User oxygen cabin data monitoring system and method for rehabilitation management

Abstract

The invention discloses a user oxygen cabin data monitoring system and method for rehabilitation management, and relates to the technical field of medical equipment management, and the system comprises an oxygen cabin state sensing module which comprises a pressure monitoring unit and an oxygen supply equipment monitoring unit, the pressure monitoring unit is used for collecting pressure data and pressure change rate in an oxygen cabin in real time, and the oxygen supply equipment monitoring unit is used for monitoring oxygen supply equipment; the oxygen supply equipment monitoring unit is used for collecting oxygen supply pipeline flow, oxygen concentration and equipment operation parameters, the abnormity analysis module receives real-time data of the oxygen cabin state sensing module, whether pressure leakage or oxygen supply abnormity exists or not is judged by comparing a preset threshold value with a dynamic base line, and the abnormity level is output. And according to the abnormity level output by the abnormity analysis module, the corresponding early warning strategy and the equipment control instruction are executed, so that the oxygen cabin data can be accurately monitored, and early warning processing can be timely performed.

Description

Technical Field

[0001] This invention relates to the field of medical equipment management technology, specifically to a user oxygen chamber data monitoring system and method for rehabilitation management. Background Technology

[0002] Hyperbaric oxygen chambers, as an important means of rehabilitation therapy, are widely used in nerve repair, trauma recovery, and chronic disease treatment. With the improvement of medical conditions, the frequency of use of hyperbaric oxygen chambers is increasing. Currently, the monitoring and management of hyperbaric oxygen chambers mainly rely on traditional manual inspections and single-parameter threshold alarm systems. Manual inspections suffer from discontinuous data recording, low efficiency, and a high risk of missed detections, and are also difficult to detect slowly occurring leaks. Existing automated systems mostly monitor only single parameters such as pressure and oxygen concentration in isolation, triggering alarms when values ​​exceed preset thresholds. This makes it difficult to identify potential risks such as slow leaks in advance, resulting in delayed operation and maintenance response and posing a threat to the continuous and safe operation of the oxygen chamber.

[0003] Therefore, the present invention provides a user oxygen chamber data monitoring system and method for rehabilitation management. Summary of the Invention

[0004] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a user oxygen chamber data monitoring system and method for rehabilitation management. Specifically, it constructs a technical system of "comprehensive monitoring - intelligent analysis - graded response" to achieve accurate monitoring and safety assurance of the oxygen chamber's operational status. Through modular design, pressure monitoring units and oxygen supply equipment monitoring units achieve full-link data coverage from the chamber environment to equipment operation. Dynamic baseline technology combined with safety thresholds accurately judges equipment anomalies. The fluctuation range of the dynamic baseline can distinguish between normal data fluctuations and abnormal situations, and the safety thresholds further determine whether an anomaly has occurred, thereby improving the accuracy of anomaly detection. The classification of anomaly levels and warning levels can maximize the maintenance of rehabilitation continuity while ensuring user safety.

[0005] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: A user oxygen chamber data monitoring system for rehabilitation management includes: The oxygen chamber status sensing module includes a pressure monitoring unit and an oxygen supply equipment monitoring unit. The pressure monitoring unit is used to collect real-time pressure data and pressure change rate inside the oxygen chamber, while the oxygen supply equipment monitoring unit is used to collect oxygen supply pipeline flow, oxygen concentration, and equipment operating parameters. The dynamic baseline calculation module is used to generate dynamic baselines for key operating parameters based on historical oxygen chamber operation data through multimodal adaptive learning. The anomaly analysis module is used to receive real-time data from the oxygen chamber status sensing module, determine whether the data fluctuation is normal through the dynamic baseline, and if it exceeds the baseline fluctuation range, determine whether there is pressure leakage or oxygen supply abnormality through the preset threshold, and output the anomaly level. The early warning control module is used to execute corresponding early warning strategies and equipment control commands based on the anomaly level output by the anomaly analysis module.

[0006] Furthermore, the dynamic baseline calculation module includes: a multimodal data clustering and working condition segmentation unit, a time series prediction and expected value generation unit, an adaptive fluctuation threshold calculation unit, and an incremental learning unit; The multimodal data clustering and operating condition division unit is configured to standardize historical operating data and use a Gaussian mixture model clustering algorithm to identify multiple typical operating modes of the oxygen chamber and assign the closest operating mode to real-time operating condition data. The time series prediction and expectation value generation unit is configured to build an independent time series prediction model for each identified operating mode, which is used to infer the dynamic expectation values ​​of key operating parameters. The adaptive fluctuation threshold calculation unit is configured to calculate the adaptive fluctuation upper and lower limits of the dynamic expected value based on the prediction uncertainty of the time series prediction model and the noise level of real-time data, which together constitute the dynamic baseline. The incremental learning unit is configured to use an exponentially weighted moving average mechanism to fine-tune the parameters of the time series forecasting model online using new normal operation data, thereby achieving progressive optimization of the dynamic baseline.

[0007] Furthermore, in the time series forecasting and expected value generation unit, the time series forecasting model includes the ARIMA model, which has the following form: (1− (1−B) d Pt=(1+ )εt; Where B is the lag operator and d is the difference order. Pt Given the historical parameter values ​​at time t, this model is used to predict the dynamic expected value of the parameters at the next time step. .

