A circadian rhythm disorder adjustment method and system across time zones based on a biological clock dynamics model

Abstract

The present application relates to a kind of based on circadian rhythm dynamics model's cross time zone circadian rhythm disorder regulation method and system, comprising the following steps: obtaining human physiological parameters, construct the circadian rhythm dynamics model and circadian rhythm disorder resynchronization iteration function of human body;Based on cross time zone flight task parameter, cross time zone flight direction and amplitude adjustment difficulty coefficient, construct cross time zone fatigue coefficient;Combining circadian rhythm dynamics model, circadian rhythm disorder resynchronization iteration function, environmental illumination parameter, cross time zone fatigue coefficient, construct cross time zone multi-task chain simulation model, the prediction of circadian rhythm phase, rhythm load and vigilance;According to the prediction result of cross time zone multi-task chain simulation model, generate individualized illumination, sleep and auxiliary intervention scheme of regulating rhythm.This regulation method effectively shortens the recovery time length of jet lag, substantially reduces the subjective fatigue degree of pilot, improves vigilance in on-duty period, realizes from experience control to mechanism, quantization, intelligent rhythm management upgrade.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology and relates to the regulation of biological clocks, particularly a method and system for regulating cross-time zone diurnal rhythm disorders based on a biological clock dynamics model. Background Technology

[0002] Jet lag syndrome, caused by long-haul flights or other cross-time zone missions, is a core physiological challenge that has long plagued the aviation industry, threatening personnel health and mission performance. When performing cross-time zone missions, the body's internal biological clock (circadian rhythm pacemaker) becomes drastically out of sync with the external light cycle (time factor) of the destination, leading to a series of symptoms such as sleep disturbances, daytime fatigue, and cognitive decline, and in severe cases, potentially inducing operational errors. Existing methods of rhythm regulation have the following problems:

[0003] 1. Piecewise function methods use the number of time zones crossed as a threshold. These methods lack the application of the intrinsic dynamics of the human biological clock. Their adjustment suggestions are usually based on experience rather than physiological models, and they cannot quantify the recovery time. Furthermore, they ignore the fundamental influence of flight direction on the adjustment rate, which limits the universality and accuracy of their predictions and adjustments.

[0004] 2. Since it is physiologically easier to delay by 12 hours than to advance by 12 hours, when flying eastward across enough time zones (e.g., more than 10), the body may choose to resynchronize in a "delayed" rather than "advanced" manner, resulting in a mode switching phenomenon. However, existing simple adjustment schemes cannot handle this complex mode switching.

[0005] 3. Existing adjustment methods mainly target the recovery process after a single cross-time zone flight, while actual flight missions are often complex mission chains involving multiple transfers and round trips. The rhythmic interference effects of each segment will accumulate and interact, resulting in inaccurate adjustment results. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and system for regulating cross-time zone diurnal rhythm disorders based on a biological clock dynamics model that is accurate in prediction and highly applicable.

[0007] To solve the above problems, the technical solution of the present invention is as follows:

[0008] A method for regulating cross-time zone circadian rhythm disorders based on a biological clock dynamics model includes the following steps:

[0009] Obtain human physiological parameters, construct a dynamic model of the human biological clock and an iterative function for resynchronization of biological clock disorders;

[0010] Based on cross-time zone mission parameters and the difficulty coefficient of cross-time zone direction and amplitude adjustment, a cross-time zone fatigue coefficient is constructed.

[0011] By combining the biological clock dynamics model, the biological clock disorder resynchronization iteration function, ambient light parameters, and cross-time zone fatigue coefficient, a cross-time zone multi-task chain simulation model is constructed, which is suitable for predicting biological clock phase, rhythm load, and alertness.

[0012] Based on the prediction results of the cross-time zone multi-task chain simulation model, personalized intervention plans for light, sleep, and assisted regulation of circadian rhythms are generated.

[0013] In a further embodiment, human physiological parameters include individual endogenous period values;

[0014] The biological clock dynamics model includes core body temperature variables, driving variables, and light sensitivity regulation variables;

[0015] The biological clock disorder resynchronization iterative function is based on the Poincaré section method and derived from the biological clock dynamics model. It is suitable for quantifying the shift of the biological clock phase after each light-dark cycle.

[0016] In a further embodiment, the biological clock disorder resynchronization iteration function includes a stable fixed point and an unstable fixed point. The stable fixed point is suitable for characterizing the phase when the biological clock is fully adapted to the local light-dark cycle. The unstable fixed point is set as the boundary threshold between the phase-advanced resynchronization mode and the phase-lagging resynchronization mode. The stable fixed point and the unstable fixed point are determined based on the individual's endogenous cycle value and the ambient light parameters of the location.

[0017] In a further embodiment, the cross-time zone fatigue factor includes the task time load factor, the task fragmentation factor, and the task chain complexity accumulation factor.

[0018] The difficulty coefficient for adjusting direction and magnitude across time zones is calculated using the weight of the direction difficulty and the absolute value of the number of time zones crossed.

[0019] In a further embodiment, the difficulty weight for crossing time zones eastward is greater than that for crossing time zones westward;

[0020] When the number of time zones crossed to the east exceeds a preset threshold, the difficulty weight of crossing time zones to the west is adopted.

[0021] In a further embodiment, the task time load coefficient is calculated using the task duration, weighted duty duration, and baseline duration, and the weighted duty duration is calculated using the duty duration and duty weight factor.

[0022] The task fragmentation coefficient is calculated using the task segment increment coefficient, the average number of task segments per day, and the baseline number of task segments.

[0023] The task chain complexity accumulation factor is calculated by adjusting the difficulty coefficient across time zones for each task segment, the task time load coefficient, and the total number of task days.

[0024] In a further embodiment, the ambient lighting parameters are obtained by querying a lighting database;

[0025] The cross-time zone multi-task chain simulation model includes a task execution phase and an inter-task dwell phase. The task execution phase is suitable for simulating phase drift under corresponding lighting conditions. The inter-task dwell phase calls the resynchronization iteration function to calculate the phase convergence process based on the lighting conditions of the destination.

[0026] In a further embodiment, the intervention program includes guidance on light exposure times, suggestions on bedtime and wake-up times, and suggestions on when to take supplementary products.

