Adaptive bio-energy control system based on dynamic multi-dimensional state modeling and deviation checking
The adaptive bioenergy control system, which uses multidimensional state reconstruction and deviation verification, solves the problems of one-sided state assessment and control strategy failure in existing technologies, and realizes real-time and predictable management of individual energy state and coordinated scheduling of multiple physical fields.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- XIAMEN LIXUE HEALTH TECHNOLOGY CO LTD
- Filing Date
- 2026-02-13
- Publication Date
- 2026-06-12
AI Technical Summary
The lack of a unified state model and closed-loop feedback mechanism in existing technologies leads to one-sided assessment of individual energy state, failure of control strategies, and isolation of physical regulation methods, making it difficult to cope with individual differences and disturbances.
A multi-dimensional state reconstruction mechanism is constructed. Physiological data is collected through a multi-modal perception interface, a dynamic state modeling engine is used to generate standard state vectors, a prediction and deviation verification module is combined to identify disturbances, and adaptive control instructions are generated based on a finite state machine to drive personalized intervention in the external physical field.
It enables real-time and predictable management of individual energy states, has anti-interference capabilities, supports the coordinated scheduling of multiple physical fields, and improves the accuracy and adaptability of energy control.
Smart Images
Figure CN122201577A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the fields of biocontrol engineering, systems physiology and intelligent health management, and in particular to an adaptive bioenergy control system based on dynamic multidimensional state modeling and deviation verification. Background Technology
[0002] The human energy metabolism system is a complex biological system characterized by high nonlinearity, dynamic time-varying behavior, and large time lag. The operational state of an individual's energy system is influenced by multiple variables, including energy substrate input, endocrine signals, neural regulation, and environmental physical fields.
[0003] However, existing technologies for regulating individual energy states have significant shortcomings at the system engineering level: The lack of a unified state model is a significant issue: current technologies often rely on discrete, single indicators (such as simply monitoring blood sugar or heart rate) to assess physical condition. There is a lack of a mechanism to heterogeneously integrate microscopic biochemical indicators with macroscopic vital signs data, making it impossible to construct a "state vector" representing the overall stability of the system. This results in assessments that are often one-sided and discontinuous.
[0004] Limitations of Open-Loop Control: Most existing physiotherapy equipment (such as phototherapy devices and thermotherapy chambers) or dietary plans employ preset, fixed programs. The system cannot perceive real-time physiological feedback from individuals, nor can it determine the effectiveness of interventions. This "blind output" model struggles to address individual differences.
[0005] Absence of Prediction-Verification: Effective control relies on comparing predicted and observed values. Current technologies struggle to generate accurate metabolic prediction trajectories based on inputs (such as energy intake). Consequently, the system cannot distinguish between normal physiological fluctuations and abnormal metabolic disturbances (such as bivariate decoupling caused by undeclared eating), leading to frequent control strategy failures.
[0006] The isolation of physical intervention methods: Physical regulation methods such as light, heat, sound, and electricity usually exist as independent tools, lacking a central control engine to integrate them under the constraints of a unified steady-state model for coordinated scheduling.
[0007] In conclusion, the industry urgently needs a system-level solution that can introduce cybernetics principles (state modeling, deviation verification, closed-loop feedback) into the field of bioenergy management. Summary of the Invention
[0008] The purpose of this invention is to solve the above-mentioned problems by providing an adaptive bioenergy control system based on dynamic multidimensional state modeling and deviation verification.
[0009] To address the aforementioned technical pain points, this invention aims to solve the following core problems: How to construct a "multidimensional state reconstruction" mechanism? Establish an algorithm that can normalize heterogeneous physiological data (biochemical / electrical signals) into a standard current state vector (such as the mitochondrial energy index MEI), making complex biological states "computable" and "comparable".
[0010] How to achieve "predictive bias verification"? Introduce an input consistency verification mechanism, which compares "model-based predicted trajectory" with "real-time observation data" to identify potential system disturbance events (such as latent metabolic disturbances), thereby solving the input uncertainty problem.
[0011] How to construct a complete closed loop of "perception-decision-execution"? Based on finite state machine (FSM) logic, the output parameters of external physical fields (light / heat / sound / electricity) are automatically adjusted according to the interval position of the state vector and the disturbance marker to achieve adaptive energy return.
[0012] The above-mentioned physical field generation method can be implemented using conventional techniques in this field. The innovation of this invention lies in the scheduling logic and state-driven mechanism. For example, the light field module can be implemented using red light or near-infrared light sources (such as LED arrays), and the magnetic field module can be implemented using a pulsed electromagnetic field generating unit or a similar magnetic stimulation device, but is not limited to these.
