Far infrared light wave oxygen cabin control method and system

Abstract

The invention relates to the technical field of environment control of health conditioning equipment, in particular to a far infrared light wave oxygen cabin control method and system, and the method comprises the following steps: obtaining real-time data of physiological parameters of a user; extracting physiological rhythm features based on the real-time data of the physiological parameters; on the basis of the physiological rhythm characteristics, predicting the change trend of the physiological rhythm characteristics or associated physiological indexes of the user in a preset time period; according to the predicted change trend and in combination with a conditioning target preset by a user, a regulation and control instruction is generated, and the regulation and control instruction is used for cooperatively regulating and controlling at least two environmental parameters selected from a far infrared radiation parameter, a light wave parameter and an oxygen supply concentration in the far infrared light wave oxygen cabin; by introducing the physiological rhythm characteristics and predicting the change trend of the physiological state of the user based on the physiological rhythm characteristics, deep evaluation of the current adaptive state of the user and prospective judgment of the future state are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental control of health conditioning equipment, and in particular to a far-infrared light wave oxygen chamber control method and system. Background Art

[0002] As a health-enhancing device, far-infrared light wave oxygen chambers are designed to improve human physiological status through the synergistic effects of far-infrared radiation, specialized light waves, and oxygen supply. Their operation relies on precise control of in-chamber environmental parameters. However, in practical applications, especially during prolonged treatment, existing technologies face numerous challenges. First, different users have significant differences in their physiological tolerance and actual needs for in-chamber environmental parameters. Preset fixed parameter modes are difficult to meet individual needs, while manual adjustment requires high user expertise. Second, some systems that incorporate physiological parameter monitoring often lag behind the body's actual internal state. Relying on only a few indicators cannot fully and accurately reflect the body's comprehensive physiological state and actual needs in a multi-parameter environment. More importantly, during prolonged treatment, the body's adaptability and physiological response to in-chamber environmental parameters evolve dynamically over time, and a parameter combination that initially provides comfort may become unsuitable in the middle and later stages of treatment.

[0003] Existing systems that make isolated adjustments based solely on initial settings or simple threshold feedback can interfere with achieving treatment goals or fail to promptly detect cumulative intolerance trends. Furthermore, the effects of far-infrared radiation and light wave parameters are closely related to individual physiological factors such as the user's body shape and skin characteristics. If existing systems fail to fully consider these factors and adjust parameters accordingly, achieving accurate and efficient control will be difficult. Furthermore, other parameters such as the cabin's carbon dioxide concentration and humidity can also affect user comfort and treatment effectiveness during long-term treatments, and if overlooked, can cause discomfort.

[0004] Therefore, the existing technology has obvious technical deficiencies in how to comprehensively consider the individual physiological characteristics of users, the dynamic changes in real-time physiological status and its nonlinear evolution trend, the specific needs of different conditioning stages and the migration of physiological tolerance boundaries, to achieve coordinated and forward-looking dynamic regulation of multiple environmental parameters in the cabin to ensure user safety and continuous comfort experience, and optimize control strategies for preset conditioning goals.

[0005] In view of the above problems, the existing technology is in urgent need of improvement. Summary of the Invention

[0006] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a far-infrared light wave oxygen chamber control method and system.

[0007] In a first aspect, the present invention provides a far-infrared light wave oxygen chamber control method, the method comprising the following steps: Obtain real-time data of users’ physiological parameters; Extracting physiological rhythm characteristics based on the real-time data of the physiological parameters, wherein the physiological rhythm characteristics reflect the user's current adaptation state to the cabin environment; Based on the physiological rhythm characteristics, predicting the changing trend of the physiological rhythm characteristics or related physiological indicators of the user within a preset time period; Generate a control instruction based on the predicted change trend and in combination with the user's preset conditioning goal, wherein the control instruction is used to coordinately control at least two environmental parameters in the far-infrared light wave oxygen chamber selected from far-infrared radiation parameters, light wave parameters, and oxygen supply concentration; The control instructions are executed to adjust the at least two environmental parameters to maintain the user's physiological state within a preset comfort zone and promote the achievement of conditioning goals.

[0008] The core innovation of this application lies in that by introducing physiological rhythm characteristics and predicting the changing trend of the user's physiological state based on them, an in-depth assessment of the user's current adaptation state and a forward-looking judgment of the future state are achieved. Based on this prediction result and the user's preset conditioning goals, instructions are generated for coordinated regulation of at least two environmental parameters such as far-infrared radiation parameters, light wave parameters, and oxygen supply concentration, thereby achieving the effect of maintaining the user's physiological state within the preset comfort zone and promoting the achievement of conditioning goals, effectively solving the problems of control lag, parameter rigidity, and low individual matching in the existing technology.

[0009] In a second aspect, a far-infrared light wave oxygen chamber control system is provided, the system comprising: A data acquisition module is used to obtain real-time data of the user's physiological parameters; A feature extraction module, configured to extract physiological rhythm features based on the real-time data of the physiological parameters; A trend prediction module, configured to predict, based on the physiological rhythm characteristics, a changing trend of the physiological rhythm characteristics or related physiological indicators of the user within a preset time period; An instruction generation module is used to generate a control instruction based on the predicted change trend and the user's preset conditioning target; The parameter control module is used to execute the control instruction to adjust the at least two environmental parameters.

[0010] Compared with the prior art, the present invention has the following beneficial effects: By obtaining the user's real-time physiological parameters, extracting physiological rhythm characteristics and predicting their changing trends, and then coordinating and regulating the cabin environment parameters according to the predicted trends and conditioning goals, and maintaining the user's physiological state within the comfort zone, it effectively solves the problems of fixed parameters, reliance on lagging indicators, lack of dynamic adaptation and personalized regulation capabilities in the existing technology. It has the advantage of being able to proactively and coordinating the cabin environment parameters according to the user's real-time physiological state and its dynamic changing trends, to achieve personalized, dynamic and precise conditioning, while ensuring the user's comfort and the achievement of conditioning goals. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 Schematic diagram of the method of the present invention.

[0012] Figure 2 Schematic diagram of the system structure of the present invention.