[0008] Furthermore, in the adaptive fluctuation threshold calculation unit, the upper and lower limits of the dynamic baseline are: The upper limit of dynamic baseline is Lt, Lt = - α ⋅ spread ( k )− β ⋅ noise ( t ); The lower limit of the dynamic baseline is Ut, Ut= + α ⋅ spread ( k )+ β ⋅ noise ( t ); in, For dynamic expected value, For the current operating mode The root mean square error of the prediction model. The standard deviation of short-term noise for real-time data. , These are configurable weighting coefficients.

[0009] Furthermore, the pressure monitoring unit specifically includes: a high-precision pressure sensor deployed inside the oxygen chamber, with a data acquisition frequency of 1 time / second; and a pressure change calculation subunit, which calculates the pressure change rate ΔP / Δt based on three consecutive pressure data acquisitions.

[0010] Specifically, the high-precision pressure sensor has an accuracy of ≤0.01KPa and collects the real-time pressure value (P) inside the oxygen chamber at a frequency of 1 time / second. The pressure change calculation subunit calculates the pressure change rate using the formula ΔP / Δt=(P3-P1) / (t3-t1).

[0011] Furthermore, the oxygen supply equipment monitoring unit specifically includes: an electromagnetic flow meter installed on the main oxygen supply line and an oxygen concentration sensor installed in the chamber; the equipment operation parameter acquisition subunit is used to acquire the oxygen supply pump operating current, valve opening and closing status and cumulative running time.

[0012] Specifically, the electromagnetic flowmeter monitors the oxygen delivery flow rate Q of the main oxygen supply line, the oxygen concentration sensor collects the actual oxygen concentration C in the chamber, and the equipment operation parameter acquisition subunit obtains the oxygen pump operating current I and the valve switch status.

[0013] Furthermore, the preset thresholds include: Preset lower limit of safe pressure: The lower limit of safe pressure for hyperbaric oxygen chamber is 120 kPa, and the lower limit of safe pressure for atmospheric oxygen chamber is 101 kPa; Preset leakage thresholds: The preset leakage threshold for hyperbaric oxygen chambers is 0.5 kPa / min, and the preset leakage threshold for atmospheric oxygen chambers is 0.3 kPa / min. Minimum oxygen supply threshold: Based on the number of users U in the cabin, the minimum oxygen supply threshold for a single cabin is 5L / minute, and the minimum oxygen supply threshold for a multi-person cabin is 5*UL / minute. Target concentration: Based on the oxygen content concentration in the rehabilitation program.

[0014] Further, determining whether there is a pressure leak or abnormal oxygen supply includes: Pressure Leakage Detection: If the real-time pressure value inside the chamber is lower than the preset lower limit of safety pressure, and the rate of pressure change ΔP / Δt is greater than the preset leakage threshold, then it is determined to be a pressure leak. Oxygen supply anomaly judgment: If the oxygen supply pipeline flow rate is less than the minimum oxygen supply threshold and the oxygen concentration in the chamber is less than the target concentration, or if the equipment operating parameters are outside the normal range, then it is judged as an oxygen supply anomaly.

[0015] Furthermore, the anomaly levels include: Level 1 Anomaly: When any of the following parameters exceeds the dynamic baseline but does not exceed the corresponding preset threshold, it is judged as a Level 1 anomaly. Level 2 anomaly: If the oxygen concentration in the chamber continuously deviates from the target oxygen concentration within ±3%, or if the flow rate of the oxygen supply pipeline continuously falls below 80% of the minimum oxygen supply threshold and the oxygen concentration in the chamber drops by more than 2% but not more than 5% within 10 seconds, or if the working current of the oxygen supply pump exceeds 125% of the rated current and lasts for more than 10 seconds, it is judged as a Level 2 anomaly. Level 3 anomaly: The real-time pressure value inside the chamber is less than 90% of the preset lower limit of safety pressure or greater than 150 kPa, or the absolute value of the pressure change rate is greater than 3 times the preset leakage threshold, or the flow rate of the oxygen supply pipeline suddenly drops to 0 L / min, or the oxygen concentration inside the chamber drops by 5%, below 75% or above 95% within 10 seconds, or the working current of the oxygen supply pump suddenly increases to twice the rated value.

[0016] Furthermore, the early warning strategy of the early warning control module includes: Level 1 Warning: Activate the cabin's audible and visual alarm and simultaneously send a text notification to the administrator's terminal; Specifically, when only a slight data deviation occurs that does not affect safe use, the cabin's audible and visual alarm will be activated, and the text notification will include at least the time of the anomaly, the oxygen chamber number, the warning level, and the reason for the warning.

[0017] Level 2 warning: Activate the cabin's audible and visual alarm, simultaneously push a text reminder to the administrator's terminal, and automatically activate the pressurization device or backup oxygen supply line; Specifically, in addition to audible and visual alarms and push text reminders, when a pressure leak is detected, the pressure replenishment device is activated, and the pressure monitoring unit collects the cabin pressure every second. When the cabin pressure rises back to a safe range, the pressure replenishment stops. When an oxygen supply abnormality is detected, the system switches to the backup oxygen supply pipeline, and the oxygen concentration is continuously monitored by the oxygen supply equipment monitoring unit. When the oxygen concentration rises back to the target value plus or minus 2% and remains stable for 30 seconds, the backup pipeline continues to operate.