[0013] The technical solution of this application is implemented as follows: This invention provides an adaptive bioenergy control system based on dynamic multidimensional state modeling and deviation verification, the system comprising: A multimodal sensing interface, configured to communicate with at least one biosensor for acquiring real-time multidimensional physiological characterization signals of an individual; The dynamic state modeling engine, containing at least one processor, is configured to run the following logic modules: State Reconstruction Module: Used to call the preset bioenergy system homeostatic model, perform heterogeneous fusion and normalization calculation on the real-time multidimensional physiological characterization signal, and generate a standardized current state vector. Prediction and Deviation Verification Module: This module is used to generate a predicted metabolic evolution trajectory for an individual based on the bioenergy system steady-state model or the individual's historical state data; and to compare the currently collected real-time observation data with the predicted metabolic evolution trajectory to calculate the state deviation value. Abnormal event determination logic: used to monitor the state deviation value, and generate a metabolic disturbance event marker when the state deviation value shows a non-linear change or exceeds a preset safety fault tolerance threshold; An adaptive closed-loop controller is configured to generate control commands using finite state machine (FSM) logic based on the distance of the current state vector relative to the target steady-state interval and in conjunction with the metabolic disturbance event markers. The network interface is configured to respond to the control command and drive the physical execution unit to apply a controllable external physical field to the individual. The external physical field includes one or a combination of light field module, thermal field module, sound field module, and electric / magnetic field module to minimize the state deviation value and cause the system to return to steady state.
[0014] As a further improvement, the bioenergy system homeostasis model is specifically the mitochondrial quality control standard (MQC) database; the current state vector is specifically represented by the mitochondrial energy index (MEI); the state reconstruction module constructs the current state vector by calculating macroscopic physiological mapping indicators such as mitochondrial membrane potential, ATP production rate, or reactive oxygen species level.
[0015] As a further improvement, the multimodal sensing interface is also configured to receive external energy intake data; the prediction and deviation verification module is configured to incorporate the external energy intake data as a feedforward variable into the generation logic of the predicted metabolic evolution trajectory, so as to verify whether the response of the real-time multidimensional physiological characterization signal meets expectations.
[0016] As a further improvement, the generation logic of the metabolic disturbance event marker includes: when no corresponding external energy intake data or exercise energy consumption data is received, an abnormal decoupling feature is detected between the blood glucose parameter (first parameter) and the blood ketone parameter (second parameter), thereby determining it as a latent metabolic disturbance event, and using the event as the input signal of the adaptive closed-loop controller.
[0017] As a further improvement, the external physical field is a composite energy field generated by photosensitive thermoacoustic driving technology, or one or more combinations of environmental ion field, pulsed electromagnetic field (PEMF) and specific frequency sound wave field.
[0018] As a further improvement, the adaptive closed-loop controller is configured to divide an individual's energy metabolism state into at least two discontinuous operating state intervals, including a steady-state region, a sub-healthy fluctuation region, and a pathological decline region; when the current state vector crosses different operating state intervals, the controller triggers a phase transition switching of the control strategy.
[0019] As a further improvement, the state reconstruction module is configured to execute a weighted mapping algorithm to map multi-dimensional heterogeneous parameters, including microscopic biochemical mapping indicators and macroscopic vital signs, into a single-dimensional MEI index, which quantitatively reflects the distance of an individual's current energy system from its optimal steady state.
[0020] As a further improvement, the system is only configured for individual energy status management, exercise performance support, or circadian rhythm optimization in non-medical settings, and the metabolic disturbance event markers are not used as a basis for disease diagnosis.
[0021] As a further improvement, the regulatory instructions also include dynamic adjustment suggestions for the proportion or timing of energy substrate intake, including carbohydrates, fats, or ketone body precursors, to induce a switching of metabolic substrates in the body.
[0022] The system provided by this invention adopts an architecture of "multimodal perception - state modeling - deviation verification - adaptive control".
[0023] (i) The multimodal sensing and dynamic state modeling system acquires real-time physiological representation signals of individuals through a multimodal sensing interface. The dynamic state modeling engine uses a preset bioenergy system homeostasis model (in a preferred embodiment, the mitochondrial quality control standard MQC) to fuse the signals and generate a standardized current state vector. This vector quantitatively reflects the distance of the individual's current energy system from the ideal homeostasis.
