A medical gas remote monitoring system

Abstract

The application discloses a medical gas remote monitoring system and relates to the technical field of gas detection, which solves the technical problem that it is difficult to detect faults of different pipelines according to the characteristics and requirements of different pipeline levels; the system comprises a data acquisition module, a template matching module and the like; the data acquisition module is used for dividing all gas supply pipelines into a gas source pipeline, a first branch pipeline, a second branch pipeline and a third branch pipeline, and processing data collected by gas pressure sensors and gas flow sensors of the pipelines to obtain characteristic spectrum data of the sensors; the template matching module is used for detecting characteristic spectrum data of the third branch pipeline and a gas supply source through a template matching algorithm; when data of the third pipeline is abnormal, the problem can be found and corrected more easily and specifically, and meanwhile, detection of the gas source pipeline, the first branch pipeline and the second branch pipeline is not affected, so that the fault tolerance and stability of the system are improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of gas detection, and relates to a medical gas detection technology, in particular to a medical gas remote monitoring system. BACKGROUND

[0002] With the modernization and digitization development of medical facilities, medical gas equipment plays a crucial role in the medical process. The traditional monitoring method of gas equipment mainly relies on manual inspection and regular maintenance, which has problems such as untimely monitoring and low accuracy. Based on the application of remote monitoring system, the remote real-time monitoring and management of gas equipment can be realized, which can quickly capture abnormal conditions and early warning of fault risk, improve the safety, reliability and efficiency of medical gas equipment, and provide more reliable technical support for medical safety. The medical gas remote monitoring system is a system that can monitor and remotely manage the running state and gas supply quality of various gas equipment in medical facilities. Through real-time collection of running data of gas equipment by sensors, data is transmitted to the remote monitoring center by network communication technology, and combined with data analysis and processing technology, the running state of gas system is monitored, analyzed and warned.

[0003] The commonly used medical gas remote monitoring system usually monitors the flow of all pipelines, and only judges the leakage fault of the pipeline through flow comparison, which is difficult to optimize and adjust the neural network model according to the characteristics and needs of different pipeline levels, so that the model is more suitable for the prediction needs of each type of pipeline. When the end pipeline data is abnormal, it will affect the detection of other pipelines. SUMMARY

[0004] The present application aims to solve at least one of the technical problems existing in the prior art; for this purpose, the present application proposes a medical gas remote monitoring system to solve the technical problem that it is difficult to optimize and adjust the neural network model according to the characteristics and needs of different pipeline levels, so that the model is more suitable for the prediction needs of each type of pipeline. When the end pipeline data is abnormal, it will affect the detection of other pipelines.

[0005] To solve the above problems, the first aspect of the present application provides a medical gas remote monitoring system, comprising:

[0006] The data acquisition module is used for dividing all gas supply pipelines into gas source pipeline, first branch pipeline, second branch pipeline and third branch pipeline, and processing the data collected by the gas pressure sensor and the gas flow sensor of each pipeline to obtain the characteristic frequency spectrum data of each sensor;

[0007] The template matching module is used for detecting the characteristic frequency spectrum data of the three-level branch pipeline and the gas supply source by a template matching algorithm, and transmitting a fault signal to the monitoring alarm module when the three-level branch pipeline or the gas supply source fails.

[0008] The historical data acquisition module is used for acquiring the historical data of the gas pressure and the gas flow of the pipeline when the gas source pipeline, the first-level branch pipeline, the second-level branch pipeline and the three-level branch pipeline leak or do not leak, and screening the gas pressure and the gas flow data of different pipelines when different numbers of gas supply points are in use.

[0009] The model construction module is used for training the neural network model for detecting the gas source pipeline, the first-level branch pipeline and the second-level branch pipeline according to the gas pressure and the gas flow data of different pipelines when different numbers of gas supply points are screened by the historical data acquisition module.

[0010] The leakage monitoring module is used for acquiring the data acquired by the data acquisition module, and inputting the acquired data into the model construction module to detect whether the pipeline is normally used, wherein the number of the three-level branch pipeline gas flow data not being zero is the number of the gas supply points in use.