[0018] Level 3 warning: Immediately cut off the oxygen supply line, activate the emergency pressure relief valve, trigger the audible and visual alarm, and call the preset emergency contact number.

[0019] A method for monitoring user oxygen chamber data for rehabilitation management includes: S1. Status Awareness: Real-time collection of oxygen chamber pressure, pressure change rate, oxygen supply pipeline flow, oxygen concentration in the chamber, and equipment operating parameters. S2. Dynamic baseline calculation: Based on historical operating data, a dynamic baseline for key operating parameters is generated through multimodal adaptive learning; S3. Anomaly Analysis: Compare the collected data with the dynamic baseline to determine whether it is within the normal fluctuation range, whether there is pressure leakage or abnormal oxygen supply, and determine the anomaly level. S4. Early warning control: Execute corresponding early warning measures and equipment control commands according to the level of abnormality until the abnormality is resolved.

[0020] (III) Beneficial Effects This invention provides a user oxygen chamber data monitoring system and method for rehabilitation management, which has the following beneficial effects: 1. The pressure monitoring unit of the oxygen chamber status sensing module collects real-time pressure data and pressure change rate inside the oxygen chamber. The oxygen supply equipment monitoring unit of the oxygen chamber status sensing module is used to collect the flow rate, oxygen concentration and equipment operating parameters of the oxygen supply pipeline. It can perform high-frequency data collection to provide data support for whether the oxygen chamber has pressure leakage or oxygen supply abnormality.

[0021] 2. The anomaly analysis module, based on real-time data from the oxygen chamber status sensing module, compares preset thresholds with the dynamic baseline to determine whether there is pressure leakage or abnormal oxygen supply, and outputs the anomaly level. It calculates the rate of pressure change in the chamber using real-time data and performs a dual comparison with the dynamic baseline and preset thresholds. The dynamic baseline can identify normal numerical fluctuations during the rehabilitation process. Combined with the rigid constraints of the preset thresholds, it can improve the accuracy of anomaly warnings and reduce ineffective responses from the oxygen chamber.

[0022] By monitoring both the internal pressure of the oxygen chamber and the oxygen supply lines, the monitoring scope covers the entire chain of risks related to chamber sealing, pipeline unobstructedness, and equipment operation, ensuring that users are in a safe environment with stable pressure and qualified oxygen supply during the rehabilitation process.

[0023] 3. The early warning control module executes corresponding early warning strategies and equipment control commands based on the anomaly level output by the anomaly analysis module. By classifying different anomalies into different levels, different early warning schemes are adopted. In the case of a Level 1 anomaly, the oxygen chamber data deviates from the threshold but does not affect the safety of use. Only a reminder is needed, and it does not interfere with the rehabilitation process. In the case of a Level 2 anomaly, the oxygen chamber data deviates significantly from the threshold, which may affect the user's safety. The system will immediately automatically repressurize or switch to a backup oxygen supply line, which can resolve the abnormal situation while maintaining the continuity of treatment. This adapts to the safety and continuity requirements of rehabilitation scenarios. In the case of a Level 3 anomaly, an emergency occurs. The system will immediately stop oxygen supply and depressurize, and issue an emergency alarm and request manual intervention, which can ensure rapid control of serious risks while avoiding excessive intervention. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the structure of a user oxygen chamber data monitoring system for rehabilitation management according to the present invention; Figure 2 This is a flowchart illustrating a user oxygen chamber data monitoring method for rehabilitation management according to the present invention. Detailed Implementation

[0025] 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.

[0026] refer to Figure 1 , Figure 1 A schematic diagram of a system structure for a user oxygen chamber data monitoring system for rehabilitation management, provided for embodiments of this application, includes: The oxygen chamber status sensing module includes a pressure monitoring unit and an oxygen supply equipment monitoring unit. The pressure monitoring unit is used to collect real-time pressure data and pressure change rate inside the oxygen chamber, while the oxygen supply equipment monitoring unit is used to collect flow rate, oxygen concentration, and equipment operating parameters of the oxygen supply pipeline.

[0027] The pressure monitoring unit includes a high-precision pressure sensor deployed inside the oxygen chamber, with a data acquisition frequency of 1 time / second; the pressure change calculation subunit calculates the pressure change rate ΔP / Δt based on three consecutive pressure data acquisitions.

[0028] The high-precision pressure sensor needs to meet the working pressure monitoring requirements of 120-150 kPa in the oxygen chamber, with a measurement accuracy of ≤0.01 kPa, and maintain the ability to detect minor pressure leaks. The high-precision pressure sensor should be deployed in the central area of ​​the top of the oxygen chamber, and kept at a distance of more than 50 cm from the airflow channel inside the chamber to avoid local pressure disturbances caused by user breathing and airflow from the chamber fan affecting the measurement accuracy. The high-precision pressure sensor acquires the absolute pressure (P) inside the chamber at a sampling frequency of 1 time / second.

