Intelligent safety early warning and active defense control system and method based on micro-high pressure cabin

Abstract

The present application relates to the technical field of micro-high pressure oxygen cabin, and especially to an intelligent safety early warning and active defense control system and method based on a micro-high pressure oxygen cabin, which collects oxygen concentration, cabin pressure and temperature in real time, and forms corresponding time series data; based on the time series data, oxygen concentration change rate, oxygen concentration change acceleration and oxygen concentration continuous growth duration are extracted to represent parameter change trend; based on the oxygen concentration change rate, oxygen concentration change acceleration, oxygen concentration continuous growth duration, cabin oxygen concentration, cabin pressure and temperature, a multi-dimensional enhanced risk assessment model is constructed to continuously assess the current operation state and obtain real-time risk value; in the present application, by constructing a multi-dimensional enhanced risk assessment model containing time accumulation enhancement component, nonlinear coupling enhancement component and prediction error feedback component, high-sensitivity identification of oxygen concentration progressive risk and compound risk is realized.

Description

Technical Field

[0001] This invention relates to an intelligent safety early warning and active defense control system and method based on a micro hyperbaric oxygen chamber, belonging to the field of micro hyperbaric oxygen chamber technology. Background Technology

[0002] Existing hyperbaric oxygen chambers are widely used in home health management, medical rehabilitation, and sports recovery. They typically provide an oxygen-rich and micro-high-pressure environment to the chamber through an oxygen supply system and a pressure regulation system, and monitor the environment inside the chamber through oxygen concentration, pressure, and temperature sensors.

[0003] Existing technologies mostly employ alarm mechanisms based on a single parameter threshold, triggering an alarm or prompting user intervention when the detected value exceeds a preset threshold.

[0004] However, the above-mentioned technical solutions have obvious shortcomings: First, they only respond when parameters exceed limits, lacking the ability to analyze parameter change trends and making it difficult to identify gradual risks such as slow accumulation of oxygen concentration in a timely manner; second, the parameters are processed independently, without considering the coupling relationship between oxygen concentration, pressure, and temperature, making it difficult to reflect the true risk state under complex working conditions; in addition, the existing system lacks predictive capabilities and automatic adjustment mechanisms, resulting in response lag and safety hazards in non-professional user scenarios. Therefore, it is urgent to improve the intelligent safety early warning and active defense control system and method based on micro hyperbaric oxygen chambers to solve the above-mentioned problems. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent safety early warning and active defense control system and method based on a micro hyperbaric oxygen chamber, which can be applied to home or small micro hyperbaric oxygen chamber scenarios to identify, warn and actively control the risks of gradual oxygen concentration, compound coupling risks and abnormal deviation risks.

[0006] Specifically, during the operation of the micro-hyperbaric oxygen chamber, the chamber's internal state parameters are first collected in real time using oxygen concentration sensors, pressure sensors, and temperature sensors, and recorded as the chamber's oxygen concentration. cabin pressure and cabin temperature And form time series data.

[0007] Subsequently, dynamic feature extraction was performed on the time series data to calculate the rate of change of oxygen concentration, the acceleration of change, and the duration of continuous increase in oxygen concentration, wherein:

[0008]

[0009]

[0010]

[0011] Among them, when consecutive sampling points satisfy At that time, take =1, otherwise take =0, thus characterizing the duration of a sustained increase in oxygen concentration. This refers to the wiping cycle or prediction step size.

[0012] Based on this, a multidimensional enhanced risk assessment model is constructed to obtain real-time risk values. The risk assessment model includes at least a basic risk component, a time-cumulative reinforcement component, a nonlinear coupling reinforcement component, and a prediction error feedback component, and its expression can be represented as:

[0013] ;

[0014] Among them, the basic risk components are based on oxygen concentration, rate of change of oxygen concentration, cabin pressure, and temperature. Basic risk components The mathematical expression is:

[0015] ;

[0016] In the formula, Basic risk weights;

[0017] The time-cumulative enhancement component based on the combined effect of the sustained increase time and rate of change of oxygen concentration. This is used to amplify slowly but steadily growing risks, where the time-cumulative amplification component... The mathematical expression is:

[0018] ;

[0019] In the formula, This is the adjustment coefficient;

[0020] Coupled enhancement component based on the nonlinear combination relationship between oxygen concentration and chamber pressure and temperature It is used to reflect the composite risk caused by the coordinated changes of multiple parameters, among which the coupling enhancement component The mathematical expression is:

[0021] ;

[0022] In the formula, This is the adjustment coefficient;

[0023] The prediction error feedback component based on the deviation between the actual oxygen concentration and the predicted oxygen concentration. It is used to identify abnormal deviations from the state, wherein the prediction error feedback component The mathematical expression is:

[0024] ;

[0025] in, To predict oxygen concentration, This is the adjustment coefficient.

