Driver fatigue early warning method, device and equipment and storage medium

Abstract

The invention provides a driver fatigue early warning method, device and equipment and a storage medium, and relates to the technical field of safety monitoring. The method comprises the following steps: acquiring hiPSC derived myocardial cells; placing the cells in vitro for culturing; detecting cell physiological indexes and calculating an ischemic tolerance score; determining a driving safety threshold value of the target driver; collecting current driving data; and when the current driving data reaches or exceeds a driving safety threshold value, an early warning signal is triggered. According to the method, by detecting the tolerance of the autologous cells of the driver in the simulated anoxic environment, accurate evaluation based on individual biological characteristics is achieved, and the defect that a general standard cannot adapt to individual differences is overcome. By converting the tolerance limit of the cellular level into the personalized driving safety threshold value and performing real-time monitoring, predictive alarm can be performed before physiological collapse, and the active prevention capability of sudden diseases caused by driving fatigue is remarkably improved.

Description

Technical Field

[0001] This application relates to the field of safety monitoring technology, and in particular to a driver fatigue early warning method, device, equipment and storage medium. Background Technology

[0002] With the rapid development of the modern transportation industry and the continuous increase in the number of motor vehicles, road traffic safety has become a core issue in the field of public safety. Among the many factors affecting traffic safety, the physical and mental health of drivers plays a crucial role. Medical research shows that cardiovascular abnormalities (such as sudden arrhythmias and acute coronary heart disease attacks) are important contributing factors to serious traffic accidents. In particular, for commercial vehicle drivers who are engaged in long-term transportation operations, their cardiovascular system often bears a special load different from daily life due to prolonged monotonous driving environments, sedentary lifestyles, and a state of high mental concentration, which significantly increases the risk of sudden health events.

[0003] Currently, driver monitoring technologies mainly fall into two categories: one is regular medical checkups, including routine electrocardiograms, blood pressure measurements, and blood biochemistry tests, used to assess an individual's baseline health level; the other is in-vehicle real-time monitoring technology, which uses wearable devices or in-vehicle sensors (such as steering wheel sensors and cameras) to collect the driver's heart rate, blood pressure, or facial expressions in real time. Existing Advanced Driver Assistance Systems (ADAS) typically integrate this function, monitoring real-time fluctuations in physiological signals and triggering alarms when a sudden increase in heart rate or an abnormal pulse is detected, reminding the driver to rest or seek medical attention. In addition, general risk scoring models based on demographic characteristics are also used to assess the probability of developing cardiovascular disease.

[0004] However, the aforementioned existing technologies have significant limitations in practical applications. First, traditional medical examinations are mostly conducted in a resting state, primarily reflecting basic physiological functions. They struggle to realistically simulate the unique mental stress, prolonged fixed posture, and physiological stress responses under potential hypoxia during driving, leading to a disconnect between examination results and actual driving risks. Second, in-vehicle real-time monitoring technology is essentially a "post-event response" mechanism, typically only triggering an alarm when physiological indicators show significant abnormalities (such as precursors to myocardial infarction), lacking the ability to "predict" potential risks. Furthermore, existing monitoring systems often employ universal threshold standards (such as a uniformly set upper limit for heart rate alarms), ignoring the significant individual differences among drivers in terms of age, genetic background, and basic physical condition. For some individuals with lower tolerance, even if physiological indicators are within the universal standard range, they may already be at their physiological limits; while for some groups with low baseline heart rates, the universal standard can easily lead to false alarms.

[0005] In summary, existing driver safety systems primarily rely on static general physical examinations and delayed surface signal monitoring. Current monitoring methods struggle to quantify an individual's tolerance limits to specific driving stresses at the cellular or mechanistic level, making it impossible to formulate precise safe driving strategies based on individual-specific physiological tolerance before physiological abnormalities occur. Bridging the gap between static physical examinations and dynamic driving, and addressing the issue of general monitoring thresholds failing to adapt to individual differences, are pressing technical challenges in the field of traffic safety. Summary of the Invention

[0006] In a first aspect, the present invention provides a driver fatigue early warning method, comprising: Obtain hiPSC-derived cardiomyocytes from a target driver; and culture the hiPSC-derived cardiomyocytes in a pre-defined in vitro hypoxic stress environment; wherein the in vitro hypoxic stress environment is used to simulate the physiological load caused by driving fatigue. Cellular physiological indicators of the hiPSC-derived cardiomyocytes under in vitro hypoxic stress were detected, and the ischemia tolerance score of the target driver was calculated based on the cellular physiological indicators. Based on the ischemia tolerance score, a correlation is established between the ischemia tolerance score and the risk level of the driving scenario, and the driving safety threshold of the target driver is determined. During vehicle operation, the target driver's current driving data is collected in real time. The current driving data is compared with the driving safety threshold. When the current driving data reaches or exceeds the driving safety threshold, a warning signal is triggered.

[0007] In an optional embodiment, the in vitro hypoxic stress environment includes a gradient oxygen concentration culture system and a biochemical stress inducer; The step of culturing the hiPSC-derived cardiomyocytes in a pre-defined in vitro hypoxic stress environment includes: The hiPSC-derived cardiomyocytes were placed under culture conditions with different oxygen concentrations, and a preset concentration of adrenaline was added to the culture medium to simulate the ischemic stress state of drivers under mild, moderate and heavy driving loads.

[0008] In an optional implementation, the detection of cellular physiological indicators of the hiPSC-derived cardiomyocytes under in vitro hypoxic stress includes: The apoptosis rate of the hiPSC-derived cardiomyocytes was detected under different oxygen concentrations. Electrophysiological signal stability parameters of the hiPSC-derived cardiomyocytes were detected using a microelectrode array; wherein the electrophysiological signal stability parameters include at least one of beat frequency, beat frequency coefficient of variation, and QT interval prolongation duration.

[0009] In an optional implementation, calculating the target driver's ischemia tolerance score based on the cellular physiological indicators includes: A first score is determined based on the apoptosis rate, and a second score is determined based on the electrophysiological signal stability parameter. Calculate the product of the first score and the first weight to obtain the first weighted score; and calculate the product of the second score and the second weight to obtain the second weighted score. The sum of the first weighted score and the second weighted score is calculated, and the sum is used as the ischemia tolerance score.

[0010] In an optional implementation, before calculating the product of the first score and the first weight to obtain the first weighted score; and before calculating the product of the second score and the second weight to obtain the second weighted score, the method further includes: Set a preset benchmark value for the first weight and a preset benchmark value for the second weight, and set the sum of the first weight and the second weight to a fixed constant; Obtain the target driver's historical health data or types of underlying diseases; When the underlying disease type is coronary artery disease risk, the value of the first weight is adjusted to be greater than the preset benchmark value of the first weight, and the value of the second weight is reduced accordingly to keep the fixed constant unchanged; When the underlying disease type is arrhythmia risk, the value of the second weight is adjusted to be greater than the preset benchmark value of the second weight, and the value of the first weight is reduced accordingly to keep the fixed constant unchanged.

[0011] In an optional implementation, the fixed constant is 100; The preset benchmark value for the first weight is 60, and the preset benchmark value for the second weight is 40; When the underlying disease type is coronary artery disease risk, adjusting the value of the first weight to be greater than the preset benchmark value of the first weight includes: setting the first weight to 70; When the underlying disease type is arrhythmia risk, adjusting the value of the second weight to be greater than the preset benchmark value of the second weight includes: setting the second weight to 50.

[0012] In an optional implementation, determining the first score based on the apoptosis rate includes: The apoptosis rate R is obtained, and (1-R) ​​is used as the first score. The determination of the second score based on the electrophysiological signal stability parameter includes: The electrophysiological signal stability parameters are normalized to obtain the second score, which ranges from 0 to 100.