[0029] The pressure change calculation subunit takes the current sampling time as the endpoint and extracts three absolute pressure data points backward. If Pn < 0 exists in the three data points, it is marked as abnormal data and automatically removed. One position is then extracted backward, and the three valid data points P1, P2, and P3 are retained, along with the corresponding sampling times t1, t2, and t3. The pressure change rate is then calculated using the formula ΔP / Δt = (P3 - P1) / (t3 - t1).

[0030] The oxygen supply equipment monitoring unit specifically includes: an electromagnetic flow meter installed on the main oxygen supply line and an oxygen concentration sensor installed in the chamber; the equipment operation parameter acquisition subunit is used to acquire the working current of the oxygen supply pump, the valve opening and closing status, and the cumulative running time.

[0031] The electromagnetic flow meter must meet the maximum oxygen supply requirements of different oxygen chambers and be installed in a horizontal pipeline section to avoid flow disturbance caused by pipeline bends affecting measurement accuracy. The electromagnetic flow meter also uses a sampling frequency of 1 time / second.

[0032] The oxygen concentration sensor must be able to measure within the oxygen chamber's range of 21%-95%, and be installed in the user's breathing area inside the oxygen chamber with splash protection to prevent user contact and moisture corrosion, thus ensuring user safety and equipment operational stability.

[0033] The equipment operation parameter acquisition subunit is connected to the oxygen chamber oxygen supply system through multiple interfaces to obtain the current parameter I, running time T, and pressure relief valve status 1 / 0 (open / closed) of the oxygen supply pump in real time.

[0034] The dynamic baseline calculation module is used to generate dynamic baselines for key operating parameters based on historical oxygen chamber operation data through multimodal adaptive learning. The dynamic baseline calculation module includes: a multimodal data clustering and operating condition segmentation unit, a time series prediction and expected value generation unit, an adaptive fluctuation threshold calculation unit, and an incremental learning unit.

[0035] The multimodal data clustering and operating condition segmentation unit is configured to standardize historical operating data and use a Gaussian mixture model clustering algorithm to identify multiple typical operating modes of the oxygen chamber and assign the closest operating mode to the real-time operating condition data.

[0036] Each mode k is characterized by a multidimensional Gaussian distribution with the following probability density function: .

[0037] In the Gaussian mixture model clustering algorithm, the selection of eigenvectors comprehensively considers the physical mechanism of oxygen chamber operation and historical data analysis. This includes at least the number of users (U) and the target pressure (P). target The system provides multi-dimensional operating condition information, including time period (H) and workday type (W). Based on posterior probability, the system uses real-time collected operating condition data X... t Assign the closest running module k*, k ∗ =argmax(k) P ( k |x t =arg max(k) .

[0038] The time series prediction and expectation value generation unit is configured to build an independent time series prediction model for each identified operating mode, which is used to infer the dynamic expectation values ​​of key operating parameters.

[0039] Taking the internal pressure P as an example, the ARIMA(p,d,q) model is expressed as follows: (1- )(1− B ) d P t =(1+ )ε t Where B is the lag operator and d is the difference order at each time step. The model is based on the previous Historical data for each time window, outputting the dynamic expected value of the parameters. : = FARM ( k ()( Pt -1, Pt −2,..., Pt - m ), This expected value This is the baseline of the dynamic baseline at that moment.

[0040] The adaptive fluctuation threshold calculation unit is configured to calculate the adaptive fluctuation upper and lower limits of the dynamic expected value based on the prediction uncertainty of the time series prediction model and the noise level of real-time data, which together constitute the dynamic baseline.

[0041] The upper and lower limits of the dynamic baseline are: The upper limit of dynamic baseline is Lt, Lt = - α ⋅ spread ( k )− β ⋅ noise ( t ); The lower limit of the dynamic baseline is Ut, Ut = + α ⋅ spread ( k )+ β ⋅ noise ( t ); in, For dynamic expected value, For the current operating mode The root mean square error of the prediction model. The standard deviation of short-term noise for real-time data. , These are configurable weighting coefficients used to balance the contributions of the two types of uncertainty.

[0042] The incremental learning unit is configured to use an exponentially weighted moving average mechanism to fine-tune the parameters of the time series forecasting model online using new normal operation data, thereby achieving progressive optimization of the dynamic baseline.

[0043] To ensure the dynamic baseline continuously adapts to changes in equipment status, the system employs an exponentially weighted moving average mechanism to fine-tune the prediction model parameters online. For any model parameter θ, the update rule is as follows: thenew = l ⋅ cold +(1− l )⋅ update .

[0044] Among them, the forgetting factor Control the extent to which historical information is retained. When new normal data batches... When generated, the system calculates the parameter increment. It also performs smooth updates, thereby enabling incremental optimization of the baseline.

[0045] The dynamic baseline calculation module, through multimodal learning and adaptive temporal prediction, upgrades the generation of dynamic baselines from static historical statistics to a computationally calculable, evolvable, and condition-sensitive intelligent process. This method significantly improves the accuracy and context-awareness of the definition of "normal" state, providing a more scientific and reliable benchmark for subsequent anomaly analysis, thereby reducing the system's false alarm and false negative rates.

