Portable breather valve online detection system and method

Abstract

The invention discloses a portable breather valve online detection system and method. The system comprises a signal acquisition module, a valve response monitoring module, a feature point identification module, a performance parameter evaluation module and a detection state output module. Signal quality is improved through continuous segment stability detection, a synchronous sudden change area is identified in dynamic data, a sudden change characteristic sampling point group is extracted, a traffic ratio threshold judgment mechanism is combined to realize accurate identification of an emergency state, and a rate change extreme point is compared with a fluctuation difference, a trigger node is calibrated, and a characteristic pairing value is extracted. A plurality of groups of flow pressure pairing sequences are formed in a test round and are compared with a standard interval to generate a compliance result, whether the inspiration and expiration state reaches the standard or not is automatically judged, and detection period label information and response state records with traceability are formed by performing unified archiving and identification processing on each detection period state. The recognition efficiency of abnormal opening and closing of the breather valve, the response speed of state feedback and the recording integrity of a detection result are improved.

Description

Technical Field

[0001] This invention belongs to the field of real-time detection technology, and in particular relates to a portable online detection system and method for breathing valves. Background Technology

[0002] The field of real-time monitoring technology mainly involves technologies for the continuous acquisition and analysis of various physical, physiological, or environmental parameters during operation or use. It encompasses core aspects such as sensor technology, signal acquisition methods, data conversion devices, and status recognition mechanisms, and is widely applied in various fields including medical monitoring, industrial control, and safety protection. Among these, a portable breathing valve online monitoring system is a specialized device used to monitor the operating status of breathing valves in breathing apparatus during use. It targets the real-time identification of potential malfunctions such as improper opening and closing, leakage, or blockage during wear. Typically, it uses pressure sensing elements to continuously measure the pressure difference across the breathing valve, combined with threshold-based triggering logic to determine the valve's operating status, and provides the user with simple visual or auditory signals to indicate the current device status.

[0003] Existing portable breathing valves primarily rely on upstream and downstream pressure difference measurement and threshold logic judgment during testing. They lack mechanisms for detecting signal stability and aligning over time, making them prone to misjudgments in scenarios with high data volatility or rapid changes in breathing rate. Furthermore, they cannot accurately capture rate abrupt changes in opening and closing behavior, leading to offsets or omissions in trigger point determination. The lack of systematic archiving capabilities for test data hinders unified management and traceability of multi-cycle test results. When continuous performance monitoring or fault behavior backtracking is required, a stable, comparable, and continuous analytical foundation is difficult to provide, impacting the response accuracy and recording reliability of online testing systems. Summary of the Invention

[0004] The problem to be solved by the present invention is to provide a portable online detection system and method for breathing valves. The system introduces a time synchronization channel construction mechanism for pressure and flow rate to ensure that there is a unified time reference between different physical quantities. It has the ability to automatically determine whether the breathing state meets the standard, thereby improving the efficiency of identifying abnormal opening and closing of breathing valves, the response speed of status feedback, and the completeness of recording detection results.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a portable online detection system for breathing valves, comprising, The signal acquisition module is used to acquire data from the pressure sensor end and the flow meter end of the breathing valve when connected, detect the time series stability of each set of signals, and construct a time-synchronized channel matrix including pressure and flow rate. The valve response monitoring module, based on the time synchronization channel matrix, identifies the synchronous abrupt change interval between pressure and flow velocity in the continuous growth segment, compares the change trend difference of the data groups before and after in turn, extracts the sampling point group whose change rate exceeds the critical flow ratio threshold, records the timestamp and change type identifier, and generates the flow pressure abrupt change parameter group. The feature point recognition module, based on the flow pressure mutation parameter group, filters the time nodes where extreme points occur in the changes of flow rate and pressure rate, calculates the rate fluctuation difference before and after the mutation interval, calibrates the exhalation trigger point and inhalation trigger point, extracts the flow rate and pressure pairing values ​​at the corresponding positions, and generates a set of opening and closing behavior feature pairs. The performance parameter evaluation module extracts the flow pressure pairing value sequence under different test rounds based on the opening and closing behavior feature set, compares it item by item with the breathing valve pressure standard range, determines whether the corresponding values ​​of exhalation pressure and inhalation pressure are all within the allowable range, and generates an opening and closing response compliance status list.

[0006] Furthermore, the time synchronization channel matrix includes a pressure signal channel, a flow rate signal channel, and a timestamp calibration column; the flow pressure mutation parameter group includes a mutation sampling point group, a change type identifier, and a synchronization timestamp; the opening and closing behavior feature pair set includes exhalation trigger point pairing value, inhalation trigger point pairing value, number information, and synchronization identifier; the opening and closing response compliance status list includes exhalation pressure compliance status, inhalation pressure compliance status, and detection cycle number.

[0007] Furthermore, the signal acquisition module includes: The signal acquisition submodule is used to acquire the pressure sensor signal data and the flow meter signal data, detect the time series stability of each group of pressure signal and flow signal, filter data segments whose fluctuation amplitude is less than the critical threshold of flow velocity change and the critical threshold of pressure fluctuation in a continuous time period, and generate effective continuous signal segment interval data. The time merging submodule compares the timestamp data of pressure signal and flow signal within the same time period based on the effective continuous signal segment interval data, filters signal pairs whose timestamp difference is less than the maximum time alignment error threshold, reads the signal reading value and time interval corresponding to each timestamp in the filtered signal pair, arranges the signal reading values ​​in a set according to the timestamp order and calculates the average value to generate a merged synchronization signal reading value sequence. The channel construction submodule reads the timestamp, pressure signal value, and flow rate signal value from the merged synchronization signal value sequence, and performs a traversal and filtering based on the time condition that the signal sampling interval is less than the maximum threshold of the sampling interval. The three data elements that meet the conditions are combined and filled into the corresponding positions of the two-dimensional matrix in order to obtain the pressure and flow rate time synchronization channel matrix.

[0008] Furthermore, the valve response monitoring module includes: The trend recognition submodule is used to obtain the pressure sequence and flow velocity sequence in the time synchronization channel matrix, extract the pressure difference and flow velocity difference between any two adjacent sampling points in time order, compare the adjacent differences to determine whether their directions are consistent, record the continuous growth segment, and generate pressure and flow velocity growth interval segment data. The flow ratio filtering submodule extracts all flow velocity values ​​within the continuous growth segment based on the pressure flow velocity growth interval segment data and calculates the average value. It then calculates the ratio by combining the rated flow value and determines whether the abrupt critical flow ratio threshold condition is met. If the condition is met, it is identified as an abrupt segment and the start and end timestamps and ratio results within the corresponding segment are extracted to generate a flow velocity abrupt identification index. The mutation calibration submodule calls the last sampling point of the identified mutation segment in the flow velocity mutation identification index, extracts the pressure difference and flow velocity difference between the segment before and after the last sampling point, and determines whether the directional product is negative. If it is negative, it is determined to be an abnormal mutation; otherwise, it is a normal change. The determination type, timestamp, and change value are integrated to establish a flow pressure mutation parameter group.