[0027] In a further embodiment, based on the prediction results of the cross-time zone multi-task chain simulation model, a personalized intervention plan for light, sleep, and assisted regulation of circadian rhythms is generated, specifically including:

[0028] Based on the intervention plan, the task assignments were quantitatively evaluated;

[0029] Optimization suggestions are generated based on the assessment results, and risk warnings are issued.

[0030] A cross-time zone circadian rhythm disorder regulation system based on a biological clock dynamics model, comprising:

[0031] The individual record management module is suitable for obtaining human physiological parameters;

[0032] The task and environment data interface module is suitable for obtaining cross-time zone task parameters, cross-time zone direction and amplitude adjustment difficulty coefficients, ambient lighting parameters, and constructing cross-time zone fatigue coefficients.

[0033] The biological clock dynamics simulation engine module is connected to the individual file management module and the task and environment data interface module, respectively. It is suitable for building human biological clock dynamics models, biological clock disorder resynchronization iterative functions, cross-time zone multi-task chain simulation models, and performing simulation predictions.

[0034] The scheduling scheme evaluation and optimization module is suitable for receiving prediction results from the biological clock dynamics simulation engine module, generating intervention plans, and outputting risk warnings and optimization strategies for task arrangement.

[0035] Compared with the prior art, the beneficial effects of the present invention are:

[0036] 1. This adjustment method is based on the human biological clock dynamics model as its core theoretical foundation. Combined with the biological clock disorder resynchronization iterative function, it accurately classifies two types of resynchronization modes: phase advance and phase lag. It quantifies and analyzes the physiological laws such as the asymmetry of east-west time difference during cross-time zone flights and the switching of multiple time zone modes. On this basis, it constructs four types of quantitative coefficients: cross-time zone adjustment difficulty, task time load, task fragmentation, and task chain complexity. Relying on the global illumination database, it builds a cross-time zone multi-task chain simulation model to simulate the dynamic evolution of the pilot's biological clock phase, rhythm load, and alertness segment by segment and time period, accurately predicting the time difference recovery time and rhythm trough periods. Then, based on the simulation results, it automatically generates personalized rhythm intervention plans such as illumination regulation, sleep schedule, and auxiliary conditioning, and supports parallel simulation of multiple scheduling plans and multi-objective risk assessment, outputting global optimization suggestions such as flight time adjustment and personnel allocation. Comparative experiments have verified that, compared with traditional experience-based intervention methods, this invention can effectively shorten the recovery time from jet lag, significantly reduce pilots' subjective fatigue, and improve alertness during duty hours, achieving an upgrade from experience-based control to mechanistic, quantitative, and intelligent rhythm management.

[0037] 2. This adjustment method achieves accurate predictions that combine mechanistic depth with individualized adaptation. On the one hand, it upgrades from rough judgments based on empirical thresholds to quantitative, dynamic, and continuous predictions based on a biological clock dynamics model. For the first time, it transforms the biological clock dynamics model into a practical tool that can quantify and calculate jet lag recovery time in any cross-time zone flight scenario. The prediction results are based on rigorous mathematical analysis, ensuring the rationality of the prediction mechanism and its inclusiveness of complex factors, surpassing empirical summaries. On the other hand, the core model parameters take into account multiple dimensions such as flight direction, flight workload, and changes at specific time points. It can also provide differentiated risk prediction and optimization schemes based on the pilot's endogenous cycle range, taking into account the individual differences of pilots with different endogenous cycles. This overcomes the shortcomings of traditional schemes that ignore individual differences and achieves truly meaningful guidance for healthy travel.

[0038] 3. This adjustment method achieves proactive and holistic decision-making, moving rhythm management from passive adaptation by pilots to proactive optimization by airline management during scheduling. Through full mission chain simulation, the system can conduct multi-objective risk assessments in the scheduling phase, considering rhythm load, duty alertness, and mission duration. It automatically identifies high-risk periods during nighttime takeoffs and landings or high-load flight phases when rhythms are at their lowest, avoiding high-risk rhythm patterns at the source. It also provides specific optimization suggestions such as flight schedule adjustments, pilot replacements, and additional stopovers, achieving a systematic improvement in operational safety and crew health. The system not only provides pilots with guidance on rhythm adjustment but also offers optimization suggestions to the company during the scheduling phase, achieving a leap from passive adjustment to proactive prevention, and from individual health management to organizational operational optimization.

[0039] 4. This adjustment method is equipped with a complete hardware system and data calibration mechanism, possessing broad industry adaptability and application promotion value. The hardware system adopts a three-tier architecture of cloud server, local workstation, and pilot terminal, and is equipped with wearable motion analyzer, body temperature acquisition device, and other physiological data collection equipment to complete dynamic model calibration. The data of each functional module is interconnected, and it can stably adapt to various cross-time zone flight missions such as civil aviation passenger, cargo, and business flights. Furthermore, in addition to the aviation field, this invention can also be extended to the rhythmic health management scenarios of cross-time zone populations such as cross-border business personnel, sports teams, and overseas personnel. It provides safety management departments with a quantifiable tool for personnel fatigue risk prevention and control based on cutting-edge biological clock theory, effectively improving the rest quality and alertness level of long-distance travelers across time zones during working hours, with broad application prospects. Attached Figure Description

[0040] Figure 1 A flowchart of a method for regulating cross-time zone circadian rhythm disorders based on a biological clock dynamics model;

[0041] Figure 2 This is a schematic diagram of a cross-time zone circadian rhythm disorder regulation system based on a biological clock dynamics model. Detailed Implementation

[0042] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0043] Example 1:

[0044] A method for regulating cross-time zone circadian rhythm disorders based on a biological clock dynamics model, such as Figure 1 As shown, it includes the following steps:

[0045] Step S101: Obtain human physiological parameters and construct a human biological clock dynamics model and a biological clock disorder resynchronization iterative function:

[0046] Obtain human physiological parameters and construct corresponding biological clock dynamics models and biological clock disorder resynchronization iterative functions based on these parameters.

[0047] Specifically, the biological clock dynamics model is expressed by formulas (1) to (5):

[0048] dC / dt = (π / 12)(A + B) (1)

[0049] In the formula, C is the core body temperature, a measurable temperature signal that fluctuates between day and night, such as being lower in the morning and higher in the evening. t is time. (π / 12) is a unit conversion factor used to convert the human body's time "hours" into "radians / hours" for internal calculations in the model, ensuring consistency in mathematical units. A is the driving signal, generated by the biological clock itself. B is the net driving force of external light signals on the system, i.e., the light-driven signal. Formula (1) describes the rate of change of body temperature, which is determined by both the driving signal of the biological clock and the light-driven signal.