[0011] The evaluation module is used for evaluating the leakage risk coefficient of the gas source pipeline, the first-level branch pipeline and the second-level branch pipeline according to the corresponding characteristic frequency spectrum data of the gas pressure and the gas flow data of the gas source pipeline, the first-level branch pipeline and the second-level branch pipeline.

[0012] The monitoring alarm module is used for alarming the gas source pipeline, the first-level branch pipeline and the second-level branch pipeline which are detected by the leakage monitoring module to be abnormally used, alarming the fault signal of the template matching module, and displaying the evaluation data of the evaluation module.

[0013] As a further scheme of the present application, the data acquisition module comprises:

[0014] The pipeline division unit is used for dividing all the pipelines connected to the gas supply source of the hospital, and the pipeline connected to the gas supply source of the hospital is set as the gas source pipeline; each area requiring medical gas of the hospital is divided according to the department, and each department area is divided into different gas supply point groups according to the position area of the required medical gas supply point, the pipeline leading to each department area from the gas source pipeline is the first-level branch pipeline, the pipeline leading to each gas supply point group in each department area from the first-level branch pipeline is the second-level branch pipeline, and the pipeline leading to each gas supply point in the gas supply point group from the second-level branch pipeline is the three-level branch pipeline.

[0015] Pipeline monitoring unit: install pressure sensors and gas flow sensors on the gas source pipeline, primary branch pipeline, secondary branch pipeline and tertiary branch pipeline for real-time monitoring of gas pressure and gas flow of each pipeline;

[0016] Gas source detection unit: install tilt sensors and gas purity sensors in the electrolytic tank and gas storage tank of the medical electronic atomization mechanism of the gas supply source for monitoring the inclination angle and gas purity of the device;

[0017] Data processing unit: for processing the data collected by each sensor, extracting the signal sequence, and performing power spectrum analysis to obtain the characteristic frequency spectrum data of each sensor.

[0018] As a further aspect of the application: the template matching module detects the characteristic frequency spectrum data of the tertiary branch pipeline and the gas supply source by template matching algorithm, and transmits the fault signal to the monitoring alarm module when the tertiary branch pipeline or the gas supply source fails, including the following steps:

[0019] For the tertiary branch pipeline, the template matching algorithm is applied to match the characteristic frequency spectrum information corresponding to the gas pressure and gas flow of the use state of the corresponding gas supply point of the tertiary branch pipeline with the preset template spectrum to determine whether the tertiary branch pipeline leaks;

[0020] For the gas supply source, the characteristic frequency spectrum information corresponding to the inclination angle and gas purity data of the electrolytic tank and gas storage tank is matched with the preset template spectrum to determine whether the electrolytic tank and gas storage tank are used normally;

[0021] If the tertiary branch pipeline and the gas supply source are used normally, further detection of other pipelines is performed by the leakage monitoring module, and if the gas supply source is not used normally, the gas supply source is determined to be faulty and the fault signal is transmitted to the monitoring alarm module;

[0022] If the tertiary branch pipeline is not used normally, the corresponding tertiary branch pipeline number generates fault information and transmits the fault signal to the monitoring alarm module for alarm.

[0023] As a further aspect of the application: screen the gas pressure and gas flow data of different pipelines when different numbers of gas supply points are in use, including the following steps:

[0024] Screen the gas pressure and gas flow data of the tertiary branch pipeline corresponding to each gas supply point when the gas supply point is in use and not in use in the historical data;

[0025] The gas pressure and gas flow data of the secondary branch pipeline when different numbers of gas supply points in the gas supply point grouping are in use.

[0026] The gas pressure and gas flow data of the primary branch pipeline when different numbers of gas supply points in the department partition are in use;

[0027] The gas pressure and gas flow data of the gas source pipeline when different numbers of gas supply points in all gas supply points supplied by the gas source are in use.