[0009] Furthermore, the feature point recognition module includes: The extreme value localization submodule, based on the flow pressure mutation parameter group, extracts the flow velocity sequence and pressure sequence of continuous sampling points within the mutation interval, detects the sampling points with the maximum and minimum change rates in each sequence, records the corresponding timestamps as maximum and minimum value points, and determines whether the order between the two points meets the definition conditions of inhalation and exhalation characteristics, filters the mutation segment sampling points that meet the inhalation and exhalation switching characteristics, and generates a rate extreme value time point sequence. The fluctuation calculation submodule extracts all velocity and pressure value sequences within the time interval between two adjacent extreme points in the velocity extreme time point sequence, calculates the difference between the maximum and minimum values ​​of the velocity sequence and the difference between the maximum and minimum values ​​of the pressure sequence, and represents the two types of differences as velocity fluctuation and pressure fluctuation, respectively. The fluctuation value pairs are arranged in the order of time intervals to obtain a velocity-pressure fluctuation pair set. The behavior calibration submodule extracts the flow velocity and pressure values ​​at the corresponding positions based on the timestamps corresponding to each pair of fluctuations in the velocity-pressure fluctuation set and the original channel data. It then sets the number and synchronization identifier number for each pair of flow-pressure values, constructs a unified structure data entry, and establishes a set of opening and closing behavior features.

[0010] Furthermore, the performance parameter evaluation module includes: The round extraction submodule is used to obtain all sampling records in the set of opening and closing behavior feature pairs, divide each feature pair according to the test batch number, extract all flow rate and pressure pair values ​​in each round, arrange each group of data in chronological order, and summarize and number all records in each round to generate a test round pair value sequence. The pressure comparison submodule identifies the corresponding pressure value in the exhalation state and compares it with the upper and lower limits of the exhalation pressure range based on each pair of flow pressure values ​​in the test round pairing value sequence. It also identifies the corresponding pressure value in the inhalation state and compares it with the upper and lower limits of the inhalation pressure range. It records the sampling point numbers that do not meet the range conditions and obtains the pressure range deviation number set. The compliance determination submodule, based on the correspondence between the abnormal sampling point numbers identified by the pressure range deviation number set and the test number of the test round paired value sequence, marks the test rounds with pressure values ​​outside the range as non-compliant, and the rest as compliant. It also summarizes the test number of each round and the corresponding compliance status identifier into a two-dimensional status array to establish a list of opening and closing response compliance statuses.

[0011] Furthermore, it also includes a detection status output module, which determines whether the exhalation pressure test of the breathing valve is compliant based on the opening and closing response compliance status list. If the status is not compliant, it controls the pressure relief valve to execute an emergency pressure relief command, determines whether the inhalation pressure test is compliant, and executes a secondary pressure relief action if the status is not compliant. The information is summarized into the status tag information of the current detection cycle, and a portable breathing valve online detection record is generated.

[0012] Furthermore, the portable breathing valve online detection record includes a detection cycle status label, a safety response status record, and a pressure relief execution identifier.

[0013] Furthermore, the detection status output module includes: The pressure determination submodule, based on the opening and closing response compliance status list, filters all exhaled pressure status labels and inhaled pressure status labels respectively, and sequentially determines whether the exhaled pressure status is compliant in each round, and determines whether the inhaled pressure status is compliant. It summarizes the two pressure relief requirement identifiers in each round and adds corresponding numbers to generate a round pressure relief determination value group. The instruction execution submodule sets instruction trigger operations for each round number in sequence according to the pressure relief demand identifier of each round in the round pressure relief judgment value group. If the exhalation pressure relief identifier does not meet the rules, an emergency pressure relief instruction is triggered. If the inhalation pressure relief identifier does not meet the rules, a secondary pressure relief instruction execution process is added. All trigger records are encoded to establish an instruction response structure and obtain the action response trigger code set. The record generation submodule, based on the trigger results of each round in the action response trigger code set, combined with the original detection number, round number, and exhalation and inhalation status label information, organizes and outputs structured result units, uniformly generates standard format labels, and establishes a portable breathing valve online detection record.

[0014] Furthermore, the present invention also provides a portable online detection method for breathing valves, utilizing the aforementioned portable online detection system for breathing valves, comprising the following steps: S1: Acquire signals, obtain data from the pressure sensor end of the breathing valve and the signal end of the flow meter in the connected state, detect the time series stability of each group of signals, and construct a time synchronization channel matrix of pressure and flow rate; S2: Monitor the valve, based on the time synchronization channel matrix, identify the synchronous abrupt change interval between pressure and flow rate in the continuous growth segment, compare the change trend difference of the data groups before and after in turn, extract the sampling point group whose change rate exceeds the critical flow ratio threshold, record the timestamp and change type identifier, and generate the flow pressure abrupt change parameter group. S3: Identify feature points. Based on the flow pressure mutation parameter group, by screening the time nodes where extreme points appear in the changes of flow rate and pressure rate, calculate the rate fluctuation difference before and after the mutation interval, calibrate the exhalation trigger point and the inhalation trigger point, extract the flow rate and pressure pairing values ​​at the corresponding positions, and generate a set of opening and closing behavior feature pairs. S4: Evaluate parameter performance. Based on the set of opening and closing behavior characteristics, extract the flow pressure pairing value sequence under different test rounds, compare it item by item with the breathing valve pressure standard range, determine whether the corresponding values ​​of exhalation pressure and inhalation pressure are all within the allowable range, and generate a list of opening and closing response compliance statuses.

