Far infrared light wave oxygen cabin control method and system

Abstract

The present application relates to the field of environment control technology of health conditioning equipment, and particularly relates to a far-infrared light wave oxygen cabin control method and system, the method comprising the following steps: acquiring real-time data of physiological parameters of a user; extracting a physiological rhythm feature based on the real-time data of the physiological parameters; predicting a change trend of the physiological rhythm feature or a related physiological index thereof of the user within a preset time period based on the physiological rhythm feature; generating a regulation instruction according to the predicted change trend and in combination with a preset conditioning target of the user, the regulation instruction being used to cooperatively regulate 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; and by introducing the physiological rhythm feature and predicting a change trend of a physiological state of the user based thereon, in-depth evaluation of a current adaptive state of the user and prospective judgment of a future state are realized.

Description

Technical Field

[0001] This invention relates to the field of environmental control technology for health conditioning equipment, and in particular to a far-infrared light wave oxygen chamber control method and system. Background Technology

[0002] Far-infrared oxygen chambers, as a health conditioning device, aim to improve the human body's physiological state through the synergistic effect of far-infrared radiation, specific light waves, and oxygen supply. Their operation relies on precise control of the chamber's environmental parameters. However, in practical applications, especially during prolonged use, existing technologies face numerous challenges. First, different users exhibit significant differences in their physiological tolerance and actual needs regarding the chamber's environmental parameters. Preset fixed parameter modes are insufficient to meet individual needs, while manual adjustment requires a high level of professional knowledge from the user. Second, some systems that incorporate physiological parameter monitoring often lag behind actual changes in the body's internal state, and relying on only a few indicators cannot comprehensively and accurately reflect the body's overall physiological state and actual needs under multi-parameter environments. More importantly, during prolonged use, the body's adaptability and physiological responses to the chamber's environmental parameters dynamically evolve over time; initially comfortable parameter combinations may become unsuitable in the later stages.

[0003] Existing systems that rely solely on initial settings or simple threshold feedback for isolated adjustments may interfere with achieving treatment goals or fail to detect cumulative intolerance trends in a timely manner. Furthermore, the effects of far-infrared radiation and light wave parameters are closely related to individual physiological factors such as the user's body type and skin characteristics. Existing systems that fail to adequately consider these factors and adjust parameters accordingly will struggle to achieve accurate and efficient regulation. Simultaneously, other parameters such as cabin carbon dioxide concentration and humidity also affect user comfort and treatment effectiveness during prolonged treatment; neglecting these factors may lead to discomfort.

[0004] Therefore, existing technologies have significant shortcomings in how to comprehensively consider the individual physiological characteristics of users, the dynamic changes and nonlinear evolution trends of real-time physiological state, 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, so as to ensure user safety and continuous comfort, and to optimize control strategies for preset conditioning goals.

[0005] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0006] The purpose of this invention is to address the shortcomings of existing technologies by proposing a far-infrared light wave oxygen chamber control method and system.

[0007] In a first aspect, the present invention provides a method for controlling a far-infrared light wave oxygen chamber, the method comprising the following steps:

[0008] Acquire real-time data of the user's physiological parameters;

[0009] Based on real-time data of the physiological parameters, physiological rhythm features are extracted, which reflect the user's current adaptation status to the cabin environment.

[0010] Based on the physiological rhythm characteristics, predict the changing trend of the physiological rhythm characteristics or their related physiological indicators of the user within a preset time period;

[0011] Based on the predicted trend and combined with the user's preset conditioning goals, a control command is generated. The control command is used to coordinate and 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.

[0012] The control instructions are executed to adjust the at least two environmental parameters in order to maintain the user's physiological state within a preset comfort zone and promote the achievement of conditioning goals.

[0013] The core innovation of this application lies in introducing physiological rhythm characteristics and predicting the changing trend of the user's physiological state based on them, thereby achieving an in-depth assessment of the user's current adaptive state and a forward-looking judgment of the future state. Based on this prediction result and the user's preset conditioning goals, instructions are generated for the coordinated regulation of at least two environmental parameters, such as far-infrared radiation parameters, light wave parameters, and oxygen supply concentration. This achieves 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 prior art.

[0014] Secondly, a far-infrared oxygen chamber control system is provided, the system comprising:

[0015] The data acquisition module is used to acquire real-time data of the user's physiological parameters;

[0016] The feature extraction module is used to extract physiological rhythm features based on real-time data of the physiological parameters;

[0017] The trend prediction module is used to predict the trend of change of the physiological rhythm characteristics or their related physiological indicators of the user within a preset time period based on the physiological rhythm characteristics.

[0018] The instruction generation module is used to generate control instructions based on the predicted trend of change and the user's preset conditioning goals.

[0019] The parameter control module is used to execute the control instructions to adjust the at least two environmental parameters.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] By acquiring users' real-time physiological parameters, extracting physiological rhythm characteristics, and predicting their changing trends, the system can then coordinately regulate the cabin environment parameters based on the predicted trends and conditioning goals, maintaining the user's physiological state within the comfort zone. This effectively solves the problems of fixed parameters, reliance on lagging indicators, and lack of dynamic adaptation and personalized regulation capabilities in existing technologies. It has the advantage of being able to proactively coordinately regulate cabin environment parameters based on the user's real-time physiological state and its dynamic changing trends, achieving personalized and dynamic precision conditioning while ensuring user comfort and the achievement of conditioning goals. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0023] Figure 2 This is a schematic diagram of the system structure of the present invention.