[0028] As a further scheme of the present application: the model construction module trains neural network models for detecting the gas source pipeline, the primary branch pipeline and the secondary branch pipeline, including the following steps:

[0029] The neural network model for detecting the secondary branch pipeline is established by taking the feature spectrum information corresponding to the gas pressure and gas flow data of the secondary branch pipeline, and the number of gas supply points in use in the gas supply point grouping as inputs to train the neural network model for detecting the secondary branch pipeline;

[0030] The neural network model for detecting the primary branch pipeline is established by taking the feature spectrum information corresponding to the gas pressure and gas flow data of the primary branch pipeline, and the number of gas supply points in use in the department partition as inputs to train the neural network model for detecting the primary branch pipeline;

[0031] The neural network model for detecting the gas source pipeline is established by taking the feature spectrum information corresponding to the gas pressure and gas flow data of the gas source pipeline, and the number of gas supply points in use in the gas supply points supplied by the gas source as inputs to train the neural network model for detecting the gas source pipeline;

[0032] The number of gas supply points in use in the gas supply point grouping is the number of pipelines with non-zero flow in the corresponding tertiary branch pipeline of the gas supply point.

[0033] As a further scheme of the present application: the leakage monitoring module inputs the collected data to the model construction module to detect whether the pipeline is in normal use, including the following steps:

[0034] The gas pressure and gas flow data of the gas source pipeline, the primary branch pipeline, the secondary branch pipeline and the tertiary branch pipeline obtained by the data acquisition module in real time are processed by the data processing unit to obtain real-time feature spectrum data of the gas pressure and gas flow data; the number of tertiary branch pipelines with non-zero gas flow data is counted as the number of gas supply points in use; the real-time feature spectrum data of the gas pressure and gas flow data and the number of gas supply points in use corresponding to the tertiary branch pipeline are input to the model construction module to detect whether the pipeline is in normal use.

[0035] As a further scheme of the present application: the evaluation module evaluates the leakage risk coefficients of the gas source pipeline, the first branch pipeline and the second branch pipeline, comprising the following steps:

[0036] If the leakage monitoring module detects that the gas source pipeline, the first branch pipeline, the second branch pipeline and the third branch pipeline are all normally used within the preset evaluation interval, the gas pressure and gas flow data in the time period in which the gas flow data of the gas source pipeline, the first branch pipeline and the second branch pipeline fluctuate greater than the preset threshold value are screened as risk detection data of the risk period;

[0037] According to the risk detection data of the risk period of the screened gas source pipeline, first branch pipeline and second branch pipeline, the leakage risk coefficients of the gas source pipeline, first branch pipeline and second branch pipeline are respectively calculated;

[0038] The pipeline whose leakage risk coefficient exceeds the preset threshold value is sent to the monitoring alarm module with the pipeline type and the gas pressure and gas flow data of the pipeline;

[0039] If it is a first branch pipeline and a second branch pipeline, the department subarea and the gas supply point grouping data of the pipeline are sent to the monitoring alarm module.

[0040] As a further scheme of the present application: the leakage risk coefficient of the second branch pipeline is calculated by the following formula:

[0041]

[0042] Wherein, N2 is the leakage risk coefficient of the second branch pipeline, P max is the maximum pressure value of the risk period of the second branch pipeline, P min is the minimum pressure value of the risk period of the second branch pipeline, q i is the total flow of the i-th third branch pipeline in the risk period in the gas supply point grouping of the second branch pipeline; Q j is the total flow of the j-th second branch pipeline in the risk period in the department subarea of the first branch pipeline, i∈(1,2,,i,,n), n is the total number of gas supply points in the gas supply point grouping.

[0043] As a further scheme of the present application: the leakage risk coefficient of the first branch pipeline is calculated by the following formula:

[0044]

[0045] Wherein, N1 is the leakage risk coefficient of the first branch pipeline, R max is the maximum pressure value of the risk period of the first branch pipeline, R min is the minimum pressure value of the risk period of the first branch pipeline, G KThe total flow of the kth primary branch pipeline in the risk period in the department partition connected with the gas source pipeline, j is (1, 2, j, m), and m is the total number of gas supply point groups in the department partition.

