Micro-hyperbaric oxygen chamber oxygen supply system and micro-hyperbaric oxygen chamber monitoring system

Abstract

The invention particularly relates to a micro-hyperbaric oxygen chamber oxygen supply system and a micro-hyperbaric oxygen chamber monitoring system. The micro-hyperbaric oxygen chamber oxygen supply system comprises a data collection module, an oxygen supply module and a monitoring module, the data analysis module is used for respectively analyzing the state information of the compressor, the blockage state information of the air filter and the leakage state information to obtain an abnormal coefficient, a blockage coefficient and a possible leakage coefficient of the compressor; the comprehensive processing module is used for comprehensively processing the compressor abnormal coefficient, the blockage coefficient and the leakage possibility coefficient to obtain a monitoring evaluation coefficient; and a judgment module. According to the method, multi-physical field data fusion and spatial feature analysis are carried out, the system achieves the crossing from single-point threshold value alarm to whole-system health assessment, the current fault can be accurately positioned, potential risks such as the compressor bearing abrasion rate and the filter accumulated dust increasing trend can be predicted through the historical data trend, and the fault diagnosis accuracy is improved. And advanced intervention of preventive maintenance becomes possible.

Description

Technical Field

[0001] The present invention relates to the technical field of medical equipment monitoring, and in particular to a micro-hyperbaric oxygen chamber oxygen supply system and a micro-hyperbaric oxygen chamber monitoring system. Background Art

[0002] As a medical device that promotes human tissue repair by precisely regulating the oxygen concentration and pressure in the chamber, the micro-hyperbaric oxygen chamber has been widely used in clinical treatment (such as carbon monoxide poisoning and wound healing), rehabilitation health care, and sports medicine.

[0003] Its core air conditioning system covers key links such as compressor air supply, filter impurity filtration, pipeline pressure control and sealing. The performance degradation of any component may cause systemic risks.

[0004] However, failures in the air conditioning system (such as compressors, filters, and pipe seals) can lead to treatment interruptions, safety risks, or decreased efficacy. Existing monitoring technologies, which are mostly based on single parameters (such as pressure and temperature), lack the ability to comprehensively assess complex failure modes (such as wear-corrosion coupling and coordinated failure of multiple components), making it difficult to achieve early warning and precise location of failures.

[0005] Therefore, a micro-hyperbaric oxygen chamber oxygen supply system and a micro-hyperbaric oxygen chamber monitoring system are needed to address the above-mentioned problems. Summary of the Invention

[0006] The purpose of the present invention is to solve the above problems and to propose a micro-hyperbaric oxygen chamber oxygen supply system and a micro-hyperbaric oxygen chamber monitoring system.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions: A micro-hyperbaric oxygen chamber oxygen supply system, comprising: Oxygen source module: provides the oxygen source required by the oxygen chamber, ensuring oxygen purity and supply; Pressure regulating module: controls the pressure in the oxygen chamber within a safe range Flow control module: adjusts the oxygen input flow to meet the needs of different treatment or usage scenarios; Pipes and interface modules: connect components and transport gases.

[0008] A micro-hyperbaric oxygen chamber monitoring system includes the following parts: Data collection module: obtain relevant data information of air conditioning; Data analysis module: analyzes the compressor status information, air filter blockage status information and leakage status information respectively to obtain the compressor abnormality coefficient, blockage coefficient and leakage possibility coefficient; Comprehensive processing module: The monitoring and evaluation coefficient is obtained by comprehensively processing the compressor abnormality coefficient, blockage coefficient and leakage possibility coefficient; Judgment module: obtains the corresponding air conditioning fault status level based on the monitoring evaluation coefficient.

[0009] Preferably, the air conditioning related data information includes compressor status information, air filter blockage status information and leakage status information.