[0024] (II) Prediction and Deviation Verification Logic The system has a built-in prediction and deviation verification module. This module generates an individual's predicted metabolic evolution trajectory based on the model or historical data. The system compares the observed data with the predicted trajectory in real time. When the deviation between the two exceeds a safety threshold (e.g., the appearance of blood glucose / blood ketone decoupling characteristics), the system generates a metabolic disturbance event marker. This mechanism enables the system to have the ability of "self-correction" and "interference resistance".
[0025] (III) Adaptive Closed-Loop Control The adaptive closed-loop controller is based on finite state machine (FSM) logic, which divides the energy metabolism process of an individual into multiple discrete operating state intervals (e.g., steady state, fluctuation, and decay). Based on the position of the current state vector and the disturbance marker, the controller determines whether the system should maintain its current state or trigger a phase transition to the next interval, and generates control commands accordingly.
[0026] (iv) The multimodal physical execution network system drives the physical execution unit through the execution network interface. In response to control commands, the system applies a controllable external physical field (including but not limited to photosensitive thermoacoustic field, environmental ion field, and electromagnetic field) to the individual. This physical field serves as a non-invasive intervention method, aiming to promote the convergence of the state vector to the baseline model. Attached Figure Description
[0027] The accompanying drawings illustrate exemplary embodiments of the present application and, together with the description thereof, serve to explain the principles of the present application. These drawings are included to provide a further understanding of the present application and are incorporated in and constitute a part of this specification.
[0028] Figure 1 The present invention is based on a system overall control architecture diagram of dynamic state modeling (showing the closed-loop logic of perception, modeling, verification, and execution).
[0029] Figure 2 Schematic diagram of the mapping relationship between the steady-state model (MQC) of bioenergy system and the state vector (MEI).
[0030] Figure 3 : Schematic diagram of input consistency verification logic based on predicted trajectory vs. real-time observation.
[0031] Figure 4 The bivariate decoupling feature is used to identify signal waveforms that conceal metabolic disturbances.
[0032] Figure 5 Logic diagram of phase transition based on finite state machine (FSM).
[0033] Figure 6 : Schematic diagram of the scheduling logic of the multimodal physics execution network. Detailed Implementation
[0034] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While some embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this application. It should be understood that the drawings and embodiments of this application are for illustrative purposes only and are not intended to limit the scope of protection of this application.
[0035] It should be noted that, where there is no conflict, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.
[0036] The names of the messages or information exchanged between multiple devices in the embodiments of this application are for illustrative purposes only and are not intended to limit the scope of these messages or information.
[0037] [Important Note: Mapping Relationships in this Embodiment] To enable those skilled in the art to better understand the technical solution of this invention, the following embodiments will use the "Mitochondrial Quality Control Standard (MQC)" as a specific implementation of the "Bioenergy System Steady-State Model" and the "Mitochondrial Energy Index (MEI)" as a specific implementation of the "Current State Vector" for detailed explanation. However, it should be understood that the scope of protection of this invention is not limited thereto.
[0038] like Figure 1 As shown, the overall architecture of the adaptive bioenergy control system of the present invention includes: a multimodal sensing interface 101, a dynamic state modeling engine 102, a prediction and deviation verification module 103, an execution network interface 104, and an adaptive closed-loop controller 106. The multimodal sensing interface 101 is used to collect real-time physiological characteristic signals of an individual 105; the dynamic state modeling engine 102 performs multimodal fusion and state modeling processing on the physiological characteristic signals to generate a dynamic energy state vector representing the individual's current energy state; the prediction and deviation verification module 103 generates a predicted metabolic evolution trajectory based on historical state data or a steady-state model, and performs deviation verification between the predicted trajectory and real-time observation data to output a metabolic disturbance event marker; the adaptive closed-loop controller 106 generates a corresponding adaptive regulation strategy based on the dynamic energy state vector and the metabolic disturbance event marker; the execution network interface 104 is used to convert the regulation strategy into executable instructions and apply corresponding physical field effects to the individual 105 or output energy scheduling suggestions. The above functional modules work together to constitute a closed-loop adaptive control system based on real-time sensing, dynamic state modeling, prediction consistency verification, and feedback execution.