[0024] In the diagram: 201, Data Acquisition Module; 202, Feature Extraction Module; 203, Trend Prediction Module; 204, Instruction Generation Module; 205, Parameter Control Module. Detailed Implementation

[0025] 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 denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0026] 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 technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0027] Application Scenario: Imagine a user undergoing a one-hour deep tissue relaxation treatment in a far-infrared light wave oxygen chamber. The user's body shape and skin physical characteristics 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, as time progresses, far-infrared heat accumulates in the body, the user's skin temperature rises at a faster rate, heart rate variability decreases, and respiratory rhythm changes. Existing systems may lack effective monitoring and control for these changes. Clearly, without addressing these issues, the practical application of far-infrared light wave oxygen chambers will be limited. Users may experience discomfort due to improper parameter settings or untimely adjustments, potentially even leading to safety risks.

[0028] Based on the above considerations, such as Figure 1 The method for controlling a far-infrared oxygen chamber, as shown, includes the following steps:

[0029] Acquire real-time data of the user's physiological parameters;

[0030] Based on real-time data of physiological parameters, physiological rhythm features are extracted, which reflect the user's current state of adaptation to the cabin environment.

[0031] Based on physiological rhythm characteristics, predict the changing trend of users' physiological rhythm characteristics or their related physiological indicators within a preset time period;

[0032] Based on the predicted trend and combined with the user's preset conditioning goals, control instructions are generated. These control instructions are used to coordinate and regulate at least two environmental parameters selected from far-infrared radiation parameters, light wave parameters, and oxygen supply concentration within the far-infrared light wave oxygen chamber.

[0033] Execute control commands 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.

[0034] Physiological rhythm characteristics refer to patterns or indicators with 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 indicators 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 adaptation status to the cabin environment. In other words, based on the extracted physiological rhythm characteristics, the user's immediate physiological response and long-term adaptation ability to environmental factors such as far-infrared radiation, light waves, and oxygen concentration can be assessed.

[0035] This application's solution acquires real-time data of the user's physiological parameters, providing foundational information for subsequent analysis and regulation. Based on this real-time data, the system further extracts physiological rhythm characteristics that reflect the user's current adaptation to the cabin environment. This allows the assessment of the user's physiological state to move beyond instantaneous values ​​and capture deeper-level regulatory patterns and adaptive changes within the body. Because of the ability to acquire and analyze these physiological rhythm characteristics, this application can predict the changing trends of the user's physiological rhythm characteristics or their associated physiological indicators over a preset time period, thereby achieving a forward-looking judgment of the user's future physiological state and avoiding the shortcomings of traditional delayed feedback control. Subsequently, based on this predicted trend and combined with the user's preset conditioning goals, the system generates control commands for the coordinated regulation of at least two environmental parameters within the far-infrared oxygen chamber, including far-infrared radiation parameters, light wave parameters, and oxygen supply concentration. The control commands are used to coordinate and regulate at least two environmental parameters selected from far-infrared radiation parameters, light wave parameters, and oxygen supply concentration within the far-infrared oxygen chamber. This means that the generated control signals can simultaneously or in conjunction with other environmental factors within the chamber, rather than simply adjusting a single parameter in isolation. Specifically, they can coordinately adjust combinations of far-infrared radiation intensity and light wave wavelength, and oxygen supply concentration and far-infrared radiation intensity, etc. The aim is to consider the comprehensive influence and interaction of different environmental parameters on the human physiological state, achieving more precise and effective environmental regulation. This coordinated regulation considers the mutual influence between different environmental parameters and their comprehensive effect on the user's physiological state, enabling a more accurate and individualized regulation strategy. Ultimately, the system executes the generated control commands, adjusting the environmental parameters within the chamber to maintain the user's physiological state within a preset comfort zone and promote the achievement of conditioning goals. Maintaining the user's physiological state within a preset comfort zone and promoting the achievement of treatment goals refers to the dual optimization objectives of the control system. On the one hand, it is necessary to ensure that the user does not experience discomfort or safety risks throughout the entire treatment process by controlling key physiological indicators such as heart rate, body temperature, and respiration within an individualized comfort range. On the other hand, it is necessary to adjust the combination of environmental parameters to maximize the treatment effect based on the user's set health goals (such as relieving muscle tension and improving sleep quality). The aim is to balance safety, comfort, and therapeutic efficacy, thereby improving user experience and treatment efficiency.

[0036] In some preferred embodiments, real-time data on the user's physiological parameters can be acquired using sensors deployed within the cabin or worn on the user. For example, a photoplethysmography (PPG) sensor can be used to acquire pulse wave signals for heart rate variability analysis, a temperature sensor can be used to acquire body surface temperature data, and a respiratory sensor can be used to acquire 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 specific algorithms, such as performing time-domain and frequency-domain analysis on the pulse wave signals to extract heart rate variability indicators, or performing pattern recognition on the respiratory signals to obtain respiratory rhythm features. A module for predicting the user's physiological rhythm features or the trends of changes in related physiological indicators can be a predictive model, such as a prediction model based on a Long Short-Term Memory (LSTM) network, which uses historical physiological rhythm data and current data to predict the direction and magnitude of changes in physiological indicators over a future period. Based on the predicted trends and the user's preset conditioning goals, the generation of control commands can be accomplished by a control logic unit. This unit calculates the target values ​​or adjustment amounts of environmental parameters such as the far-infrared radiation intensity, parameters of specific light waves (such as wavelength and intensity), and oxygen supply concentration, according to a preset control strategy or optimization algorithm. Finally, executing the control commands to adjust the 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.

[0037] As one embodiment of the present invention, the step of generating a control command based on the predicted trend and the user's preset conditioning target, and using the control command 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:

[0038] When the predicted trend of change indicates a mismatch between the adjustment requirement for the first environmental parameter in the far-infrared oxygen chamber and the user's preset conditioning target for regulating that first environmental parameter:

[0039] A first adjustment scheme is determined for the first environmental parameter, which aims to maintain the user's physiological state within a preset comfort zone.