[0046] As a further scheme of the present application, the leakage risk coefficient of the gas source pipeline is calculated by the following formula:

[0047]

[0048] Wherein, N1 is the leakage risk coefficient of the gas source pipeline, D max is the maximum pressure of the gas source pipeline in the risk period, D min is the minimum pressure of the gas source pipeline in the risk period, G K is the total flow of the kth primary branch pipeline in the risk period in the department partition connected with the gas source pipeline; F is the total flow of the gas source pipeline in the risk period, k is (1, 2, j, w), and w is the total number of department partitions in the gas supply source.

[0049] Compared with the prior art, the present application has the following beneficial effects:

[0050] The model construction module trains the neural network model for detecting the gas source pipeline, the primary branch pipeline and the secondary branch pipeline according to the gas pressure and gas flow data of different pipelines when different numbers of gas supply points are screened by the historical data acquisition module; the neural network model is trained for each pipeline level, so that the model is more focused on the characteristics and data rules of each type of pipeline, the accuracy of model prediction is improved, the model complexity is reduced, and the calculation efficiency is improved. According to the characteristics and requirements of different pipeline levels, the neural network model is individually optimized and adjusted, so that the model is more suitable for the prediction requirements of each type of pipeline. Since the model is trained for each pipeline level, when the data of the three-level pipeline is abnormal, it can be more easily found and corrected, and at the same time, the detection of the gas source pipeline, the primary branch pipeline and the secondary branch pipeline is not affected, the fault tolerance and stability of the system are improved. BRIEF DESCRIPTION OF DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0052] Fig. 1 The present application is a system framework schematic diagram;

[0053] Fig. 2A schematic diagram of a framework of a data acquisition module of the present application DETAILED DESCRIPTION

[0054] The technical solutions of the present application will be described clearly and completely below in conjunction with embodiments. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0055] Please refer to Figs. 1-2 The first aspect embodiment of the present application provides a medical gas remote monitoring system, comprising:

[0056] The data acquisition module is used to divide all gas supply pipelines into a gas source pipeline, a first branch pipeline, a second branch pipeline and a third branch pipeline, and process the data collected by the gas pressure sensors and the gas flow sensors of each pipeline to obtain characteristic frequency spectrum data of each sensor.

[0057] The template matching module is used to detect the characteristic frequency spectrum data of the third branch pipeline and the gas source of the gas supply by a template matching algorithm, and transmit a fault signal to the monitoring and alarming module when a fault occurs in the third branch pipeline or the gas source of the gas supply.

[0058] The historical data acquisition module is used to obtain historical data of the gas pressure and the gas flow of the pipeline when the gas source pipeline, the first branch pipeline, the second branch pipeline and the third branch pipeline have a leak or do not have a leak, and to screen the gas pressure and the gas flow data of different pipelines when different numbers of gas supply points are in use.

[0059] The model construction module is used to train a neural network model for detecting the gas source pipeline, the first branch pipeline and the second branch pipeline, respectively, according to the gas pressure and the gas flow data of different pipelines when different numbers of gas supply points are screened by the historical data acquisition module.

[0060] The leak monitoring module is used to obtain the data acquired by the data acquisition module in real time, and input the acquired data into the model construction module to detect whether the pipeline is used normally, wherein the number of the third branch pipeline gas flow data that is not zero is the number of gas supply points in use.

[0061] The evaluation module is used to evaluate the leakage risk coefficient of the gas source pipeline, the first branch pipeline and the second branch pipeline according to the corresponding characteristic frequency spectrum data of the gas pressure and the gas flow data of the gas source pipeline, the first branch pipeline, the second branch pipeline and the third branch pipeline.

[0062] The monitoring alarm module is used for alarming the non-normal use of the gas source pipeline, the first branch pipeline and the second branch pipeline detected by the leakage monitoring module, alarming the fault signal of the template matching module, and displaying the evaluation data of the evaluation module.