[0013] In an optional implementation, establishing the correlation between the ischemia tolerance score and the driving scenario risk level, and determining the driving safety threshold of the target driver, includes: The ischemia tolerance score is divided into multiple preset intervals; each preset interval corresponds to a risk level. For each risk level, a corresponding upper limit for a single safe driving session and a continuous driving rest interval are set as the driving safety threshold; the higher the risk level, the shorter the upper limit for a single safe driving session.

[0014] In a second aspect, the present invention provides a driver fatigue warning device, comprising: A culture module is used to obtain hiPSC-derived cardiomyocytes from a target driver; and to culture the hiPSC-derived cardiomyocytes in a preset in vitro hypoxic stress environment; wherein the in vitro hypoxic stress environment is used to simulate the physiological load caused by driving fatigue. The detection module is used to detect the cellular physiological indicators of the hiPSC-derived cardiomyocytes under the in vitro hypoxic stress environment, and to calculate the ischemia tolerance score of the target driver based on the cellular physiological indicators. The evaluation module is used to establish the correlation between the ischemia tolerance score and the risk level of the driving scenario based on the ischemia tolerance score, and to determine the driving safety threshold of the target driver. The data acquisition module is used to collect the target driver's current driving data in real time during vehicle operation; The warning module is used to compare the current driving data with the driving safety threshold, and to trigger a warning signal when the current driving data reaches or exceeds the driving safety threshold.

[0015] Thirdly, the present invention provides a computer device including a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the driver fatigue warning method described in any of the foregoing embodiments.

[0016] Fourthly, the present invention provides a computer storage medium storing a computer program, which, when executed on a processor, implements the driver fatigue warning method according to any one of the foregoing embodiments.

[0017] The embodiments of this application have the following beneficial effects: This method obtains hiPSC-derived cardiomyocytes from the target driver and constructs a detection model using the driver's own biological sample, thus fully preserving the individual driver's genetic background and specific physiological characteristics. Compared to traditional monitoring methods that use universal thresholds, this autologous cell-based detection method can accurately reflect the differences in individual tolerance to fatigue and hypoxia, effectively avoiding false alarms or missed alarms caused by individual differences in physical condition, and achieving a highly personalized health risk assessment.

[0018] By culturing and detecting cells in an in vitro hypoxic stress environment simulating the physiological load of driving fatigue, this method recreates the ischemic and hypoxic state that may be encountered during driving at the cellular level. This not only compensates for the deficiency of conventional static physical examinations in reflecting dynamic driving stress, but also allows for in-depth exploration of the intrinsic tolerance limits of cardiac tissue under specific stress conditions. This assessment method, based on cellular physiological mechanisms, is more revealing of potential cardiovascular risks than simply relying on surface signal monitoring, ensuring a close correlation between the assessment results and actual driving scenarios.

[0019] Furthermore, this method predetermines a driving safety threshold based on a calculated ischemia tolerance score and compares real-time driving data with this personalized threshold, thereby achieving a shift from "post-event warning" to "pre-event prevention." The system can trigger an early warning when the driver's physiological load approaches their cellular tolerance limit but before substantial lesions occur, effectively preventing sudden cardiovascular events induced by long-distance driving or fatigue accumulation, and significantly improving the active safety of road traffic. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of this application, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of this application and therefore should not be considered as a limitation on the scope of protection of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the hardware operating environment involved in an embodiment of the driver fatigue warning method of the present invention; Figure 2 This is a flowchart illustrating Embodiment 1 of the driver fatigue warning method of the present invention; Figure 3This is a flowchart illustrating the refinement of step S200 (S210~S220) in Embodiment 2 of the driver fatigue warning method of the present invention; Figure 4 This is a flowchart illustrating the refinement of step S200 (S230~S250) in Embodiment 2 of the driver fatigue warning method of the present invention; Figure 5 This is a flowchart illustrating the refinement of step S200 (S260~S290) in Embodiment 2 of the driver fatigue warning method of the present invention; Figure 6 This is a detailed flowchart of step S300 in Embodiment 3 of the driver fatigue warning method of the present invention; Figure 7 This is a detailed flowchart of step S600 in Embodiment 4 of the driver fatigue warning method of the present invention; Figure 8 This is a schematic diagram of the module connection of the driver fatigue warning device of the present invention. Detailed Implementation

[0022] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0023] The components of the embodiments of this application described and illustrated in the accompanying drawings can be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0024] In the following, the terms “comprising,” “having,” and their cognates, which may be used in various embodiments of this application, are intended only to indicate a particular feature, number, step, operation, element, component, or combination thereof, and should not be construed as excluding, firstly, the presence of one or more other features, numbers, steps, operations, elements, components, or combinations thereof, or adding the possibility of one or more features, numbers, steps, operations, elements, components, or combinations thereof.

[0025] Furthermore, the terms "first," "second," and "third" are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0026] Unless otherwise specified, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of this application pertain. Terms (such as those defined in commonly used dictionaries) shall be interpreted as having the same meaning as in their contextual meaning in the relevant technical field and shall not be construed as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of this application.

[0027] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0028] like Figure 1 The diagram shown is a structural schematic of the hardware operating environment of the terminal involved in an embodiment of the present invention.

[0029] The driver fatigue warning system (device) of this invention can be a PC, or a mobile terminal device such as a smartphone, tablet, or laptop. The driver fatigue warning system may include: a processor 1001, such as a CPU; a network interface 1004; a user interface 1003; a memory 1005; and a communication bus 1002. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen, an input unit such as a keyboard, or a remote control; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be a high-speed RAM memory or a stable memory, such as a disk storage device. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001. Optionally, the driver fatigue warning system may also include RF (Radio Frequency) circuitry, audio circuitry, a Wi-Fi module, etc. In addition, the driver fatigue warning system can also be equipped with other sensors such as gyroscopes, barometers, hygrometers, thermometers, and infrared sensors, which will not be elaborated here.

[0030] Those skilled in the art will understand that Figure 1 The system (device) shown is not intended to limit it and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Figure 1 As shown, the memory 1005, which is a computer-readable storage medium, may include an operating system, a data interface control program, a network connection program, and a driver fatigue warning program.

[0031] In summary, the method provided by this invention utilizes autologous hiPSC-derived cardiomyocytes from the driver to construct an in vitro model. Under hypoxic stress conditions simulating driving fatigue, it quantifies the physiological tolerance of cells, thereby replacing universal monitoring standards with precise assessments based on individual biological characteristics, solving the problem of existing technologies being unable to adapt to individual differences. Simultaneously, by establishing a correlation between tolerance scores and driving safety thresholds, and dynamically comparing real-time data with these personalized thresholds, this method can identify risks before physiological abnormalities occur, elevating driving safety monitoring from a passive response to an active early warning based on cellular tolerance mechanisms, effectively reducing the risk of cardiovascular accidents induced by driving fatigue.

[0032] Example 1 Reference Figure 2 This embodiment provides a driver fatigue warning method, including: Step S100: Obtain hiPSC-derived cardiomyocytes from the target driver; and place the hiPSC-derived cardiomyocytes in a preset in vitro hypoxic stress environment for culture; wherein the in vitro hypoxic stress environment is used to simulate the physiological load caused by driving fatigue.

[0033] This step can include two core actions: first, "digital preparation of biological samples", which involves reprogramming the driver's own somatic cells (such as peripheral blood mononuclear cells) into human induced pluripotent stem cells (hiPSCs) and further directing their differentiation into cardiomyocytes; second, "simulation of physical environment", which involves creating a special external environment in a laboratory culture dish to force these cells into a physiological stress state similar to that of human fatigue.

[0034] Based on the principles of stem cell biology, hiPSC retains the complete genetic information and epigenetic characteristics of the donor (i.e., the target driver). Therefore, the cardiomyocytes derived from hiPSC can truly reflect the physiological characteristics of the individual driver (such as ion channel function and metabolic capacity).