[0040] Based on the first adjustment scheme, assess the degree of negative impact of the first adjustment scheme on the achievement of the conditioning goal. If the degree of negative impact reaches the first preset threshold, determine a second adjustment scheme to adjust at least one second environmental parameter that works in conjunction with the first environmental parameter. The second adjustment scheme is used to reduce the degree of negative impact.

[0041] If the negative impact still reaches the second preset threshold after the second adjustment scheme is determined, the adjustment range of the first adjustment scheme of the first environmental parameter will be adjusted, provided that the user's physiological state is maintained within the preset comfort zone.

[0042] The generated control instructions include adjustments to the first environmental parameter determined based on the aforementioned process, and adjustments to at least one second environmental parameter when corresponding conditions are met.

[0043] The predicted trend indicating the need to adjust the first environmental parameter within the far-infrared oxygen chamber refers to the direction and magnitude of adjustment required to maintain or improve the user's physiological state, based on predicted physiological indicator trends. This can be determined by analyzing the predicted trend using physiological or machine learning models. The user's preset conditioning goal and its control requirement for the first environmental parameter refer to the state or range to which the environmental parameter needs to be adjusted to achieve the user's set conditioning purpose. This can be determined by consulting a preset conditioning plan database or based on the target parameter range input by the user. The first environmental parameter refers to one of the far-infrared radiation parameter, light wave parameter, or oxygen supply concentration. The first adjustment plan refers to the adjustment strategy for the first environmental parameter, prioritizing maintaining the user's physiological state within a preset comfort zone. The preset comfort zone refers to the state within which the user's physiological parameters are maintained within a specific range. The first environmental parameter refers to the environmental state, which can be determined based on individual historical data or general physiological models; the degree of negative impact refers to the degree to which the first adjustment plan hinders or weakens the achievement of the conditioning goal, which can be calculated using a quantitative assessment model; the first and second preset thresholds are used to determine whether the degree of negative impact has reached the critical value that requires further measures, which can be set based on clinical experience or user feedback data; the second environmental parameter refers to at least one of the far-infrared radiation parameter, light wave parameter, and oxygen supply concentration, other than the first environmental parameter, which have a relationship of mutual influence or joint action on the user's physiological state; the second adjustment plan refers to the adjustment strategy for the second environmental parameter to reduce the negative impact of the first adjustment plan on the conditioning goal; the adjustment range of the first adjustment plan for adjusting the first environmental parameter refers to the fine-tuning of the adjustment strategy for the first environmental parameter while maintaining the comfort zone, in order to seek a balance between comfort and conditioning goal.

[0044] This application's solution addresses the discrepancy between the predicted adjustment needs for a first environmental parameter within the far-infrared oxygen chamber, as indicated by a trend reversal, and the user's pre-set conditioning goals. Instead of directly adjusting according to the conditioning goals, it prioritizes maintaining the user's physiological state within a pre-set comfort zone. This ensures basic comfort and safety for the user during conditioning. Furthermore, the solution assesses the negative impact of this first adjustment on achieving the conditioning goals. If the impact reaches a first pre-set threshold, a second adjustment is determined, adjusting at least one second environmental parameter that works in conjunction with the first environmental parameter. This adjustment attempts to compensate for any potential loss in conditioning effectiveness due to prioritizing the comfort zone by adjusting other relevant parameters. If, even after introducing the second adjustment, the negative impact still reaches the second pre-set threshold, the solution adjusts the magnitude of the first adjustment to the first environmental parameter while maintaining the user's physiological state within the pre-set comfort zone, achieving a more refined balance between comfort and conditioning goals. The final generated control instructions incorporate the adjustments to the environmental parameters determined through this series of decision-making processes. This conflict-handling logic, combined with basic physiological rhythm prediction and conditioning goal setting, enables the generated regulatory instructions to more intelligently balance the user's immediate physiological needs with long-term conditioning goals. This avoids the problems that may arise from simply adjusting according to predictions or goals, and improves the effectiveness of regulation and user experience.

[0045] As one embodiment of the present invention, the step of predicting the changing trend of a user's physiological rhythm characteristics or related physiological indicators within a preset time period based on physiological rhythm characteristics includes the following: When predictions are made separately for multiple different physiological rhythm characteristics, and there are preset conflict conditions among the preliminary physiological indicator change trend prediction results obtained from these different physiological rhythm characteristics, the step includes:

[0046] Obtain preliminary prediction results of the changing trends of physiological indicators corresponding to multiple different physiological rhythm characteristics;

[0047] Obtain at least one of the preset conditioning goals of the current far-infrared light wave oxygen chamber and the current conditioning stage;

[0048] Based on the preset conflict conditions, and at least one of the preset conditioning goals and the current conditioning stage, an integrated criterion is determined to generate a single comprehensive physiological indicator trend from the prediction results of multiple preliminary physiological indicator trend changes.

[0049] By applying integrated criteria, the prediction results of multiple preliminary physiological indicators are processed to obtain the trend of a single comprehensive physiological indicator, and the trend of the single comprehensive physiological indicator is used as the predicted trend.