[0026] Furthermore, the system predicts the oxygen concentration within a future time window based on the current state, obtaining the predicted oxygen concentration:

[0027] ;

[0028] Then, based on the real-time risk value and predicting oxygen concentration The current status is classified and judged, and when the risk reaches the warning level or control level, the corresponding warning signal or control strategy is generated.

[0029] When the risk level reaches the preset control level, the system generates a control strategy based on the risk level, adjusting the oxygen supply flow rate. and exhaust flow Perform nonlinear adjustment;

[0030] The oxygen supply flow rate can be set to decrease exponentially as the risk value increases: The exhaust flow rate can be set to increase non-linearly with increasing risk value. ,in, As the baseline flow rate, , This is the adjustment coefficient.

[0031] Finally, based on the feedback data after control execution, the system adaptively updates the parameters of the risk assessment model, realizing a closed-loop control process of "collection-extraction-assessment-prediction-control-feedback".

[0032] Compared with the prior art, the beneficial effects of the present invention are:

[0033] This invention constructs a multidimensional enhanced risk assessment model that includes time-cumulative enhancement components, nonlinear coupling enhancement components, and prediction error feedback components, achieving highly sensitive identification of gradual and compound risks in oxygen concentration. Simultaneously, by combining trend prediction and nonlinear active control mechanisms, intervention can be initiated before the risk reaches a dangerous threshold, effectively preventing oxygen concentration runaway. This significantly improves the accuracy and lead time of risk identification, reduces reliance on manual intervention, and enhances the safety, stability, and intelligence of the micro-hyperbaric oxygen chamber in home and complex environments. Attached Figure Description

[0034] Figure 1 This is a flowchart of the intelligent safety early warning and active defense control method based on a micro hyperbaric oxygen chamber according to the present invention;

[0035] Figure 2 This is a system block diagram of the intelligent safety early warning and active defense control system based on the micro hyperbaric oxygen chamber of the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] like Figures 1-2 As shown, the intelligent safety early warning and active defense control system and method based on a micro hyperbaric oxygen chamber provided in this embodiment includes the following steps:

[0038] Real-time collection of oxygen concentration inside the cabin cabin pressure and temperature And generate corresponding time series data;

[0039] Based on time series data, the rate of change of oxygen concentration, which characterizes the trend of parameter variation, is extracted. Acceleration of oxygen concentration change and the duration of continuous increase in oxygen concentration ;

[0040] Based on the rate of change of oxygen concentration Acceleration of oxygen concentration change Duration of continuous increase in oxygen concentration and cabin oxygen concentration cabin pressure and temperature A multi-dimensional enhanced risk assessment model is constructed to continuously assess the current operating status and obtain real-time risk values. Identification of gradual risks that have not reached the preset threshold but have a continuous growth trend;

[0041] Based on time series data and oxygen concentration change rate Acceleration of oxygen concentration change and the duration of continuous increase in oxygen concentration The trend of oxygen concentration change within a preset time window is predicted to obtain the predicted oxygen concentration value. ;

[0042] Based on real-time risk value and predicted oxygen concentration values The system divides the current operating status into multiple risk levels and outputs a warning signal when the risk level reaches the preset warning level. By introducing trend characteristics and prediction mechanisms, it can identify the gradual risk of oxygen concentration in advance, transforming the system from a traditional "over-limit alarm" to a "trend warning", significantly improving the lead time for safety warnings.

[0043] In one possible embodiment, the following steps are also included:

[0044] When the risk level reaches the preset control level, a control strategy is generated based on the degree of risk to nonlinearly adjust the oxygen supply and exhaust processes, thereby suppressing the oxygen concentration growth trend and reducing the overall risk level.

[0045] Based on real-time data after control execution, the parameters in the risk assessment model are dynamically updated, and the model is corrected according to the prediction error to form an adaptive closed-loop control process. This realizes a closed-loop adjustment mechanism from early warning to active control, reduces manual intervention, and improves the safety and automation level of the system in non-professional use scenarios.