[0035] The mechanism of the aforementioned "extracorporeal hypoxic stress environment" lies in simulating the relative myocardial ischemia (ischemia / hypoxia) state caused by prolonged sitting, mental stress, and changes in blood rheology during driving. By reducing the partial pressure of oxygen in the environment, cells are forced to undergo anaerobic metabolism, thereby inducing a cellular stress response similar to actual driving fatigue.

[0036] Specifically, cell acquisition can involve collecting peripheral blood, extracting PBMCs, inducing them into hiPSCs through reprogramming factors (such as Sendai virus), and then differentiating them into beating cardiomyocytes through small molecule regulators (such as Wnt signaling pathway regulators).

[0037] In terms of environment construction, a three-gas incubator can be used to precisely control the oxygen concentration (e.g., set the oxygen concentration below the normal physiological level of the human body) and maintain a constant temperature (37°C) and pH environment.

[0038] The above steps achieve "surrogate testing." Instead of subjecting the driver to dangerous extreme fatigue tests, a "cellular surrogate" is used to test physiological limits in a laboratory. This method is non-invasive (to humans), highly safe, and exhibits high individual specificity.

[0039] Step S200: Detect the cellular physiological indicators of the hiPSC-derived cardiomyocytes under the in vitro hypoxic stress environment, and calculate the ischemia tolerance score of the target driver based on the cellular physiological indicators.

[0040] This step involves converting biological responses into digital data. Under specific stress conditions, we observe how cells "perform" and quantify this performance into a specific numerical value (score) using algorithms.

[0041] The specific processing can include monitoring two core states of the cell: "whether the structure is damaged" (e.g., whether apoptosis or necrosis has occurred) and "whether the function is normal" (e.g., whether the beating is regular and whether the electrical signal transduction is delayed). Then, a computational model is set up to reduce the dimensionality of the collected multidimensional biological data and calculate a score that comprehensively reflects the cell's "stress resistance," namely, the "ischemia tolerance score."

[0042] The synergistic effect lies in the fact that a single indicator is often one-sided (for example, cells are alive but not moving, or moving erratically but not dead). Combining structural and functional indicators can provide a comprehensive assessment of the health of the cardiovascular system.

[0043] Specifically, detection methods include using flow cytometry to detect apoptosis markers (such as Annexin V) and using microelectrode arrays (MEAs) to record field potential signals.

[0044] In terms of the algorithm, for example, it can be S = A × w1 + B × w2. Where A is the cell survival rate (positive indicator), B is the electrical signal stability (positive indicator), and w1 and w2 are weighting coefficients.

[0045] In addition to routine physiological indicators, it is possible to expand the detection of cellular metabolites (such as lactic acid accumulation) and calcium ion transient amplitude to more precisely characterize tolerance.

[0046] Step S300: Based on the ischemia tolerance score, establish the correlation between the ischemia tolerance score and the risk level of the driving scenario, and determine the driving safety threshold of the target driver.

[0047] This step is a crucial one in the transition from "bioinformation" to "management information," mapping abstract scores to specific driving rule restrictions.

[0048] Specifically, a risk mapping model can be established. The logic is: the lower the cell tolerance score, the more vulnerable the driver's cardiovascular system, the higher the corresponding risk level, and the shorter the allowable safe driving time (threshold) should be.

[0049] This step can output a set of personalized parameters, such as: "Zhang San's maximum long-distance driving time is 4 hours per trip" (while the general regulatory standard may be 8 hours).

[0050] Its advantage lies in breaking the "one-size-fits-all" general management rules and customizing a "safety red line" for each driver that matches their actual physiological capabilities, thus achieving scientific preventive management.

[0051] Specifically, a lookup table or piecewise function can be constructed. For example: score > 80 points: risk level I, safety threshold = 8 hours; score 60-80 points: risk level II, safety threshold = 6 hours; score < 60 points: risk level III, safety threshold = 4 hours.

[0052] Step S400: During the vehicle's operation, the target driver's current driving data is collected in real time.

[0053] In this step, the system enters the online monitoring phase. After the vehicle is started, it continuously collects data streams related to the driver's status.

[0054] Specifically, onboard sensors and computing units can be used: time data includes the duration of a single continuous drive and the cumulative driving time recorded by the onboard timer. Physiological data can include real-time heart rate and respiratory rate collected via a smart steering wheel or wearable devices. Environmental data can include GPS positioning and vehicle speed recording. These data collection actions in this step provide real-time "input variables" for subsequent comparison steps.

[0055] Step S500: Compare the current driving data with the driving safety threshold. When the current driving data reaches or exceeds the driving safety threshold, trigger a warning signal.

[0056] This step is the final step in closed-loop control. It involves real-time comparison between the "current situation" (data from step S400) and the "allowed limits" (thresholds determined in step S300). The mechanism relies on logical comparison operations.

[0057] It should be noted that the "threshold" here is not a rigid, fixed value, but rather originates from personalized data derived from hiPSC cell experiments in steps S100, S200, and S300. Therefore, this simple comparison actually embodies profound biological wisdom. It ensures that the warning is not based on general regulations, but on the individual driver's life limits.

[0058] Specifically, the comparison program can be executed by the central processing unit of the vehicle's ADAS system. The triggering condition can be the current continuous driving time T. curr =4.1 hours, while the driver's personalized threshold T safe =4.0 hours. The warning output can be generated as an electrical signal to drive a buzzer alarm, a pop-up notification on the dashboard, seat vibration feedback, or to send a data packet to the backend.

[0059] This step enables a certain degree of advance warning. When the driver subjectively feels "still able to drive," or when routine physical signs have not yet shown obvious abnormalities, but the physiological load is actually approaching the cellular tolerance limit, a forced intervention and reminder can be issued to avoid sudden accidents.

[0060] Furthermore, it should be noted that, considering the dynamic changes in driver health, the driving safety thresholds established by this method can be periodically calibrated. For example, for low-risk drivers, an assessment every two years is recommended; for high-risk drivers with chronic diseases, calibration every 6-12 months is recommended. Additionally, if the vehicle's warning system triggers a Level 2 or Level 3 warning while driving, the driver should be advised to undergo a new cellular assessment to update their safety thresholds. This dynamic calibration mechanism ensures the long-term effectiveness and accuracy of the warning model.

[0061] Example 2 This embodiment provides a driver fatigue early warning method. Based on the aforementioned implementation method, the in vitro hypoxic stress environment includes a gradient oxygen concentration culture system and a biochemical stress inducer.

[0062] This embodiment specifies the "environment" and clarifies the two core elements that constitute this environment: (1) Gradient oxygen concentration culture system: It can refer to a series of environments that are not a single hypoxic environment, but a series of environments with different oxygen content levels (from near normal to extreme hypoxia).

[0063] (2) Biochemical stress inducers: can refer to chemical substances added in addition to the physical environment (oxygen) to induce stress response in cells through biochemical pathways.

[0064] The principle behind this is based on multidimensional simulation, meaning that real driving fatigue is a complex physiological process. Simple hypoxia can only simulate insufficient blood supply, while adding biochemical inducers can simulate the neurohumoral regulatory changes caused by mental stress. Combining the two allows for a more comprehensive recreation of real driving scenarios.

[0065] In addition to adrenaline, "biochemical stress inducers" can also include, but are not limited to, cortisol (a stress hormone that mimics stress hormones), angiotensin, and other endogenous substances related to cardiovascular stress.

[0066] In step S100, the hiPSC-derived cardiomyocytes are cultured in a pre-defined in vitro hypoxic stress environment, including: Step S110: The hiPSC-derived cardiomyocytes are placed under culture conditions with different oxygen concentrations, and a preset concentration of adrenaline is added to the culture medium to simulate the ischemic stress state of a driver under mild, moderate and heavy driving loads.