[0050] Among these, multiple different physiological rhythm characteristics refer to the characteristic information corresponding to various periodically fluctuating physiological processes existing in the human body, such as diurnal rhythm, super-diurnal rhythm, and bi-diurnal rhythm. These characteristics can be extracted from the user's real-time physiological parameter data, reflecting the changes in the body's internal state at different time scales. Preliminary physiological indicator change trend prediction results refer to the preliminary judgment on the direction and magnitude of change of a certain physiological indicator (such as heart rate, body temperature, blood pressure, etc.) in the future period obtained by independently predicting each specific physiological rhythm characteristic. Each result corresponds to the influence of a physiological rhythm. Preset conflict conditions refer 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 certain physiological indicator will increase, while another prediction result indicates that the same physiological indicator will decrease. Preset conditioning goals refer to the health that the user expects to achieve before using the far-infrared light wave oxygen chamber. Or a comfortable state, such as promoting blood circulation, relieving muscle fatigue, and improving sleep quality, this goal guides the direction and focus of environmental regulation; the current conditioning stage refers to the specific time period or stage of the conditioning process that the user is currently in, such as the initial adaptation stage, the core conditioning stage, and the later consolidation stage, etc. Different stages may require different environmental parameter settings and physiological response priorities; the integration criterion refers to a decision logic or algorithm used to comprehensively consider factors such as the type of conflict, the preset conditioning goal, and the current conditioning stage when multiple preliminary physiological indicator trend prediction results conflict, to determine how to weigh, combine, or select these preliminary results to generate a single, guiding comprehensive prediction result; the single comprehensive physiological indicator trend refers to the final prediction result obtained after processing with the integration criterion, which represents the most likely direction and magnitude of change of the user's physiological indicators in the current situation, as believed by the system, and is used to guide subsequent environmental parameter regulation.

[0051] This application's solution, by acquiring preliminary physiological indicator trend predictions corresponding to multiple different physiological rhythm characteristics, can comprehensively understand the potential impact of different physiological rhythms on the user's physiological state. When there are preset conflict conditions among these preliminary predictions, the solution does not perform simple numerical averaging, but further acquires at least one of the preset conditioning goals of the current far-infrared light wave oxygen chamber and the current conditioning stage. Based on the preset conflict conditions, and the acquired preset conditioning goals and at least one of the current conditioning stages, the system determines an integrated criterion for generating a single comprehensive physiological indicator trend from multiple preliminary physiological indicator trend predictions. The determination of this integrated criterion considers 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 integrated criterion is applied to process the multiple preliminary physiological indicator trend predictions to obtain a single comprehensive physiological indicator trend. Thus, a prediction result that comprehensively considers the influence of multiple physiological rhythms, conflict situations, conditioning goals, and stages is used as the final predicted trend for subsequent regulation command generation. This allows the prediction results to more accurately reflect the user's actual needs and physical condition in complex scenarios where multiple physiological rhythms are involved and conflict, thus providing a more reliable basis for environmental regulation of far-infrared oxygen chambers. In this way, based on the prediction based on physiological rhythm characteristics proposed in claim 1, this solution further solves the problem of predicting multiple conflicting physiological rhythms, improves the accuracy of prediction, and thus enhances the precision and effectiveness of environmental regulation.

[0052] As one embodiment of the present invention, based on preset conflict conditions and at least one of the obtained preset conditioning goals and the current conditioning stage, a step is taken to determine 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 goal or the current conditioning stage changes, the step includes:

[0053] Obtain the new status of the preset treatment goal that has changed or the current treatment stage;

[0054] Based on the new state, guide information is determined from multiple pre-configured integrated criteria associated with different preset treatment goals or different treatment stages. Then, an integrated criterion that is suitable for the new state is matched and selected.

[0055] The selected integration criteria are used to determine the guidance information, and the integration criteria are determined in conjunction with the preset conflict conditions.

[0056] Specifically, the new state refers to the specific value or type after the preset treatment goal or the current treatment stage has changed, and its purpose is to clarify the latest treatment situation. The integration criterion determination guidance information refers to the pre-configured rules, strategies or configuration data used to guide how to determine the integration criteria based on the preset treatment goal or treatment stage. It can be implemented in the form of lookup tables, decision trees, rule sets or parameter configuration files, and its purpose is to provide a flexible and configurable way to adapt to different treatment needs and stages.

[0057] This application's solution first obtains the new state when the preset treatment goal or current treatment stage changes. This new state forms the basis for subsequent adjustments; only by clearly defining the new treatment goal or stage can the integration criteria be adjusted in a targeted manner. Then, based on the new state, it matches and selects one integration criterion determination guideline from pre-configured guidelines associated with different preset treatment goals or stages. This step is crucial; the pre-configured guideline allows for the rapid identification of an integration criterion that matches the new state, avoiding the complex process of redesigning integration criteria and improving the system's adaptability. Finally, the selected integration criterion determination guideline is applied, combined with preset conflict conditions, to determine the integration criterion applicable to the current state, ensuring its effectiveness and accuracy. In this way, the system can dynamically adjust the integration criteria according to changes in the treatment goal or stage, thereby better adapting to user needs and improving treatment effectiveness. 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 generation of subsequent regulatory instructions and improving the overall regulatory effect.

[0058] As one embodiment of the present invention, after executing the application integration criterion, processing the prediction results of multiple preliminary physiological indicator change trends, obtaining 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:

[0059] Based on the degree of consistency among the prediction results of multiple preliminary physiological indicators that participate in generating the trend of a single comprehensive physiological indicator, or based on the preset importance information of the source physiological rhythm characteristics of each of the prediction results of multiple preliminary physiological indicators, the reliability measure of the trend of a single comprehensive physiological indicator is calculated and obtained.

[0060] The reliability metric is compared with a preset reliability judgment threshold.

[0061] If the reliability metric fails to meet the standard defined by the reliability judgment threshold, then when generating control instructions for regulating the environmental parameters inside the far-infrared oxygen chamber based on the changing trend of a single comprehensive physiological indicator, one or more execution characteristics of the control instructions will be adjusted.

