型及应用研究》", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB2611427A (en) * | 2022-11-04 | 2023-04-05 | Polar Electro Oy | Predicting awake-time alertness |
| GB2611427B (en) * | 2022-11-04 | 2023-10-18 | Polar Electro Oy | Predicting awake-time alertness |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN100508875C (en) | Method and system for predicting human cognitive performance | |
| JP4638041B2 (en) | Method and system for predicting human cognitive ability using actigraph data | |
| JP4113434B2 (en) | Method, program, storage medium and apparatus for predicting human cognitive ability using data from actigraph | |
| JP4638040B2 (en) | Method and apparatus for predicting human cognitive ability | |
| Chen et al. | Unobtrusive sleep monitoring using smartphones | |
| US9241658B2 (en) | Personal fatigue risk management system and method | |
| US8781796B2 (en) | Systems and methods for individualized alertness predictions | |
| US5433223A (en) | Method for predicting alertness and bio-compatibility of work schedule of an individual | |
| Mallis et al. | Summary of the key features of seven biomathematical models of human fatigue and performance | |
| US7898426B2 (en) | Alertness estimator | |
| JP5775868B2 (en) | System for providing behavioral therapy for insomnia and control method thereof | |
| KR102053604B1 (en) | Method for sleeping analysis and device for sleeping analysis using the same | |
| Wilson et al. | Understanding fatigue in a naval submarine: Applying biomathematical models and workload measurement in an intensive longitudinal design | |
| CN114916919A (en) | System, method and application for predicting user alertness and fatigue risk index | |
| Matthews et al. | Monitoring task fatigue in contemporary and future vehicles: a review | |
| JP2019162382A (en) | Sleep improvement support apparatus, tissue improvement support apparatus, and program | |
| JP2018509270A (en) | Mental distress monitoring system | |
| WO2020223033A9 (en) | Method and system for measuring, predicting and optimizing human alertness | |
| Gundel et al. | A critical review of existing mathematical models for alertness | |
| Sun et al. | Development of a biomathematical model for human alertness and fatigue risk assessment based on the concept of energy | |
| Jia et al. | Research and Application of Fatigue Management of Apron Control in Beijing Daxing International Airport | |
| Basner et al. | Markov state transition models for the prediction of changes in sleep structure induced by aircraft noise | |
| KR102437402B1 (en) | Method and device for analyzing personalized biorhythm | |
| Lin et al. | Applications of non‐invasive sensor devices to personalised health care | |
| Çevik et al. | Conceptualization of Aviation Cabin Crew’s Fatigue Risk Assessment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20220819 |