[0013] In the figure: 201, data acquisition module; 202, feature extraction module; 203, trend prediction module; 204, instruction generation module; 205, parameter control module. DETAILED DESCRIPTION

[0014] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and are not to be construed as limiting the present invention.

[0015] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the specified features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0016] Application scenario: Imagine a user undergoing a one-hour deep tissue relaxation treatment in a far-infrared oxygen chamber. Their body shape and skin physical properties differ from standard settings. The chamber is initially set to preset parameters. For the first twenty minutes of the treatment, the user feels comfortable. However, over time, the far-infrared heat accumulates in the body, causing the user's skin temperature to rise faster, heart rate variability to decrease, and respiratory rhythm to change. However, existing systems may lack effective monitoring and control capabilities. Clearly, if these issues are not addressed, the practical application of the far-infrared oxygen chamber will be limited. Improper parameter settings or untimely adjustments may cause user discomfort, or even pose safety risks.

[0017] Based on the above considerations, Figure 1 A far-infrared light wave oxygen chamber control method is shown, and the method includes the following steps: Obtain real-time data of users’ physiological parameters; Based on real-time data of physiological parameters, physiological rhythm characteristics are extracted, which reflect the user's current state of adaptation to the cabin environment; Based on physiological rhythm characteristics, predict the changing trend of the user's physiological rhythm characteristics or related physiological indicators within a preset time period; Based on the predicted change trend and in combination with the user's preset conditioning goals, a control instruction is generated, and the control instruction is used to coordinately control at least two environmental parameters in the far-infrared light wave oxygen chamber selected from far-infrared radiation parameters, light wave parameters, and oxygen supply concentration; The control instructions are executed to adjust at least two environmental parameters to maintain the user's physiological state within a preset comfort zone and promote the achievement of conditioning goals.

[0018] Physiological rhythm characteristics refer to patterns or indicators that exhibit periodic or regular changes, extracted from raw physiological parameters. These can be achieved using techniques such as heart rate variability analysis, respiratory rate variability analysis, and skin temperature fluctuation pattern analysis. For example, heart rate variability can be obtained by analyzing electrocardiogram signals, or respiratory rhythm patterns can be obtained by analyzing respiratory signals. These physiological rhythm characteristics reflect the user's current state of adaptation to the cabin environment. This means that based on the extracted physiological rhythm characteristics, the user's immediate physiological response and long-term adaptability to environmental factors such as far-infrared radiation, light waves, and oxygen concentration can be assessed.

[0019] The solution of this application obtains real-time data of the user's physiological parameters, providing basic information for subsequent analysis and control. Based on this real-time data, the system further extracts physiological rhythm characteristics that can reflect the user's current state of adaptation to the cabin environment. This makes the assessment of the user's physiological state no longer limited to instantaneous values, but can capture deeper regulatory patterns and adaptive changes within the body. Precisely because of the ability to obtain and analyze these physiological rhythm characteristics, this application is able to predict the changing trends of the user's physiological rhythm characteristics or their related physiological indicators within a preset time period based on these characteristics, thereby achieving a forward-looking judgment of the user's future physiological state and avoiding the shortcomings of traditional hysteresis feedback control. Subsequently, based on this predicted changing trend and combined with the user's preset conditioning goals, the system generates control instructions for collaboratively controlling at least two environmental parameters in the far-infrared light wave oxygen chamber, including far-infrared radiation parameters, light wave parameters, and oxygen supply concentration. The control instructions are used to coordinately control at least two environmental parameters selected from far-infrared radiation parameters, light wave parameters, and oxygen supply concentration in the far-infrared light wave oxygen cabin. This means that the generated control signal can adjust multiple environmental factors in the cabin simultaneously or in conjunction with each other, rather than just adjusting a certain parameter in isolation. Specifically, the far-infrared radiation intensity and light wave wavelength combination, oxygen supply concentration and far-infrared radiation intensity, etc. can be coordinated to adjust. Its purpose is to take into account the comprehensive influence and interaction of different environmental parameters on the physiological state of the human body, and to achieve more refined and more effective environmental control. This coordinated control takes into account the mutual influence between different environmental parameters and the comprehensive effect on the physiological state of the user, and can achieve a more accurate and more individualized control strategy. Ultimately, the system executes the generated control instructions to adjust the environmental parameters in the cabin to maintain the user's physiological state within the preset comfort zone and promote the achievement of conditioning goals. Maintaining the user's physiological state within the preset comfort zone and promoting the achievement of conditioning goals refers to the dual optimization goals of the control system. On the one hand, it is necessary to ensure that the user will not experience discomfort or safety risks during the entire conditioning process, and to control key physiological indicators such as heart rate, body temperature, and respiration within an individualized comfort range. On the other hand, according to the health goals set by the user (such as relieving muscle tension, improving sleep quality, etc.), the combination of environmental parameters should be adjusted to maximize the conditioning effect. The purpose is to balance safety, comfort and efficacy, and improve user experience and conditioning efficiency.

[0020] In some preferred embodiments, real-time data on the user's physiological parameters can be obtained using sensors placed within the cabin or worn by the user. For example, a photoplethysmography sensor can be used to obtain pulse wave signals for analyzing heart rate variability, a temperature sensor can be used to obtain body surface temperature data, and a respiratory sensor can be used to obtain respiratory rate and pattern data. Based on this real-time data, the extraction of physiological rhythm features can be performed by a processing unit that runs a specific algorithm, such as performing time and frequency domain analysis on the pulse wave signal to extract heart rate variability indicators, or performing pattern recognition on the respiratory signal to obtain respiratory rhythm features. The module used to predict the changing trends of the user's physiological rhythm features or related physiological indicators can be a prediction model, such as a prediction model based on a long short-term memory network (LSTM), which uses historical physiological rhythm data and current data to predict the direction and magnitude of changes in physiological indicators over a period of time in the future. Based on the predicted trends and the user's preset conditioning goals, a control logic unit can generate control instructions. This unit, based on a preset control strategy or optimization algorithm, calculates the target values ​​or adjustments for environmental parameters such as far-infrared radiation intensity, specific light wave parameters (such as wavelength and intensity), and oxygen supply concentration. Ultimately, executing the control instructions to adjust environmental parameters can be achieved by driving the corresponding actuators, such as adjusting the power output of the far-infrared emitter, controlling the brightness and color of the LED light source, and adjusting the opening of the oxygen mixing valve.