[0067] In a preferred embodiment, to more comprehensively simulate the complex physiological stresses of driving, the in vitro hypoxic stress environment may further include cortisol or the application of irregular electrical stimulation to simulate the combined effects of mental stress or sudden road condition shock. The above-mentioned step of "placing the hiPSC-derived cardiomyocytes under culture conditions with different oxygen concentrations" is a step in the "grading test". It does not mean placing all cells in the same harsh environment, but rather dividing the cells into multiple groups and exposing them to different degrees of hypoxia.

[0068] The treatment process can be as follows: Set up multiple parallel experimental groups. For example: Group A: Normoa control group (21% or 18% oxygen concentration). Group B: Mild hypoxia group (e.g., 12%-15% oxygen concentration). Group C: Severe hypoxia group (e.g., 5%-8% oxygen concentration).

[0069] The principle behind this is to establish a dose-response relationship. This approach can depict a tolerance curve. By observing the changes in cell state as oxygen concentration gradually decreases, the driver's "breakdown threshold" can be precisely identified. Furthermore, different oxygen concentration gradients physically correspond to different driving conditions (e.g., mild hypoxia corresponds to daily driving, while severe hypoxia corresponds to driving at high altitudes or microcirculatory disturbances caused by prolonged traffic jams).

[0070] Specifically, a three-gas incubator with independent control of multiple chambers can be used, or a portable hypoxic culture chamber can be used to adjust the gas intake ratio (nitrogen / oxygen / carbon dioxide) of each chamber.

[0071] The aforementioned step of "adding a predetermined concentration of adrenaline to the culture medium" is a key operation in simulating "mental stress." This involves adding a specific dose of a chemical substance—adrenaline—to the liquid environment (culture medium) in which cells live.

[0072] The principle behind this is that adrenaline is the main hormone secreted by the body in response to sudden events, stress, and anxiety. It acts directly on the β receptors of myocardial cells, increasing heart rate and contractility, while also increasing myocardial oxygen consumption.

[0073] In terms of the synergistic mechanism (hypoxia + adrenaline), it is an extremely severe "double whammy." On the one hand, hypoxia leads to a reduction in energy (ATP) supply; on the other hand, adrenaline forces cells to work frantically (increased energy consumption). This "supply and demand imbalance" perfectly simulates the extreme physiological state of "physical and mental exhaustion" and "driving while forcing oneself to stay awake" during drowsy driving.

[0074] This combination significantly improves the model's realism and sensitivity. While hypoxia alone may not induce certain hidden arrhythmias, the addition of adrenaline stimulation exposes underlying ion channel defects, thereby greatly enhancing the accuracy of early warning.

[0075] For example, different concentrations of adrenaline solutions (such as 0.1 μmol / L, 1 μmol / L) can be prepared and added to the culture system when changing the medium.

[0076] The aforementioned steps aim to simulate the ischemic stress state of drivers under mild, moderate, and severe driving loads. This clarifies the mapping relationship between laboratory procedures (oxygenation and drug administration) and the real world (driving load). A complete "extracorporeal driving load spectrum" has been established through these steps. For example, it could be as follows: (1) Mild load: high oxygen concentration + low adrenaline concentration (simulation: just got on the highway, slightly nervous).

[0077] (2) Moderate load: moderate oxygen concentration + moderate adrenaline (simulation: driving continuously for 2 hours, feeling slightly tired).

[0078] (3) Heavy load: low oxygen concentration + high adrenaline concentration (simulation: driving continuously for more than 4 hours, extremely fatigued and encountering congested road conditions).

[0079] For example, it could be: A. Mild stress combination: 18% O2 + adrenaline 0.3μmol / L + ES1 (simulating daily commuting stress); B. Moderate stress combination: 10% O2 + adrenaline 0.6μmol / L + ES2 (simulating long-distance driving stress); C. Severe load combination: 5% O2 + adrenaline 1μmol / L + ES2 (simulating traffic congestion + sudden fright extreme stress); D. Combined all-factor combination: 5% O2 + adrenaline 1μmol / L + cortisol 10 -5 mol / L + ES3 (simulating multiple driving stress superposition).

[0080] This method transforms the subsequent "tolerance score" from an isolated number into one with rich contextual meanings, providing solid data support for the formulation of tiered early warning strategies.

[0081] refer to Figure 3 In some embodiments, step S200, detecting cellular physiological indicators of the hiPSC-derived cardiomyocytes under in vitro hypoxic stress, includes: Step S210: Detect the apoptosis rate of the hiPSC-derived cardiomyocytes under different oxygen concentrations.

[0082] This step focuses on the "survival status" or "structural integrity" of cells. "Apoptosis rate" is a quantitative indicator that reflects the proportion of cells that have initiated an irreversible programmed cell death process under specific stress conditions.

[0083] It's important to note that when cardiomyocytes suffer severe ischemia and hypoxia, exceeding their self-repair capabilities, the mitochondrial pathway or death receptor pathway is activated, leading to the outward flipping of phosphatidylserine residues on the cell membrane, DNA breaks, and ultimately cell disintegration. Detecting this indicator is precisely to capture the critical point from cell "life" to "death."

[0084] Specifically, cells placed in different oxygen gradient environments can be labeled and stained separately to obtain a set of data, such as: the apoptosis rate is 5% (normal) at 18% oxygen concentration; and the apoptosis rate soars to 60% (collapse) at 5% oxygen concentration.

[0085] This step represents the lowest level of safety defense. It reveals the "hard power" of the driver's heart tissue: whether, under extreme circumstances, the myocardial cells will die directly. This is the core basis for assessing the risk of long-term irreversible damage.

[0086] For example, the Annexin V-FITC / PI double staining kit can be used in conjunction with flow cytometry for detection; or the TUNEL staining method can be used for counting under a microscope.

[0087] Step S220: Detect the electrophysiological signal stability parameters of the hiPSC-derived cardiomyocytes using a microelectrode array; wherein the electrophysiological signal stability parameters include at least one of beat frequency, beat frequency coefficient of variation, and QT interval prolongation duration.

[0088] The aforementioned "detection of electrophysiological signal stability parameters of hiPSC-derived cardiomyocytes via microelectrode arrays" refers to the "functional state" or "working performance" of the cells. Microelectrode arrays (MEAs) are a non-invasive, high-throughput detection technology that can record changes in the field potential of cardiomyocyte populations at the bottom of a culture dish in real time without damaging the cells.

[0089] The synergistic effect lies in the fact that "apoptosis rate" examines the state of cell apoptosis, that is, "whether cells have died or not," while "electrophysiological parameters" examine "whether the arrhythmias are normal or not." The combination of the two constitutes a two-dimensional assessment system of "structure + function." This synergy is extremely important because many sudden deaths do not originate from myocardial necrosis, but from functional malignant arrhythmias (i.e., cells are alive, but beating erratically).

[0090] Specifically, differentiated cardiomyocytes can be directly seeded onto culture plates covered with microelectrodes, and voltage signal waveforms can be continuously recorded during hypoxia experiments.

[0091] This step is highly sensitive and real-time. It can keenly detect minute fluctuations in electrical signals in the early stages, before cells die and only metabolic abnormalities occur, greatly improving the foresight of early warning.

[0092] Among the aforementioned electrophysiological signal stability parameters, beat frequency refers to the number of heartbeats per minute. The principle is that the heart rate may increase compensatorily in the early stages of hypoxia, and then slow down due to energy depletion in the later stages. The coefficient of variation of beat frequency is a statistical indicator (standard deviation / mean) measuring the regularity of the heartbeat rhythm. A higher value indicates a more erratic heartbeat, suggesting a higher risk of arrhythmia. QT interval prolongation simulates the change in the time from the start of the Q wave to the end of the T wave in an electrocardiogram, representing the total time of ventricular depolarization and repolarization. It should be noted that hypoxia can lead to potassium channel dysfunction, prolonging repolarization time (long QT syndrome), which is a very high-risk signal for inducing fatal arrhythmias such as torsades de pointes (TdP).