[0046] The anomaly analysis module receives real-time data from the oxygen chamber status sensing module, determines whether data fluctuations are normal through a dynamic baseline, and if they exceed the baseline fluctuation range, determines whether there is a pressure leak or oxygen supply anomaly through a preset threshold and outputs the anomaly level.

[0047] The dynamic baseline is used to determine whether the real-time parameters fall within the corresponding baseline fluctuation range. If they are within the range, they are considered normal data, and the data fluctuations are considered normal fluctuations. There is no need to determine whether pressure leakage or oxygen supply abnormality has occurred. If the real-time data has exceeded the dynamic baseline range, then a threshold judgment is performed.

[0048] Preset thresholds include: Preset lower limit of safe pressure: The lower limit of safe pressure for hyperbaric oxygen chamber is 120 kPa, and the lower limit of safe pressure for atmospheric oxygen chamber is 101 kPa; Preset leakage thresholds: The preset leakage threshold for hyperbaric oxygen chambers is 0.5 kPa / min, and the preset leakage threshold for atmospheric oxygen chambers is 0.3 kPa / min. Minimum oxygen supply threshold: Based on the number of users U in the cabin, the minimum oxygen supply threshold for a single cabin is 5L / minute, and the minimum oxygen supply threshold for a multi-person cabin is 5*UL / minute. Target concentration: Based on the oxygen content concentration in the rehabilitation program.

[0049] Determining whether a pressure leak or oxygen supply abnormality has occurred includes: Pressure leakage detection: If the real-time pressure value is lower than the preset safety pressure limit, or the pressure change rate ΔP / Δt is greater than the preset leakage threshold, then it is determined to be a pressure leak.

[0050] Lower limit of safety pressure P min The standards are formulated based on current hyperbaric oxygen chamber types and rehabilitation treatment standards; for example, the P value of a single-person hyperbaric oxygen chamber. min The pressure is 120 kPa, formulated according to the requirements of "Medical Air Pressurized Oxygen Chamber" to ensure treatment effectiveness and user comfort. The P value of a normal pressure oxygen chamber is... min The pressure is 101 kPa, which needs to be slightly higher than the standard atmospheric pressure to prevent backflow of outside air. The preset leakage threshold V0 is determined based on the oxygen chamber volume and sealing level. For example, the high-pressure oxygen chamber has a smaller volume and higher sealing requirements, so V0 = 0.5 kPa / min. The sealing requirements for the atmospheric pressure oxygen chamber are slightly lower, so V0 = 0.3 kPa / min.

[0051] If the current real-time pressure value P remains less than P for 10 seconds min If the absolute value of the pressure change rate ΔP / Δt is greater than V0, it is determined to be a pressure leak, and the abnormality level needs to be further determined.

[0052] Oxygen supply anomaly judgment: If the pipeline flow rate is less than the minimum oxygen supply threshold and the oxygen concentration in the chamber is less than the target concentration, or if the equipment operating parameters are outside the normal range, then it is judged as an oxygen supply anomaly.

[0053] Minimum oxygen supply threshold Q min The oxygen chamber designation is based on the current number of users in the oxygen chamber and must meet the oxygen demand of adults per minute. For example, a single-person chamber Q... m ᵢ n =5L / min, increasing according to the number of people in the multi-person cabin. Target concentration is 85%±3%. Normal operating parameters for the equipment include the oxygen pump current I, which is within the normal range of I of the rated current. 额 85%-115%, whether the valve opening and closing status is consistent with the instruction. For example, when oxygen is supplied during rehabilitation treatment, the valve must be in the 1 (open) state, and the continuous running time of the oxygen chamber should be recorded.

[0054] Anomaly levels include: Level 1 Anomaly: When any of the following parameters exceeds the dynamic baseline but does not exceed the corresponding preset threshold, it is judged as a Level 1 anomaly. Level 2 anomaly: If the oxygen concentration in the chamber continuously deviates from the target oxygen concentration within ±3%, or if the flow rate of the oxygen supply pipeline continuously falls below 80% of the minimum oxygen supply threshold and the oxygen concentration in the chamber drops by more than 2% but not more than 5% within 10 seconds, or if the working current of the oxygen supply pump exceeds 125% of the rated current and lasts for more than 10 seconds, it is judged as a Level 2 anomaly. Level 3 anomaly: The real-time pressure value inside the chamber is less than 90% of the preset lower limit of safety pressure or greater than 150 kPa, or the absolute value of the pressure change rate is greater than 3 times the preset leakage threshold, or the flow rate of the oxygen supply pipeline suddenly drops to 0 L / min, or the oxygen concentration inside the chamber drops by 5%, below 75% or above 95% within 10 seconds, or the working current of the oxygen supply pump suddenly increases to twice the rated value.

[0055] An anomaly is classified as Level 1 when one or more of the following conditions are met: The cabin pressure P has exceeded the dynamic baseline fluctuation range but is still greater than the lower safe pressure limit P. m ᵢ n The absolute value of the pressure change rate ΔP / Δt is greater than 50% of the preset leakage threshold V0 but less than or equal to V0, and the pressure value is stable within the baseline range; the oxygen concentration has exceeded the dynamic baseline fluctuation range but is still fluctuating within ±3% of the target value; the flow rate is lower than the corresponding dynamic baseline minimum but higher than the minimum oxygen supply Q.m ᵢ n Furthermore, the oxygen concentration did not decrease; the equipment current was slightly exceeded, and the duration was less than 5 seconds.