[0015] The advantages and positive effects of this invention are: This invention introduces a time synchronization channel construction mechanism for pressure and flow rate, ensuring a unified time reference across different physical quantities. Signal quality is improved through continuous segment stability detection, identifying synchronous abrupt change regions in dynamic data and extracting abrupt change feature sampling point groups. Combined with a flow ratio threshold determination mechanism, this enables accurate identification of sudden states. By comparing extreme points of rate change with fluctuation differences, trigger nodes are calibrated and feature pairing values ​​are extracted. Multiple sets of flow-pressure pairing sequences are formed in test rounds and compared with standard intervals to generate compliance results, achieving automatic judgment of whether the breathing state meets the standards. By uniformly archiving and labeling the status of each detection cycle, traceable detection cycle label information and response status records are formed, improving the efficiency of identifying abnormal opening and closing of the breathing valve, the response speed of status feedback, and the completeness of the detection result recording. Attached Figure Description

[0016] Figure 1 This is a schematic diagram illustrating the working principle of an embodiment of the present invention.

[0017] Figure 2 This is a schematic diagram of the system framework of an embodiment of the present invention.

[0018] Figure 3 This is a schematic diagram of the working principle of the signal acquisition module in an embodiment of the present invention.

[0019] Figure 4 This is a schematic diagram of the working principle of the valve response monitoring module in an embodiment of the present invention.

[0020] Figure 5 This is a schematic diagram illustrating the working principle of the feature point recognition module in an embodiment of the present invention.

[0021] Figure 6 This is a schematic diagram illustrating the working principle of the performance parameter evaluation module in an embodiment of the present invention.

[0022] Figure 7 This is a schematic diagram of the working principle of the detection status output module in an embodiment of the present invention. Detailed Implementation

[0023] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. 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.

[0024] The embodiments of the present invention will be further described below with reference to the accompanying drawings: like Figure 1 , Figure 2 As shown, the present invention provides a portable online detection system for breathing valves, including a signal acquisition module, a valve response monitoring module, a feature point recognition module, and a performance parameter evaluation module.

[0025] The signal acquisition module is used to acquire data from the pressure sensor and flow meter of the breathing valve in the connected state, detect the time series stability of each signal set, and construct a time synchronization channel matrix including pressure and flow rate by merging readings within consecutive time periods (requiring a pressure / flow signal sampling interval ≤ 10ms; time alignment accuracy ≤ ±1ms). The time synchronization channel matrix includes a pressure signal channel, a flow rate signal channel, and a timestamp calibration column.

[0026] Specifically, such as Figure 2 , Figure 3 As shown, the signal acquisition module includes a signal acquisition submodule, a time merging submodule, and a channel construction submodule.

[0027] The signal acquisition submodule is used to acquire pressure sensor signal data and flow meter signal data, detect the time series stability of each pressure signal and flow signal, filter data segments whose fluctuation amplitude is less than the critical threshold of flow velocity change and the critical threshold of pressure fluctuation within a continuous time period, and generate effective continuous signal segment interval data.

[0028] Specifically, when acquiring data from the pressure sensor and flow meter of the breathing valve in a connected state, it is necessary to first connect the pressure sensor and flow meter signal sources respectively through a bus interface or analog acquisition channel. The sampling device should have a sampling accuracy setting function of ≤10ms, and record the pressure value (unit Pa) and flow value (unit L / min) with synchronous timestamps. In a practical scenario, for example, using a digital pressure sensor to record the pressure value in the breathing mask, changing from 0Pa to 1000Pa, a total of 1000 sets of signal data are collected within 10 seconds. Each set of data includes three items: timestamp, pressure value, and flow value. Subsequently, in the process of detecting the time series stability of each pressure and flow signal, "time series stability" needs to be defined as the signal fluctuation value being less than a signal change threshold within a certain continuous time period, where the critical threshold for pressure fluctuation is... The threshold for flow rate change was set to ±50 Pa and ±1.2 L / min. This setting was based on a 5-second moving window monitoring result of an average inspiratory pressure change of ±45 Pa and an average respiratory flow rate of 4.5 L / min over 10 seconds during human respiration. When filtering the signal, if the pressure change between three consecutive sampling points was less than 50 Pa and the flow rate change was less than 1.2 L / min, the signal segment was determined to be a stable time period. Then, the signal reading values ​​at each moment within the stable segment were extracted and the corresponding timestamps were used to construct a data structure. The binding process between the time index in the list and the signal value was implemented through programming. Finally, for each selected stable signal segment, the start and end times, signal type, original signal sequence value, and signal segment length were recorded, and the data were combined to generate intermediate results that can be used for subsequent signal analysis, thus generating effective continuous signal segment interval data.

[0029] The time merging submodule compares the timestamp data of pressure signals and flow signals within the same time period based on the effective continuous signal segment interval data. It filters signal pairs whose timestamp difference is less than the maximum time alignment error threshold, reads the signal reading value and time interval corresponding to each timestamp in the filtered signal pairs, arranges the signal reading values ​​in a set according to the timestamp order, calculates the average value, and generates a merged synchronization signal reading value sequence.

[0030] When comparing timestamp data of pressure and flow signals within the same time period based on valid continuous signal segment interval data, the pressure signal time series and flow velocity signal time series are first arranged in ascending order of time. Then, the time difference between each pair of signal points is calculated. Specifically, all pressure signal points within the same time period are selected and their nearest neighboring flow signal points are used for time difference calculation. i Timestamp T i Search for the closest value to T in the flow sequence. i Timestamp T j Calculate Δt=|T i -Tj The algorithm checks if Δt is less than the maximum alignment error threshold of 1ms. If the condition is met, the signal pair is retained for subsequent calculations. For example, if a pressure signal has time points of 100.003s, 100.013s, and 100.023s, and a flow signal has time points of 100.002s, 100.012s, and 100.022s, then the time difference between the three pairs of signals is 1ms, which can be identified as a synchronization signal pair. Then, the signal reading value S from each signal pair is extracted. i With S j and the sampling interval ΔT between them. i Using the weighted average formula Savg = (S i +S j ) / 2, perform this calculation on all synchronization signal points respectively, construct a new sequence of synchronization signal read values, and record the timestamp, average value and source signal pair index for each point in the sequence, thereby constructing stable and aligned synchronization signal data and generating a merged sequence of synchronization signal read values.

[0031] The channel construction submodule reads the timestamp, pressure signal value, and flow rate signal value from the merged synchronization signal value sequence, and performs a traversal and filtering based on the time condition that the signal sampling interval is less than the maximum threshold of the sampling interval. The three data elements that meet the conditions are combined and filled into the corresponding positions of the two-dimensional matrix in order to obtain the pressure and flow rate time synchronization channel matrix.