[0046] In one possible embodiment, the risk assessment model includes at least:

[0047] Basic risk components based on oxygen concentration, rate of change of oxygen concentration, cabin pressure, and temperature. Basic risk components The mathematical expression is: In the formula, Basic risk weights;

[0048] The time-cumulative enhancement component based on the combined effect of the sustained increase time and rate of change of oxygen concentration. This is used to amplify slowly but steadily growing risks, where the time-cumulative amplification component... The mathematical expression is: In the formula, This is the adjustment coefficient;

[0049] Coupled enhancement component based on the nonlinear combination relationship between oxygen concentration and chamber pressure and temperature It is used to reflect the composite risk caused by the coordinated changes of multiple parameters, among which the coupling enhancement component The mathematical expression is: In the formula, This is the adjustment coefficient;

[0050] The prediction error feedback component based on the deviation between the actual oxygen concentration and the predicted oxygen concentration. It is used to identify abnormal deviations from the state, wherein the prediction error feedback component The mathematical expression is: A multidimensional enhanced risk model is constructed, which integrates time effects, nonlinear coupling and predictive feedback to improve the ability to identify complex working conditions and hidden risks.

[0051] In one possible embodiment, the rate of change and acceleration of oxygen concentration change are obtained by performing continuous difference operations on time series data, thereby achieving accurate quantification of parameter change trends and improving the system's response sensitivity to dynamic changes.

[0052] In one possible embodiment, the duration of continuous increase in oxygen concentration is obtained by statistically analyzing the length of time during which the rate of change in oxygen concentration is continuously positive, and is used to distinguish between short-term fluctuations and continuous increase states, effectively distinguishing between random fluctuations and continuous risks, improving the accuracy of early warnings, and reducing false alarms.

[0053] In one possible embodiment, the time-cumulative enhancement component is calculated by combining the duration of oxygen concentration increase with its rate of change, so as to improve the risk assessment results when the increase time is longer and the rate of change is greater, enhance the ability to identify "slow but continuously deteriorating" risks, and solve the problem that traditional methods are difficult to detect progressive risks.

[0054] In one possible embodiment, the coupling enhancement component is constructed based on the nonlinear relationship between oxygen concentration and cabin pressure and temperature, and the risk value is enhanced and corrected when oxygen concentration and pressure or temperature change simultaneously, thereby improving the risk identification capability in multi-parameter coupling scenarios and avoiding missed detections caused by single parameter judgment.

[0055] In one possible embodiment, the prediction error feedback component is determined based on the deviation between the actual oxygen concentration and the predicted oxygen concentration, and improves the risk assessment result when the deviation exceeds a preset range. This can identify abnormal changes or system mismatches and improve the system's response capability to sudden anomalies.

[0056] In one possible embodiment, when the risk level reaches a preset control level, in the process of generating a control strategy based on the risk level, the oxygen supply flow rate is adjusted in an exponentially decreasing manner as the risk value increases, and the exhaust flow rate is adjusted in a non-linearly increasing manner as the risk value increases. Through the non-linear control strategy, smoother and more efficient risk suppression is achieved, avoiding system oscillation or control lag.

[0057] The system includes a data acquisition module for collecting multi-source state parameters within the cabin; a feature extraction module for extracting dynamic feature parameters such as rate of change, acceleration of change, and duration of increase; an enhanced risk assessment module for constructing a risk assessment model that includes a basic risk component, a time-cumulative enhancement component, a coupled enhancement component, and a prediction error feedback component, and calculating real-time risk values; a predictive analysis module for predicting oxygen concentration change trends; an early warning determination module for determining the risk level based on the risk value and prediction results; a control execution module for adjusting the oxygen supply and exhaust processes according to the risk level; and an adaptive update module for updating the parameters of the risk assessment model based on feedback data. This complete, systematic, closed-loop architecture enhances the feasibility of system engineering and its industrial application capabilities.

[0058] Example 1: Progressive Risk Identification Based on Time-Accumulated Reinforcement

[0059] This embodiment is applicable to home-use single-person hyperbaric oxygen chambers, and focuses on solving the problem that traditional threshold alarms cannot detect the slow accumulation of oxygen concentration in advance.

[0060] During operation, the system uses a fixed sampling period. =1-2s to collect cabin oxygen concentration cabin pressure and temperature First, the rate of change of oxygen concentration is calculated based on continuous sampling data: Simultaneously calculate the duration of the continuous increase in oxygen concentration: Where a certain sampling point satisfies At that time, take =1, otherwise =0.