[0039] like Figure 2As shown, the bioenergy system steady-state model 201 describes the baseline operating state of an individual's energy metabolism system, and includes a multidimensional set of energy metabolism parameters, a threshold matrix, and steady-state constraints. After being fused, standardized, and weighted by mapping and normalization functions 202, the bioenergy system steady-state model 201 is converted into a unified representation for calculation and comparison, thereby generating a dynamic energy state vector 203. The dynamic energy state vector 203 is a single or low-dimensional numerical representation used to characterize the degree of deviation of the individual 105's current energy system relative to the steady-state baseline, and serves as the input basis for subsequent state interval determination, predicted trajectory verification, and closed-loop control logic.
[0040] like Figure 3 As shown, the system generates a predicted metabolic evolution trajectory 301 for an individual within a predetermined time window based on historical state data or a bioenergy system steady-state model 201, and compares the predicted metabolic evolution trajectory with the currently collected real-time observation data 302. The comparison process uses a deviation calculation unit 303 to quantitatively evaluate the difference between the predicted trajectory and the real-time observation; when the difference exceeds a preset safety threshold, the system generates a corresponding metabolic disturbance event marker 304 to characterize whether there is an abnormal shift in the current energy state, and serves as the input basis for subsequent state determination or regulation logic.
[0041] like Figure 4 As shown, the variation curves of the first parameter 401 and the second parameter 402 are plotted on the same time axis. Under the condition that no corresponding external energy input or energy consumption change is detected, when the first parameter 401 and the second parameter 402 exhibit opposite, asynchronous, or weakened correlation characteristics within the time interval shown in the figure, this time interval is marked as the decoupling interval 403. The decoupling interval 403 is used to characterize the abnormal response relationship between multiple parameters and can serve as one of the reference features for system state assessment or subsequent processing logic.
[0042] like Figure 5 As shown, the system constructs a finite state machine (FSM) based on the interval distribution of the dynamic energy state vector, dividing the individual energy operation state into a steady state region 501, a metastable / fluctuating region 502, a disturbed / abnormal region 503, and a recovering state 504. When the dynamic energy state vector crosses or deviates from the preset state interval boundary, the finite state machine triggers the corresponding state transition and switches the corresponding control strategy accordingly to achieve adaptive management and smooth transition of different operation state intervals.
[0043] like Figure 6As shown, the system constructs a multimodal physical field execution network, with the execution network interface 601 serving as a unified scheduling node. Based on adaptive control commands, it selectively invokes and combines different physical field modules. The physical field modules include a light field module 602, a thermal field module 603, a sound field module 604, and an electric / magnetic field module 605. Each module can be activated independently, in parallel, or sequentially according to a preset strategy to apply corresponding external physical field effects to the individual 606. This execution network does not limit the specific physical field type, execution order, or combination method, thus supporting flexible physical environment adjustment for individuals in different energy states.
[0044] (I) Implementation Example of Multidimensional State Reconfiguration Based on MQC In this embodiment, the dynamic state modeling engine runs the Mitochondrial Quality Control Criterion (MQC) database.
[0045] Input: The system collects multidimensional data such as blood glucose (Gk), blood ketones (Kb), heart rate variability (HRV), and skin conductance (GSR).
[0046] Processing: The state reconstruction module calls the MQC threshold matrix to calculate the weighted combination of the above parameters. For example, it calculates the insulin signal decay rate and the degree of lipid oxidation activation.
[0047] Output: Generates a normalized value between 0 and 100—the MEI index (i.e., the state vector). This index no longer represents a single indicator, but rather the "overall stability of the mitochondrial energy system." The following non-limiting example illustrates the state vector calculation process. This example is only for understanding the technical concept of the invention and does not constitute a limitation on the scope of protection.
[0048] In one implementation scenario, the system collects the following normalized physiological parameters at time point t: Micrometabolic indicators ; Autonomic Nervous System Indicators ; Physical response indicators .
[0049] The mitochondrial energy index ( The following exemplary calculation model can be used:
[0050] Wherein: α is a metabolic sensitivity adjustment parameter, and its typical value range can be set to 0.5–1.5, which is used to control the smoothness of the nonlinear mapping curve; These are the weighting coefficients, and their sum can be normalized to 1.
[0051] In different application scenarios, the weighting coefficients can be dynamically adjusted according to the system objectives. For example: In the context of weight loss goals, improve Weights are used to amplify the influence of metabolic indicators; In stress recovery scenarios, improve Weighting to enhance autonomic nervous system regulatory factors; In environmental response assessment scenarios, improve Weights.
[0052] As a specific numerical example: when , , α=1.0, , , When, it can be calculated that: when the stated When the value is >80, the system can determine that the current state is in the efficient steady-state range; when When the value is less than 60, the system can determine that it is in a metabolic imbalance range and trigger the recovery logic of the state machine. The above calculation model is only a preferred embodiment, and those skilled in the art can make equivalent substitutions for the parameter form, mapping function type, or weight allocation method without departing from the spirit of the invention.