[0032] When calling the merged synchronization signal to read the timestamp, pressure signal value, and flow rate signal value from the value sequence, the sequence must first be traversed, and the triplet information of each signal must be extracted in timestamp order. A sampling interval threshold of 10ms is set. Based on this threshold, it is determined whether the time difference between two adjacent signal points meets the continuity condition. If the time difference is less than or equal to 10ms, it is determined to be a valid record point that can be added to the synchronization channel data. For example, if the merged timestamp sequence is 100.003s, 100.013s, and 100.023s, then its sampling interval is 10ms, which meets the set condition. Then, each valid triplet signal is filled into a two-dimensional channel matrix in sequence, where the first column is the timestamp, the second column is the merged pressure signal value, and the third column is the merged flow rate signal value. The matrix structure is shown below: This matrix enables the construction of a unified data structure for signal synchronization channels. This structure facilitates subsequent signal trend extraction and pattern recognition operations, enabling the acquisition of the pressure-flow-rate-time synchronization channel matrix.

[0033] The valve response monitoring module, based on a time-synchronized channel matrix, identifies synchronous abrupt changes in pressure and flow velocity within a continuously increasing segment. It compares the difference in change trends between preceding and following data sets, extracting sampling point groups whose rate of change exceeds the critical flow ratio threshold [the abrupt change judgment value defined by the international standard for measuring gas flow rate using a critical flow nozzle (default ratio 0.85), used to identify abnormal flow-pressure abrupt changes. This threshold is verified through Reynolds number correlation testing, with a recognition accuracy ≥95% as the criterion]. The module records the timestamp and change type identifier, generating a flow-pressure abrupt change parameter set. The critical flow ratio threshold is the abrupt change judgment value defined by the international standard for measuring gas flow rate using a critical flow nozzle. The flow-pressure abrupt change parameter set includes the abrupt change sampling point group, change type identifier, and synchronization timestamp.

[0034] Specifically, such as Figure 2 , Figure 4 As shown, the valve response monitoring module includes a trend identification submodule, a flow ratio screening submodule, and a mutation calibration submodule.

[0035] The trend recognition submodule is used to obtain the pressure sequence and flow velocity sequence in the time synchronization channel matrix, extract the pressure difference and flow velocity difference between any two adjacent sampling points in time order, compare the adjacent differences to determine whether their directions are consistent, record the continuous growth segment, and generate pressure and flow velocity growth interval segment data.

[0036] After obtaining the pressure and flow velocity sequences from the time synchronization channel matrix, it is necessary to analyze the pressure changes between adjacent sampling points in chronological order. With change in flow velocity Extraction is performed, and direction is determined. To confirm whether a unidirectional growth trend exists, a valid growth interval is defined as a segment with at least three consecutive growth points. The start and end times, flow velocity and pressure ranges, and directions of change for this interval are recorded. Taking a specific sampling segment as an example, with a sampling time interval of 0.01 seconds, the flow velocity and pressure changes recorded continuously starting from the 20th second are as follows: Table 1 Sample Values ​​for Continuously Growing Segments Calculate the pressure difference: ; ; ; Calculate the velocity difference: ; ; ; All sampling points satisfy the following conditions. If three consecutive upward segments meet the set threshold, they are determined to be effective growth segments. The start and end times of the recorded segments are 20.000s to 20.030s, the pressure range is 935Pa to 1000Pa, and the flow rate range is 11.2L / min to 14.3L / min, generating pressure and flow rate growth segment data.

[0037] The flow ratio filtering submodule extracts all flow velocity values ​​within the continuous growth segment based on the pressure flow velocity growth interval data and calculates the average value. It then calculates the ratio by combining the rated flow value and determines whether the critical flow ratio threshold condition for sudden change is met. If the condition is met, it is identified as a sudden change segment and the start and end timestamps and ratio results within the corresponding segment are extracted to generate a flow velocity sudden change identification index.

[0038] Based on the pressure-velocity growth interval data, the cumulative average flow rate change is calculated using sampling point data within the growth interval, and the ratio of instantaneous average flow rate to rated flow rate is used to determine whether there is a sudden change. First, the flow rate sequence of continuously sampled points is extracted. And calculate the average value, and set the rated flow rate as... The judgment criteria are: ; calculate: ; ; Comparison results: ; If the last sampling point is Then we have: ; ; The mutation determination criteria are met, therefore, under this sampling sequence, this segment is identified as a mutation segment, and the flow velocity mutation identification index is obtained.

[0039] The mutation calibration submodule calls the last sampling point of the mutation segment identified in the flow velocity mutation identification index, extracts the pressure difference and flow velocity difference between the segment before and after the point, and determines whether the product of the directions is negative. If it is negative, it is determined to be an abnormal mutation; otherwise, it is a normal change. The determination type, timestamp, and change value are integrated to establish a flow pressure mutation parameter group.

[0040] The last sampling point identified as a sudden change in flow velocity is called from the flow velocity change identification index value for further judgment. The direction of pressure change and the direction of flow velocity change before and after the sampling point are compared sequentially, and the following calculations are performed respectively: ; ; If the pressure at the next sampling point drops after the mutation... The flow rate continued to rise to Then we have: ; ; ; According to the principle of direction judgment, if If the mutation point is abnormal, it is considered an "abnormal mutation"; otherwise, it is considered a "normal change". Therefore, the mutation type here is "abnormal mutation". Finally, the timestamp, flow velocity change value, pressure change value and direction judgment result are integrated into the classification matrix to establish the flow pressure mutation parameter group.

[0041] The feature point recognition module, based on the flow-pressure abrupt change parameter set, filters time nodes where extreme points occur in the changes in flow rate and pressure rate, calculates the rate fluctuation difference before and after the abrupt change interval, calibrates the exhalation trigger point and inhalation trigger point, extracts the flow rate and pressure pair values ​​at the corresponding locations, and processes each pair of values ​​with a number and synchronization identifier to generate an opening and closing behavior feature set. The opening and closing behavior feature set includes the exhalation trigger point pair value, the inhalation trigger point pair value, the number information, and the synchronization identifier.

[0042] like Figure 2 , Figure 5 As shown, specifically, the feature point recognition module includes an extreme value localization submodule, a fluctuation calculation submodule, and a behavior calibration submodule.

[0043] The extreme value localization submodule extracts the flow velocity and pressure sequences of continuous sampling points within the mutation interval based on the flow and pressure mutation parameter set. It detects the sampling points with the maximum and minimum change rates in each sequence, records the corresponding timestamps as the maximum and minimum points, and determines whether the order between the two points meets the definition conditions of inhalation and exhalation characteristics. It then filters the sampling points of the mutation segment that meet the inhalation-exhalation switching characteristics and generates a sequence of extreme rate time points.