[0021] As an embodiment of the present invention, based on the predicted change trend and in combination with the user's preset conditioning goals, a control instruction is generated, and the control instruction is used to coordinately control at least two environmental parameters selected from far-infrared radiation parameters, light wave parameters, and oxygen supply concentration in the far-infrared light wave oxygen chamber. The steps include: When the adjustment requirement for the first environmental parameter in the far-infrared light wave oxygen chamber indicated by the predicted change trend is inconsistent with the adjustment requirement for the first environmental parameter set by the user's preset conditioning target: determining a first adjustment plan for the first environmental parameter, the first adjustment plan being configured to maintain the user's physiological state within a predetermined comfort zone; Based on the first adjustment plan, assess the degree of negative impact of the first adjustment plan on the achievement of the conditioning goal, and if the degree of negative impact reaches a first preset threshold, determine a second adjustment plan for adjusting at least one second environmental parameter coordinated with the first environmental parameter, wherein the second adjustment plan is used to reduce the degree of negative impact; If the degree of negative impact still reaches the second preset threshold after the second adjustment plan is determined, then the adjustment range of the first adjustment plan for the first environmental parameter is adjusted, provided that the user's physiological state remains within the preset comfort zone; The generated control instruction includes an adjustment to the first environmental parameter determined based on the aforementioned process and an adjustment to at least one second environmental parameter when a corresponding condition is met.

[0022] Among them, the adjustment demand for the first environmental parameter in the far-infrared light wave oxygen chamber indicated by the predicted change trend refers to the inference of the direction and magnitude of adjustment of a certain environmental parameter in order to maintain the stability or improvement of the user's physiological state based on the predicted trend of changes in physiological indicators. It can be determined by analyzing the predicted trend based on a physiological model or a machine learning model; the user's preset conditioning target's regulation demand for the first environmental parameter refers to the state or range to which a certain environmental parameter needs to be adjusted in order to achieve the conditioning purpose set by the user. It can be determined by referring to the preset conditioning program database or according to the target parameter range input by the user; the first environmental parameter refers to one of the far-infrared radiation parameter, light wave parameter, and oxygen supply concentration; the first adjustment program refers to an adjustment strategy determined for the first environmental parameter with the priority condition of maintaining the user's physiological state within the preset comfort zone; the preset comfort zone refers to the state in which the user's physiological parameters are maintained within a specific range. state, and the range can be determined based on individual historical data or a general physiological model; the degree of negative impact refers to the degree of obstruction or weakening of the realization of the conditioning goal caused by the first adjustment plan, which can be calculated using a quantitative evaluation model; the first preset threshold and the second preset threshold are used to determine whether the degree of negative impact has reached the critical value requiring further measures, which can be set based on clinical experience or user feedback data; at least one second environmental parameter coordinated with the first environmental parameter refers to at least one other item other than the first environmental parameter among the far-infrared radiation parameter, light wave parameter, and oxygen supply concentration, and there is a relationship between them that mutually influences or acts together on the user's physiological state; the second adjustment plan refers to an adjustment strategy for the second environmental parameter to reduce the negative impact of the first adjustment plan on the conditioning goal; the adjustment amplitude of the first adjustment plan for adjusting the first environmental parameter refers to fine-tuning the adjustment strategy of the first environmental parameter on the premise of maintaining the comfort zone, so as to seek a balance between comfort and conditioning goals.

[0023] The solution of the present application, when the predicted trend of change indicates that the adjustment requirement for a first environmental parameter within the far-infrared light wave oxygen chamber is inconsistent with the user's preset conditioning target for that first environmental parameter, does not directly adjust according to the conditioning target. Instead, it prioritizes determining a first adjustment plan to maintain the user's physiological state within a preset comfort zone. This ensures the user's basic comfort and safety during the conditioning process. On this basis, the solution further evaluates the degree of negative impact of the first adjustment plan on the achievement of the conditioning target. If the impact reaches a first preset threshold, a second adjustment plan is determined to adjust at least one second environmental parameter that is coordinated with the first environmental parameter. By adjusting other related parameters, the solution attempts to compensate for the potential loss of conditioning effect due to the priority of maintaining the comfort zone. If the negative impact still reaches the second preset threshold even after the introduction of the second adjustment plan, the solution adjusts the adjustment range of the first adjustment plan for the first environmental parameter, assuming that the user's physiological state remains within the preset comfort zone, to achieve a more refined trade-off between comfort and conditioning target. The final control instruction contains the adjustment of the environmental parameters determined through this series of decision-making processes. This conflict resolution logic is combined with basic physiological rhythm feature prediction and conditioning target setting, so that the generated control instructions can more intelligently balance the user's immediate physiological needs and long-term conditioning goals, avoiding the problems that may arise from simply adjusting according to predictions or targets, and improving the effectiveness of control and user experience.

[0024] As an embodiment of the present invention, the step of predicting a change trend of a user's physiological rhythm characteristics or related physiological indicators within a preset time period based on the physiological rhythm characteristics, when predictions are made separately based on multiple different physiological rhythm characteristics and there is a preset conflict condition between the preliminary physiological indicator change trend prediction results obtained based on the different physiological rhythm characteristics, includes: Obtain preliminary physiological indicator change trend prediction results corresponding to multiple different physiological rhythm characteristics; Obtain at least one of the preset conditioning target of the current far-infrared light wave oxygen chamber and the current conditioning stage; Determining, based on a preset conflict condition and at least one of the preset conditioning goal and the current conditioning stage, an integrated criterion for generating a single comprehensive physiological indicator change trend from the plurality of preliminary physiological indicator change trend prediction results; The integrated criterion is applied to process the prediction results of multiple preliminary physiological index change trends to obtain a single comprehensive physiological index change trend, and the single comprehensive physiological index change trend is used as the predicted change trend.