[0050] dA / dt = (π / 12)[μ(A - (4*A^3) / 3) - C((24 / (0.99669τc))^2 + kB)] (2)

[0051] Formula (2) describes the core of the biological clock as a van der Bohr oscillator, which oscillates continuously with a period close to 24 hours. In the formula, C is the core body temperature, a measurable temperature signal with diurnal fluctuations, such as low in the morning and high in the evening. t is time. (π / 12) is a unit conversion factor used to convert the human body's time "hours" to "radians / hour" used in the model's internal calculations, ensuring consistent mathematical units. A is the driving signal, generated by the biological clock itself. B is the net driving force of the system from external light signals. (A - (4*A^3) / 3) is nonlinear damping, which provides a limiting pull when the oscillator's amplitude is too large and a thrust when the oscillator's amplitude is too small, ensuring that the oscillation of the oscillator neither stops nor increases indefinitely, forming a stable and rhythmic "limiting loop" oscillation. μ is the "stiffness" parameter. τc represents an individual's endogenous circadian rhythm, which is the cycle of the body's natural rhythm in complete darkness without any time cues (usually slightly longer than 24 hours); this is the most crucial individual parameter in the model, and its specific value varies from person to person. kB indicates that the light-driven signal also feeds back to the biological clock itself, simulating that light can not only directly drive the biological clock but also alter its internal sensitivity and response.

[0052] dn / dt = γ[α(I)* f(t) *(1 - n) - β*n] (3)

[0053] Formula (3) describes the dynamic change of the light sensitivity regulation variable n of the eye / brain, which is suitable for simulating the process of the human body gradually "adapting" to light exposure and gradually "recovering sensitivity" after entering darkness. In the formula, n is the light sensitivity regulation variable, which takes a value between 0 and 1. t is time. f(t) is the external light-dark switch, which is 1 during the day (light period) and 0 at night (dark period). α(I) is a function of light intensity 1, which represents how "strong" the light is, see formula (5) for details. γ is the overall gain rate parameter of the light processing process, in h. -1 In the model, the reference value γ = 0.0075 h -1 It scales the rate of the entire phototransduction equation, determining the dynamic speed at which the light signal is converted into neural drive, i.e., the sensitivity regulation variable n; β is the decay rate constant of retinal cells recovering from the "stimulated state" to the "ready state" during phototransduction, in units of h. -1 The model's default reference value is β = 0.45 h. -1 It determines how quickly the retina's sensitivity to light deteriorates over time.

[0054] Under illumination (f(t)=1), n ​​tends to 1 (becoming insensitive to light to prevent overstimulation), and the rate is controlled by α(I).

[0055] In the dark (f(t)=e), n will tend to 0 (regaining sensitivity to light and preparing to receive the morning light), and the rate is controlled by β.

[0056] B = G*α(I) *f(t) *(1 - n)* (1 - 0.4C) *(1 - 0.4A) (4)

[0057] Formula (4) defines the net drive of the external light signal on the system, that is, the light signal B that ultimately drives the biological clock. It is not a simple "drive with light", but a complex product that depends on the state of the biological clock itself. In the formula, B is the net drive of the external light signal on the system, that is, the light drive signal. α(I) is a function of light intensity 1, indicating how "strong" the light is, see formula (5) for details. f(t) is the external light switch, which is 1 during the day (light period) and 0 at night (dark period). n is the sensitivity adjustment variable, which takes a value between 0 and 1. C is the core body temperature of the human body, which is a measurable temperature signal with diurnal fluctuations, such as low in the morning and high in the evening. A is the drive signal, which is generated by the biological clock itself. G*α(I) *f(t) is the basic light signal. (1 - n) indicates that at a higher sensitivity n, that is, when adapting to strong light, the light drive effect weakens; this explains why the effect of continuous light saturates. (1 - 0.4C) and (1 - 0.4A) indicate that the current values ​​of core body temperature C and driving signal A also have a reverse effect on the system's sensitivity to light. This means that at different times of the day (when the values ​​of C and A are different), the same light exposure has drastically different effects on the biological clock (e.g., morning light advances the phase, while evening light delays the phase), which is the mathematical manifestation of the human body's "phase response curve".

[0058] α(I) =α0 (I / I0)^p (5)

[0059] In the formula, α(I) is a function of light intensity I, indicating how "strong" the light is. I is the light intensity. α0 is the light response gain parameter, which calibrates the overall driving intensity of light stimulation on the biological clock system. It is a coefficient obtained by fitting the experimental data of light stimulation in the photopic range. In the classic parameter set of the FJK model, α0 = 0.05; I0 is the basal light intensity, with the dimension lux, which is the reference value for normalizing the light intensity. In the classic parameter set of the model, I0 = 9500 lux; p is the sensitivity exponent, which characterizes the sensitivity of the biological clock to the nonlinear response of light intensity. It is also derived from the fitting of the photopic range light stimulation experiment. In the classic parameter set of the model, p = 0.5. Formula (5) is the light intensity response function, which describes the nonlinear response of the biological clock system to light intensity I. When p = 0.5, the response is proportional to the square root of the light intensity. This means that the effect improves significantly from dim to medium brightness, but the improvement slows down from bright to very bright. This aligns with biological observations: indoor light differs greatly from morning light, but the difference between midday sunlight and brighter sunlight is smaller.

[0060] The entire circadian clock dynamics model, that is, Equations (1) to (5), constitutes a three-dimensional dynamical system. A and C form an autonomous oscillator, while n and B form a sophisticated light signal processor. The light signal B is the output of this processor, which is injected as an input into the oscillator, namely Equations (1) and (2), thereby adjusting the oscillator to a state synchronized with the external 24-hour light-dark cycle.

[0061] Circadian clock disorder resynchronization iteration function:

[0062] First, a fixed observation surface is selected on a stable state (referred to as the LD-resumed solution) that has been synchronized with the local light cycle (e.g., 12 hours of light / 12 hours of dark, denoted as LD). x is defined as the time when the circadian clock state reaches this observation surface, which is the time elapsed since the last start of illumination (0 < x < 24 hours).