[0062] Among them, the reliability measure of the trend of a single comprehensive physiological indicator is a quantitative indicator used to assess the reliability of the 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 is a pre-set standard value used to define whether the reliability of the trend of a single comprehensive physiological indicator has reached an acceptable level. It can be a fixed value or a dynamically adjusted range. One or more execution characteristics of the control command refer to the attributes that the control command has in actual application, such as the magnitude of adjustment, the frequency of adjustment, or the combination of environmental parameters that act. It can be a limitation on the strength, speed or range of the command.

[0063] This application's solution avoids directly using potentially uncertain predictions 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, which reflects the degree of consistency among the initial predictions involved in the integration, or the importance of these initial results derived from different physiological rhythm features. For example, if multiple initial predictions are highly consistent, or if the comprehensive trend closely matches predictions derived from important physiological rhythm features (such as heart rate variability), the reliability metric is high. Conversely, if the initial predictions differ significantly, or if the comprehensive trend deviates from predictions derived from important physiological rhythm features, the reliability metric is low. Subsequently, the calculated reliability metric is compared with a preset reliability judgment threshold. This threshold sets a minimum acceptable reliability standard. It is through this comparison that the system can identify comprehensive trends with insufficient reliability. When the reliability metric is 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 regulatory instructions based on this uncertain trend, but will adjust the execution characteristics of the instructions when generating subsequent regulatory instructions. Such adjustments can include limiting the magnitude of regulation, reducing the frequency of regulation, or carefully selecting the combination of regulation parameters. In this way, based on comprehensive prediction using multiple physiological rhythm characteristics, this application further introduces a self-assessment and risk control mechanism for the reliability of prediction results. This effectively solves the problem of low reliability of the overall trend caused by conflicts or differences in importance in preliminary prediction results, and significantly improves the safety and effectiveness of subsequent environmental parameter regulation.

[0064] As one embodiment of the present invention, the step of adjusting one or more execution characteristics of the control command includes:

[0065] The system obtains a reliable measure of the trend of a single comprehensive physiological indicator, the user's preset conditioning goals, and feedback information on the historical regulatory effects of adjustment options for different performance characteristics under the conditioning goals, which are associated with the user's historical conditioning data.

[0066] The system can obtain a variety of preset execution characteristic adjustment options, including at least one of the following: 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 environmental parameters, and changing the combination method of environmental parameters involved in coordinated regulation.

[0067] Based on the obtained reliability metrics, the user's preset conditioning goals, and historical conditioning effect feedback information, and combined with the obtained multiple performance characteristic adjustment options, an performance characteristic adjustment plan is determined. The determination of the performance characteristic adjustment plan is based on the premise that the user's physiological state is maintained within the preset comfort zone, and aims to reduce the potential negative impact on the achievement of conditioning goals.

[0068] The established execution characteristic adjustment scheme is applied to adjust one or more execution characteristics of the control command.

[0069] Among them, the reliability metric of the trend of a single comprehensive physiological indicator refers to the quantitative assessment of the credibility or predictive accuracy of the trend of a single comprehensive physiological indicator obtained after integration processing. It can be achieved by calculation methods based on the consistency of preliminary prediction results or the importance of the source physiological rhythm characteristics. Its purpose is to indicate whether the current prediction results are sufficiently reliable to guide subsequent regulatory decisions. Historical regulatory effect feedback information refers to the data set that records the actual physiological response or regulatory effect produced by the user under specific regulatory goals when performing regulation in the past. It can be stored in a structured database or log file. Its purpose is to provide a reference based on individual historical experience for the current regulation characteristic adjustment decision. Regulatory characteristic adjustment options refer to a variety of pre-set and selectable strategies or schemes for modifying the regulation command execution method. These can include specific methods such as limiting the adjustment range of environmental parameter target values, extending or shortening the parameter update cycle, and adding, deleting or replacing the set of synergistic regulatory parameters. Its purpose is to provide multiple alternative adjustment means to deal with insufficient predictive reliability. Regulatory characteristic adjustment A plan refers to a specific strategy for modifying the execution method of a control command, formed by selecting or combining various preset execution characteristic adjustment options based on the current situation (including reliability metrics, conditioning goals, and historical feedback). It can be a single adjustment option or a combination of multiple adjustment options, and its purpose is to provide a specific and executable adjustment command. Maintaining the user's physiological state within 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.) are kept within a preset range that is comfortable and safe for the user is the primary and insurmountable constraint. This can be achieved through real-time monitoring and threshold judgment, and its purpose is to ensure the user's safety and experience. Minimizing the potential negative impact on the achievement of the conditioning goal means that, while maintaining the user's physiological comfort zone, the plan with the least impact on achieving the preset conditioning goal is selected from the feasible adjustment options. This can be achieved by assessing the degree of impact of different adjustment options on the physiological indicators or subjective feelings related to the conditioning goal, and its purpose is to retain the conditioning effect as much as possible while dealing with predictive uncertainties.

[0070] This application's solution obtains a reliability metric for the trend of a single comprehensive physiological indicator, the user's preset conditioning goals, and historical regulatory effect feedback information correlated with the user's historical conditioning data. Combined with multiple preset execution characteristic adjustment options, an execution characteristic adjustment plan is determined based on this information. This plan is determined on the premise that the user's physiological state is maintained within a preset comfort zone, and aims to minimize potential negative impacts on achieving the conditioning goals. Because it comprehensively considers the reliability of the current prediction, the user's personalized conditioning needs, and their past conditioning experience, and weighs and selects from multiple preset adjustment strategies based on this, it can adopt a more intelligent and refined adjustment method when the reliability of the trend of a single comprehensive physiological indicator is insufficient. This avoids the negative consequences that may result from simple and crude adjustments, thereby maximizing the effectiveness of the conditioning process while ensuring the user's safety and comfort. Compared with the basic solution that relies solely on predictive trends to generate instructions or makes simple adjustments when predictive reliability is insufficient, this method can more effectively cope with complex and variable individual physiological responses and predictive uncertainties, improving the adaptability and robustness of the regulation.