[0010] Preferably, the process of obtaining the compressor abnormality coefficient includes the following parts: Obtain the status information of the compressor in the startup state; including the current, voltage, temperature, operating speed and torque of the compressor motor; Set a standard parameter value of any parameter in the state information of the compressor in the startup state, calculate the difference between the parameter value in the startup state and the standard parameter value corresponding to the parameter, and obtain the standard deviation value corresponding to the parameter; Set the allowable floating range of the standard deviation value of the parameter. If the standard deviation value is not within its corresponding allowable floating range, the standard deviation value will be marked as deviating from the standard deviation value; Mark the time zone between the start time of the compressor and the current time as the operating time zone, obtain the time points corresponding to the maximum deviation standard deviation value and the minimum deviation standard deviation value in the operating time zone, and calculate the difference between the two time points to obtain the duration between the two time points, and mark it as a span of different time lengths; Perform standard deviation calculation on the standard deviation value in the operating time zone to obtain the standard deviation wave value; Record the generation time of any deviation from the standard deviation value in the operating time zone, calculate the time difference between the adjacent generation times, and obtain the adjacent time difference value; calculate the standard deviation of the adjacent time difference values ​​in the operating time zone to obtain the deviation time fluctuation value; Perform weighted calculation on the spanning time length, standard deviation wave value, and deviation time fluctuation value to obtain the abnormal single coefficient corresponding to the parameter; After assigning corresponding weight factors to various parameters of the compressor, the abnormal coefficient corresponding to each parameter is multiplied by its corresponding weight factor, and the sum is calculated to obtain the abnormal coefficient of the compressor.

[0011] Preferably, the process of obtaining the blocking coefficient includes the following parts: Obtaining the pressure value at each location in the pipeline on the air inlet side of the filter at a preset time interval, wherein the pressure value at each location in the pipeline is obtained by a pressure sensor pre-installed in the pipeline; A pressure threshold is preset, and the pressure value at each position in the pipeline is compared with the pressure threshold. A pressure value greater than the pressure threshold is recorded as an abnormal pressure value, and the difference between each abnormal pressure value and the pressure threshold is calculated to obtain a pressure deviation value; Count the number of pressure deviation values ​​and divide it by the number of all pressure values ​​to get the degree of deviation; Arrange the pressure deviation values ​​in descending order according to their numerical values, extract the four largest pressure deviation values, and use the pressure sensor positions corresponding to the four largest pressure deviation values ​​as endpoints. Connect the four endpoints with straight lines to form a tetrahedron, and calculate the volume of the tetrahedron as the quantized value. The congestion coefficient is obtained by weighting the deviation value and the quantization value.

[0012] Preferably, the process of obtaining the leakage possibility coefficient includes the following parts: Obtain flange image information at the pipeline connection, perform feature extraction on each flange sub-region, extract features related to wear and corrosion, and mark the extracted features as damaged areas and corroded areas respectively; Calculate the number of pixels in the damaged and corroded areas of each flange sub-region, and convert the number of pixels in the damaged and corroded areas of each flange sub-region into actual areas based on the image resolution to obtain the damaged and corroded areas of each flange sub-region; Obtain the overlapping parts of the damaged area and the corroded area of ​​each flange sub-area in turn, as well as the area of ​​the overlapping parts, which are recorded as the overlapping area; accumulate the overlapping areas of each flange sub-area to obtain the total overlapping area value; Obtain the contours of the damaged area and the corroded area in each flange area, and arrange marking points along the contours of the damaged area and the corroded area in sequence; Connect any two marked points on the damaged area contour with a straight line, record the straight line as the damaged line, sort the obtained damaged lines in descending order according to their numerical values, and extract the largest damaged line; obtain the largest damaged line in each flange sub-area in turn; The maximum damage line in each flange area is accumulated to obtain the damage span value; Following the above process of analyzing the damaged area to obtain the damage span value, the corrosion area in each flange sub-area is analyzed to obtain the corrosion span value; The corrosion value is obtained by multiplying the damage span value and the corrosion span value; The leakage possibility coefficient is obtained by comprehensively analyzing the total overlapping face value and the corruption value.