[0044] Based on the flow pressure mutation parameter set, the flow velocity sequence corresponding to each mutation interval needs to be extracted first. With pressure sequence After expanding each sequence in chronological order, for and The sequence performs extreme value localization operations, which involves determining the sampling indices and corresponding timestamps of the maximum and minimum points within the segment, checking whether the located maximum point is after the minimum point, and selecting time periods that meet the criteria of inhalation starting at the minimum and exhalation ending at the maximum point as effective respiratory transition segments for subsequent identification of critical nodes. For example, if the time series in a certain mutation segment is... to The corresponding flow velocity sampling values ​​are [2.1, 1.8, 1.5, 1.2, 1.3, 1.6, 2.0] L / min, then the minimum value appears in... The maximum value appears This period can be identified as the inhalation-exhalation transition segment; the above judgment is performed on multiple sets of mutation segments, and all extreme points that meet the rules are uniformly numbered with timestamps and arranged in chronological order to obtain the sequence of extreme rate time points.

[0045] The fluctuation calculation submodule extracts all velocity and pressure value sequences within the time interval between two adjacent extreme points in the velocity extreme time point sequence, calculates the difference between the maximum and minimum values ​​of the velocity sequence and the difference between the maximum and minimum values ​​of the pressure sequence, and represents the two types of differences as velocity fluctuation and pressure fluctuation, respectively. The fluctuation value pairs are arranged in the order of time intervals to obtain the velocity-pressure fluctuation pair set.

[0046] Each pair of extreme points in the time sequence of call rate extremes is used as the segment boundary, and the complete velocity sequence within the corresponding time period needs to be extracted separately. With pressure sequence Using the start and end indexes of the segment as boundaries, all elements of that segment... The value obtained by subtracting the maximum and minimum values ​​is defined as the flow velocity fluctuation value. Similarly, extract all pressure values ​​within this segment. Calculate the difference between its maximum and minimum values. These constitute the fluctuation pairs within the segment. For example, if the sampled flow velocity values ​​within a certain segment are [1.2, 1.4, 1.8, 2.1, 2.4], then the flow velocity fluctuation value is... If the pressure sequence is [940, 960, 985, 990, 1010], then the pressure fluctuation value is... These fluctuation values ​​are paired and arranged according to the extreme value segment number to form a two-dimensional array structure with a uniform structure. The above operation is performed on all segments to obtain the set of velocity pressure fluctuations.

[0047] The behavior calibration submodule extracts the flow velocity and pressure values ​​at the corresponding locations based on the timestamps corresponding to each pair of fluctuations in the velocity-pressure fluctuation data set, combined with the original channel data. It then sets the number and synchronization identifier number for each pair of flow-pressure values, constructs a unified structure data entry, and establishes a set of opening and closing behavior features.

[0048] Based on the velocity and pressure fluctuations and the recorded fluctuation results for each group of segments, the original velocity value at the corresponding timestamp for each fluctuation segment needs to be extracted from the time synchronization channel matrix. With pressure value Using this time point as the behavioral feature observation point, the extracted flow velocity values ​​and pressure values ​​were paired and numbered. The numbering format adopted was... ,in The segment number and synchronization identifier are set to a unified identifier with the same precision as the timestamp. For example, if the ID is ID_03, the synchronization identifier is TS_8.030, the flow rate is 2.4 L / min, and the pressure is 1010 Pa, then the record is [ID_03, TS_8.030, 2.4, 1010]. This process is repeated to complete the calibration of all paired values ​​and generate a set of opening and closing behavior feature pairs.

[0049] The performance parameter evaluation module extracts the flow pressure paired value sequences from different test rounds based on the opening and closing behavior characteristic set, organizes and classifies them, and compares them item by item with the standard range of breathing valve pressure [the pressure range specified by the medical ventilator safety standard (exhalation pressure: -0.5 to +0.5 kPa; inhalation pressure: -2.0 to -0.2 kPa), as the compliance judgment benchmark. This range is determined through clinical validation (sample size > 1000 cases), and the measurement standard is to cover 99% of normal operating conditions] to determine whether the corresponding values ​​of exhalation pressure and inhalation pressure are all within the allowable range, and to determine the unit compliance of each test cycle, generating an opening and closing response compliance status list. The breathing valve pressure standard range is the pressure range specified by the medical ventilator safety standard. The opening and closing response compliance status list includes exhalation pressure compliance status, inhalation pressure compliance status, and test cycle number.

[0050] Specifically, such as Figure 2 , Figure 6 As shown, the performance parameter evaluation module includes a round extraction submodule, a stress comparison submodule, and a compliance determination submodule.

[0051] The round extraction submodule is used to obtain all sampled records of the opening and closing behavior feature pairs, divide each feature pair according to the test batch number, extract all flow rate and pressure pair values ​​in each round, arrange each group of data in chronological order, and summarize and number all records in each round to generate a test round pair value sequence.

[0052] To obtain the characteristics of opening and closing behavior for all sampled records in the set, it is necessary to first identify the test round number field contained in the records, classify all data based on different numbers, and then extract the corresponding flow rate and pressure values ​​from each category of data, and sort them by timestamp from smallest to largest to ensure that each data sequence is continuous and non-overlapping in the time dimension. Subsequently, based on the record structure, extract the triplet combination of time point, flow rate value, and pressure value to generate the complete paired value sequence structure under each test round. For example, if the number of samples under test round 1 is... There are 10 sets of data, with times ranging from 0.000s to 0.090s. Flow velocity values ​​(e.g., [2.1, 2.4, 2.7...]) and pressure values ​​(e.g., [-0.3, -0.4, -0.5...]) are recorded. These 10 sets of data should be archived as number "1". Rounds 2 and 3 should be organized in the same way to ensure that a time series of paired flow velocity and pressure values ​​is established for all sampling rounds. At the same time, the original round number should be retained in the structure to maintain the accuracy of subsequent indexing and comparison. Finally, a paired value sequence for the test rounds is generated.

[0053] The pressure comparison submodule identifies the corresponding pressure value in the exhalation state based on each pair of flow pressure values ​​in the test round pairing value sequence and compares it with the upper limit of the exhalation pressure range (0.5 kPa) and the lower limit (-0.5 kPa). It also identifies the corresponding pressure value in the inhalation state and compares it with the upper limit of the inhalation pressure range (-0.2 kPa) and the lower limit (-2.0 kPa). It records the sampling point numbers that do not meet the range conditions and obtains the pressure range deviation number set.