[0063] If the circadian clock of a trans-timezone pilot reaches this observation surface at a certain phase x (i.e., the pilot is not synchronized with the local time due to jet lag), then after a complete local sunlight cycle (24 hours), when his circadian clock state returns to this observation surface again, the corresponding new phase is Π(x). The new phase Π(x) is defined as the circadian clock disorder resynchronization iteration function, as shown in Equation (6):

[0064] Π(x)=(x+ρ(x))mod24 (6)

[0065] In the equation, Π(x) is the circadian clock disorder resynchronization iteration function, which is a one-dimensional return map constructed based on the Poincaré section method, derived from the circadian clock dynamics model. It is used to quantify the phase evolution law of the human circadian clock after being disturbed by the light-dark cycle, and is the core mathematical tool for analyzing the generation, recovery and mode switching of jet lag. x is the phase, with the unit of hour (h), which is an independent variable with a value range of 0 ≤ x < 24, describing the time offset of the human circadian clock relative to the external 24-hour light-dark cycle. ρ(x) is the return duration, with the unit of hour (h). After the circadian clock passes through the selected Poincaré section (A=0, A'<0) at phase x, the time it takes to return to this section is calculated by combining the equations of the circadian clock dynamics model with environmental parameters (light intensity I, photoperiod N) and individual parameters (endogenous period τc). mod24 is the modulo operation. Since the earth's light-dark cycle is a standard 24 hours and the phase has a 24-hour periodicity, the result of x+ρ(x) is modulo 24 to constrain the output phase within the interval [0,24), ensuring that the domain and range of the map are consistent.

[0066] Π(x) usually has two special points:

[0067] 1. Stable fixed point x_s: satisfies Π(x_s) = x_s. If the pilot's phase is exactly x_s, then it will still be x_s at this time tomorrow, meaning he has fully adapted to the local time. x_s is the phase stably synchronized with the local LD ​​cycle.

[0068] 2. Unstable fixed point x_u: x_u is a watershed and satisfies Π(x_u) = x_u, but it is extremely sensitive. If the phase deviates slightly from x_u, the next iteration will move away from it. If the initial misalignment phase x_0 is to the left of x_u, the iteration will move to the left (phase advance) towards x_s; if x_0 is to the right of x_u, the iteration will move to the right (phase delay) towards x_s. Therefore, x_u directly determines whether resynchronization is performed in an "advance" or "delay" mode. The distance between x_u and x_s is key to determining the asymmetry and severity of the time difference.

[0069] By calculating x_1 = Π(x_0), x_2 = Π(x_1)..., the evolution of the pilot's circadian rhythm phase over time is predicted until it converges to x_s. The number of iterations (days) required for convergence is the predicted jet lag recovery time.

[0070] Step S103: Based on the cross-time zone task parameters and the difficulty coefficient of cross-time zone direction and amplitude adjustment, construct the cross-time zone fatigue coefficient:

[0071] Building upon the simulation of the circadian rhythm dynamics model, the impact of various factors in complex flight missions on rhythm dysregulation (time difference) is further quantified, constructing a cross-time zone fatigue coefficient. These quantified factors will serve as key inputs or adjustment parameters for model simulation, enabling more accurate calculation of cumulative rhythm load and prediction of recovery trajectories. The functional relationships of each influencing factor are defined as follows:

[0072] 1. Difficulty level of adjusting direction and magnitude across time zones:

[0073] The direction (east / west) of flight across time zones and the number of time zones crossed are the core factors affecting rhythm resynchronization. Based on empirical research, the baseline adjustment difficulty for eastward flight (phase advance) is set to be higher than that for westward flight (phase delay), as shown in formula (7):

[0074] Dz=α dir ×∣Z∣ (7)

[0075] In the formula, Dz represents the difficulty coefficient for cross-time zone adjustment; the larger the coefficient, the longer the baseline recovery time required for prediction. Z represents the number of time zones crossed, with positive values ​​for eastward rows and negative values ​​for westward rows. α dir The directional difficulty weight is selected as α based on the flight direction. east or α westBased on empirical data, the following settings are used: α when flying eastward. east =1.0; α when flying westward west =2 / 3 (reflecting that the westward adjustment rate is approximately 1.5 times that of the eastward adjustment). When the number of time zones crossed eastward exceeds a preset personal threshold (e.g., 11 hours), the system adopts a "delay mode," at which point α... dir Using α west value.

[0076] Cross-timezone mission parameters include mission time load factor, mission fragmentation factor, and mission chain complexity accumulation factor, specifically:

[0077] 2. Task time load factor:

[0078] Long-duty flights and shifts can lead to acute fatigue, potentially exacerbating circadian rhythm disorders and decreased subjective feelings and cognitive performance. This coefficient quantifies the immediate workload of the flight mission itself, as shown in formula (8):

[0079] F t =1+(T flight +k*T duty ) / T base (8)

[0080] In the formula, F t T represents the task time load coefficient, serving as a multiplicative factor for "initial inequality" in the rhythm disorder model. flight T represents the planned mission duration, i.e., the planned flight duration, in hours. duty T represents the planned duty duration, i.e., the planned duty period, in hours. k is the duty weighting factor (e.g., set to 0.5), representing the fatigue impact of ground waiting during the duty period. base The base duration is set to, for example, 8 hours, representing the workload of a standard workday.

[0081] 3. Task fragmentation coefficient:

[0082] Performing multiple flight segments in a single day (mission fragmentation) will frequently interrupt rest opportunities and may involve multiple takeoffs and landings with varying environmental conditions, increasing the difficulty of maintaining a stable rhythm, as shown in formula (9):

[0083] F s =1+β*(S-S0) (9)

[0084] In the formula, F sS is the mission fragmentation coefficient. S is the average number of mission segments per day, i.e., the average number of flight segments per mission day. S0 is the baseline number of mission segments, i.e., the baseline number of flight segments, set to 1 (for direct flight missions). β is the mission segment increment coefficient, i.e., the increment of the impact of each additional flight segment on rhythm stability, which can be set based on expert evaluation or empirical data, for example, β=0.1.