[0013] Preferably, the monitoring and evaluation coefficient is obtained by comprehensively processing the compressor abnormality coefficient, the blockage coefficient and the leakage possibility coefficient, which specifically includes: After normalizing the compressor abnormality coefficient, blockage coefficient and leakage possibility coefficient, the compressor abnormality coefficient and blockage coefficient are used as the two right-angled sides of a right triangle respectively, and the remaining side is connected to form a complete right triangle. The blockage coefficient is used as the height of the right triangle to construct a triangular pyramid model, and the volume of the triangular pyramid model is calculated and recorded as the monitoring evaluation coefficient.

[0014] Preferably, the value ranges of the three preset thresholds, the value range of each threshold corresponds to an air conditioning fault state level, and the monitoring evaluation coefficient is matched with the value ranges of the three thresholds to obtain the air conditioning fault state level corresponding to the monitoring evaluation coefficient, including slight abnormality, moderate fault, and severe fault.

[0015] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. Through multi-physics field data fusion and spatial feature analysis, the present invention has achieved a leap from "single-point threshold alarm" to "full system health assessment". It can not only accurately locate the current fault, but also predict potential risks based on historical data trends, such as the wear rate of compressor bearings and the growth trend of filter dust accumulation, making preventive maintenance possible in advance.

[0016] 2. This invention fills the gap in traditional technologies in complex fault assessment and graded treatment through precise diagnosis based on multi-dimensional data fusion and intelligent response driven by geometric models, and provides full-chain technical support of "monitoring-analysis-decision-making-treatment" for the safe operation of micro-hyperbaric oxygen chambers. It has significant clinical application value and industry promotion significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Further details, features and advantages of the present application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which: Figure 1 is a flow chart of the present invention; DETAILED DESCRIPTION

[0018] Several embodiments of the present application will be described in more detail below with reference to the accompanying drawings so that those skilled in the art can implement the present application. The present application can be embodied in many different forms and for many different purposes and should not be limited to the embodiments described herein. These embodiments are provided to make the present application comprehensive and complete and to fully convey the scope of the present application to those skilled in the art. The embodiments do not limit the present application.

[0019] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the relevant art and / or the context of this specification, and will not be interpreted in an idealized or overly formal sense unless expressly defined as such herein.

[0020] See also Figure 1 As shown, the present invention provides a technical solution: A micro-hyperbaric oxygen chamber oxygen supply system, comprising: Oxygen source module: provides the oxygen source required by the oxygen chamber, ensuring oxygen purity and supply; include: Oxygen cylinder set / oxygen concentrator: Oxygen cylinder set: stores high-pressure liquid or gaseous oxygen and outputs oxygen at a stable pressure through a pressure reducing device.