[0093] The method described in this embodiment can yield a set of quantitative data reflecting cardiac electrical stability, which can be used for subsequent scoring calculations.

[0094] For example, MEA data analysis software can be used to automatically identify the characteristic peaks of the field potential waveform and calculate the peak spacing (calculate the frequency and coefficient of variation) and waveform width (calculate the QT interval).

[0095] refer to Figure 4 In some embodiments, step S200, calculating the ischemia tolerance score of the target driver based on the cellular physiological indicators, includes: Step S230: Determine a first score based on the apoptosis rate and a second score based on the electrophysiological signal stability parameter.

[0096] This step is a "standardization" or "preprocessing" step for the data. Because the physical dimensions and numerical ranges of "apoptosis rate" (percentage) and "electrophysiological parameters" (such as milliseconds, coefficient of variation) are completely different, they cannot be added directly. They must first be converted into fractions of the same dimension (e.g., both are 0-100 points).

[0097] The specific processing procedure involves mapping the original experimental data to a dimensionless score space.

[0098] The first score represents the score for the "structural survival dimension". The second score represents the score for the "functional stability dimension".

[0099] Step S240: Calculate the product of the first score and the first weight to obtain the first weighted score; and calculate the product of the second score and the second weight to obtain the second weighted score.

[0100] This step reflects the "focus on key aspects." By introducing weighting coefficients, the importance of different dimensions is quantitatively allocated.

[0101] It should be noted that in health risk assessment, different physiological abnormalities pose varying degrees of threat to life. In some cases, structural damage is more fatal, while in others, functional disorders are more dangerous. Multiplication amplifies the impact of key dimensions.

[0102] Step S250: Calculate the sum of the first weighted score and the second weighted score, and use the sum as the ischemia tolerance score.

[0103] This step is the final data fusion step. A unique comprehensive index is generated using the "linear weighted summation" method. For example, it can be formed as follows (Formula 1): S = S1 × W1 + S2 + W2; Where S is the ischemia tolerance score, S1 is the first score, S2 is the second score, W1 is the first weight, and W2 is the second weight.

[0104] The method presented in this embodiment is simple and robust. This weighted summation model is the most mature method in multi-criteria decision analysis, which can intuitively reflect the comprehensive tolerance and facilitates subsequent threshold division.

[0105] Further reference Figure 5Before step S240, which calculates the product of the first score and the first weight to obtain the first weighted score, and before calculating the product of the second score and the second weight to obtain the second weighted score, the method further includes: Step S260: Set a preset benchmark value for the first weight and a preset benchmark value for the second weight, and set the sum of the first weight and the second weight to a fixed constant.

[0106] In this step, the initial state and boundary constraints of the model are established.

[0107] The above benchmark values ​​can be based on the default configuration (e.g., balanced configuration) for the general healthy population.

[0108] The aforementioned fixed constant (normalization constraint) can be, for example, W1+W2=100 or 1.0. This ensures that no matter how the weights are adjusted, the full score remains constant, preventing the total score from being artificially inflated or deflated due to weight adjustments, thus maintaining the fairness of the scoring system.

[0109] Step S270: Obtain the target driver's historical health data or basic disease types.

[0110] Step S280: When the underlying disease type is coronary artery disease risk, the value of the first weight is adjusted to be greater than the preset benchmark value of the first weight, and the value of the second weight is reduced accordingly to keep the fixed constant unchanged.

[0111] It should be noted that the pathological basis for this is that the core risk of coronary artery disease (such as coronary heart disease) lies in myocardial infarction (i.e., cell necrosis) caused by insufficient blood supply. For this group of people, whether cells "survive" (apoptosis rate) is a life-or-death indicator.

[0112] The specific processing logic is that after the system automatically recognizes the label, it increases W1 (structural weight) and decreases W2.

[0113] The data processing in this step makes the model more sensitive to cell death. For high-risk individuals with coronary heart disease, even a small amount of cell apoptosis can cause a significant drop in the total score, thus triggering an alarm earlier.

[0114] Step S290: When the underlying disease type is arrhythmia risk, the value of the second weight is adjusted to be greater than the preset benchmark value of the second weight, and the value of the first weight is reduced accordingly to keep the fixed constant unchanged.

[0115] It should be noted that the pathological basis can be that the risk of sudden death in patients with arrhythmia (such as long QT syndrome) lies in the disorder of electrical signals. At this time, the cells may not be dead yet, but the electrical signals are already disordered.

[0116] The specific processing logic is to increase W2 (functional weight).

[0117] The data processing in this step enables the true implementation of the "precision medicine" concept in engineering. The same model framework, through parameter fine-tuning, has been adapted to populations with different pathological characteristics.

[0118] Furthermore, the fixed constant is 100; the preset benchmark value of the first weight is 60, and the preset benchmark value of the second weight is 40; when the underlying disease type is coronary artery disease risk, adjusting the value of the first weight to be greater than the preset benchmark value of the first weight includes: setting the first weight to 70; when the underlying disease type is arrhythmia risk, adjusting the value of the second weight to be greater than the preset benchmark value of the second weight includes: setting the second weight to 50.

[0119] The example provided in this embodiment is a specific case. The numerical values ​​are set as a fixed constant = 100 (out of 100).

[0120] Baseline values: W1=60, W2=40. This indicates that, by default, the model considers "cell survival" (structure) to be slightly more important than "signal stability" (function) (6:4 ratio).

[0121] Adjusted for coronary artery disease: W1=70, W2=30. Further enhances sensitivity to structural damage.

[0122] Arrhythmia adjustment: W2=50, W1=50. Functional stability is elevated to an equal level of importance with structural stability.

[0123] This embodiment provides clearly defined and executable operational parameters. These values ​​are derived from experimental experience or big data regression analysis, and have extremely high practical reference value.

[0124] Further, step S230, determining a first score based on the apoptosis rate, includes: Step S231: Obtain the cell apoptosis rate R, and use (1-R) ​​as the first score; This step defines the calculation logic for the first score: inversion.

[0125] The principle is that the apoptosis rate R is a "negative indicator" (the higher the value, the worse, such as 80% mortality). Scoring systems are usually "positive" (the higher the score, the better). Therefore, 1-R (i.e., survival rate) is used as the score. For example, a 5% apoptosis rate results in a score of 0.95 (or 95 points). The resulting function formula is (Formula 2): S1=1 R (or 100×(1-R), depending on the normalization standard).

[0126] Step S230, determining the second score based on the electrophysiological signal stability parameter, includes: Step S232: Normalize the electrophysiological signal stability parameters to obtain the second score with a value of 0 to 100.

[0127] This step defines how the second score is handled.

[0128] It should be noted that since the units of electrophysiological parameters (such as coefficient of variation CV and QT duration T) vary and are usually "the smaller the better" or "the best in a specific range", they need to be mapped to 0-100 points through a normalization function.

[0129] Alternatively, linear interpolation can be used (Formula 3): ; In addition, a segmented scoring method can also be used (e.g., 100 points for coefficient of variation <5%, 80 points for 5%-10%, etc.).

[0130] This method eliminates the differences in physical dimensions, allowing electrical signal data to be mathematically weighted with apoptosis rate data.

[0131] In one specific embodiment of this application, the ischemia tolerance score can be calculated using the following formula: Tolerance score = (1 - apoptosis rate) × first weight + electrophysiological signal stability score × second weight. The baseline values ​​of the first and second weights (e.g., 60 and 40, respectively) can be determined through pre-experimental data analysis. More preferably, the weight coefficients can be dynamically adjusted based on the driver's individual health condition. For example, as described in the foregoing embodiments, for high-risk individuals with coronary heart disease, the first weight representing structural damage can be increased to 70; for high-risk individuals with arrhythmia, the second weight representing functional disorders can be increased to 50. This dynamic adjustment mechanism makes the scoring model more sensitive to specific risks, further improving the accuracy of personalized early warning.