[0085] 4. Task chain complexity cumulative factor:

[0086] For complex mission chains involving multiple cross-time zone flights and transfers, the rhythm disorder effect accumulates. This factor assesses the overall complexity of the entire mission sequence, as shown in Equation (10):

[0087] Ac=∑ i=1 n (D z,i *F t,i )*[1+log(1+L)] (10)

[0088] In the formula, Ac is the task chain complexity accumulation factor, used to adjust the total integral of the predicted rhythm load for the entire task cycle. n is the total number of task segments in the task chain, i.e., the total number of flight segments. i is the i-th task segment, i.e., the flight segment. D z,i and F t,i These represent the cross-timezone adjustment difficulty coefficient and the task time load coefficient for the i-th task segment, respectively. L is the total number of task days, i.e., the total calendar days of the task chain, and its impact on task duration is marginally decreasing, expressed in logarithmic form.

[0089] Step S105: Combining the biological clock dynamics model, the biological clock disorder resynchronization iteration function, ambient light parameters, and cross-time zone fatigue coefficient, a cross-time zone multi-task chain simulation model is constructed, suitable for predicting biological clock phase, rhythm load, and alertness.

[0090] Ambient lighting parameters are obtained by querying a lighting database, which integrates historical and predicted lighting data (day length N, light intensity I) for cities and seasons worldwide. The input to the cross-time zone multi-mission chain simulation model includes a complete flight mission plan with multiple segments and intermediate stops.

[0091] Key decision points in the cross-time zone multi-task chain simulation model: When encountering an eastward flight segment, the system will determine whether the number of time zones crossed exceeds the pilot's preset threshold. If it does not exceed the threshold, the "phase advance" mode (α=1.0 baseline) is used; if it exceeds the threshold, it will automatically switch to the "phase delay" mode (α=1.5 baseline).

[0092] Model simulation process: The pilot's current rhythm state is used as the initial phase x s depThe system performs a daily dynamic simulation of the mission chain: for each flight segment and stopover period in the mission, it calls the circadian rhythm dynamics model, combining personal parameters (τc) and destination environmental parameters (N, I), to dynamically generate or call the circadian rhythm resynchronization iterative function Π(x) under that environment. The model simulates the dynamic evolution of the pilot's circadian rhythm phase throughout the mission, including:

[0093] 1. Mission execution phase: Based on the flight duration and cabin lighting conditions, simulate the micro-changes in phase under the "free-running" state, that is, simulate the phase drift under the corresponding lighting conditions.

[0094] 2. During the intermission phase, calculate the phase x0 of the pilot's biological clock relative to the local target phase x after arriving at the new time zone / latitude. s dest The difference. Utilizing the destination's circadian rhythm disruption resynchronization iterative function, through iterative calculation, the convergence from x0 to x is accurately predicted. s dest The required time, i.e., the predicted recovery time curve.

[0095] 3. Iterative calculation: Using this endpoint phase as the initial phase of the next flight segment, repeat the above process to finally obtain quantitative indicators such as the rhythm phase curve, alertness prediction curve, and cumulative rhythm load throughout the entire mission cycle.

[0096] Step S107: Based on the prediction results of the cross-time zone multi-task chain simulation model, generate personalized intervention plans for light, sleep, and assisted regulation of circadian rhythms:

[0097] Based on a cross-timezone multi-task chain simulation model, personalized intervention plans are generated for each task stage, including:

[0098] Lighting schedule guidance: Based on the x in the resynchronization iteration function for biological clock disruption u With x s Based on the positional relationship and the pilot's current phase x0, it accurately determines whether exposure to light at that destination / time period will cause the phase to advance or delay. Accordingly, it generates a specific light schedule for the pilot, for example: "During a stopover in city A, it is recommended to avoid strong light between 8:00 AM and 11:00 AM local time, and seek light between 3:00 PM and 6:00 PM local time."

[0099] Bedtime and wake-up time recommendations: Provide optimal bedtime and wake-up time recommendations based on predicted circadian rhythm troughs and peaks.

[0100] Recommended timing for supplemental products: If it involves taking melatonin or other health supplements, the recommended timing can be provided based on the phase response curve of the substance.

[0101] Multi-scenario simulation and evaluation: When scheduling flights, airlines can input multiple task drafts. The model performs a complete simulation of each draft from steps S101 to S107, and quantitatively evaluates and compares multiple objectives such as minimizing rhythmic load, maximizing alertness during critical duty periods, and total task duration.

[0102] Risk warning and optimization suggestions: The model identifies high-risk periods in the draft that may cause pilots to be in a rhythm trough during nighttime takeoffs and landings or high-load flight phases, and automatically proposes optimization suggestions, such as fine-tuning flight schedules, suggesting adding stopovers at specific latitudes to take advantage of the light regulation effect, or replacing pilots with those more suitable for the rhythm characteristics of the mission.

[0103] Example 2:

[0104] A cross-time zone circadian rhythm disorder regulation system based on a biological clock dynamics model, such as Figure 2 As shown, it includes:

[0105] The individual file management module is suitable for acquiring human physiological parameters and storing and maintaining the calibrated circadian rhythm dynamics model parameters for each pilot.

[0106] The mission and environment data interface module connects to the flight mission system and environment database, and is suitable for obtaining cross-time zone mission parameters, cross-time zone direction and amplitude adjustment difficulty coefficients, and ambient lighting parameters, and constructing cross-time zone fatigue coefficients.

[0107] The biological clock dynamics simulation engine module is the core computing module. It has a built-in biological clock dynamics model and is connected to the individual record management module and the task and environment data interface module. It is suitable for building human biological clock dynamics models, biological clock disorder resynchronization iterative functions, cross-time zone multi-task chain simulation models, and performing simulation predictions.

[0108] The scheduling scheme evaluation and optimization module is suitable for receiving the prediction results of the biological clock dynamics simulation engine module, generating intervention schemes, performing multi-objective optimization calculations and risk assessments, providing quantitative comparisons and optimization suggestions on the impact of different scheduling plans on pilot rhythms, and outputting risk warnings and optimization strategies for task arrangements.

[0109] Pilot Interaction Terminal: Used to push plans to pilots and receive their subjective feedback for continuous model optimization.

[0110] The present invention also provides a computer device, including a processor component comprising at least one processor; a storage component comprising a memory; program instructions comprising a computer program stored in the memory; the processor component being configured to read and execute the program instructions in the storage unit to implement steps S101 to S107.