[0021] Oxygen concentrator (such as PSA pressure swing adsorption oxygen concentrator): extracts oxygen from the air through physical adsorption technology and is suitable for scenarios requiring continuous oxygen supply; Oxygen purity detection device: real-time monitoring of oxygen purity (usually required to be ≥90%) to ensure compliance with medical or usage standards; Backup gas source switching system: When the main gas source fails, it automatically switches to the backup gas source (such as another set of oxygen cylinders or emergency oxygen generator) to ensure the continuity of oxygen supply; Pressure regulation module: controls the pressure in the oxygen chamber within a safe range (micro-high pressure usually refers to 0.1~0.3MPa gauge pressure) and achieves stable pressure regulation; include: Decompression device: reduces the high-pressure oxygen in the oxygen cylinder group to a pressure suitable for use in the oxygen chamber (e.g. 0.5~1MPa); Pressure stabilizing valve / proportional valve: Dynamically adjusts the air intake volume through a closed-loop control system to maintain constant cabin pressure and avoid excessive pressure fluctuations; Safety valve: automatically releases pressure when the cabin pressure exceeds the set threshold to prevent overpressure danger; Flow control module: adjusts the oxygen input flow to meet the needs of different treatment or usage scenarios; include: Flow meter: displays oxygen flow in real time (unit: L / min). Common types include rotor flowmeter and electromagnetic flowmeter. Flow control valve: manual or electric control valve, accurately controls the intake flow according to demand, and supports continuous adjustment; Pipeline and interface module: connect components and transport gas; include: Pressure-resistant pipeline: stainless steel or food-grade plastic pipe, resistant to slightly high-pressure environment (pressure ≥ 0.6MPa); Quick-connect connector / valve: convenient for equipment connection and maintenance, including air inlet valve, exhaust valve, emergency vent valve, etc. Filter: removes impurities (such as dust and oil mist) in the gas to ensure gas cleanliness; A micro-hyperbaric oxygen chamber monitoring system includes the following parts: Data collection module: obtains relevant data information of air conditioning; the relevant data information of air conditioning includes compressor status information, air filter blockage status information and leakage status information; Data analysis module: analyzes the compressor status information, air filter blockage status information and leakage status information respectively to obtain the compressor abnormality coefficient, blockage coefficient and leakage possibility coefficient; The process of obtaining the compressor abnormality coefficient includes the following parts: Obtain the status information of the compressor in the startup state; including the current, voltage, temperature, operating speed and torque of the compressor motor; Set a standard parameter value of any parameter in the state information of the compressor in the startup state, calculate the difference between the parameter value in the startup state and the standard parameter value corresponding to the parameter, and obtain the standard deviation value corresponding to the parameter; Set the allowable floating range of the standard deviation value of the parameter. If the standard deviation value is not within its corresponding allowable floating range, the standard deviation value will be marked as deviating from the standard deviation value; Mark the time zone between the start time of the compressor and the current time as the operating time zone, obtain the time points corresponding to the maximum deviation standard deviation value and the minimum deviation standard deviation value in the operating time zone, and calculate the difference between the two time points to obtain the duration between the two time points, and mark it as a span of different time lengths; Perform standard deviation calculation on the standard deviation value in the operating time zone to obtain the standard deviation wave value; Record the generation time of any deviation from the standard deviation value in the operating time zone, calculate the time difference between the adjacent generation times, and obtain the adjacent time difference value; calculate the standard deviation of the adjacent time difference values ​​in the operating time zone to obtain the deviation time fluctuation value; Perform weighted calculation on the spanning time length, standard deviation wave value, and deviation time fluctuation value to obtain the abnormal single coefficient corresponding to the parameter; Mark the span of different time length, standard deviation wave value, and time deviation wave value as 、 、 The subsequent entry formula: ; Get the abnormal single coefficient corresponding to the parameter; in 、 、 are the maximum allowable spanning time difference length, reference standard deviation wave value, and reference time deviation fluctuation value respectively; a1, a2, and a3 are the weight factors corresponding to the spanning time difference length, standard deviation wave value, and time deviation fluctuation value respectively; After assigning corresponding weight factors to the various parameters of the compressor, the abnormal coefficients corresponding to the various parameters and their corresponding weight factors are multiplied and the sum is calculated to obtain the abnormal coefficient of the compressor; The process of obtaining the blocking coefficient includes the following parts: Obtaining the pressure value at each location in the pipeline on the air inlet side of the filter at a preset time interval, wherein the pressure value at each location in the pipeline is obtained by a pressure sensor pre-installed in the pipeline; Sensor deployment: Install pressure sensors evenly (recommended ≥6 to form a spatial lattice) at locations prone to airflow disturbances, such as the inlet, middle, elbow, and near the filter interface of the filter intake side duct. A pressure threshold is preset, and the pressure value at each position in the pipeline is compared with the pressure threshold. A pressure value greater than the pressure threshold is recorded as an abnormal pressure value, and the difference between each abnormal pressure value and the pressure threshold is calculated to obtain a pressure deviation value; The pressure threshold is set based on the historical data of the filter during normal operation and the pressure average is calculated. and standard deviation ,Pick As a threshold value (k is a safety factor, usually 1.5-2, corresponding to a confidence interval of approximately 93%-98%); or directly set a fixed threshold value based on the filter design parameters (e.g., if the absolute pressure corresponding to normal suction vacuum is 95kPa, the threshold value is set to 90kPa); Count the number of pressure deviation values ​​and divide it by the number of all pressure values ​​to get the degree of deviation; Arrange the pressure deviation values ​​in descending order according to their numerical values, extract the four largest pressure deviation values, and use the pressure sensor positions corresponding to the four largest pressure deviation values ​​as endpoints. Connect the four endpoints with straight lines to form a tetrahedron, and calculate the volume of the tetrahedron as the quantized value. The four largest pressure deviation values ​​are recorded as 、 、 、 ; Calculate the volume of a tetrahedron using the vector mixed product formula:

[0022] The volume of the tetrahedron reflects the degree of spatial discreteness of the blockage area. If the four endpoints are concentrated on one side of the pipe, the volume is small, which may correspond to a local blockage of the filter element. If the endpoints are dispersed and the volume is large, it may correspond to a uniform blockage of the filter element or multiple obstructions in the pipe. The congestion coefficient is obtained by weighting the deviation value and the quantization value; Preset weight factors for the deviation value and the quantized value, multiply the deviation value and the quantized value by their corresponding weight factors, and sum them to obtain the congestion coefficient; The process of obtaining the leakage possibility coefficient includes the following parts: Obtain flange image information at the pipeline connection, perform feature extraction on each flange sub-region, extract features related to wear and corrosion, and mark the extracted features as damaged areas and corroded areas respectively; Flange image acquisition: Sensor configuration: Deploy an industrial camera (recommended resolution ≥ 2000 × 1500 pixels, frame rate ≥ 30fps), paired with an LED ring light source (to eliminate shadows) and an optical filter (to enhance metal surface features); Imaging requirements: Shoot perpendicular to the flange plane to ensure that the field of view covers the entire flange (field of view FOV ≥ 120°). After calibration, the pixel accuracy reaches the millimeter level (e.g. 1 pixel = 0.1mm). Sub-area division strategy: Based on prior knowledge of flange structure, functional areas such as bolt holes and sealing surfaces are automatically identified and divided into targeted areas; Calculate the number of pixels in the damaged and corroded areas of each flange sub-region, and convert the number of pixels in the damaged and corroded areas of each flange sub-region into actual areas based on the image resolution to obtain the damaged and corroded areas of each flange sub-region; Feature extraction algorithm: Wear characteristics: Surface roughness changes are identified through local texture analysis (LBP operator), and the wear area is extracted by combining threshold segmentation (Otsu algorithm); Corrosion features: Segment the rusted area based on the HSV color space (Saturation ≥ 0.4, Value ≥ 0.3) combined with morphological operations (opening operation to remove small noise points); Obtain the overlapping parts of the damaged area and the corroded area of ​​each flange sub-area in turn, as well as the area of ​​the overlapping parts, which are recorded as the overlapping area; accumulate the overlapping areas of each flange sub-area to obtain the total overlapping area value; Obtain the contours of the damaged area and the corroded area in each flange area, and arrange marking points along the contours of the damaged area and the corroded area in sequence; Connect any two marked points on the damaged area contour with a straight line, record the straight line as the damaged line, sort the obtained damaged lines in descending order according to their numerical values, and extract the largest damaged line; obtain the largest damaged line in each flange sub-area in turn; The maximum damage line in each flange area is accumulated to obtain the damage span value; Following the above process of analyzing the damaged area to obtain the damage span value, the corrosion area in each flange sub-area is analyzed to obtain the corrosion span value; The corrosion value is obtained by multiplying the damage span value and the corrosion span value; Total overlap value: reflects the size of the area where wear and corrosion act together. The larger the value, the more serious the deterioration of sealing performance. Corrosion value: the product of the damage span value and the corrosion span value, which represents the degree of damage to the structural integrity (geometric amplification effect); The leakage possibility coefficient is obtained by comprehensively analyzing the total overlapping face value and the corruption value; Preset the weight factors of the total overlapping face value and the corruption value, multiply the total overlapping face value and the corruption value with their corresponding weight factors, and sum them to obtain the leakage possibility coefficient; Comprehensive processing module: The monitoring and evaluation coefficient is obtained by comprehensively processing the compressor abnormality coefficient, blockage coefficient and leakage possibility coefficient; Specifically include: After normalizing the compressor abnormality coefficient, blockage coefficient, and leakage possibility coefficient, the compressor abnormality coefficient and blockage coefficient are used as the two right-angled sides of a right triangle, and the remaining side is connected to form a complete right triangle. The blockage coefficient is used as the height of the right triangle to construct a triangular pyramid model. The volume of the triangular pyramid model is calculated and recorded as the monitoring evaluation coefficient. Judgment module: obtains the corresponding air conditioning fault status level based on the monitoring evaluation coefficient and performs corresponding processing; Three groups of threshold value ranges are preset. Each threshold value range corresponds to an air conditioning fault status level. The monitoring evaluation coefficient is matched with the three threshold value ranges to obtain the air conditioning fault status level corresponding to the monitoring evaluation coefficient, including slight abnormality, moderate fault, and severe fault. When the fault status level is slightly abnormal: strengthen monitoring, record abnormal data trends, arrange regular maintenance, and prevent fault escalation; When the fault status is moderate: immediately initiate troubleshooting to locate the source of the abnormality (such as pipeline leakage, decreased compressor efficiency), and prioritize repairing or replacing damaged components to prevent the fault from spreading. When the fault status level is serious fault: emergency shutdown, power off, prohibition of further operation, professional team comprehensive inspection, replacement of seriously damaged parts, re-debug the system.