[0025] Among them, multiple different physiological rhythm characteristics refer to the characteristic information corresponding to various physiological processes with periodic fluctuations in the human body, such as circadian rhythm, ultradian rhythm, sub-daily rhythm, etc. These characteristics can be extracted from the real-time data of the user's physiological parameters, reflecting the changes in the body's internal state at different time scales; the preliminary physiological indicator change trend prediction result refers to the preliminary judgment on the direction and amplitude of changes of a certain physiological indicator (such as heart rate, body temperature, blood pressure, etc.) in the future period obtained by independent prediction based on each specific physiological rhythm feature. Each result corresponds to the influence of a physiological rhythm; the preset conflict condition refers to a series of pre-set rules or patterns used to identify situations where there are contradictions or inconsistencies between multiple preliminary physiological indicator change trend prediction results. For example, one prediction result indicates that a physiological indicator will increase, while another prediction result indicates that the same physiological indicator will decrease; the preset conditioning target refers to the health goal set by the user before using the far-infrared light wave oxygen chamber. Or comfortable state, such as promoting blood circulation, relieving muscle fatigue, improving sleep quality, etc. This goal has a guiding role in the direction and focus of environmental regulation; the current conditioning stage refers to the specific time period or link of the user's current conditioning process, such as the initial adaptation stage, the core conditioning stage, the later consolidation stage, etc. Different stages may require different environmental parameter settings and physiological response priorities; the integration criterion refers to a decision-making logic or algorithm, which is used to comprehensively consider factors such as the conflict type, preset conditioning goals and the current conditioning stage when there is a conflict in the prediction results of multiple preliminary physiological indicator change trends, and determine how to weigh, combine or select these preliminary results to generate a single, guiding comprehensive prediction result; the change trend of a single comprehensive physiological indicator refers to the final prediction result obtained after processing the integration criterion. This result represents the system's view that the most likely change direction and amplitude of the user's physiological indicators in the current situation is used to guide subsequent environmental parameter regulation.

[0026] The solution of the present application obtains preliminary physiological indicator trend prediction results corresponding to multiple different physiological rhythm characteristics, providing a comprehensive understanding of the potential impact of different physiological rhythms on the user's physiological state. When there is a preset conflict condition between these preliminary prediction results, the solution does not perform a simple numerical averaging, but instead further obtains at least one of the preset conditioning target and the current conditioning stage of the current far-infrared light wave oxygen chamber. Based on the preset conflict condition and at least one of the preset conditioning target and the current conditioning stage, the system determines an integration criterion for generating a single comprehensive physiological indicator trend from the multiple preliminary physiological indicator trend prediction results. The determination of this integration criterion takes into account the nature of the conflict, the user's desired goals, and the current conditioning process, making conflict resolution more targeted and intelligent. Subsequently, the determined integration criterion is applied to process the multiple preliminary physiological indicator trend prediction results to obtain a single comprehensive physiological indicator trend. Thus, a prediction result that comprehensively considers the influence of multiple physiological rhythms, conflict conditions, conditioning targets, and stages is used as the final predicted trend for subsequent control instruction generation. This allows prediction results to more accurately reflect the user's actual needs and physical condition in complex scenarios where multiple physiological rhythms influence and conflict with each other, providing a more reliable basis for environmental control in the far-infrared light wave oxygen chamber. In this way, this solution, building on the physiological rhythm feature-based prediction proposed in claim 1, further addresses the challenge of predicting multiple physiological rhythm conflicts, improving prediction accuracy and, in turn, enhancing the precision and effectiveness of environmental control.

[0027] As an embodiment of the present invention, based on a preset conflict condition and at least one of the preset conditioning target and the current conditioning stage, a step of determining an integrated criterion for generating a single comprehensive physiological indicator change trend from multiple preliminary physiological indicator change trend prediction results, when the preset conditioning target or the current conditioning stage changes, includes: Obtain the changed preset conditioning target or the new status of the current conditioning stage; Based on the new state, determining guidance information from a plurality of pre-configured integrated criteria associated with different preset conditioning goals or different conditioning stages, matching and selecting an integrated criterion that is compatible with the new state to determine guidance information; The selected integration criteria are applied to determine the guidance information, and the integration criteria are determined in combination with the preset conflict conditions.

[0028] Among them, the new state specifically refers to the specific value or type after the preset conditioning target or the current conditioning stage changes, and its purpose is to clarify the latest conditioning situation currently in place; the integration criterion determination guidance information specifically refers to the pre-configured rules, strategies or configuration data used to guide how to determine the integration criteria based on the preset conditioning target or conditioning stage. It can be implemented in the form of a lookup table, decision tree, rule set or parameter configuration file, and its purpose is to provide a flexible and configurable way to adapt to different conditioning needs and stages.

[0029] The solution of the present application first obtains the new state after the change when the preset conditioning target or the current conditioning stage changes. This is the basis for subsequent adjustments. Only when the new conditioning target or conditioning stage is clear can the integration criteria be adjusted in a targeted manner. Then, based on the new state, the guidance information is determined from a plurality of pre-configured integration criteria associated with different preset conditioning targets or different conditioning stages, and an integration criterion that is compatible with the new state is matched and selected to determine the guidance information. This step is the core. Through the pre-configured guidance information, the integration criterion determination method that matches the new state can be quickly found, avoiding the complex process of redesigning the integration criterion and improving the system's adaptability. Finally, the selected integration criterion is applied to determine the guidance information, and combined with the preset conflict conditions, the integration criterion applicable to the current state is determined, ensuring the effectiveness and accuracy of the integration criterion. In this way, the system can dynamically adjust the integration criterion according to the changes in the conditioning target or conditioning stage, so as to better adapt to the needs of the user and improve the conditioning effect. This ability to dynamically adjust and integrate criteria makes the process of generating a single comprehensive trend from multiple preliminary forecast results more targeted and accurate, thereby optimizing the subsequent generation of control instructions and improving the overall control effect.

[0030] As an embodiment of the present invention, after executing the step of applying the integrated criterion to process multiple preliminary physiological indicator change trend prediction results to obtain a single comprehensive physiological indicator change trend, and using the single comprehensive physiological indicator change trend as the predicted change trend, the method further includes: Calculating and obtaining a reliability measure value of the single comprehensive physiological indicator change trend based on the degree of consistency between multiple preliminary physiological indicator change trend prediction results that participate in generating the single comprehensive physiological indicator change trend, or based on preset importance information of the physiological rhythm characteristics of the sources of the multiple preliminary physiological indicator change trend prediction results; comparing the reliability measure value with a preset reliability judgment threshold; If the reliability measurement value fails to meet the standard defined by the reliability judgment threshold, then when a control instruction for controlling the environmental parameters in the far-infrared light wave oxygen chamber is subsequently generated based on the change trend of a single comprehensive physiological indicator, one or more execution characteristics of the control instruction will be adjusted.