[0111] The present invention provides a computer program product including computer-executable instructions, which, when executed on a processor of an electronic device, enable the electronic device to perform steps S101 to S107.

[0112] This invention also provides a computer-readable storage medium storing computer program instructions. The storage medium includes non-volatile memory (e.g., solid-state drives, flash memory) or temporary storage media (e.g., memory cache). The computer-readable storage medium stores an independent product, which implements steps S101 to S107 in the form of software functional units. Essentially, the part of the technical solution of this invention that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute steps S101 to S107. The aforementioned storage medium includes, but is not limited to, semiconductor memories (e.g., ROM, RAM, Flash), magnetic storage media (e.g., hard disks, magnetic tapes), optical storage media (e.g., CDs, DVDs), and other physical storage devices capable of carrying program code (e.g., USB flash drives, portable hard drives, etc.).

[0113] Based on a method and system for regulating cross-time zone circadian rhythm disorders based on a biological clock dynamics model, the present invention also conducted the following experiments and set up a control group to verify the actual effect of the present invention.

[0114] I. Implementation of the Overall Architecture

[0115] The invention comprises four main components: hardware system, software system, personnel samples, and test scenarios. The method and system of this invention are fully implemented at a large civil aviation crew management center, and the target users are civil aviation passenger pilots.

[0116] 1. System hardware composition (electrical / connection relationships):

[0117] The entire system adopts a three-tier architecture of cloud server + local workstation + pilot terminal, with hardware connected via enterprise intranet + 4G / 5G wireless communication.

[0118] (1) Core computing unit: Cloud simulation server:

[0119] Configuration: High-performance computing server, equipped with parallel computing module, deploying biological clock dynamics simulation engine, lighting database, and flight data interface;

[0120] Connection relationship: It communicates bidirectionally with the local workstation via the intranet, receives task data and individual parameters, and returns simulation results and optimization solutions; it stores global city illumination, time zone and seasonal data.

[0121] (2) Local management workstation:

[0122] Target users: airline schedulers, crew health managers, and company managers;

[0123] Functions: Enter flight assignments, retrieve individual pilot files, view risk assessment reports, and export scheduling optimization plans;

[0124] Connection relationship: The intranet connects to the cloud server, and the local area network connects to printers and large-screen display devices.

[0125] (3) Pilot interactive terminal (smart tablet / smartwatch):

[0126] Target user: Pilots on duty;

[0127] Functions: Receive personalized circadian rhythm adjustment plans, light / sleep reminders, and report subjective fatigue feelings;

[0128] Connection relationship: Wireless access to cloud server for real-time data synchronization.

[0129] (4) Auxiliary data acquisition equipment:

[0130] Wearable motion sensors and body temperature scanners can be selected: real-time collection of the pilot's core body temperature (C) and activity rhythm for dynamic calibration of model parameters, and Bluetooth connection to the pilot's terminal.

[0131] 2. Software module correspondence:

[0132] The software modules strictly correspond to the five functional modules described in the invention, with unidirectional / bidirectional data flow between modules:

[0133] The individual profile management module connects to a cloud server (to store individual parameters such as τc).

[0134] The task and environment data interface module connects to the flight scheduling system and the global light database;

[0135] The biological clock dynamics simulation engine connects to the core computing modules (model calculation, phase simulation).

[0136] The scheduling plan evaluation and optimization module connects to the local workstation (results display, plan editing).

[0137] Pilot interaction module, connected to the pilot terminal (command push, feedback collection).

[0138] II. Implementation Samples and Test Scenario Settings:

[0139] 1. Subject grouping:

[0140] Sixty adult civil aviation flight trainees, aged 20 to 26, without sleep disorders or endocrine diseases, were selected and randomly divided into an experimental group (30 people, using the method of this invention) and a control group (30 people, using the traditional experience-based time difference adjustment scheme).

[0141] (1) Individualized core parameter calibration:

[0142] Following the calibration method of the biological clock dynamics model, the endogenous periodicity τc of each pilot was measured in a constant-dark laboratory environment, and the results were statistically analyzed in groups:

[0143] Normal biological clock group: τc = 24.2 h (12 people in each of the two groups);

[0144] Slower biological clock group: τc=24.6 h (9 people in each of the two groups);

[0145] Faster biological clock group: τc=23.4 h (9 people in each of the two groups).

[0146] (2) Intervention rules for the control group: The control group uses existing traditional techniques.

[0147] The solution adopts industry-standard practices: those flying east should sleep earlier and those flying west should sleep later; for those crossing time zones by more than 8 hours, it is uniformly recommended to take melatonin, without individualized differentiation or dynamic simulation.

[0148] 2. Typical flight mission scenario (multi-segment, cross-time zone mission chain):

[0149] High-frequency international round-trip multi-segment missions in civil aviation are selected to simulate real and complex duty scenarios. The specific mission chain is as follows:

[0150] Total mission duration: 7 days; Route combination: City A → City B (crossing 13 time zones to the east) → City C (crossing 3 time zones to the west) → City A (crossing 16 time zones to the east).

[0151] Environmental parameters: The execution period is summer in the Northern Hemisphere. The sunshine duration in city A is N=15 h, the sunshine duration in city B / city C is N=14 h, the outdoor light intensity is I=1000 lux, and the cabin light intensity is I=100 lux.

[0152] Single-segment flight time: City A to City B takes 14 hours, City B to City C takes 6 hours, City C to City A takes 15 hours; the average number of segments per day is S=2, and the baseline number of segments is S0=1.

[0153] III. Specific Implementation Steps (corresponding to the entire process of steps S101 to S107 of the invention method):

[0154] Step S101: Loading the biological clock dynamics model and matching individual parameters:

[0155] The cloud server loads the complete set of equations of biological clock dynamics, namely equations (1) to (5), the biological clock disorder resynchronization iteration function Π(x), the fixed point solution algorithm, and fixed general parameters μ, k, γ, etc.

[0156] The individualized parameter τc of each pilot is retrieved from the individual file management module, and a unique biological clock dynamics model is independently constructed for each pilot.

[0157] By combining the destination photoperiod N and the light intensity I, an iterative function Π(x) for resynchronization of biological clock disorder under the corresponding environment is generated, and the stable fixed point x_s and the unstable fixed point x_u under this environment are solved.