[0063] Specifically, in the embodiment, the pipeline division module divides all pipelines connected by the hospital's gas supply gas source into a gas source pipeline, a first branch pipeline, a second branch pipeline and a third branch pipeline; the historical data acquisition module acquires historical data of gas pressure and gas flow of the gas source pipeline, the first branch pipeline, the second branch pipeline and the third branch pipeline when the pipelines appear leakage and do not appear leakage, and screens the gas pressure and gas flow data of different pipelines when different numbers of gas supply points are in use; the model construction module trains the neural network model for detecting the gas source pipeline, the first branch pipeline and the second branch pipeline, respectively, according to the gas pressure and gas flow data of different pipelines when different numbers of gas supply points are screened by the historical data acquisition module; this facilitates training of the neural network model for each pipeline level, makes the model more focused on the characteristics and data rules of each type of pipeline, improves the accuracy of model prediction, trains the model of different pipeline levels respectively, reduces the complexity of the model, and improves the calculation efficiency. According to the characteristics and requirements of different pipeline levels, the neural network model is individually optimized and adjusted, so that the model is more suitable for the prediction requirements of each type of pipeline. Since the model is specially trained for each pipeline level, when the third pipeline data is abnormal, it can be more easily found and corrected, and at the same time, the detection of the gas source pipeline, the first branch pipeline and the second branch pipeline is not affected, improving the fault tolerance and stability of the system.

[0064] In one embodiment of the present application, the data acquisition module comprises:

[0065] The pipeline division unit is used for dividing all pipelines connected by the hospital's gas supply gas source, and the pipelines connected at the hospital's gas supply gas source are set as gas source pipelines; each department zone of the hospital requiring medical gas is divided according to the department, and each department zone is divided into different gas supply point groups according to the location area of the required medical gas supply point; the pipelines leading to each department zone from the gas source pipeline are first branch pipelines, the pipelines leading to each gas supply point group in each department zone from the first branch pipeline are second branch pipelines, and the pipelines leading to each gas supply point in the gas supply point group from the second branch pipeline are third branch pipelines.

[0066] The pipeline monitoring unit is provided with pressure sensors and gas flow sensors in the gas source pipeline, the first branch pipeline, the second branch pipeline and the third branch pipeline, and is used for real-time monitoring of the gas pressure and gas flow of each pipeline.

[0067] The gas source detection unit is provided with an inclination sensor and a gas purity sensor in the electrolytic tank and the gas storage tank of the medical electronic atomization mechanism of the gas supply source, for monitoring the inclination angle and the gas purity of the device;

[0068] The data processing unit is used for processing the data collected by the sensors, extracting the signal sequence, and performing power spectrum analysis to obtain the characteristic frequency spectrum data of the sensors.

[0069] In one embodiment of the present application, the template matching module detects the characteristic frequency spectrum data of the three-level branch pipes and the gas supply source by using a template matching algorithm, and transmits a fault signal to the monitoring and alarm module when a fault occurs in the three-level branch pipes or the gas supply source, including the following steps:

[0070] For the three-level branch pipes, the template matching algorithm is applied to match the characteristic frequency spectrum information corresponding to the gas pressure and the gas flow of the use state of the gas supply point of the three-level branch pipes with the preset template spectrum, so as to determine whether the three-level branch pipes have a leakage;

[0071] For the gas supply source, the characteristic frequency spectrum information corresponding to the inclination angle and the gas purity data of the electrolytic tank and the gas storage tank is matched with the preset template spectrum, so as to determine whether the electrolytic tank and the gas storage tank are used normally;

[0072] If the three-level branch pipes and the gas supply source are used normally, the leakage monitoring module is used to detect other pipes, if the gas supply source is not used normally, it is determined that the gas supply source has a fault, and a fault signal is transmitted to the monitoring and alarm module;

[0073] If the three-level branch pipes are not used normally, the corresponding three-level branch pipe number generates a fault information, and a fault signal is transmitted to the monitoring and alarm module for alarm.