[0031] Among them, the reliability measure value of the change trend of a single comprehensive physiological indicator refers to a quantitative indicator used to evaluate the reliability of the change trend of a single comprehensive physiological indicator obtained after integration processing. It can be calculated based on the degree of dispersion of the preliminary prediction results or the degree of alignment with the prediction results of important physiological rhythm characteristics; the preset reliability judgment threshold refers to a pre-set standard value used to define whether the reliability of the change trend of a single comprehensive physiological indicator reaches an acceptable level. It can be a fixed value or a dynamically adjusted range; one or more execution characteristics of the control instruction refer to the properties of the control instruction in actual application, such as the adjustment amplitude, adjustment frequency, or the combination of environmental parameters, which can be a limitation on the instruction intensity, speed or range.

[0032] The solution of this application avoids directly using potentially uncertain prediction results for regulation by adding a reliability assessment step after generating a single comprehensive physiological indicator trend. Specifically, the system calculates a reliability metric for the comprehensive trend. This metric reflects the degree of consistency between the integrated preliminary prediction results or the importance of different physiological rhythm features derived from these preliminary results. For example, if multiple preliminary prediction results are highly consistent, or if the comprehensive trend closely matches the prediction results derived from important physiological rhythm features (such as heart rate variability), the reliability metric is high. Conversely, if the preliminary prediction results differ significantly, or if the comprehensive trend deviates from the prediction results derived from important physiological rhythm features, the reliability metric is low. The calculated reliability metric is then compared with a preset reliability threshold. This threshold sets a minimum acceptable reliability standard. This comparison allows the system to identify comprehensive trends with insufficient reliability. When the reliability metric falls below the threshold, it indicates that the comprehensive trend generated based on the current information has high uncertainty. In this case, the system will not directly generate aggressive regulation instructions based on this uncertain trend, but will adjust the execution characteristics of the instructions when subsequently generating regulation instructions. This adjustment can be a limitation on the control amplitude, a reduction in the control frequency, or a prudent selection of the control parameter combination. In this way, the present application further introduces a self-assessment and risk control mechanism for the reliability of the prediction results based on the comprehensive prediction of multiple physiological rhythm characteristics, effectively solving the problem of low reliability of the comprehensive trend caused by conflicts or differences in importance of the initial prediction results, and significantly improving the safety and effectiveness of subsequent environmental parameter regulation.

[0033] As an embodiment of the present invention, the step of adjusting one or more execution characteristics of the control instruction includes: Obtaining a reliability measurement value of a change trend of a single comprehensive physiological indicator, a user's preset conditioning goal, and historical conditioning effect feedback information for different execution characteristic adjustment options under the conditioning goal associated with the user's historical conditioning data; Obtaining a plurality of preset execution characteristic adjustment options, the execution characteristic adjustment options including at least one of changing the target value adjustment range of one or more environmental parameters in the far-infrared light wave oxygen chamber, changing the environmental parameter update frequency, and changing the combination of environmental parameters involved in the coordinated regulation; Based on the obtained reliability measurement value, the user's preset conditioning goal, and historical conditioning effect feedback information, and in combination with the obtained multiple performance characteristic adjustment options, an execution characteristic adjustment plan is determined. The determination of the execution characteristic adjustment plan is based on the premise that the user's physiological state is maintained in the preset comfort zone and aims to reduce the potential negative impact on the achievement of the conditioning goal; Apply the determined execution characteristic adjustment scheme to adjust one or more execution characteristics of the control instruction.

[0034] Among them, the reliability measure of the change trend of a single comprehensive physiological indicator refers to a quantitative assessment of the credibility or prediction accuracy of the change trend of a single comprehensive physiological indicator obtained after integration processing, which can be achieved by a calculation method based on the consistency of the preliminary prediction results or the importance of the source physiological rhythm characteristics. Its purpose is to indicate whether the current prediction result is reliable enough to guide subsequent regulatory decisions; historical regulatory effect feedback information refers to a data set that records the actual physiological response or conditioning effect generated by the user under specific conditioning goals for different regulatory instruction execution characteristic adjustment methods during past conditioning. It can be stored in a structured database or log file. Its purpose is to provide a reference based on individual historical experience for current execution characteristic adjustment decisions; execution characteristic adjustment options refer to pre-set, optional strategies or plans for modifying the execution method of regulatory instructions, which may include limiting the adjustment range of environmental parameter target values, extending or shortening the parameter update cycle, and increasing, decreasing or replacing the collaborative regulatory parameter set. Its purpose is to provide a variety of alternative adjustment means to deal with insufficient prediction reliability; execution characteristic adjustment A plan refers to a specific strategy for modifying the execution mode of control instructions, which is selected or combined from a variety of preset execution characteristic adjustment options according to the current situation (including reliability measurement, conditioning goals and historical feedback). It can be a single adjustment option or a combination of multiple adjustment options. Its purpose is to provide a specific executable adjustment instruction. The premise of maintaining the user's physiological state in the preset comfort zone means that when determining the execution characteristic adjustment plan, ensuring that the user's physiological indicators (such as heart rate, body temperature, respiratory rate, etc.) remain within the pre-set range that is comfortable and safe for the user will be the primary and insurmountable constraint. This can be achieved through real-time monitoring and threshold judgment. Its purpose is to ensure the safety and experience of the user. The goal of reducing the potential negative impact on the achievement of conditioning goals means that, under the premise of maintaining the user's physiological comfort zone, the plan with the least impact on the achievement of the preset conditioning goals is selected from feasible adjustment options. This can be achieved by evaluating the degree of impact of different adjustment options on physiological indicators or subjective feelings related to the conditioning goals. Its purpose is to retain the conditioning effect as much as possible while coping with prediction uncertainty.