[0056] An anomaly is classified as Level 2 when one or more of the following conditions are met: oxygen concentration consistently < target concentration - 3% or consistently > target concentration + 3%; pipeline flow rate consistently below the minimum oxygen supply threshold Q. m ᵢ n The oxygen concentration is 80% of the rated current and decreases by more than 2% but not more than 5% within 10 seconds; the equipment current exceeds 125% of the rated current and lasts for more than 10 seconds.

[0057] An anomaly is classified as Level 3 when one or more of the following conditions are met: the cabin pressure value P is less than the preset lower safety pressure limit P. m ᵢ n A leak is considered severe if 90% of the pressure changes, or if the absolute value of the pressure change rate ΔP / Δt is greater than 3V0; if the chamber pressure P is greater than 150 kPa, indicating a failure of the pressure replenishment device; if the pipeline flow rate suddenly drops to 0 L / min; if the oxygen concentration decreases by 5% within 10 seconds; if the oxygen concentration is less than 75% or greater than 95%; or if the equipment current suddenly increases to 2*I. 额 This means the equipment is short-circuited.

[0058] The early warning control module executes corresponding early warning strategies and equipment control commands based on the anomaly level output by the anomaly analysis module.

[0059] In response to anomalies of the corresponding level, the early warning control module's strategies include: Level 1 Warning: Activate the cabin's audible and visual alarm and simultaneously send a text notification to the administrator's terminal; Level 2 warning: Activate the cabin's audible and visual alarm, simultaneously push a text reminder to the administrator's terminal, and automatically activate the pressurization device or backup oxygen supply line; Level 3 warning: Immediately cut off the oxygen supply line, activate the emergency pressure relief valve, trigger the audible and visual alarm, and call the preset emergency contact number.

[0060] In response to a Level 1 warning, the warning control module activates the cabin's audible and visual alarm, including activating the yellow LED indicator light inside the cabin, which flashes slowly at a frequency of 1Hz (one second on, one second off), with a brightness ≤30 cd / m². 2 To avoid strong light irritating the user's eyes; at the same time, the buzzer works in an intermittent mode of 1 second sound and 1 second silence, working in conjunction with the indicator light, and the volume is controlled at a normal conversational level of 50-60dB, and automatically turns silent only after 5 seconds in the initial stage of the abnormality, so as to prevent continuous noise from affecting the user's rehabilitation process; the text reminder includes at least the time of the abnormality, the oxygen chamber number, the warning level, and the reason for the warning.

[0061] Once the abnormal situation corresponding to the Level 1 abnormality disappears for 10 seconds, the Level 1 abnormality is lifted and the Level 1 warning measures are cancelled. The Level 1 warning is for minor abnormalities and scenarios that do not affect the safety of the rehabilitation process. Its main purpose is to alert to risks without interfering with treatment. By using low-intensity warnings, the safety and continuity of rehabilitation treatment can be balanced.

[0062] In response to the Level 2 warning, the warning control module also activates the cabin's audible and visual alarms, including activating the yellow LED indicator light inside the cabin and keeping it constantly lit with a brightness of ≤30 cd / m². 2 To avoid strong light irritating the user's eyes; the buzzer also operates in continuous mode, with the volume controlled at 60-70dB slightly higher than normal conversation, until the level 2 abnormal state is resolved, to ensure the user's safety during the recovery process; the text reminder includes at least the time of the abnormality, the oxygen chamber number, the warning level, the reason for the warning, and the remedial measures; When a level-two anomaly is identified as a pressure leak, and the situation does not improve within 10 seconds, the pressure replenishment device is automatically activated. The initial output power of the pressure replenishment device is set to 50%, and pressure changes are collected every 3 seconds. If the pressure recovery rate is <0.3 kPa / min, the power is automatically increased to 100%. If the pressure recovery rate is >0.8 kPa / min, the power is automatically reduced to 30% to prevent overpressure. This process continues until the level-two anomaly disappears.

[0063] When the level 2 anomaly is an oxygen supply anomaly, and it does not improve within 10 seconds, it will automatically switch to the backup oxygen supply line and shut down the oxygen supply line. If the backup line flow is normal, it will continue to run until the main line is repaired. If the backup line is abnormal, a level 3 warning will be triggered immediately. When the concentration in the chamber stabilizes at ±2% of the target, the level 2 warning will end and a maintenance notification will be sent.

[0064] Level 2 early warning is used to intervene through automated equipment to quickly bring abnormal data back to a safe range, avoiding treatment interruptions that could affect rehabilitation outcomes.