[0071] As one embodiment of the present invention, the step of determining an execution characteristic adjustment scheme based on the acquired reliability metric value, the user's preset conditioning target, and historical conditioning effect feedback information, combined with the acquired multiple execution characteristic adjustment options, includes:

[0072] A multi-dimensional decision matrix is ​​pre-configured, which includes the range of reliability measurement values, the user's preset treatment target type, and the type of historical treatment effect feedback information. The matrix cells store the optimal execution characteristic adjustment scheme corresponding to the combination of each dimension.

[0073] The system obtains the reliability metrics of the current situation, the user's preset treatment goals, and historical treatment effect feedback information, and searches for cells that match the current situation in the multidimensional decision matrix to determine the performance adjustment plan.

[0074] The multidimensional decision matrix refers to a data structure used to store the output results corresponding to different input conditions. It can be implemented using lookup tables, multidimensional arrays, or database structures, and its purpose is to transform the multi-factor decision-making process into a lookup operation. The dimension of the matrix refers to the categories of input variables that constitute the multidimensional decision matrix. Specifically, it includes different value ranges of reliability metrics, different types of user-preset conditioning goals, and different types of historical conditioning effect feedback information. Its purpose is to cover the combination of key factors affecting the selection of execution characteristic adjustment schemes. The adaptive execution characteristic adjustment scheme stored in the matrix cells refers to the adaptive execution characteristic adjustment strategy that is pre-determined and stored under each specific dimension combination in the matrix. Specifically, it can be the numerical value or range of the adjustment magnitude of the target value of environmental parameters, the setting of the update frequency of environmental parameters, or a list of combinations of environmental parameters participating in coordinated conditioning. Its purpose is to provide an optimized or verified adjustment scheme for each situation that can balance user comfort and the achievement of conditioning goals.

[0075] This application's solution pre-configures a multi-dimensional decision matrix, using key factors influencing the selection of performance characteristic adjustment schemes (reliability metric range, conditioning target type, and historical conditioning effect feedback information type) as dimensions of the matrix. The matrix cells store corresponding adaptation adjustment schemes for each dimension combination. During actual operation, the system acquires the reliability metric, conditioning target, and historical conditioning effect feedback information of the current situation, and then uses this information as an index to search within the pre-configured multi-dimensional decision matrix. This lookup method allows the system to locate the matrix cell matching the current situation, directly retrieving the adaptation performance characteristic adjustment scheme stored in that cell. This method avoids complex calculations or reasoning processes, especially when considering the combined effects of multiple factors, improving decision-making efficiency and accuracy. The scheme determination always prioritizes maintaining the user's physiological state within a preset comfort zone and aims to minimize potential negative impacts on the achievement of conditioning targets, ensuring the safety and effectiveness of the adjustment scheme. This lookup-based method efficiently utilizes various input information to quickly determine a pre-optimized or validated adjustment scheme, providing a fast and stable decision-making mechanism for dynamic conditioning methods.

[0076] As one embodiment of the present invention, the step of calculating and obtaining a reliability metric value for the trend of a single comprehensive physiological indicator includes:

[0077] The system obtains the prediction results of multiple preliminary physiological indicators, P_i(t), the confidence level C_i(t) corresponding to P_i(t), the dynamic importance weight W_i(t) corresponding to the i-th physiological rhythm feature, the trend of a single comprehensive physiological indicator P_comp(t), the preset maximum value range of physiological indicators P_range_max, and the preset balance factor alpha.

[0078] The weighted standard deviation of the predicted trend of multiple preliminary physiological indicators P_i(t) is calculated. 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.

[0079] Based on the weighted standard deviation and P_range_max, calculate the first reliability component R_C_weighted(t). The formula for calculating R_C_weighted(t) is: R_C_weighted(t) = Max(0, 1 - (weighted standard deviation / P_range_max)).

[0080] Calculate the absolute deviation between the predicted trend of the i-th preliminary physiological indicator P_i(t) and the trend of the single comprehensive physiological indicator P_comp(t);

[0081] Based on the absolute deviation and P_range_max, calculate the similarity score between the predicted trend of the i-th preliminary physiological indicator P_i(t) and the trend of the single comprehensive physiological indicator P_comp(t).

[0082] The second reliability component R_I_aligned(t) is obtained by weighted summing the similarity score with the products of W_i(t) and C_i(t) and dividing by the sum of the products of W_i(t) and C_i(t). The formula for calculating 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)];

[0083] 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 measure R(t) of the trend of change of a single comprehensive physiological index is calculated. The formula for calculating R(t) is: R(t) = alpha * R_C_weighted(t) + (1 - alpha) * R_I_aligned(t).

[0084] Where N represents the number of preliminary physiological indicator trend prediction results that participate in generating a single comprehensive physiological indicator trend;

[0085] P_i(t) represents the prediction result of the trend of change of the i-th preliminary physiological indicator, indicating the direction and magnitude of the change of the physiological indicator predicted by a specific physiological rhythm feature;

[0086] C_i(t) represents the confidence level of the i-th preliminary prediction result, indicating the reliability of the preliminary prediction result itself;

[0087] W_i(t) is the dynamic importance weight of the i-th physiological rhythm feature, representing the relative importance of the physiological rhythm feature in assessing the reliability of the overall trend;

[0088] P_comp(t) represents the trend of change of a single integrated physiological indicator that has been generated, and indicates the trend of change of the final physiological indicator after integration processing.

[0089] P_range_max is the preset maximum range of values ​​for physiological indicators;

[0090] alpha is a preset balancing factor used to balance the contributions of consistency components and importance alignment components in the final reliability metric.