[0035] The solution of the present application obtains a reliability measure of the change trend of a single comprehensive physiological indicator, the user's preset conditioning target, and historical control effect feedback information associated with the user's historical conditioning data, and combines it with multiple preset execution characteristic adjustment options to determine an execution characteristic adjustment plan based on this information. The determination of this plan is based on the premise that the user's physiological state is maintained in the preset comfort zone and aims to reduce the potential negative impact on the achievement of the conditioning target. It is precisely because it comprehensively considers the reliability of the current prediction, the user's personalized conditioning needs, and their past conditioning experience, and uses this as a basis for weighing and selecting among multiple preset adjustment strategies that it is possible to adopt a more intelligent and refined adjustment method when the reliability of the change trend of a single comprehensive physiological indicator is insufficient, avoiding the negative consequences that may be caused by simple and crude adjustments, thereby maintaining the effectiveness of the conditioning process to the greatest extent possible while ensuring the safety and comfort of the user. Compared with the basic solution that only relies on the prediction trend to generate instructions, or performs simple adjustments when the prediction reliability is insufficient, this method can more effectively cope with complex and changing individual physiological responses and prediction uncertainties, and improve the adaptability and robustness of the regulation.

[0036] As an embodiment of the present invention, the steps of determining an execution characteristic adjustment plan based on the obtained reliability metric, the user's preset adjustment target, and historical adjustment effect feedback information, in combination with the obtained multiple execution characteristic adjustment options, include: A multi-dimensional decision matrix is ​​pre-configured. The multi-dimensional decision matrix includes the reliability measurement value range, the user-preset adjustment target type, and the type of historical adjustment effect feedback information. The matrix cells store the preferred execution characteristic adjustment plan corresponding to each dimensional combination; Obtain the reliability measurement value of the current situation, the user's preset adjustment target, and historical adjustment effect feedback information, and find the cell that matches the current situation in the multidimensional decision matrix to determine the execution feature adjustment plan.

[0037] Among them, the multidimensional decision matrix refers to a data structure used to store the corresponding output results under different input conditions. It can be implemented using a lookup table, a multidimensional array or a database structure. Its purpose is to convert the multi-factor decision-making process into a table lookup operation; the dimension of the matrix refers to the category of input variables that constitute the multidimensional decision matrix, which specifically includes different value ranges of reliability measurement values, different types of conditioning targets preset by users, and different types of historical control effect feedback information. Its purpose is to cover the combination of key factors that affect the selection of execution characteristic adjustment plans; the adaptive execution characteristic adjustment plan stored in the matrix cell refers to the adaptive execution characteristic adjustment strategy determined and stored in advance under each specific dimension combination in the matrix. It can specifically be the value or range of the adjustment amplitude of the environmental parameter target value, the setting of the environmental parameter update frequency, or the combination list of environmental parameters participating in collaborative regulation. Its purpose is to provide each scenario with an optimized or verified adjustment plan that can balance user comfort and the achievement of conditioning goals.

[0038] The solution of this application pre-configures a multidimensional decision matrix, using key factors influencing the selection of execution characteristic adjustment plans (reliability metric value range, adjustment target type, and historical adjustment effect feedback information type) as the matrix's dimensions. The matrix cells then store adaptive adjustment plans corresponding to each combination of dimensions. During actual operation, the system obtains the reliability metric value, adjustment target, and historical adjustment effect feedback information for the current context, then uses this information as an index to search within the pre-configured multidimensional decision matrix. This table lookup allows the system to locate the matrix cell that matches the current context and directly retrieve the adaptive execution characteristic adjustment plan stored there. This approach avoids complex calculations or reasoning, improving decision-making efficiency and accuracy, particularly when multiple factors and their combined effects need to be considered. The selection of the adjustment plan is always based on maintaining the user's physiological state within a preset comfort zone and minimizing potential negative impacts on the achievement of adjustment goals, ensuring the safety and effectiveness of the adjustment plan. This table lookup-based approach efficiently utilizes multiple inputs to quickly determine a pre-optimized or verified adjustment plan, providing a fast and stable decision-making mechanism for dynamic control methods.

[0039] As an embodiment of the present invention, the step of calculating and obtaining a reliability measurement value of a change trend of a single comprehensive physiological indicator includes: Obtain multiple preliminary physiological indicator change trend prediction results P_i(t), the confidence C_i(t) corresponding to P_i(t), the dynamic importance weight W_i(t) corresponding to the i-th physiological rhythm feature, the change trend of a single comprehensive physiological indicator P_comp(t), the preset maximum value range P_range_max of the physiological indicator, and the preset balance factor alpha; Calculate the weighted standard deviation of the prediction results of the change trend of multiple preliminary physiological indicators P_i(t). The weighted standard deviation is calculated based on P_i(t) and the product of W_i(t) and C_i(t) as weights to obtain the weighted standard deviation value; Calculate the first reliability component R_C_weighted(t) based on the weighted standard deviation and P_range_max. The calculation formula of R_C_weighted(t) is: R_C_weighted(t) = Max(0, 1 - (weighted standard deviation / P_range_max)); Calculate the absolute deviation between the predicted result of the change trend of the i-th preliminary physiological indicator P_i(t) and the change trend of the single comprehensive physiological indicator P_comp(t); Based on the absolute deviation and P_range_max, calculate the similarity score between the prediction result of the change trend of the i-th preliminary physiological indicator P_i(t) and the change trend of the single comprehensive physiological indicator P_comp(t); Perform a weighted summation of the similarity score and the product of W_i(t) and C_i(t), and divide it by the sum of the products of W_i(t) and C_i(t) to obtain the second reliability component R_I_aligned(t). The calculation formula of R_I_aligned(t) is: R_I_aligned(t) = Sum(i=1 to N) [W_i(t) * C_i(t) * (1 - abs(P_i(t) - P_comp(t)) / P_range_max)] / Sum(i=1 to N) [W_i(t) * C_i(t)]; Based on the first reliability component R_C_weighted(t), the second reliability component R_I_aligned(t), and the balance factor alpha, the reliability measurement value R(t) of the change trend of a single comprehensive physiological indicator is calculated. The calculation formula of R(t) is: R(t) = alpha * R_C_weighted(t) + (1 - alpha) * R_I_aligned(t).