[0158] Example (using a pilot with τc = 24.2 h and an urban environment B as an example)

[0159] The model calculations yielded: x_s = 5.96 h, x_u = 16.87 h; the determination rule was: if the initial phase x0 < 16.87 h, the biological clock would resynchronize in a phase-advanced mode; if x0 > 16.87 h, it would resynchronize in a phase-lagging mode.

[0160] Step S103: Quantitative calculation of flight mission impact coefficient:

[0161] Based on the parameters of this mission, the four major load factors are calculated in batches, taking the flight segment from city A to city B (crossing 13 time zones eastward, Z=+13) as an example:

[0162] Difficulty coefficient Dz for cross-time zone adjustment: Dongfei benchmark weight α east =1.0, crossing time zones 13 > individual threshold 11, triggering delayed resynchronization mode, switching weight α. west =2 / 3;Dz=α west ×∣Z∣=2 / 3×13≈8.67.

[0163] Flight and duty load factor Ft: Flight duration T flight =14 h, duty duration T duty =18 h, weight k=0.5, benchmark value T base =8 h; Ft=1+(14+0.5×18) / 8=3.875.

[0164] Mission fragmentation coefficient Fs: Daily average number of flight segments S=2, baseline S0=1, incremental coefficient β=0.1; Fs=1+0.1×(2-1)=1.1.

[0165] The cumulative complexity factor Ac for the mission chain is calculated as follows: the total number of mission days L=7, the three segments are calculated sequentially and then summed, and finally the entire mission chain Ac≈26.3.

[0166] Step S105: Full Task Chain Dynamic Simulation and Rhythm Prediction:

[0167] Data import: Input the three flight segments, dwell time, local lighting parameters, and load factor into the simulation engine;

[0168] Initial state: The pilot is fully synchronized in city A, with an initial phase of x0 = x_s = 6.0 h;

[0169] Phased simulation:

[0170] First segment: City A → City B: During the flight phase, phase drift is simulated under low cabin lighting (I=100 lux). After landing, the phase difference is calculated, and the resynchronization time is predicted by iterating Π(x).

[0171] Transit phase: City B → City C: Using the final phase of the previous flight segment as the initial phase, continue the simulation;

[0172] Return route: City C → City A: Identifies the direction and time zone number of the cross-time zone and automatically switches to resynchronization mode;

[0173] Output results: Generates a 7-day continuous biological clock phase curve, alertness curve, and daily rhythm load value.

[0174] Step S107: Generation and delivery of personalized rhythm adjustment plans:

[0175] After the simulation is completed, the system combines the trends of position and phase changes to automatically generate three types of intervention plans, which are then pushed to the pilot via a smart terminal. An example of one plan is shown below (τc = 24.2 h pilot, stay period in city B):

[0176] Lighting intervention plan: Avoid strong light from 08:00 to 11:00 local time (near the x_u interval); actively accept natural light from 15:00 to 18:00 to accelerate phase convergence.

[0177] Sleep-Wake-Up Plan: Recommended bedtime: 22:00 local time, wake-up time: 07:00 local time, avoid working during the low points of the circadian rhythm.

[0178] Suggested supplementary treatment: If melatonin is needed, it is recommended to take it at 21:00 local time to match the phase response pattern.

[0179] Multi-objective optimization and risk assessment of scheduling:

[0180] The airline provided three alternative scheduling plans. The system simulated each of the three plans one by one, quantifying and scoring them from three dimensions: rhythm load, duty alertness, and task duration (maximum score 100 points, higher score is better), and identifying high-risk periods.

[0181] Option 1 (original plan): The high-risk period is the return night flight, with an average rhythm load of 72 minutes;

[0182] Option 2 (fine-tuning flight schedules): Avoiding rhythmic lows, with an average rhythmic load of 88 minutes;

[0183] Option 3 (Replace Pilot): Match pilots with fast biological clocks, with an average circadian rhythm load of 85 minutes;

[0184] Optimization conclusion: Option 2 is recommended, and risk points for each flight segment are marked for scheduling personnel to refer to and implement.

[0185] IV. Comparison of Experimental Data and Results (Experimental Group and Control Group):

[0186] After completing the 7-day mission, a dual-dimensional evaluation of objective indicators and subjective scores was conducted. Objective indicators were collected by a motion analyzer and an alertness tester, while subjective fatigue scores were calculated using the aviation standard fatigue scale (0 to 10 points, with higher scores indicating more severe fatigue).

[0187] 1. Statistics of core evaluation indicators are shown in Table 1:

[0188] Table 1

[0189]

[0190] 2. Data Analysis and Results Explanation:

[0191] Jet lag recovery efficiency: The average recovery time of the experimental group was shortened by about 23 hours compared with the control group, a reduction of 30.9%; the individualized scheme based on the biological clock dynamics model of this invention significantly improves the speed of biological clock resynchronization recovery.

[0192] Fatigue level: The subjective fatigue score of the experimental group decreased by 53.6%, and the pilots' perceived fatigue was significantly relieved, proving that the light and sleep intervention program effectively improved the rhythm disorder.

[0193] Duty safety: The alertness compliance rate of the experimental group increased by 18.07 percentage points, and the high-risk operation period was significantly reduced, directly improving flight operation safety.

[0194] Individualized adaptability: This invention demonstrates significant advantages for three types of people with fast, slow, and normal biological clocks, verifying the model's compatibility with different τc parameters and overcoming the "one-size-fits-all" shortcomings of traditional empirical solutions.

[0195] 3. Verification of special phenomena (corresponding to basic theories):

[0196] When the samples spanned more than 11 time zones, both groups of samples exhibited a reverse resynchronization phenomenon, i.e., phase delay. The model of this invention can identify and adjust the intervention plan in advance. The control group did not identify this pattern, and the recovery time increased significantly.

[0197] In summer, under long daylight hours (increased N), people with slower biological clocks (τc=24.6 h) experience a greater jet lag. This invention, based on a dynamic photoperiod correction scheme, specifically enhances light intervention, with outstanding results.

[0198] V. Supplementary Notes on System Operation:

[0199] Model dynamic calibration: Pilots report their subjective feelings daily through terminals, wearable devices collect physiological data, and the system fine-tunes τc and light response parameters every 24 hours to ensure long-term simulation accuracy;

[0200] Multi-scenario expansion: This example is for international passenger flights, but it can also be adapted to cargo flights, business flights, crew rotations, and other scenarios. Only the flight duration, number of segments, and regional lighting parameters need to be modified.