[0091] R_C_weighted(t) represents the weighted consistency component;

[0092] R_I_aligned(t) represents the importance alignment component;

[0093] R(t) is a measure of the reliability of the trend of a single comprehensive physiological indicator.

[0094] First, by calculating the weighted standard deviation, the dispersion among the predicted results of different physiological rhythm features is measured. The weights consider the confidence level of the predicted results themselves and the importance of the corresponding physiological rhythm features, making the consistency assessment more accurate. Based on the weighted standard deviation and the maximum range of physiological indicators, the first reliability component is calculated, which intuitively reflects the degree of convergence of the predicted results. Simultaneously, the absolute deviation between each preliminary prediction and the overall trend is calculated and converted into a similarity score. Then, these similarity scores are weighted and averaged according to confidence and importance weights to obtain the second reliability component, which reflects the alignment of the overall trend with those more important and reliable preliminary predictions. Finally, the first and second reliability components are weighted and combined using a balancing factor to obtain the reliability metric for the trend of a single comprehensive physiological indicator. This method comprehensively considers the dispersion among the predicted results and their consistency with the final comprehensive trend, and incorporates the reliability of the predicted results themselves and the relative importance of their source physiological rhythm features in the evaluation process. This allows the reliability metric to reflect the true credibility of the comprehensive trend more comprehensively and precisely, overcoming the limitations of relying solely on a single factor for evaluation.

[0095] like Figure 2 The far-infrared light wave oxygen chamber control system shown includes:

[0096] The data acquisition module 201 is used to acquire real-time data of the user's physiological parameters;

[0097] The feature extraction module 202 is used to extract physiological rhythm features based on real-time data of physiological parameters;

[0098] Trend prediction module 203 is used to predict the trend of changes in a user's physiological rhythm characteristics or related physiological indicators within a preset time period based on physiological rhythm characteristics.

[0099] The instruction generation module 204 is used to generate control instructions based on the predicted trend of change and the user's preset conditioning target. The control instructions are used to coordinate the control of 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.

[0100] The parameter control module 205 is used to execute control commands to adjust at least two environmental parameters in order to maintain the user's physiological state within a preset comfort zone and promote the achievement of conditioning goals.

[0101] Among them, the data acquisition module 201 refers to the unit used to acquire real-time data of the user's physiological parameters. It can be implemented using various physiological sensors, data acquisition circuits or wireless receiving devices, and its purpose is to provide basic data for subsequent processing.

[0102] Among them, the feature extraction module 202 refers to the unit used to extract physiological rhythm features from real-time data based on physiological parameters. It can be implemented by a digital signal processor executing a specific signal processing algorithm. Its purpose is to extract key information reflecting physiological state from the raw data.

[0103] Among them, the trend prediction module 203 refers to the unit used to predict the changing trend of the user's physiological rhythm characteristics or related physiological indicators within a preset time period based on physiological rhythm characteristics. It can be implemented by using an embedded processor to execute the prediction model algorithm, and its purpose is to achieve a forward-looking judgment of future physiological state.

[0104] Among them, the instruction generation module 204 is a unit used to generate control instructions based on the predicted trend and the user's preset control target. It can be implemented by using a microcontroller to execute decision logic algorithms. Its purpose is to determine the specific control scheme based on the prediction results and user needs.

[0105] The parameter control module 205 is a unit used to execute control commands to adjust at least two environmental parameters. It can be implemented using an actuator drive circuit, a power regulator, or a valve controller. Its purpose is to convert the generated control commands into actual changes to the cabin environmental parameters.

[0106] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A method for controlling a far-infrared oxygen chamber, characterized in that, The method includes the following steps: Acquire real-time data of the user's physiological parameters; Based on real-time data of the physiological parameters, physiological rhythm features are extracted, which reflect the user's current adaptation status to the cabin environment. Based on the physiological rhythm characteristics, predict the changing trend of the physiological rhythm characteristics or their related physiological indicators of the user within a preset time period; Based on the predicted trend and combined with the user's preset conditioning goals, a control command is generated. The control command is used to coordinate and 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 control instructions are executed to adjust the at least two environmental parameters in order to maintain the user's physiological state within a preset comfort zone and promote the achievement of the conditioning goals; The step of predicting the trend of change of the user's physiological rhythm characteristics or their associated physiological indicators within a preset time period based on the physiological rhythm characteristics includes the following when predictions are made separately based on multiple different physiological rhythm characteristics, and there are preset conflict conditions among the preliminary physiological indicator trend prediction results obtained from these different physiological rhythm characteristics: Obtain preliminary prediction results of the physiological indicator change trends corresponding to each of the multiple different physiological rhythm characteristics; Obtain at least one of the preset conditioning goals of the current far-infrared light wave oxygen chamber and the current conditioning stage; Based on the preset conflict conditions and at least one of the preset conditioning goals and the current conditioning stage, an integrated criterion is determined to generate a single comprehensive physiological indicator change trend from the predicted results of the multiple preliminary physiological indicator change trends. The integrated criteria are applied to process the prediction results of the multiple preliminary physiological indicators to obtain the single comprehensive physiological indicator trend, and the single comprehensive physiological indicator trend is used as the predicted trend.