[0065] In response to the Level 3 warning, the warning control module immediately closes the valves to cut off the oxygen supply line and activates the emergency pressure relief valve to release the pressure inside the cabin; simultaneously, it activates the audible and visual alarms inside and outside the cabin, including activating the red strobe lights installed in the four corners inside the cabin and the top of the cabin outside, which flash rapidly at a frequency of 5Hz with a brightness ≥100cd / m². 2 It alerts users and those around them through a comprehensive visual impact; at the same time, the buzzer operates in a high-frequency, rapid mode, keeping the volume at 110dB until manually turned off, ensuring that the user has clearly perceived the danger; it immediately dials the emergency contact number according to the preset priority until it receives manual confirmation.

[0066] Level 3 early warning is designed for scenarios involving sudden malfunctions that directly endanger user safety. It is used to immediately terminate the source of risk and maximize user safety. Through mandatory safety measures, high-intensity early warnings, and emergency communication, it enables instantaneous response to fatal risks, allowing users to quickly escape danger.

[0067] refer to Figure 2 , Figure 2 A method for monitoring user oxygen chamber data for rehabilitation management, provided in this application, includes the following steps: S1. Status Awareness: Real-time collection of oxygen chamber pressure, pressure change rate, oxygen supply pipeline flow, oxygen concentration in the chamber, and equipment operating parameters. S2. Dynamic baseline calculation: Based on historical operating data, a dynamic baseline for key operating parameters is generated through multimodal adaptive learning; S3. Anomaly Analysis: Compare the collected data with the dynamic baseline to determine whether it is within the normal fluctuation range, whether there is pressure leakage or abnormal oxygen supply, and determine the anomaly level. S4. Early warning control: Execute corresponding early warning measures and equipment control commands according to the level of abnormality until the abnormality is resolved.

[0068] It should be noted that, in addition to the specific embodiments described above, those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Although the description of this application is presented in conjunction with preferred embodiments, this does not mean that the features of this application are limited to these embodiments. On the contrary, the purpose of describing the application in conjunction with the embodiments is to cover other options or modifications that may be derived based on the claims of this application. To provide a thorough understanding of this application, many specific details are included in the above description, and this application may also be implemented without using these details. Furthermore, to avoid confusion or obscuring the focus of this application, some specific details will be omitted in the description. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other.

[0069] In the description of this embodiment, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set up," "connected," and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this embodiment based on the specific circumstances.

[0070] Although this application has been illustrated and described with reference to certain preferred embodiments, those skilled in the art should understand that the above description is a further detailed explanation of the application in conjunction with specific embodiments, and should not be construed as limiting the specific implementation of the application to these descriptions. Those skilled in the art can make various changes in form and detail, including some simple deductions or substitutions, without departing from the spirit and scope of this application.

Claims

1. A user oxygen chamber data monitoring system for rehabilitation management, characterized in that, include: The oxygen chamber status sensing module includes a pressure monitoring unit and an oxygen supply equipment monitoring unit; The pressure monitoring unit is used to collect real-time pressure data and pressure change rate inside the oxygen chamber, and the oxygen supply equipment monitoring unit is used to collect oxygen supply pipeline flow rate, oxygen concentration and equipment operating parameters. The dynamic baseline calculation module is used to generate dynamic baselines for key operating parameters based on historical oxygen chamber operation data through multimodal adaptive learning. The anomaly analysis module is used to receive real-time data from the oxygen chamber status sensing module, determine whether the data fluctuation is normal through the dynamic baseline, and if it exceeds the baseline fluctuation range, determine whether there is pressure leakage or oxygen supply abnormality through the preset threshold, and output the anomaly level. The early warning control module is used to execute corresponding early warning strategies and equipment control commands based on the anomaly level output by the anomaly analysis module.

2. The user oxygen chamber data monitoring system for rehabilitation management according to claim 1, characterized in that, The dynamic baseline calculation module includes: a multimodal data clustering and working condition segmentation unit, a time series prediction and expected value generation unit, an adaptive fluctuation threshold calculation unit, and an incremental learning unit; The multimodal data clustering and operating condition division unit is configured to standardize historical operating data and use a Gaussian mixture model clustering algorithm to identify multiple typical operating modes of the oxygen chamber and assign the closest operating mode to real-time operating condition data. The time series prediction and expectation value generation unit is configured to build an independent time series prediction model for each identified operating mode, which is used to infer the dynamic expectation values ​​of key operating parameters. The adaptive fluctuation threshold calculation unit is configured to calculate the adaptive fluctuation upper and lower limits of the dynamic expected value based on the prediction uncertainty of the time series prediction model and the noise level of real-time data, which together constitute the dynamic baseline. The incremental learning unit is configured to use an exponentially weighted moving average mechanism to fine-tune the parameters of the time series prediction model online using new normal operation data, thereby achieving progressive optimization of the dynamic baseline.

3. The user oxygen chamber data monitoring system for rehabilitation management according to claim 2, characterized in that, In the time series prediction and expected value generation unit, the time series prediction model includes an ARIMA model, which has the following form: (1− (1−B) d Pt=(1+ )εt; Where B is the lag operator and d is the difference order. Pt Given the historical parameter values ​​at time t, this model is used to predict the dynamic expected value of the parameters at the next time step. .