[0132] Example 3 Reference Figure 6 This embodiment provides a driver fatigue early warning method. Based on the aforementioned implementation method, step S300, which establishes the correlation between the ischemia tolerance score and the risk level of the driving scenario, and determines the driving safety threshold of the target driver, includes: Step S310: Divide the ischemia tolerance score into multiple preset intervals; each preset interval corresponds to a risk level.

[0133] This step is a "discretization" or "grading" process. Tolerance scores (e.g., 35 points, 82 points) are continuous variables, which are not convenient for direct use in real-time traffic management. Dividing them into several intervals (e.g., "Excellent, Good, Average, Poor") and assigning them level labels (risk levels) facilitates the subsequent development of tiered management strategies.

[0134] Specifically, several threshold points can be set to map score ranges to risk levels. For example: [85,100] -> Risk Level I (low risk); [70,85) -> Risk Level II; ...; [0,40) -> Risk Level V (extremely high risk).

[0135] For example, it could be the following levels: (1) Level 1 (low risk): Tolerance score ≥ 85 points, no obvious apoptosis and electrophysiological disorders; (2) Level II (lower risk): Tolerance score ≤ 70 points < 85 points, no abnormalities under mild ischemia, apoptosis rate < 5% under severe ischemia; (3) Level 3 (medium risk): 55 points ≤ tolerance score < 70 points, apoptosis rate of 5%-10% under moderate ischemic environment, mild disturbance of electrophysiological signals; (4) Level 4 (higher risk): 40 points ≤ tolerance score < 55 points, apoptosis rate ≥ 8% under mild ischemic environment, and electrophysiological signals are significantly disordered; (5) Level 5 (extremely high risk): Tolerance score < 40 points, significant cell damage and electrophysiological abnormalities appear in all gradient ischemic environments.

[0136] This step simplifies the system's decision-making logic. The vehicle-mounted system does not need to calculate a strategy for each specific score; it only needs to identify the current risk level to invoke the corresponding contingency plan, reducing the complexity of real-time calculations and improving the system's response efficiency.

[0137] In addition, the number of levels can be adjusted according to actual needs, and can be a simple three levels (green / yellow / red) or a more refined five or ten levels.

[0138] Step S320: For each of the risk levels, a corresponding upper limit for a single safe driving time and a continuous driving rest interval are set as the driving safety threshold; the higher the risk level, the shorter the upper limit for a single safe driving time.

[0139] This step is the "Rule Configuration" step. It clarifies the specific restrictions corresponding to each risk level.

[0140] The maximum safe driving time for a single trip can refer to the maximum time a driver is allowed to drive continuously without taking a long break (e.g., 4 hours).

[0141] The aforementioned continuous driving rest interval can refer to the shortest time required for mandatory rest after reaching the upper limit (e.g., a 20-minute rest).

[0142] In terms of physiological recovery mechanisms, drivers with higher risk levels (poor tolerance) experience more fatigue and slower recovery of their myocardial cells under hypoxic conditions. Therefore, it is essential to reduce cardiac load and prevent cumulative damage by shortening working hours and increasing rest frequency.

[0143] This step generates a "personalized driving ability table." For example, a Level I driver is allowed to drive for 4 hours with a 15-minute rest. A Level III driver is only allowed to drive for 2 hours with a mandatory 30-minute rest. This directly translates the biological concept of "poor physical condition" into the operational concept of "more rest," providing executable and monitorable quantitative indicators that directly guide the intervention logic of the ADAS system.

[0144] For example, it could be the following: (1) Level 1 risk: Long-distance driving safety duration ≤ 8 hours, continuous driving ≤ 4 hours requires rest; (2) Level 2 risk: Long-distance driving safety duration ≤ 6 hours, continuous driving ≤ 3 hours requires rest; (3) Level 3 risk: Long-distance driving safety duration is ≤4 hours per trip, and continuous driving is ≤2 hours, so rest is required. Night driving is not recommended. (4) Level 4 risk: Single driving time ≤ 2 hours, continuous driving ≤ 1 hour requires rest, restricting commercial vehicle driving qualifications; (5) Level 5 risk: Prohibited from engaging in motor vehicle driving work, and it is recommended to undergo clinical cardiovascular disease screening immediately.

[0145] The statement above, "The higher the risk level, the shorter the upper limit of safe driving time per session," defines a negative correlation between the risk level and the threshold. This is a protection logic based on safety redundancy design. Higher risk means the cardiovascular system is closer to the brink of collapse, therefore a larger safety margin (i.e., a shorter driving time) must be provided to ensure that physiological limits are not reached under any circumstances.

[0146] This logic, together with the preceding "score-level" logic, forms a closed loop: poor cellular tolerance → low score → high risk level → short permissible driving time. This chain ensures that the final restrictions are strictly matched to the driver's actual physiological state.

[0147] Specifically, a mapping function or lookup table can be stored in the database of the vehicle system: Tmax=f(Risk Level ), where f is a monotonically decreasing function.

[0148] For example (Formula 4): Tmax =Base Time ×(1-k×Risk Level ), where k is the reduction factor.

[0149] Furthermore, step S400 involves real-time collection of the target driver's current driving data, including: Step S410: Collect the real-time physiological data and cumulative driving time of the target driver.

[0150] This step involves dual-channel data input for the vehicle-mounted terminal.

[0151] The aforementioned cumulative driving time can be a deterministic, linear physical quantity (how long you drove).

[0152] The aforementioned real-time physiological data can be fluctuating and complex biomass (such as real-time ECG waveforms and heart rate variability HRV).

[0153] This method focuses on both "external load" (time) and "internal response" (physiological signals) to ensure comprehensive monitoring.

[0154] Step S500: When the current driving data reaches or exceeds the driving safety threshold, a warning signal is triggered, including: Step S510: When the cumulative driving time reaches the upper limit of the single safe driving time in the driving safety threshold, a level one warning is triggered; The above steps are preventative alarms based on "time limit rules".

[0155] The first-level warning can be defined as a milder prompt (such as a voice reminder "You have been driving for 3 hours, it is recommended to take a rest"), because at this time there may not be any substantial danger, but only the preset management red line has been reached.

[0156] In this step, a personalized threshold can be calculated based on the previous steps (e.g., Zhang San can only drive for 3 hours). Once the timer reaches its limit, intervention is immediate, thus achieving "preemptive intervention." Forced rest breaks the chain of fatigue accumulation before it actually occurs. This is the simplest and most effective proactive safety measure.

[0157] Step S520: When the trend of change shown by the real-time physiological data matches the abnormal characteristics exhibited by the hiPSC-derived cardiomyocytes under the in vitro hypoxic stress environment, a secondary warning is triggered.

[0158] This step is for an emergency alarm based on "pattern matching".

[0159] The above-mentioned abnormal feature matching does not simply examine whether the heart rate exceeds 100, but rather confirms whether the current waveform trend matches the "cell waveform under specific conditions" in the laboratory.

[0160] The aforementioned Level 2 warning can be defined as an emergency intervention (such as vibration or speed limit) because it means that danger is occurring.

[0161] The specific algorithm can be as follows: extract features, such as a sudden prolongation of the QT interval and a flattened T wave, from the in vitro experimental data; perform real-time comparison, calculating the driver's current physiological feature vector in real time; and calculate similarity, such as cosine similarity and Euclidean distance. If the similarity is high, it indicates that the driver is reenacting the "collapse process" in the laboratory.

[0162] This method enables cross-temporal and spatiotemporal linkage between the "in vitro model" and "in vivo measurements." The in vitro model provides reference information (alerting the system: if this waveform appears, it indicates danger). The onboard system monitors in real time based on the reference information, and can issue an immediate warning once a match is found.