2. The far-infrared light wave oxygen chamber control method according to claim 1, characterized in that, The step of generating a control command based on the predicted trend and the user's preset conditioning target, wherein the control command 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 predicted trend of change indicates a need to adjust the first environmental parameter within the far-infrared oxygen chamber, which is inconsistent with the user's preset conditioning target's need to regulate that first environmental parameter: A first adjustment scheme for the first environmental parameter is determined, wherein the first adjustment scheme is to maintain the user's physiological state within a preset comfort zone; Based on the first adjustment scheme, the degree of negative impact of the first adjustment scheme on the achievement of the conditioning target is evaluated. If the degree of negative impact reaches a first preset threshold, a second adjustment scheme is determined to adjust at least one second environmental parameter that works in conjunction with the first environmental parameter. The second adjustment scheme is used to reduce the degree of negative impact. If, after determining the second adjustment scheme, the degree of negative impact still reaches the second preset threshold, then, under the premise that the user's physiological state is maintained within the preset comfort zone, the adjustment range of the first adjustment scheme of the first environmental parameter is adjusted. The generated control instructions include adjustments to the first environmental parameter determined based on the aforementioned process, and adjustments to the at least one second environmental parameter when corresponding conditions are met.

3. The far-infrared light wave oxygen chamber control method according to claim 1, 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 conditions and at least one of the obtained preset conditioning goals and the current conditioning stage, includes the following when the preset conditioning goals or the current conditioning stage change: Obtain the new status of the preset treatment goal that has changed or the current treatment stage; Based on the new state, from the pre-configured multiple integrated criteria that are associated with different preset treatment goals or different treatment stages, determine guidance information, match and select one integrated criteria determination guidance information that is suitable for the new state; The selected integration criteria are used to determine the guidance information, and the integration criteria are determined in conjunction with the preset conflict conditions.

4. The far-infrared light wave oxygen chamber control method according to claim 1, characterized in that, After executing the steps of applying the integrated criteria, processing the prediction results of the multiple preliminary physiological indicators, obtaining the single comprehensive physiological indicator trend, and using the single comprehensive physiological indicator trend as the predicted trend, the method further includes: Based on the degree of consistency among the prediction results of the multiple preliminary physiological indicators that participated in generating the trend of the single comprehensive physiological indicator, or based on the preset importance information of the source physiological rhythm characteristics of each of the multiple preliminary physiological indicator trend prediction results, the reliability metric of the trend of the single comprehensive physiological indicator is calculated and obtained. The reliability metric is compared with a preset reliability judgment threshold. If the reliability metric fails to meet the standard defined by the reliability judgment threshold, then when generating control instructions for regulating the environmental parameters inside the far-infrared oxygen chamber based on the changing trend of the single comprehensive physiological index, one or more execution characteristics of the control instructions will be adjusted.

5. The far-infrared light wave oxygen chamber control method according to claim 4, characterized in that, The steps of adjusting one or more execution characteristics of the control command include: The system obtains a reliability metric for the trend of the single comprehensive physiological indicator, the user's preset conditioning goals, and feedback information on the historical regulatory effects of the adjustment options for different performance characteristics associated with the user's historical conditioning data under the conditioning goals. The system acquires a variety of preset execution characteristic adjustment options, including at least one of the following: changing the target value adjustment range of one or more environmental parameters in the far-infrared oxygen chamber, changing the update frequency of the environmental parameters, and changing the combination method of the environmental parameters involved in the coordinated regulation. Based on the acquired reliability metric, the user's preset conditioning target, and the historical conditioning effect feedback information, and in combination with the acquired multiple performance characteristic adjustment options, an performance characteristic adjustment scheme is determined. The determined execution characteristic adjustment scheme is applied to adjust one or more execution characteristics of the control command.

6. The far-infrared light wave oxygen chamber control method according to claim 5, characterized in that, The determination of the performance characteristic adjustment scheme is based on the premise that the user's physiological state is maintained within the preset comfort zone, and aims to reduce the potential negative impact on the achievement of the conditioning goal.

7. The far-infrared light wave oxygen chamber control method according to claim 6, characterized in that, The step of determining an execution characteristic adjustment scheme based on the acquired reliability metric, the user-preset conditioning target, and the historical conditioning effect feedback information, combined with the acquired multiple execution characteristic adjustment options, includes: A multi-dimensional decision matrix is ​​pre-configured, and the matrix cells store the optimal execution characteristic adjustment schemes corresponding to the combination of each dimension. The system obtains the reliability metric value of the current situation, the user's preset conditioning goal, and the historical conditioning effect feedback information, and searches for the cell that matches the current situation in the multidimensional decision matrix to determine the performance characteristic adjustment plan.

8. The far-infrared light wave oxygen chamber control method according to claim 7, characterized in that, The multidimensional decision matrix includes the reliability metric range, the user-preset conditioning target type, and the type of historical conditioning effect feedback information.

9. A far-infrared light wave oxygen chamber control system, characterized in that, The system includes: The data acquisition module is used to acquire real-time data of the user's physiological parameters; The feature extraction module is used to extract physiological rhythm features based on real-time data of the physiological parameters; The trend prediction module is used to predict the trend of change of the physiological rhythm characteristics or their related physiological indicators of the user within a preset time period based on the physiological rhythm characteristics. It is also used to obtain preliminary prediction results of the changing trends of physiological indicators corresponding to multiple different physiological rhythm characteristics; Obtain at least one of the preset conditioning goals of the current far-infrared light wave oxygen chamber and the current conditioning stage; Based on the preset conflict conditions and at least one of the preset conditioning goals and the current conditioning stage, an integrated criterion is determined to generate a single comprehensive physiological indicator change trend from the predicted results of the multiple preliminary physiological indicator change trends. The integrated criteria are applied to process the prediction results of the multiple preliminary physiological indicators to obtain the single comprehensive physiological indicator trend, and the single comprehensive physiological indicator trend is used as the predicted trend. The instruction generation module is used to generate control instructions based on the predicted change trend and the user's preset conditioning goals. The control instructions are used to coordinate and 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 parameter control module is used to execute the control instructions to adjust the at least two environmental parameters in order to maintain the user's physiological state within a preset comfort zone and promote the achievement of conditioning goals.

Images