[0054] For each record in the paired value sequence of the test round, it is necessary to classify the state type of each pair of sampling points first. Records with flow rate values ​​greater than 0 are determined to be in the exhalation state, and those with flow rate values ​​less than 0 are determined to be in the inhalation state. The corresponding pressure values ​​are extracted from the two types of records respectively. Then, the medical breathing valve pressure standard is used as the judgment benchmark. The exhalation pressure threshold range is [-0.5, +0.5] kPa, and the inhalation pressure threshold range is [-2.0, -0.2] kPa. A comparison operation is performed on all extracted pressure values. If a pressure value in the exhalation state is less than -0.5 or greater than 0.5, or a pressure value in the inhalation state is less than -2.0 or greater than -0.2, it is marked as abnormal. For example, if the flow rate of a sampling point in round 2 is 2.1 L / min and the pressure value is 0.7 kPa, it is marked as an out-of-bounds point because the pressure is greater than the upper limit of 0.5 kPa for exhalation. If there are such pressure out-of-bounds point numbers in this round, their corresponding numbers are recorded and a number set is established. Finally, the pressure range deviation number set is obtained.

[0055] The compliance determination submodule, based on the correspondence between the abnormal sampling point numbers identified by the pressure range deviation number set and the test number of the test round paired value sequence, marks the test rounds with pressure values ​​outside the range as non-compliant, and the rest as compliant. It also summarizes the test number of each round and the corresponding compliance status identifier into a two-dimensional status array to establish a list of opening and closing response compliance statuses.

[0056] Based on all the abnormal numbers recorded in the pressure range deviation number set, each abnormal number needs to be compared with the test numbers carried in the original records of the test round pairing value sequence. It is determined whether each round number contains any abnormal number. If it does, the round is marked as non-compliant. If all sampling point numbers do not appear in the abnormal number set, they are marked as compliant. Then, a two-dimensional status table is created, with row labels representing the test round number and column fields representing the compliance status identifier, with values ​​of "compliant" or "non-compliant". For example, if round 3 contains abnormal numbers A07 and A09, it is marked as "non-compliant". If round 1 and round 2 do not have any matching abnormal numbers, they are marked as "compliant" respectively. The detection results of all rounds are summarized through this rule, and finally, a list of opening and closing response compliance statuses is established.

[0057] Preferably, the system also includes a detection status output module. Based on the opening and closing response compliance status list, it determines whether the exhalation pressure test of the breathing valve is compliant. If the status is non-compliant, it controls the pressure relief valve to execute an emergency pressure relief command [pressure relief action specification defined by the international standard for safety valves (response time ≤ 50ms), used for safety protection when pressure exceeds the limit]. It then determines whether the inhalation pressure test is compliant. If the status is non-compliant, it executes a secondary pressure relief action. This information is summarized into the current detection cycle status tag information, generating an online detection record for the portable breathing valve. The online detection record for the portable breathing valve includes a detection cycle status tag, a safety response status record, and a pressure relief execution identifier.

[0058] Specifically, such as Figure 2 , Figure 7 As shown, the detection status output module includes a pressure determination submodule, an instruction execution submodule, and a record generation submodule.

[0059] The pressure determination submodule, based on the list of open / close response compliance statuses, filters all exhaled pressure status labels and inhaled pressure status labels respectively, and sequentially determines whether the exhaled pressure status is compliant in each round, and determines whether the inhaled pressure status is compliant. It then summarizes the two pressure relief requirement identifiers in each round and adds corresponding numbers to generate a round pressure relief determination value group.

[0060] Based on the list of open / close response compliance states, each test round recorded in the list needs to be analyzed item by item. The exhaled pressure state field and inhaled pressure state field contained in each data entry are extracted, and the state value is set to two discrete types: "compliant" or "non-compliant". Then, a logical judgment is performed on the exhaled pressure state. If the content of the field is "non-compliant", the exhaled pressure relief demand value of the record is defined as 1. If it is "compliant", the pressure relief demand value is defined as 0. Similarly, the same judgment is performed on the inhaled pressure state field to generate a secondary pressure relief demand value. Then, the two binary results are integrated and encoded with the round number field. For example, the data with round number "R03" has an exhaled pressure state of "non-compliant" and an inhaled pressure state of "compliant", so its pressure relief demand group code is [R03, 1, 0]. After processing all rounds in this way, a two-dimensional data matrix is ​​constructed, with the fields being round number, exhaled pressure relief demand identifier, and inhaled pressure relief demand identifier, respectively. To ensure the accuracy of the judgment operation, the status fields should first be standardized by converting all status fields into full-width character codes for "compliant" or "non-compliant" to avoid ambiguity caused by differences in data sources. Simultaneously, data records containing missing values, illegal characters, or multiple status descriptions should be removed to ensure consistent processing of logical judgment results across rounds. Based on a practical test example, if in rounds R01 to R05, R01 and R03 have non-compliant exhalation status records, and R04 has a non-compliant inhalation status record, then the corresponding pressure relief requirement judgments are: R01=[1,0], R03=[1,0], R04=[0,1], and the rest are [0,0]. Finally, a complete two-dimensional pressure relief judgment data table is constructed based on the results of each round, generating round pressure relief judgment value groups.

[0061] The instruction execution submodule sets instruction trigger operations for each round number according to the pressure relief requirement identifier of each round in the round pressure relief judgment value group. If the exhalation pressure relief identifier does not meet the rules, an emergency pressure relief instruction is triggered. If the inhalation pressure relief identifier does not meet the rules, a secondary pressure relief instruction execution process is added. After encoding all trigger records, an instruction response structure is established, and the action response trigger code set is obtained.

[0062] The system retrieves each set of identifier values ​​recorded in the round-based pressure relief judgment value group, sequentially reading the round number field and two pressure relief requirement fields to determine if any identifier value is 1. If the exhalation pressure relief identifier value is 1, the corresponding round is marked as requiring an emergency pressure relief action. The emergency pressure relief action must meet the international standard requirement of a response time not exceeding 50 milliseconds. In actual operation, this response time is measured by the control system's clock module; exceeding this value should be considered a response anomaly and recorded. If the inhalation pressure relief identifier is 1, the corresponding round number is recorded and the system is placed in the secondary pressure relief command execution queue. The secondary command can be set as a delayed trigger action, with a delay range of 50 to 100 milliseconds. The execution action triggers the solenoid valve via a pulse signal to release the tubing. For all trigger actions, a response feedback flag must be provided. When the response is complete, the feedback signal status is recorded as 1; otherwise, it is recorded as 0. The round number, action type (exhalation or inhalation), trigger timestamp, and feedback status are then assembled into a structured record. For example, if round R02 triggers both exhalation and inhalation pressure relief actions, the following records are generated: [R02, exhalation, 18.353s, 1] and [R02, inhalation, 18.401s, 1]. If round R03 only triggers an exhalation action and the inhalation status is compliant, only one record is generated: [R03, exhalation, 19.024s, 1]. After all rounds of operations are completed, they are summarized into a response action instruction log and managed with unified numbering to obtain the action response trigger code set.