4. The user oxygen chamber data monitoring system for rehabilitation management according to claim 2, characterized in that, In the adaptive fluctuation threshold calculation unit, the upper and lower limits of the dynamic baseline are: The upper limit of dynamic baseline is Lt, Lt = - α ⋅ σpred ( k )− β ⋅ σnoise ( t ); The lower limit of the dynamic baseline is Ut, Ut = + α ⋅ σpred ( k )+ β ⋅ σnoise ( t ); in, For dynamic expected value, For the current operating mode The root mean square error of the prediction model. The standard deviation of short-term noise for real-time data. , These are configurable weighting coefficients.

5. The user oxygen chamber data monitoring system for rehabilitation management according to claim 1, characterized in that, The pressure monitoring unit specifically includes: The high-precision pressure sensor deployed inside the oxygen chamber has a data acquisition frequency of 1 time per second; The pressure change calculation subunit calculates the pressure change rate ΔP / Δt based on three consecutive pressure data acquisitions.

6. The user oxygen chamber data monitoring system for rehabilitation management according to claim 1, characterized in that, The oxygen supply equipment monitoring unit specifically includes: An electromagnetic flow meter is installed on the main oxygen supply line, and an oxygen concentration sensor is installed in the chamber; the equipment operation parameter acquisition subunit is used to obtain the working current of the oxygen supply pump, the valve opening and closing status, and the cumulative running time.

7. The user oxygen chamber data monitoring system for rehabilitation management according to claim 1, characterized in that, The preset threshold includes: Preset lower limit of safe pressure: The lower limit of safe pressure for hyperbaric oxygen chamber is 120 kPa, and the lower limit of safe pressure for atmospheric oxygen chamber is 101 kPa; Preset leakage thresholds: The preset leakage threshold for hyperbaric oxygen chambers is 0.5 kPa / min, and the preset leakage threshold for atmospheric oxygen chambers is 0.3 kPa / min. Minimum oxygen supply threshold: Based on the number of users U in the cabin, the minimum oxygen supply threshold for a single cabin is 5L / minute, and the minimum oxygen supply threshold for a multi-person cabin is 5*UL / minute. Target concentration: Based on the oxygen content concentration in the rehabilitation program.

8. A user oxygen chamber data monitoring system for rehabilitation management according to claim 1, characterized in that, The determination of whether there is a pressure leak or abnormal oxygen supply includes: Pressure Leakage Detection: If the real-time pressure value inside the chamber is lower than the preset lower limit of safety pressure, and the rate of pressure change ΔP / Δt is greater than the preset leakage threshold, then it is determined to be a pressure leak. Oxygen supply anomaly judgment: If the oxygen supply pipeline flow rate is less than the minimum oxygen supply threshold and the oxygen concentration in the chamber is less than the target concentration, or if the equipment operating parameters are outside the normal range, then it is judged as an oxygen supply anomaly.

9. A user oxygen chamber data monitoring system for rehabilitation management according to claim 1, characterized in that, The anomaly levels include: Level 1 Anomaly: When any of the following parameters exceeds the dynamic baseline but does not exceed the corresponding preset threshold, it is judged as a Level 1 anomaly. Level 2 anomaly: If the oxygen concentration in the chamber continuously deviates from the target oxygen concentration within ±3%, or if the flow rate of the oxygen supply pipeline continuously falls below 80% of the minimum oxygen supply threshold and the oxygen concentration in the chamber drops by more than 2% but not more than 5% within 10 seconds, or if the working current of the oxygen supply pump exceeds 125% of the rated current and lasts for more than 10 seconds, it is judged as a Level 2 anomaly. Level 3 anomaly: The real-time pressure value inside the chamber is less than 90% of the preset lower limit of safety pressure or greater than 150 kPa, or the absolute value of the pressure change rate is greater than 3 times the preset leakage threshold, or the flow rate of the oxygen supply pipeline suddenly drops to 0 L / min, or the oxygen concentration inside the chamber drops by 5%, below 75% or above 95% within 10 seconds, or the working current of the oxygen supply pump suddenly increases to twice the rated value.

10. A user oxygen chamber data monitoring system for rehabilitation management according to claim 1, characterized in that, The strategies of the early warning control module include: Level 1 Warning: Activate the cabin's audible and visual alarm and simultaneously send a text notification to the administrator's terminal; Level 2 warning: Activate the cabin's audible and visual alarm, simultaneously push a text reminder to the administrator's terminal, and automatically activate the pressurization device or backup oxygen supply line; Level 3 warning: Immediately cut off the oxygen supply line, activate the emergency pressure relief valve, trigger the audible and visual alarm, and call the preset emergency contact number.

11. A method for monitoring user oxygen chamber data for rehabilitation management, characterized in that, Includes the following steps: S1. Status Awareness: Real-time collection of oxygen chamber pressure, pressure change rate, oxygen supply pipeline flow, oxygen concentration in the chamber, and equipment operating parameters. S2. Dynamic baseline calculation: Based on historical operating data, a dynamic baseline for key operating parameters is generated through multimodal adaptive learning; S3. Anomaly Analysis: Compare the collected data with the dynamic baseline to determine whether it is within the normal fluctuation range, whether there is pressure leakage or abnormal oxygen supply, and determine the anomaly level. S4. Early warning control: Execute corresponding early warning measures and equipment control commands according to the level of abnormality until the abnormality is resolved.

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