[0061] When the system detects that the oxygen concentration has not reached the preset alarm threshold, but Continuously positive and As growth continues, it triggers the time-accumulated enhancement component: ;

[0062] At this point, even if the oxygen concentration is not exceeded, the system can still... The increasing trend was identified as a gradual risk in advance and a warning was issued.

[0063] During the control phase, the system adjusts oxygen supply and exhaust based on the risk value, with the oxygen supply flow rate and exhaust flow rate adjusted as follows: , ,in This is the baseline flow rate under normal operating conditions.

[0064] It can identify the hidden risk of "oxygen concentration continuously rising but not yet exceeding the limit", avoiding the lag problem of traditional systems that only alarm after exceeding the limit.

[0065] Example 2: Composite Risk Identification Based on Nonlinear Coupling

[0066] This embodiment is applicable to long-term operation or high ambient temperature scenarios, and is used to identify the combined risks between oxygen concentration, pressure and temperature.

[0067] The system collects the oxygen concentration inside the cabin. cabin pressure and temperature In addition to the basic risk component, a nonlinear coupling enhancement component is introduced:

[0068] ;

[0069] When the oxygen concentration inside the cabin increases, and the pressure or temperature continues to rise, This will significantly increase, thus affecting the total risk value: Enhanced corrections received;

[0070] For example, in high-temperature environments, even if the increase in oxygen concentration is not significant, the system can still identify complex risk states in advance because the temperature term is introduced into the coupling enhancement component, thus avoiding missed detections caused by a single oxygen concentration judgment.

[0071] During the control phase, if If the preset control level is exceeded, the system will not only reduce the oxygen supply flow rate but also increase the exhaust capacity and limit the rate of pressure rise, so that the cabin environment returns to a safe range.

[0072] By nonlinearly coupling oxygen concentration with pressure and temperature, the ability to identify complex risks under complex operating conditions is improved, making it particularly suitable for high-temperature or continuous operation scenarios.

[0073] Example 3: Active Defense Control Based on Predictive Feedback

[0074] This embodiment is applicable to scenarios with long continuous usage time, and focuses on achieving pre-control through prediction and feedback mechanisms.

[0075] The system first calculates the rate of change and acceleration of change of oxygen concentration based on historical sampling data, and then predicts the oxygen concentration within the future time window accordingly. When the prediction results show: When the oxygen concentration is about to approach the preset safety threshold in the future time window, the system will enter the early warning state in advance, without waiting for the actual oxygen concentration to reach the limit value.

[0076] Meanwhile, the system uses the deviation between the actual oxygen concentration and the predicted oxygen concentration as a prediction error feedback component: ;

[0077] If the deviation between the actual value and the predicted value continues to increase, it indicates that there may be abnormal fluctuations, oxygen supply mismatch, or sensor drift in the system. The system will automatically increase the risk level and switch to a more conservative control strategy.

[0078] During the control process, the system adjusts the oxygen supply flow and exhaust flow in a coordinated manner. The oxygen supply flow decreases exponentially with the increase of the risk value, while the exhaust flow increases nonlinearly with the increase of the risk value, thereby achieving early suppression before the risk is formed.

[0079] After control is executed, the system continues to collect new data. , and Data, and recalculate and Based on this, the model parameters are adjusted to achieve adaptive closed-loop updates.

[0080] This achieves a shift from "post-event alarm" to "pre-event prediction and active suppression," which can significantly reduce the probability of oxygen concentration runaway and improve the overall safety margin of the system.

[0081] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0082] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for intelligent safety early warning and active defense control based on a micro hyperbaric oxygen chamber, characterized in that, Includes the following steps: Real-time collection of oxygen concentration inside the cabin cabin pressure and temperature And generate corresponding time series data; Based on time series data, the rate of change of oxygen concentration, which characterizes the trend of parameter variation, is extracted. Acceleration of oxygen concentration change and the duration of continuous increase in oxygen concentration ; Based on the rate of change of oxygen concentration Acceleration of oxygen concentration change Duration of continuous increase in oxygen concentration and cabin oxygen concentration cabin pressure and temperature A multi-dimensional enhanced risk assessment model is constructed to continuously assess the current operating status and obtain real-time risk values. Identification of gradual risks that have not reached the preset threshold but have a continuous growth trend; Based on the time series data and the rate of change in oxygen concentration Acceleration of oxygen concentration change and the duration of continuous increase in oxygen concentration The trend of oxygen concentration change within a preset time window is predicted to obtain the predicted oxygen concentration value. ; Based on the real-time risk value and predicted oxygen concentration values The system divides the current operating status into multiple risk levels and outputs a warning signal when the risk level reaches the preset warning level.