[0023] The above formulas are obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The influencing weight factors and specific coefficient values ​​in the formula are set by technical personnel in this field according to actual conditions, and can be adjusted and modified later.

[0024] The above description of the embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A micro-hyperbaric oxygen chamber oxygen supply system, characterized in that: include: Oxygen source module: provides the oxygen source required by the oxygen chamber, ensuring oxygen purity and supply; Pressure regulating module: controls the pressure in the oxygen chamber within a safe range Flow control module: adjusts the oxygen input flow to meet the needs of different treatment or usage scenarios; Pipes and interface modules: connect components and transport gases.

2. A micro-hyperbaric oxygen chamber monitoring system, according to the micro-hyperbaric oxygen chamber oxygen supply system of claim 1, characterized in that: Includes the following sections: Data collection module: obtain relevant data information of air conditioning; Data analysis module: analyzes the compressor status information, air filter blockage status information and leakage status information respectively to obtain the compressor abnormality coefficient, blockage coefficient and leakage possibility coefficient; Comprehensive processing module: The monitoring and evaluation coefficient is obtained by comprehensively processing the compressor abnormality coefficient, blockage coefficient and leakage possibility coefficient; Judgment module: obtains the corresponding air conditioning fault status level based on the monitoring evaluation coefficient.

3. A micro-hyperbaric oxygen chamber monitoring system according to claim 2, characterized in that: The relevant data information of air conditioning includes compressor status information, air filter blockage status information and leakage status information.

4. A micro-hyperbaric oxygen chamber monitoring system according to claim 3, characterized in that: The process of obtaining the compressor abnormality coefficient includes the following parts: Get the status information of the compressor in the startup state; Including the current, voltage, temperature, operating speed and torque of the compressor motor; Set a standard parameter value of any parameter in the state information of the compressor in the startup state, calculate the difference between the parameter value in the startup state and the standard parameter value corresponding to the parameter, and obtain the standard deviation value corresponding to the parameter; Set the allowable floating range of the standard deviation value of the parameter. If the standard deviation value is not within its corresponding allowable floating range, the standard deviation value will be marked as deviating from the standard deviation value; Mark the time zone between the start time of the compressor and the current time as the operating time zone, obtain the time points corresponding to the maximum deviation standard deviation value and the minimum deviation standard deviation value in the operating time zone, and calculate the difference between the two time points to obtain the duration between the two time points, and mark it as a span of different time lengths; Perform standard deviation calculation on the standard deviation value in the operating time zone to obtain the standard deviation wave value; Record any generation time that deviates from the standard deviation value within the operating time zone, calculate the time difference between its adjacent generation times, and obtain the adjacent time difference value; The standard deviation of the adjacent time difference values ​​in the operating time zone is calculated to obtain the bias time fluctuation value; Perform weighted calculation on the spanning time length, standard deviation wave value, and deviation time fluctuation value to obtain the abnormal single coefficient corresponding to the parameter; After assigning corresponding weight factors to various parameters of the compressor, the abnormal coefficient corresponding to each parameter is multiplied by its corresponding weight factor, and the sum is calculated to obtain the abnormal coefficient of the compressor.