[0163] The method provided in this embodiment only reports genuine emergencies and does not report false positives (for example, a fast heart rate caused by excitement will not match the hypoxia feature, so it will not report false positives); even if the heart rate value is normal, if the electrical signal morphology shows ischemic features (QT prolongation), it can still be identified.

[0164] For example, waveform matching can be performed using dynamic time warping (DTW) algorithms or trained machine learning classifiers.

[0165] Example 4 Reference Figure 7 This embodiment provides a driver fatigue warning method. Based on the aforementioned implementation, the method further includes step S600, a scene recognition step: Step S610: Obtain the vehicle's current location information and / or road condition data, and identify the current driving scenario type based on the location information and / or road condition data; the driving scenario type includes at least one of highway scenario, urban congestion scenario, and high-altitude hypoxia scenario.

[0166] This step is the system's "Environmental Perception" port. It uses external sensors to determine the vehicle's external environment.

[0167] The above driving scenario types can be used as classification tags for external stress. Among them, highways correspond to monotony, high speed, and the need for prolonged mental concentration (corresponding to moderate mental stress); urban congestion corresponds to frequent starts and stops and emotional irritability (corresponding to high adrenaline stress); and high-altitude hypoxia corresponds to physical hypoxia (corresponding to extremely severe hypoxia stress).

[0168] Specifically, regarding location information, GPS / BeiDou modules can be used to obtain altitude (to determine if it is a high-altitude area) and road type (highway / urban area). Traffic data can be obtained using vehicle-to-everything (V2X) or navigation map APIs to obtain real-time congestion indices.

[0169] Step S620: Based on the driving scenario type, dynamically call the ischemia tolerance score of the hiPSC-derived cardiomyocytes under the corresponding load intensity in the in vitro hypoxic stress environment to correct the driving safety threshold.

[0170] This step is based on the core logic of "dynamic correction." It breaks the limitation of "using a single threshold indefinitely."

[0171] The aforementioned dynamic call responds to the "gradient oxygen concentration" experiment performed in the previous implementation. If a "plateau scenario" is identified, the system retrieves the worst score measured at "5% oxygen concentration" from the database. If a "plain highway" is identified, the score at "15% oxygen concentration" is retrieved.

[0172] The principle is that the environment-model mapping means that changes in the physical world environment (such as increased altitude) directly correspond to changes in the environmental parameters of the laboratory model (decreased oxygen concentration).

[0173] Its correction logic can be expressed as (Formula 5): Threshold final =Threshold base ×f(Scene Factor ).

[0174] The method provided in this embodiment ensures that the warning threshold always matches the current external pressure. For example, a driver might be able to drive for 4 hours on flat ground but only 2 hours on a plateau. The system can automatically complete this switch without manual intervention. This greatly improves the system's applicability and safety in extreme environments (such as transportation on the Sichuan-Tibet Highway) and solves the problem of general models failing under special road conditions.

[0175] In some implementations, triggering the warning signal includes executing tiered intervention measures; the tiered intervention measures include: issuing audible and visual alerts, controlling seat vibration, adjusting the in-vehicle air conditioning temperature, limiting the vehicle's maximum speed, or sending a notification to a preset emergency contact.

[0176] The aforementioned tiered intervention measures constitute the system's "physical feedback" to the human. Audio-visual cues serve as the visual / auditory channel (Level 1 warning). Seat vibration / air conditioning adjustment serves as the tactile / physical channel (Level 2 warning, using cold air or vibration to physically wake the driver). Speed ​​limits serve as the vehicle control channel (Level 3 warning, mandatory intervention to prevent loss of control). Notifying contacts serves as the social assistance channel (post-incident support).

[0177] This method constructs a multi-layered safety defense line from "reminder" to "awakening" and then to "vehicle control," ensuring that the most appropriate means are available to protect the safety of people and vehicles at every stage of a risk event.

[0178] Example 5: Model Validation To verify that the method disclosed in this application can effectively simulate hypoxic stress and quantify the tolerance of cardiomyocytes, a gradient hypoxia experiment was conducted in this embodiment.

[0179] 1. Experimental Method: HiPSC-derived cardiomyocytes from the same healthy driver were divided into five groups and cultured for 6 hours in oxygen concentrations of 18% (normospheric control), 15% (mild hypoxia), 12% (moderate hypoxia), 8% (severe hypoxia), and 5% (very severe hypoxia), respectively. The apoptosis rate and electrophysiological signals of each group were detected using the method described in Example 1, and tolerance scores were calculated.

[0180] 2. Experimental Results: The results of physiological indicators of cells in each group are shown in the table below (data are expressed as mean ± standard deviation, n=3): Table 1. Results of Physiological Indicators

[0181] 3. Analysis: The experimental results show that as the oxygen concentration gradient decreases, the apoptosis rate of hiPSC-derived cardiomyocytes increases significantly, while the beat frequency decreases, the coefficient of variation of beat frequency and the duration of QT interval prolongation all increase significantly, leading to a gradient decrease in electrophysiological signal stability and the final tolerance score. This strongly demonstrates that the in vitro hypoxia model constructed in this invention is effective and can accurately reproduce and quantify the degree of cardiomyocyte damage under different hypoxic loads.

[0182] Example 6: Validation of the effectiveness of individualized risk assessment To verify that this method can distinguish individual differences among drivers with different health backgrounds and thus achieve "personalized" risk assessment, this embodiment selects two categories of drivers—healthy and high-risk—for comparison.

[0183] 1. Experimental subjects and methods: Experimental Group A: hiPSC-derived cardiomyocytes (hiPSC-CM) derived from a 28-year-old healthy driver (with no history of cardiovascular disease or family history).

[0184] Experimental Group B: hiPSC-CM derived from a 35-year-old high-risk driver (with a family history of coronary heart disease and a relatively fast resting heart rate).

[0185] Both groups of cells were cultured under the same 5% extreme hypoxia condition for 6 hours, and various indicators were detected and calculated.

[0186] 2. Experimental Results: The comparison data of the two groups of cells under 5% oxygen concentration are shown in the table below (data are expressed as mean ± standard deviation, n=3): Table 2. Comparative data at 5% oxygen concentration

[0187] 3. Analysis: Experimental results show that under the same intensity of hypoxic stress, cardiomyocytes from high-risk drivers exhibited significantly higher apoptosis rates and more severe electrophysiological disturbances, with their final tolerance score (29.5 points) being far lower than that of healthy drivers (61.3 points). This demonstrates that the method of this invention can sensitively detect the intrinsic tolerance differences of cardiomyocytes among different individuals, providing a reliable biological basis for achieving personalized and precise driver fatigue early warning, and strongly supporting the "personalized" technical effect of this invention.

[0188] Example 7: Comparison of effects with conventional techniques To verify the superiority of this method over existing technologies (such as conventional resting electrocardiogram) in warning of potential risks, this embodiment provides a specific comparative case.

[0189] 1. Case Background: A 32-year-old male long-distance driver occasionally experienced chest tightness, but multiple routine resting electrocardiogram (ECG) examinations at the hospital showed "no obvious abnormalities," placing him in the "low-risk" group under current technology (there is a risk of underreporting).

[0190] 2. Comparative Test: Existing technology detection: its most recent resting ECG result.

[0191] The method of this invention involves collecting peripheral blood from the driver to prepare hiPSC-CM and conducting an in vitro stress test at a 5% oxygen concentration.

[0192] 3. Comparison Results: Table 3. Comparison Results

[0193] 4. Analysis: This case clearly demonstrates that hidden cardiovascular risks that cannot be detected by conventional resting electrocardiograms can be successfully identified by the method of this invention. By simulating the physiological load during driving, this method can expose high-risk individuals who are "normal at rest but have insufficient functional reserves under stress," thus solving the pain points of existing technologies that "can only monitor but cannot predict" and are "easy to miss," demonstrating significant inventiveness and practical value.