[0040] Wherein, N is the number of preliminary physiological indicator change trend prediction results involved in generating a single comprehensive physiological indicator change trend; P_i(t) is the prediction result of the change trend of the i-th preliminary physiological indicator, which represents the change direction and amplitude of the physiological indicator predicted by the specific physiological rhythm characteristics; C_i(t) is the confidence level of the i-th preliminary prediction result, indicating the reliability of the preliminary prediction result itself; W_i(t) is the dynamic importance weight of the i-th physiological rhythm feature, which indicates the relative importance of the physiological rhythm feature in evaluating the reliability of the comprehensive trend; P_comp(t) is the change trend of the generated single comprehensive physiological index, which represents the change trend of the final physiological index after integration processing; P_range_max is the preset maximum value range of the physiological indicator; Alpha is a preset balancing factor used to balance the contributions of the consistency component and the importance alignment component in the final reliability measure; R_C_weighted(t) is the weighted consistency component; R_I_aligned(t) is the importance alignment component; R(t) is the reliability measurement value of the change trend of a single comprehensive physiological indicator.

[0041] First, the dispersion between predictions of different circadian rhythm features is measured by calculating a weighted standard deviation. The weights take into account the confidence of the predictions themselves and the importance of the corresponding circadian rhythm features, making the consistency assessment more accurate. Based on the weighted standard deviation and the maximum value range of the physiological indicator, the first reliability component is calculated, which intuitively reflects the degree of convergence of the predictions. Simultaneously, the absolute deviation between each preliminary prediction and the overall trend is calculated and converted into a similarity score. These similarity scores are then weighted and averaged based on the confidence and importance weights to obtain the second reliability component, which reflects the degree of alignment between the overall trend and those of the more important and reliable preliminary predictions. Finally, the first and second reliability components are weighted and combined using a balancing factor to obtain a reliability measure for the trend of a single overall physiological indicator. This approach comprehensively considers the dispersion between predictions and their consistency with the final overall trend, and incorporates the reliability of the predictions themselves and the relative importance of the circadian rhythm features from which they are derived into the assessment process. This enables the reliability measure to more comprehensively and precisely reflect the true credibility of the overall trend, overcoming the limitations of relying solely on a single factor.

[0042] like Figure 2 A far-infrared light wave oxygen chamber control system is shown, the system comprising: The data acquisition module 201 is used to obtain real-time data of the user's physiological parameters; A feature extraction module 202 is used to extract physiological rhythm features based on real-time data of physiological parameters; The trend prediction module 203 is used to predict the changing trend of the user's physiological rhythm characteristics or related physiological indicators within a preset time period based on the physiological rhythm characteristics; The instruction generation module 204 is used to generate a control instruction based on the predicted change trend and the user's preset conditioning target, and the control instruction is used to coordinately control at least two environmental parameters selected from the group consisting of far-infrared radiation parameters, light wave parameters, and oxygen supply concentration in the far-infrared light wave oxygen chamber; The parameter control module 205 is used to execute the control instructions to adjust at least two environmental parameters to maintain the user's physiological state within a preset comfort zone and promote the achievement of the conditioning goal.

[0043] The data acquisition module 201 is a unit for acquiring real-time data of the user's physiological parameters, which can be implemented by various physiological sensors, data acquisition circuits or wireless receiving devices, and its purpose is to provide basic data for subsequent processing; The feature extraction module 202 is a unit for extracting physiological rhythm features based on real-time data of physiological parameters. It can be implemented by using a digital signal processor to execute a specific signal processing algorithm. Its purpose is to extract key information reflecting the physiological state from the raw data. The trend prediction module 203 is a unit for predicting the changing trend of the user's physiological rhythm characteristics or related physiological indicators within a preset time period based on the physiological rhythm characteristics. It can be implemented by using an embedded processor to execute a prediction model algorithm, and its purpose is to achieve forward-looking judgment of future physiological status; The instruction generation module 204 is a unit for generating control instructions based on the predicted change trend and the user's preset conditioning target. It can be implemented by using a microcontroller to execute a decision logic algorithm. Its purpose is to determine a specific control plan based on the predicted results and user needs. Among them, the parameter control module 205 refers to a unit used to execute control instructions to adjust at least two environmental parameters. It can be implemented by an actuator drive circuit, a power regulator or a valve controller. Its purpose is to convert the generated control instructions into actual changes to the environmental parameters in the cabin.

[0044] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. Various changes and improvements are possible without departing from the spirit and scope of the present invention, and such changes and improvements fall within the scope of the invention as claimed.

Claims

1. A far infrared light wave oxygen chamber control method, characterized in that: The method comprises the following steps: Obtain real-time data of users’ physiological parameters; Extracting physiological rhythm characteristics based on the real-time data of the physiological parameters, wherein the physiological rhythm characteristics reflect the user's current adaptation state to the cabin environment; Based on the physiological rhythm characteristics, predicting the changing trend of the physiological rhythm characteristics or related physiological indicators of the user within a preset time period; Generate a control instruction based on the predicted change trend and in combination with the user's preset conditioning goal, wherein the control instruction is used to coordinately control at least two environmental parameters in the far-infrared light wave oxygen chamber selected from far-infrared radiation parameters, light wave parameters, and oxygen supply concentration; The control instructions are executed to adjust the at least two environmental parameters to maintain the user's physiological state within a preset comfort zone and promote the achievement of conditioning goals.

2. A far-infrared light wave oxygen chamber control method according to claim 1, characterized in that: The step of generating a control instruction based on the predicted change trend and in combination with the conditioning target preset by the user, wherein the control instruction is used to coordinately control at least two environmental parameters selected from far-infrared radiation parameters, light wave parameters, and oxygen supply concentration in the far-infrared light wave oxygen chamber includes: When the adjustment requirement for the first environmental parameter in the far-infrared light wave oxygen chamber indicated by the predicted change trend is inconsistent with the regulation requirement for the first environmental parameter set by the user's preset conditioning target: determining a first adjustment plan for the first environmental parameter, wherein the first adjustment plan is configured to maintain the user's physiological state within a predetermined comfort zone; Based on the first adjustment plan, evaluating the degree of negative impact of the first adjustment plan on the achievement of the conditioning goal, and if the degree of negative impact reaches a first preset threshold, determining a second adjustment plan for adjusting at least one second environmental parameter coordinated with the first environmental parameter, wherein the second adjustment plan is used to reduce the degree of negative impact; If the negative impact still reaches the second preset threshold after the second adjustment plan is determined, then adjusting the adjustment range of the first adjustment plan for the first environmental parameter, provided that the user's physiological state remains within the preset comfort zone; The generated control instruction includes the adjustment of the first environmental parameter determined based on the above process and the adjustment of the at least one second environmental parameter when the corresponding condition is met.