[0063] The record generation submodule, based on the trigger results of each round in the action response trigger code set, combined with the original detection number, round number, and exhalation and inhalation status label information, organizes and outputs structured result units, generates standardized format labels, and establishes a portable breathing valve online detection record.

[0064] Based on each response result recorded in the action response trigger code set, the detection number and round number fields in the original data are compared to cross-integrate the action trigger information and compliance status information. First, the round number and action type fields are extracted from each response code record. Then, the call-out status and inhalation status fields corresponding to the round number are extracted from the open / close response compliance status list. The two are aligned and combined into a unified structure according to their numbers. Then, the fields "Pressure Relief Execution Status" and "Secondary Response Status" are added. The assignment of these fields depends on whether there is a corresponding action record in the response code set. If there is a call-out response code under the round number, the pressure relief status is set to "executed"; otherwise, it is set to "not executed". If there is an inhalation action record, the secondary response status is set to "executed". The secondary response status is set to "executed" otherwise. The six fields—detection number, round number, exhalation status, inhalation status, pressure relief execution status, and secondary response status—are ultimately merged into a standard structure. The field order is strictly uniform: detection number, round number, exhalation status, inhalation status, pressure relief response, secondary response. For example, if detection number D017 has an exhalation status of "non-compliant" and an inhalation status of "compliant" in round R05, and the response code records that R05 triggered the exhalation action, then the output of this record would be: [D017, R05, non-compliant, compliant, executed, not executed]. All round-related records are sorted in ascending order by number and output as a record table, establishing an online detection record for the portable breathing valve.

[0065] The above embodiments demonstrate that the device and method of the present invention can effectively solve the problem of plugging gas wells with annular leakage and high success rate, and have significant engineering application value.

[0066] The advantages and positive effects of this invention are: This invention introduces a time synchronization channel construction mechanism for pressure and flow rate, ensuring a unified time reference across different physical quantities. Signal quality is improved through continuous segment stability detection, identifying synchronous abrupt change regions in dynamic data and extracting abrupt change feature sampling point groups. Combined with a flow ratio threshold determination mechanism, this enables accurate identification of sudden states. By comparing extreme points of rate change with fluctuation differences, trigger nodes are calibrated and feature pairing values ​​are extracted. Multiple sets of flow-pressure pairing sequences are formed in test rounds and compared with standard intervals to generate compliance results, achieving automatic judgment of whether the breathing state meets the standards. By uniformly archiving and labeling the status of each detection cycle, traceable detection cycle label information and response status records are formed, improving the efficiency of identifying abnormal opening and closing of the breathing valve, the response speed of status feedback, and the completeness of the detection result recording.

[0067] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A portable online testing system for breathing valves, characterized in that: include, The signal acquisition module is used to acquire data from the pressure sensor end and the flow meter end of the breathing valve when connected, detect the time series stability of each set of signals, and construct a time-synchronized channel matrix including pressure and flow rate. The valve response monitoring module, based on the time synchronization channel matrix, identifies the synchronous abrupt change interval between pressure and flow velocity in the continuous growth segment, compares the change trend difference of the data groups before and after in turn, extracts the sampling point group whose change rate exceeds the critical flow ratio threshold, records the timestamp and change type identifier, and generates the flow pressure abrupt change parameter group. The feature point recognition module, based on the flow pressure mutation parameter group, filters the time nodes where extreme points occur in the changes of flow rate and pressure rate, calculates the rate fluctuation difference before and after the mutation interval, calibrates the exhalation trigger point and inhalation trigger point, extracts the flow rate and pressure pairing values ​​at the corresponding positions, and generates a set of opening and closing behavior feature pairs. The performance parameter evaluation module extracts the flow pressure pairing value sequence under different test rounds based on the opening and closing behavior feature set, compares it item by item with the breathing valve pressure standard range, determines whether the corresponding values ​​of exhalation pressure and inhalation pressure are all within the allowable range, and generates an opening and closing response compliance status list.

2. The portable breathing valve online detection system according to claim 1, characterized in that: The time synchronization channel matrix includes a pressure signal channel, a flow rate signal channel, and a timestamp calibration column; the flow pressure mutation parameter group includes a mutation sampling point group, a change type identifier, and a synchronization timestamp; the opening and closing behavior feature pair set includes exhalation trigger point pairing value, inhalation trigger point pairing value, number information, and synchronization identifier; the opening and closing response compliance status list includes exhalation pressure compliance status, inhalation pressure compliance status, and detection cycle number.

3. The portable breathing valve online detection system according to claim 1 or 2, characterized in that: The signal acquisition module includes: The signal acquisition submodule is used to acquire the pressure sensor signal data and the flow meter signal data, detect the time series stability of each group of pressure signal and flow signal, filter data segments whose fluctuation amplitude is less than the critical threshold of flow velocity change and the critical threshold of pressure fluctuation in a continuous time period, and generate effective continuous signal segment interval data. The time merging submodule compares the timestamp data of pressure signal and flow signal within the same time period based on the effective continuous signal segment interval data, filters signal pairs whose timestamp difference is less than the maximum time alignment error threshold, reads the signal reading value and time interval corresponding to each timestamp in the filtered signal pair, arranges the signal reading values ​​in a set according to the timestamp order and calculates the average value to generate a merged synchronization signal reading value sequence. The channel construction submodule reads the timestamp, pressure signal value, and flow rate signal value from the merged synchronization signal value sequence, and performs a traversal and filtering based on the time condition that the signal sampling interval is less than the maximum threshold of the sampling interval. The three data elements that meet the conditions are combined and filled into the corresponding positions of the two-dimensional matrix in order to obtain the pressure and flow rate time synchronization channel matrix.