2. The intelligent safety early warning and active defense control method based on a micro hyperbaric oxygen chamber according to claim 1, characterized in that: It also includes the following steps: When the risk level reaches the preset control level, a control strategy is generated based on the degree of risk to nonlinearly adjust the oxygen supply and exhaust processes, thereby suppressing the oxygen concentration growth trend and reducing the overall risk level. Based on real-time data after control execution, the parameters in the risk assessment model are dynamically updated, and the model is corrected according to the prediction error to form an adaptive closed-loop control process.

3. The intelligent safety early warning and active defense control method based on a micro hyperbaric oxygen chamber according to claim 1, characterized in that: The risk assessment model includes at least: Basic risk components based on oxygen concentration, rate of change of oxygen concentration, cabin pressure, and temperature. Basic risk components The mathematical expression is: ； In the formula, Basic risk weights; The time-cumulative enhancement component based on the combined effect of the sustained increase time and rate of change of oxygen concentration. This is used to amplify slowly but steadily growing risks, where the time-cumulative amplification component... The mathematical expression is: ； In the formula, This is the adjustment coefficient; Coupled enhancement component based on the nonlinear combination relationship between oxygen concentration and chamber pressure and temperature It is used to reflect the composite risk caused by the coordinated changes of multiple parameters, among which the coupling enhancement component The mathematical expression is: ; In the formula, This is the adjustment coefficient; The prediction error feedback component based on the deviation between the actual oxygen concentration and the predicted oxygen concentration. It is used to identify abnormal deviations from the state, wherein the prediction error feedback component The mathematical expression is: 。 4. The intelligent safety early warning and active defense control method based on a micro hyperbaric oxygen chamber according to claim 1, characterized in that: The rate of change and acceleration of the oxygen concentration were obtained by performing continuous difference operations on the time series data.

5. The intelligent safety early warning and active defense control method based on a micro hyperbaric oxygen chamber according to claim 1, characterized in that: The duration of continuous increase in oxygen concentration is obtained by statistically analyzing the length of time during which the rate of change in oxygen concentration is continuously positive, and is used to distinguish between short-term fluctuations and continuous increase.

6. The intelligent safety early warning and active defense control method based on a micro hyperbaric oxygen chamber according to claim 3, characterized in that: The time-cumulative enhancement component is calculated by combining the duration of oxygen concentration increase with its rate of change, in order to improve the risk assessment results when the increase time is longer and the rate of change is greater.

7. The intelligent safety early warning and active defense control method based on a micro hyperbaric oxygen chamber according to claim 3, characterized in that: The coupling enhancement component is constructed based on the nonlinear relationship between oxygen concentration and cabin pressure and temperature, and enhances the risk value when oxygen concentration and pressure or temperature change simultaneously.

8. The intelligent safety early warning and active defense control method based on a micro hyperbaric oxygen chamber according to claim 3, characterized in that: The prediction error feedback component is determined based on the deviation between the actual oxygen concentration and the predicted oxygen concentration, and improves the risk assessment result when the deviation exceeds a preset range.

9. The intelligent safety early warning and active defense control method based on a micro hyperbaric oxygen chamber according to claim 2, characterized in that: When the risk level reaches the preset control level, in the process of generating a control strategy based on the risk level, the oxygen supply flow rate adopts an adjustment method that decreases exponentially with the increase of the risk value, while the exhaust flow rate adopts an adjustment method that increases non-linearly with the increase of the risk value.

10. The intelligent safety early warning and active defense control method based on a micro hyperbaric oxygen chamber according to any one of claims 1-9, wherein the method is implemented through an intelligent safety early warning and active defense control system based on a micro hyperbaric oxygen chamber during operation, characterized in that: The system includes a data acquisition module for collecting multi-source status parameters inside the cabin; The feature extraction module is used to extract dynamic feature parameters such as rate of change, acceleration of change, and duration of growth. The enhanced risk assessment module is used to construct a risk assessment model that includes a basic risk component, a time-accumulated reinforcement component, a coupled reinforcement component, and a prediction error feedback component, and to calculate the real-time risk value. The predictive analysis module is used to predict the trend of oxygen concentration changes; The early warning determination module is used to determine the risk level based on the risk value and prediction results; The control execution module is used to adjust the oxygen supply and exhaust processes according to the risk level; The adaptive update module is used to update the parameters of the risk assessment model based on feedback data.

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