3. A far-infrared light wave oxygen chamber control method according to claim 1, characterized in that: The step of predicting the changing trend of the physiological rhythm feature or its associated physiological indicators of the user within a preset time period based on the physiological rhythm feature, when predictions are made separately based on multiple different physiological rhythm features of the physiological rhythm feature, and when there is a preset conflict condition between the preliminary physiological indicator changing trend prediction results obtained based on the different physiological rhythm features, includes: Obtaining preliminary physiological indicator change trend prediction results corresponding to each of the multiple different physiological rhythm characteristics; Obtain at least one of the preset conditioning target of the current far-infrared light wave oxygen chamber and the current conditioning stage; Determining, based on the preset conflict condition and at least one of the preset conditioning goal and the current conditioning stage, an integration criterion for generating a single comprehensive physiological indicator change trend from the multiple preliminary physiological indicator change trend prediction results; The integration criterion is applied to process the multiple preliminary physiological indicator change trend prediction results to obtain the single comprehensive physiological indicator change trend, and the single comprehensive physiological indicator change trend is used as the predicted change trend.

4. A far-infrared light wave oxygen chamber control method according to claim 3, characterized in that: The step of determining an integrated criterion for generating a single comprehensive physiological indicator change trend from the multiple preliminary physiological indicator change trend prediction results based on the preset conflict condition and at least one of the preset conditioning target and the current conditioning stage, when the preset conditioning target or the current conditioning stage changes, includes: Obtaining the changed preset conditioning target or the new state of the current conditioning stage; Based on the new state, determining guidance information from a plurality of pre-configured integrated criteria associated with different preset conditioning goals or different conditioning stages, matching and selecting an integrated criterion that is compatible with the new state to determine guidance information; The selected integration criterion is applied to determine the guidance information, and the integration criterion is determined in combination with the preset conflict condition.

5. A far-infrared light wave oxygen chamber control method according to claim 3, characterized in that: After executing the step of applying the integration criterion, processing the multiple preliminary physiological indicator change trend prediction results, obtaining the single comprehensive physiological indicator change trend, and using the single comprehensive physiological indicator change trend as the predicted change trend, the method further includes: Calculating and obtaining a reliability measure value of the single comprehensive physiological indicator change trend based on a degree of consistency among the multiple preliminary physiological indicator change trend prediction results that participate in generating the single comprehensive physiological indicator change trend, or based on preset importance information of the physiological rhythm characteristics of the sources of the multiple preliminary physiological indicator change trend prediction results; Comparing the reliability metric with a preset reliability judgment threshold; If the reliability metric fails to meet the standard defined by the reliability judgment threshold, one or more execution characteristics of the control instruction are adjusted when a control instruction for controlling the environmental parameters in the far-infrared light wave oxygen chamber is subsequently generated based on the change trend of the single comprehensive physiological indicator.

6. A far-infrared light wave oxygen chamber control method according to claim 5, characterized in that: The step of adjusting one or more execution characteristics of the control instruction includes: Obtaining a reliability measurement value of the change trend of the single comprehensive physiological indicator, a user's preset conditioning goal, and historical conditioning effect feedback information for different execution characteristic adjustment options under the conditioning goal, which is associated with the user's historical conditioning data; Acquiring a plurality of preset execution characteristic adjustment options, the execution characteristic adjustment options including at least one of changing the target value adjustment range of one or more environmental parameters in the far-infrared light wave oxygen chamber, changing the update frequency of the environmental parameters, and changing the combination of environmental parameters involved in collaborative regulation; Determining an execution characteristic adjustment plan based on the obtained reliability metric, the user's preset adjustment target, and the historical adjustment effect feedback information, and in combination with the obtained multiple execution characteristic adjustment options; Apply the determined execution characteristic adjustment scheme to adjust one or more execution characteristics of the control instruction.

7. A far-infrared light wave oxygen chamber control method according to claim 6, characterized in that: The determination of the execution characteristic adjustment plan is based on the premise that the user's physiological state is maintained in a preset comfort zone, and aims to reduce the potential negative impact on the achievement of the conditioning goal.

8. A far-infrared light wave oxygen chamber control method according to claim 7, characterized in that: The step of determining an execution characteristic adjustment scheme based on the obtained reliability metric, the user's preset conditioning target, and the historical conditioning effect feedback information, and in combination with the obtained multiple execution characteristic adjustment options, includes: A multi-dimensional decision matrix is ​​pre-configured, and the matrix cells store the preferred execution characteristic adjustment scheme corresponding to each dimension combination; The reliability measurement value of the current situation, the conditioning target preset by the user, and the historical conditioning effect feedback information are obtained, and a cell matching the current situation is searched in the multidimensional decision matrix to determine the execution characteristic adjustment plan.

9. A far-infrared light wave oxygen chamber control method according to claim 8, characterized in that: The multidimensional decision matrix includes the reliability measurement value range, the conditioning target type preset by the user, and the type of the historical conditioning effect feedback information.

10. A far infrared light wave oxygen chamber control system, characterized in that: The system includes: A data acquisition module is used to obtain real-time data of the user's physiological parameters; A feature extraction module, configured to extract physiological rhythm features based on the real-time data of the physiological parameters; A trend prediction module, configured to predict, based on the physiological rhythm characteristics, a changing trend of the physiological rhythm characteristics or related physiological indicators of the user within a preset time period; an instruction generation module for generating a control instruction based on the predicted change trend and in combination with a conditioning goal preset by the user, wherein the control instruction is used to coordinately control at least two environmental parameters in the far-infrared light wave oxygen chamber selected from far-infrared radiation parameters, light wave parameters, and oxygen supply concentration; The parameter control module is used to execute the control instruction to adjust the at least two environmental parameters to maintain the user's physiological state within a preset comfort zone and promote the achievement of conditioning goals.

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