4. The portable breathing valve online detection system according to claim 1 or 2, characterized in that: The valve response monitoring module includes: The trend recognition submodule is used to obtain the pressure sequence and flow velocity sequence in the time synchronization channel matrix, extract the pressure difference and flow velocity difference between any two adjacent sampling points in time order, compare the adjacent differences to determine whether their directions are consistent, record the continuous growth segment, and generate pressure and flow velocity growth interval segment data. The flow ratio filtering submodule extracts all flow velocity values ​​within the continuous growth segment based on the pressure flow velocity growth interval segment data and calculates the average value. It then calculates the ratio by combining the rated flow value and determines whether the abrupt critical flow ratio threshold condition is met. If the condition is met, it is identified as an abrupt segment and the start and end timestamps and ratio results within the corresponding segment are extracted to generate a flow velocity abrupt identification index. The mutation calibration submodule calls the last sampling point of the identified mutation segment in the flow velocity mutation identification index, extracts the pressure difference and flow velocity difference between the segment before and after the last sampling point, and determines whether the directional product is negative. If it is negative, it is determined to be an abnormal mutation; otherwise, it is a normal change. The determination type, timestamp, and change value are integrated to establish a flow pressure mutation parameter group.

5. The portable breathing valve online detection system according to claim 1 or 2, characterized in that: The feature point recognition module includes: The extreme value localization submodule, based on the flow pressure mutation parameter group, extracts the flow velocity sequence and pressure sequence of continuous sampling points within the mutation interval, detects the sampling points with the maximum and minimum change rates in each sequence, records the corresponding timestamps as maximum and minimum value points, and determines whether the order between the two points meets the definition conditions of inhalation and exhalation characteristics, filters the mutation segment sampling points that meet the inhalation and exhalation switching characteristics, and generates a rate extreme value time point sequence. The fluctuation calculation submodule extracts all velocity and pressure value sequences within the time interval between two adjacent extreme points in the velocity extreme time point sequence, calculates the difference between the maximum and minimum values ​​of the velocity sequence and the difference between the maximum and minimum values ​​of the pressure sequence, and represents the two types of differences as velocity fluctuation and pressure fluctuation, respectively. The fluctuation value pairs are arranged in the order of time intervals to obtain a velocity-pressure fluctuation pair set. The behavior calibration submodule extracts the flow velocity and pressure values ​​at the corresponding positions based on the timestamps corresponding to each pair of fluctuations in the velocity-pressure fluctuation set and the original channel data. It then sets the number and synchronization identifier number for each pair of flow-pressure values, constructs a unified structure data entry, and establishes a set of opening and closing behavior features.

6. The portable breathing valve online detection system according to claim 1 or 2, characterized in that: The performance parameter evaluation module includes: The round extraction submodule is used to obtain all sampling records in the set of opening and closing behavior feature pairs, divide each feature pair according to the test batch number, extract all flow rate and pressure pair values ​​in each round, arrange each group of data in chronological order, and summarize and number all records in each round to generate a test round pair value sequence. The pressure comparison submodule identifies the corresponding pressure value in the exhalation state and compares it with the upper and lower limits of the exhalation pressure range based on each pair of flow pressure values ​​in the test round pairing value sequence. It also identifies the corresponding pressure value in the inhalation state and compares it with the upper and lower limits of the inhalation pressure range. It records the sampling point numbers that do not meet the range conditions and obtains the pressure range deviation number set. The compliance determination submodule, based on the correspondence between the abnormal sampling point numbers identified by the pressure range deviation number set and the test number of the test round paired value sequence, marks the test rounds with pressure values ​​outside the range as non-compliant, and the rest as compliant. It also summarizes the test number of each round and the corresponding compliance status identifier into a two-dimensional status array to establish a list of opening and closing response compliance statuses.

7. The portable breathing valve online detection system according to claim 1 or 2, characterized in that: It also includes a detection status output module, which determines whether the exhalation pressure test of the breathing valve is compliant based on the opening and closing response compliance status list. If the status is not compliant, it controls the pressure relief valve to execute an emergency pressure relief command, determines whether the inhalation pressure test is compliant, and executes a secondary pressure relief action if the status is not compliant. It summarizes the status tag information of the current detection cycle and generates a portable breathing valve online detection record.

8. The portable breathing valve online detection system according to claim 7, characterized in that: The portable breathing valve online detection record includes a detection cycle status label, a safety response status record, and a pressure relief execution identifier.

9. The portable breathing valve online detection system according to claim 7, characterized in that: The detection status output module includes: The pressure determination submodule, based on the opening and closing response compliance status list, filters all exhaled pressure status labels and inhaled pressure status labels respectively, and sequentially determines whether the exhaled pressure status is compliant in each round, and determines whether the inhaled pressure status is compliant. It summarizes the two pressure relief requirement identifiers in each round and adds corresponding numbers to generate a round pressure relief determination value group. The instruction execution submodule sets instruction trigger operations for each round number in sequence according to the pressure relief demand identifier of each round in the round pressure relief judgment value group. If the exhalation pressure relief identifier does not meet the rules, an emergency pressure relief instruction is triggered. If the inhalation pressure relief identifier does not meet the rules, a secondary pressure relief instruction execution process is added. All trigger records are encoded to establish an instruction response structure and obtain the action response trigger code set. The record generation submodule, based on the trigger results of each round in the action response trigger code set, combined with the original detection number, round number, and exhalation and inhalation status label information, organizes and outputs structured result units, uniformly generates standard format labels, and establishes a portable breathing valve online detection record.

10. A portable online detection method for breathing valves, characterized in that: The portable breathing valve online detection system according to any one of claims 1 to 9 includes the following steps: S1: Acquire signals, obtain data from the pressure sensor end of the breathing valve and the signal end of the flow meter in the connected state, detect the time series stability of each group of signals, and construct a time synchronization channel matrix of pressure and flow rate; S2: Monitor the valve, based on the time synchronization channel matrix, identify the synchronous abrupt change interval between pressure and flow rate in the continuous growth segment, compare the change trend difference of the data groups before and after in turn, extract the sampling point group whose change rate exceeds the critical flow ratio threshold, record the timestamp and change type identifier, and generate the flow pressure abrupt change parameter group. S3: Identify feature points. Based on the flow pressure mutation parameter group, by screening the time nodes where extreme points appear in the changes of flow rate and pressure rate, calculate the rate fluctuation difference before and after the mutation interval, calibrate the exhalation trigger point and the inhalation trigger point, extract the flow rate and pressure pairing values ​​at the corresponding positions, and generate a set of opening and closing behavior feature pairs. S4: Evaluate parameter performance. Based on the set of opening and closing behavior characteristics, extract the flow pressure pairing value sequence under different test rounds, compare it item by item with the breathing valve pressure standard range, determine whether the corresponding values ​​of exhalation pressure and inhalation pressure are all within the allowable range, and generate a list of opening and closing response compliance statuses.

Images