[0053] (II) Example of "Two-Variable Decoupling" Judgment Based on Deviation Verification This is the core algorithm for identifying latent interference in this system (corresponding to claim 4).
[0054] Scenario setting: The individual is in a "fasting / fat mobilization state".
[0055] Model prediction: Based on the MQC model, the system predicts that within the next 30 minutes, the blood ketone level should show a linear upward trend (>0.5mmol / L), and the blood glucose level should remain at the baseline.
[0056] Real-time observation: The sensor detected a sudden suppression of blood ketone production (stopping the rise or fall), while blood glucose did not show a significant decrease.
[0057] Deviation verification: The verification module calculates that there is a significant nonlinear deviation between the "predicted trajectory" and the "measured data", and identifies the "decoupling feature".
[0058] Judgment and Intervention: The system determines that an "undeclared energy intake event" (i.e., latent metabolic interference) has occurred, generates a system disturbance flag, and instructs the controller to suspend the "deep repair mode" and instead execute the "glucose stabilization mode".
[0059] (III) Implementation Example of Closed-Loop Physical Field Control Based on FSM In this embodiment, the adaptive closed-loop controller divides the energy metabolism process into three intervals: Zone A (glucose energy supply zone): MEI < 60, the goal is to stabilize blood sugar.
[0060] Interval B (switching oscillation zone): 60≤MEI<80, the goal is to induce lipid oxidation.
[0061] Interval C (Ketone body steady-state region): MEI≥80, the goal is deep repair.
[0062] Closed-loop operation logic: When MEI=65 (in interval B), the controller instruction execution network outputs a medium-frequency pulsed electromagnetic field (PEMF) in conjunction with infrared light of a specific frequency (external physical field) to assist the activity of the mitochondrial electron transport chain.
[0063] If the MEI is detected to rise to 81, the FSM triggers a phase transition switch, and the system automatically enters "interval C". At this time, the physical field is adjusted to a low-frequency acoustic resonance mode to maintain a steady state.
[0064] If the above-mentioned "decoupling characteristics" are detected, the system will automatically revert to the "Interval A" strategy.
[0065] To facilitate understanding of the state reconstruction process of this invention, a non-limiting MEI (mitochondrial energy index) calculation example is provided below: Assume the system is The following three sets of normalized physiological parameters were collected at various times: Microscopic biochemical indicators ( ): Calculated from continuous blood glucose monitoring values ( With blood ketone levels ( The reciprocal of the derived glucose ketone body index (GKI), weighted =0.4; Macroscopic autonomic nervous system indicators ( ): Mapped from the time-domain index (rMSSD) of heart rate variability (HRV), with weights =0.3; Physical field response index ( ): Rate of change of skin conductance response (GSR) relative to baseline, weighted =0.3.
[0066] The state reconstruction module performs the following vector operations to generate the current mitochondrial energy index. :
[0067]
[0068] Where α is a preset metabolic flexibility constant.
[0069] Example decision logic is as follows: If the calculation yields If the value is ≥85 (>80), the system determines that it is currently in the "high-efficiency steady-state region", and the state machine maintains the current strategy. like If the value is ≤55, the system identifies it as "metabolic imbalance" and triggers a phase transition to the "repair state".
[0070] (iv) Execution of network interface and physical field generation The network interface serves as the system's output level, connecting to various physical generation units. Under the constraints of the MQC standard, the system can dynamically schedule the following physical fields: Photosensitive thermoacoustic field: Utilizing the photothermal effect to improve local microcirculation.
[0071] Environmental ion field: regulates the charge distribution in the microenvironment.
[0072] Sound wave field: Using sound wave frequency to induce brain waves or cell rhythms to synchronize.
[0073] (v) Restrictions on non-medical use This system and its method are primarily applied to the fields of individual energy state assessment and adaptive control scheduling. The MEI index and regulation suggestions generated by the system are used to achieve engineering modeling of the energy state and steady-state range adjustment. The technical solution of this invention belongs to the field of control systems and signal processing, and does not involve diagnostic or treatment steps for specific diseases.
[0074] (vi) Beneficial effects System-level predictability: By introducing state modeling and deviation verification, this invention solves the problem of the "black box" nature of biological systems, enabling energy management to have industrial-level predictability.