[0201] VI. Implementation Summary:

[0202] This experiment fully implemented a method and system for regulating circadian rhythm disorders across time zones based on a biological clock dynamics model: relying on the biological clock dynamics model and the biological clock disorder resynchronization iterative function to achieve accurate rhythm simulation, combining flight mission quantification coefficients to characterize fatigue load, generating personalized intervention plans through multi-segment dynamic simulation, and prioritizing them in the scheduling process to achieve global optimization.

[0203] Compared with traditional experience-based solutions, this invention achieves an upgrade from qualitative guidance to quantitative calculation, from general solutions to individualized adaptation, and from post-event remediation to pre-event prevention. It effectively shortens jet lag recovery time, reduces pilot fatigue, and enhances duty alertness. It is fully adaptable to the complex cross-time zone flight scenarios in civil aviation and can also be extended to the rhythm management of cross-border business personnel, sports teams, overseas personnel, and other groups, with a wide range of applications.

[0204] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for regulating cross-time zone circadian rhythm disorders based on a biological clock dynamics model, characterized in that, Includes the following steps: Obtain human physiological parameters, construct a dynamic model of the human biological clock and an iterative function for resynchronization of biological clock disorders; Based on cross-time zone mission parameters and the difficulty coefficient of cross-time zone direction and amplitude adjustment, a cross-time zone fatigue coefficient is constructed. Combining the aforementioned biological clock dynamics model, the aforementioned biological clock disorder resynchronization iteration function, ambient light parameters, and the aforementioned cross-time zone fatigue coefficient, a cross-time zone multi-task chain simulation model is constructed, which is suitable for predicting biological clock phase, rhythm load, and alertness. Based on the prediction results of the cross-time zone multi-task chain simulation model, personalized intervention plans for light, sleep, and assisted regulation of circadian rhythms are generated.

2. The method for regulating cross-time zone circadian rhythm disorders based on a biological clock dynamics model according to claim 1, characterized in that, The human physiological parameters include individual endogenous period values; The biological clock dynamics model includes core body temperature variables, driving variables, and light sensitivity regulation variables; The biological clock disorder resynchronization iterative function is based on the Poincaré section method and derived from the biological clock dynamics model, and is suitable for quantifying the shift of the biological clock phase after each light-dark cycle.

3. The method for regulating cross-time zone circadian rhythm disorders based on a biological clock dynamics model according to claim 2, characterized in that, The biological clock disorder resynchronization iterative function includes a stable fixed point and an unstable fixed point. The stable fixed point is suitable for characterizing the phase when the biological clock is fully adapted to the local light-dark cycle. The unstable fixed point is set as the boundary threshold between the phase-leading resynchronization mode and the phase-lag resynchronization mode. The stable fixed point and the unstable fixed point are determined based on the individual's endogenous cycle value and the ambient light parameters of the location.

4. The method for regulating cross-time zone circadian rhythm disorders based on a biological clock dynamics model according to claim 3, characterized in that, The cross-time zone fatigue factor includes the task time load factor, the task fragmentation factor, and the task chain complexity accumulation factor. The difficulty coefficient for adjusting the direction and amplitude across time zones is calculated using the direction difficulty weight and the absolute value of the number of time zones crossed.

5. The method for regulating cross-time zone circadian rhythm disorders based on a biological clock dynamics model according to claim 4, characterized in that, The difficulty weight for directions crossing time zones eastward is greater than that for directions crossing time zones westward. When the number of time zones crossed to the east exceeds a preset threshold, the directional difficulty weight for crossing time zones to the west is applied.

6. The method for regulating cross-time zone circadian rhythm disorders based on a biological clock dynamics model according to claim 5, characterized in that, The task time load coefficient is calculated using the task duration, weighted duty duration, and baseline duration. The weighted duty duration is calculated using the duty duration and duty weight factor. The task fragmentation coefficient is calculated by the task segment increment coefficient, the average number of task segments per day, and the baseline number of task segments. The task chain complexity accumulation factor is calculated using the cross-time zone adjustment difficulty coefficient, the task time load coefficient, and the total number of task days for each task segment.

7. The method for regulating cross-time zone circadian rhythm disorders based on a biological clock dynamics model according to claim 6, characterized in that, The ambient lighting parameters are obtained by querying a lighting database; The cross-time zone multi-task chain simulation model includes a task execution phase and an inter-task dwell phase. The task execution phase is suitable for simulating phase drift under corresponding lighting conditions. The inter-task dwell phase calls the resynchronization iteration function to calculate the phase convergence process according to the lighting conditions of the destination.

8. The method for regulating cross-time zone circadian rhythm disorders based on a biological clock dynamics model according to claim 7, characterized in that, The intervention plan includes guidelines on light exposure times, suggestions on bedtime and wake-up times, and suggestions on when to take supplementary products.

9. The method for regulating cross-time zone circadian rhythm disorders based on a biological clock dynamics model according to claim 8, characterized in that, Based on the prediction results of the cross-time zone multi-task chain simulation model, personalized intervention plans for light, sleep, and assisted regulation of circadian rhythms are generated, specifically including: Based on the intervention plan, the task assignments were quantitatively evaluated; Optimization suggestions are generated based on the assessment results, and risk warnings are issued.

10. A cross-time zone circadian rhythm disorder regulation system based on a biological clock dynamics model, using the cross-time zone circadian rhythm disorder regulation method based on a biological clock dynamics model as described in any one of claims 1 to 9, characterized in that, include: The individual record management module is suitable for obtaining human physiological parameters; The task and environment data interface module is suitable for obtaining cross-time zone task parameters, cross-time zone direction and amplitude adjustment difficulty coefficients, ambient lighting parameters, and constructing cross-time zone fatigue coefficients. The biological clock dynamics simulation engine module is connected to the individual file management module and the task and environment data interface module, respectively. It is suitable for constructing a biological clock dynamics model of the human body, a biological clock disorder resynchronization iterative function, a cross-time zone multi-task chain simulation model, and performing simulation prediction. The scheduling scheme evaluation and optimization module is suitable for receiving the prediction results of the biological clock dynamics simulation engine module, generating intervention schemes, and outputting risk warnings and optimization strategies for task arrangement.

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