5. A micro-hyperbaric oxygen chamber monitoring system according to claim 4, characterized in that: The process of obtaining the blocking coefficient includes the following parts: Obtaining the pressure value at each location in the pipeline on the air inlet side of the filter at a preset time interval, wherein the pressure value at each location in the pipeline is obtained by a pressure sensor pre-installed in the pipeline; A pressure threshold is preset, and the pressure value at each position in the pipeline is compared with the pressure threshold. A pressure value greater than the pressure threshold is recorded as an abnormal pressure value, and the difference between each abnormal pressure value and the pressure threshold is calculated to obtain a pressure deviation value; Count the number of pressure deviation values ​​and divide it by the number of all pressure values ​​to get the degree of deviation; Arrange the pressure deviation values ​​in descending order according to their numerical values, extract the four largest pressure deviation values, and use the pressure sensor positions corresponding to the four largest pressure deviation values ​​as endpoints. Connect the four endpoints with straight lines to form a tetrahedron, and calculate the volume of the tetrahedron as the quantized value. The congestion coefficient is obtained by weighting the deviation value and the quantization value.

6. A micro-hyperbaric oxygen chamber monitoring system according to claim 5, characterized in that: The process of obtaining the leakage possibility coefficient includes the following parts: Obtain flange image information at the pipeline connection, perform feature extraction on each flange sub-region, extract features related to wear and corrosion, and mark the extracted features as damaged areas and corroded areas respectively; Calculate the number of pixels in the damaged and corroded areas of each flange sub-region, and convert the number of pixels in the damaged and corroded areas of each flange sub-region into actual areas based on the image resolution to obtain the damaged and corroded areas of each flange sub-region; Obtain the overlapping parts of the damaged area and the corroded area of ​​each flange sub-area in turn, as well as the area of ​​the overlapping parts, which are recorded as the overlapping area; accumulate the overlapping areas of each flange sub-area to obtain the total overlapping area value; Obtain the contours of the damaged area and the corroded area in each flange area, and arrange marking points along the contours of the damaged area and the corroded area in sequence; Connect any two marked points on the outline of the damaged area with a straight line, record the straight line as the damaged line, sort the obtained damaged lines in descending order according to their numerical values, and extract the largest damaged line; obtain the largest damaged line in each flange sub-area in turn; The maximum damage line in each flange area is accumulated to obtain the damage span value; Following the above process of analyzing the damaged area to obtain the damage span value, the corrosion area in each flange sub-area is analyzed to obtain the corrosion span value; The corrosion value is obtained by multiplying the damage span value and the corrosion span value; The leakage possibility coefficient is obtained by comprehensively analyzing the total overlapping face value and the corruption value.

7. A micro-hyperbaric oxygen chamber monitoring system according to claim 5, characterized in that: The monitoring and evaluation coefficients are obtained by comprehensively processing the compressor abnormality coefficient, blockage coefficient and leakage possibility coefficient, including: After normalizing the compressor abnormality coefficient, blockage coefficient and leakage possibility coefficient, the compressor abnormality coefficient and blockage coefficient are used as the two right-angled sides of a right triangle respectively, and the remaining side is connected to form a complete right triangle. The blockage coefficient is used as the height of the right triangle to construct a triangular pyramid model, and the volume of the triangular pyramid model is calculated and recorded as the monitoring evaluation coefficient.

8. A micro-hyperbaric oxygen chamber monitoring system according to claim 7, characterized in that: Three groups of threshold value ranges are preset, and the value range of each group of threshold values ​​corresponds to an air conditioning fault status level. The monitoring evaluation coefficient is matched with the value range of the three groups of threshold values ​​to obtain the air conditioning fault status level corresponding to the monitoring evaluation coefficient, including slight abnormality, moderate fault, and serious fault.

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