[0194] refer to Figure 8 This application also provides a driver fatigue warning device, comprising: The culture module 10 is used to obtain hiPSC-derived cardiomyocytes from the target driver; and to culture the hiPSC-derived cardiomyocytes in a preset in vitro hypoxic stress environment; wherein the in vitro hypoxic stress environment is used to simulate the physiological load caused by driving fatigue. The detection module 20 is used to detect the cellular physiological indicators of the hiPSC-derived cardiomyocytes under the in vitro hypoxic stress environment, and to calculate the ischemia tolerance score of the target driver based on the cellular physiological indicators. Evaluation module 30 is used to establish the correlation between the ischemia tolerance score and the risk level of the driving scenario based on the ischemia tolerance score, and to determine the driving safety threshold of the target driver. The data acquisition module 40 is used to collect the current driving data of the target driver in real time during vehicle operation. The warning module 50 is used to compare the current driving data with the driving safety threshold, and to trigger a warning signal when the current driving data reaches or exceeds the driving safety threshold.

[0195] It is understood that the device in this embodiment corresponds to the driver fatigue warning method in the above embodiment, and the options in the above embodiment are also applicable to this embodiment, so they will not be described again here.

[0196] This application also provides a computer device, which includes a processor and a memory. The memory stores a computer program, and the processor executes the computer program to implement the driver fatigue warning method described in any of the foregoing embodiments.

[0197] The processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including at least one of a Central Processing Unit (CPU), Graphics Processing Unit (GPU), Network Processor (NP), Digital Signal Processor (DSP), Application-Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0198] The memory can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The memory is used to store computer programs, and the processor can execute the computer programs accordingly after receiving execution instructions.

[0199] This application also provides a computer storage medium storing a computer program, which, when executed on a processor, implements the driver fatigue warning method according to any one of the foregoing embodiments.

[0200] The computer storage medium can be a readable storage medium, a non-volatile storage medium, or a volatile storage medium. For example, the computer storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0201] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that, in alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0202] In addition, the functional modules or units in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0203] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part 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 smartphone, personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0204] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for driver fatigue early warning, characterized in that, include: Obtain hiPSC-derived cardiomyocytes from the target driver; Furthermore, the hiPSC-derived cardiomyocytes are cultured in a pre-defined in vitro hypoxic stress environment; wherein the in vitro hypoxic stress environment is used to simulate the physiological load caused by driving fatigue. Cellular physiological indicators of the hiPSC-derived cardiomyocytes under in vitro hypoxic stress were detected, and the ischemia tolerance score of the target driver was calculated based on the cellular physiological indicators. Based on the ischemia tolerance score, a correlation is established between the ischemia tolerance score and the risk level of the driving scenario, and the driving safety threshold of the target driver is determined. During vehicle operation, the target driver's current driving data is collected in real time. The current driving data is compared with the driving safety threshold. When the current driving data reaches or exceeds the driving safety threshold, a warning signal is triggered.

2. The driver fatigue early warning method as described in claim 1, characterized in that, The in vitro hypoxic stress environment includes a gradient oxygen concentration culture system and a biochemical stress inducer; The step of culturing the hiPSC-derived cardiomyocytes in a pre-defined in vitro hypoxic stress environment includes: The hiPSC-derived cardiomyocytes were placed under culture conditions with different oxygen concentrations, and a preset concentration of adrenaline was added to the culture medium to simulate the ischemic stress state of drivers under mild, moderate and heavy driving loads.

3. The driver fatigue early warning method as described in claim 2, characterized in that, The detection of cellular physiological indicators of the hiPSC-derived cardiomyocytes under in vitro hypoxic stress includes: The apoptosis rate of the hiPSC-derived cardiomyocytes was detected under different oxygen concentrations. Electrophysiological signal stability parameters of the hiPSC-derived cardiomyocytes were detected using a microelectrode array; wherein the electrophysiological signal stability parameters include at least one of beat frequency, beat frequency coefficient of variation, and QT interval prolongation duration.

4. The driver fatigue early warning method as described in claim 3, characterized in that, The calculation of the target driver's ischemia tolerance score based on the cellular physiological indicators includes: A first score is determined based on the apoptosis rate, and a second score is determined based on the electrophysiological signal stability parameter. Calculate the product of the first score and the first weight to obtain the first weighted score; and calculate the product of the second score and the second weight to obtain the second weighted score. The sum of the first weighted score and the second weighted score is calculated, and the sum is used as the ischemia tolerance score.

5. The driver fatigue early warning method as described in claim 4, characterized in that, The product of the first score and the first weight is calculated to obtain the first weighted score; And, before calculating the product of the second score and the second weight to obtain the second weighted score, the process also includes: Set a preset benchmark value for the first weight and a preset benchmark value for the second weight, and set the sum of the first weight and the second weight to a fixed constant; Obtain the target driver's historical health data or types of underlying diseases; When the underlying disease type is coronary artery disease risk, the value of the first weight is adjusted to be greater than the preset benchmark value of the first weight, and the value of the second weight is reduced accordingly to keep the fixed constant unchanged; When the underlying disease type is arrhythmia risk, the value of the second weight is adjusted to be greater than the preset benchmark value of the second weight, and the value of the first weight is correspondingly reduced to keep the fixed constant unchanged; and / or, The step of determining the first score based on the apoptosis rate includes: obtaining the apoptosis rate R and using (1-R) ​​as the first score; The step of determining the second score based on the electrophysiological signal stability parameter includes: normalizing the electrophysiological signal stability parameter to obtain the second score with a value of 0 to 100.

6. The driver fatigue early warning method as described in claim 5, characterized in that, The fixed constant is 100; The preset benchmark value for the first weight is 60, and the preset benchmark value for the second weight is 40; When the underlying disease type is coronary artery disease risk, adjusting the value of the first weight to be greater than the preset benchmark value of the first weight includes: setting the first weight to 70; When the underlying disease type is arrhythmia risk, adjusting the value of the second weight to be greater than the preset benchmark value of the second weight includes: setting the second weight to 50.

7. The driver fatigue early warning method as described in claim 1, characterized in that, The process of establishing the correlation between the ischemia tolerance score and the risk level of the driving scenario, and determining the driving safety threshold of the target driver, includes: The ischemia tolerance score is divided into multiple preset intervals; each preset interval corresponds to a risk level. For each risk level, a corresponding upper limit for a single safe driving session and a continuous driving rest interval are set as the driving safety threshold; the higher the risk level, the shorter the upper limit for a single safe driving session.

8. A driver fatigue warning device, characterized in that, include: A culture module for obtaining hiPSC-derived cardiomyocytes from the target driver; Furthermore, the hiPSC-derived cardiomyocytes are cultured in a pre-defined in vitro hypoxic stress environment; wherein the in vitro hypoxic stress environment is used to simulate the physiological load caused by driving fatigue. The detection module is used to detect the cellular physiological indicators of the hiPSC-derived cardiomyocytes under the in vitro hypoxic stress environment, and to calculate the ischemia tolerance score of the target driver based on the cellular physiological indicators. The evaluation module is used to establish the correlation between the ischemia tolerance score and the risk level of the driving scenario based on the ischemia tolerance score, and to determine the driving safety threshold of the target driver. The data acquisition module is used to collect the target driver's current driving data in real time during vehicle operation; The warning module is used to compare the current driving data with the driving safety threshold, and to trigger a warning signal when the current driving data reaches or exceeds the driving safety threshold.

9. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the driver fatigue warning method according to any one of claims 1-7.

10. A computer storage medium, characterized in that, It stores a computer program that, when executed on a processor, implements the driver fatigue warning method according to any one of claims 1-7.

Images