[0075] Extremely strong anti-interference capability: The unique input consistency verification mechanism (such as sugar-ketone decoupling judgment) enables the system to identify and eliminate the interference of user behavior errors (such as false dietary reports) on the control logic.
[0076] Extensive hardware compatibility: The definition of "external physical field" in the claims enables this system architecture to not only support existing phototherapy / thermotherapy devices, but also to be seamlessly compatible with future quantum / magnetic field modulation devices, giving it extremely high commercial expansion value.
[0077] Those skilled in the art should understand that the above embodiments are merely for illustrative purposes and are not intended to limit the scope of this application. Those skilled in the art can make other changes or modifications based on the above disclosure, and these changes or modifications still fall within the scope of this application.
Claims
1. An adaptive bioenergy control system based on dynamic multidimensional state modeling and deviation verification, characterized in that, The system includes: A multimodal sensing interface, configured to communicate with at least one biosensor for acquiring real-time multidimensional physiological characterization signals of an individual; The dynamic state modeling engine, containing at least one processor, is configured to run the following logic modules: State reconstruction module: used to call the preset bioenergy system steady state model, perform heterogeneous fusion and normalization calculation on the real-time multidimensional physiological characterization signal, and generate a standardized current state vector; Prediction and Deviation Verification Module: Used to generate a predicted metabolic evolution trajectory for an individual based on the bioenergy system steady-state model or the individual's historical state data; and to compare the real-time multidimensional physiological characterization signal or the current state vector with the predicted metabolic evolution trajectory to calculate the state deviation value; Abnormal event determination logic: used to monitor the state deviation value, and generate a metabolic disturbance event marker when the state deviation value shows a non-linear change or exceeds a preset safety fault tolerance threshold; An adaptive closed-loop controller is configured to generate control commands using finite state machine logic based on the distance of the current state vector relative to the target steady-state interval and in conjunction with the metabolic disturbance event markers. The network interface is configured to respond to the control command and drive the physical execution unit to apply a controllable external physical field to the individual, including one or a combination of light field module, thermal field module, sound field module, and electric / magnetic field module, to minimize the state deviation value and cause the system to return to steady state.
2. The system according to claim 1, characterized in that, The bioenergy system homeostasis model is specifically a mitochondrial quality control standard database; the current state vector is specifically represented by the mitochondrial energy index; the state reconstruction module constructs the current state vector by calculating macroscopic physiological mapping indicators such as mitochondrial membrane potential, ATP production rate, or reactive oxygen species level.
3. The system according to claim 1, characterized in that, The multimodal sensing interface is also configured to receive external energy intake data; the prediction and deviation verification module is configured to incorporate the external energy intake data as a feedforward variable into the generation logic of the predicted metabolic evolution trajectory to verify whether the response of the real-time multidimensional physiological characterization signal meets expectations.
4. The system according to claim 3, characterized in that, The generation logic of the metabolic disturbance event marker includes: when no corresponding external energy intake data or exercise energy consumption data is received, an abnormal decoupling change feature between blood glucose parameters and blood ketone parameters is detected, thereby determining it as a latent metabolic disturbance event, and using the event as the input signal of the adaptive closed-loop controller.
5. The system according to claim 1, characterized in that, The external physical field is a composite energy field generated by photosensitive thermoacoustic driving technology, or one or more combinations of environmental ion fields, pulsed electromagnetic fields, and specific frequency sound wave fields.
6. The system according to claim 1, characterized in that, The adaptive closed-loop controller is configured to divide an individual's energy metabolism state into at least two discontinuous operating state intervals, including a steady-state region, a sub-healthy fluctuation region, and a pathological decline region; when the current state vector crosses different operating state intervals, the controller triggers a phase transition switch of the control strategy.
7. The system according to claim 2, characterized in that, The state reconstruction module is configured to execute a weighted mapping algorithm to map multi-dimensional heterogeneous parameters, including microscopic biochemical mapping indicators and macroscopic vital signs, into a single-dimensional MEI index. This index quantitatively reflects the distance of an individual's current energy system from its optimal steady state.
8. The system according to claim 1, characterized in that, The system is configured only for individual energy status management, exercise performance support, or circadian rhythm optimization in non-medical settings, and the metabolic disturbance event markers are not used as a basis for disease diagnosis.
9. The system according to claim 1, characterized in that, The regulatory instructions also include dynamic adjustment suggestions for the proportion or timing of energy substrate intake, including carbohydrates, fats, or ketone body precursors, to induce a shift in metabolic substrates in the body.