[0074] Specifically, in the present embodiment, the three-level branch pipes and the gas supply source are detected first to avoid the influence of the faults of the three-level branch pipes and the gas supply source on the detection of the branch pipes, and the three-level branch pipes and the gas supply source are convenient for fault detection, and after the faults of the three-level branch pipes and the gas supply source are eliminated, the gas source pipes, the first-level branch pipes and the second-level branch pipes can be detected more scientifically.

[0075] In one embodiment of the present application, the gas pressure and the gas flow data of different pipes are screened when different numbers of gas supply points are in use, including the following steps:

[0076] In the historical data, the gas pressure and the gas flow data of the three-level branch pipes corresponding to the gas supply points are screened when the gas supply points are in use and not in use;

[0077] The gas pressure and gas flow data of the secondary branch pipe when different numbers of gas supply points in the gas supply point grouping are in use;

[0078] The gas pressure and gas flow data of the primary branch pipe when different numbers of gas supply points in the department partition are in use;

[0079] The gas pressure and gas flow data of the gas source pipe when different numbers of gas supply points in all gas supply points supplied by the gas supply gas source are in use.

[0080] In one embodiment of the present application, the model construction module trains neural network models for detecting the gas source pipe, the primary branch pipe and the secondary branch pipe, including the following steps:

[0081] The neural network model for detecting the secondary branch pipe is established by taking the feature spectrum information corresponding to the gas pressure and gas flow data of the secondary branch pipe and the number of gas supply points in use in the gas supply point grouping as input to train the neural network model for detecting the secondary branch pipe;

[0082] The neural network model for detecting the primary branch pipe is established by taking the feature spectrum information corresponding to the gas pressure and gas flow data of the primary branch pipe and the number of gas supply points in use in the department partition as input to train the neural network model for detecting the primary branch pipe;

[0083] The neural network model for detecting the gas source pipe is established by taking the feature spectrum information corresponding to the gas pressure and gas flow data of the gas source pipe and the number of gas supply points in use in all gas supply points supplied by the gas supply gas source as input to train the neural network model for detecting the gas source pipe;

[0084] The number of gas supply points in use in the gas supply point grouping is the number of pipes with non-zero flow in the corresponding tertiary branch pipe of the gas supply point.

[0085] In one embodiment of the present application, the leakage monitoring module inputs the collected data into the model construction module to detect whether the pipe is used normally, including the following steps:

[0086] The gas pressure and gas flow data of the gas source pipeline, the first branch pipeline, the second branch pipeline and the third branch pipeline acquired by the data acquisition module in real time are processed by the data processing unit to obtain real-time characteristic spectrum data of the gas pressure and gas flow data; the number of the third branch pipeline gas flow data not being zero is counted as the number of the gas supply point in the use state; the real-time characteristic spectrum data of the gas pressure and gas flow data and the number of the third branch pipeline corresponding gas supply point in the use state are input to the model construction module to detect whether the pipeline is normally used.

[0087] In one embodiment of the present application, the evaluation module evaluates the leakage risk coefficient of the gas source pipeline, the first branch pipeline and the second branch pipeline, including the following steps:

[0088] If the leakage monitoring module detects that the gas source pipeline, the first branch pipeline, the second branch pipeline and the third branch pipeline are all normally used within the preset evaluation interval time, the gas pressure and gas flow data in the time period when the gas flow data fluctuation of the gas source pipeline, the first branch pipeline and the second branch pipeline is greater than the preset threshold value are screened as risk detection data of the risk period;

[0089] According to the risk detection data of the risk period of the screened gas source pipeline, the first branch pipeline and the second branch pipeline, the leakage risk coefficients of the gas source pipeline, the first branch pipeline and the second branch pipeline are respectively calculated;

[0090] The pipeline whose leakage risk coefficient exceeds the preset threshold value is sent to the monitoring alarm module with the pipeline type and the gas pressure and gas flow data of the pipeline;

[0091] If it is the first branch pipeline and the second branch pipeline, the department partition and the gas supply point grouping data of the pipeline are sent to the monitoring alarm module.

[0092] In one embodiment of the present application, the leakage risk coefficient of the second branch pipeline is calculated by the following formula:

[0093]

[0094] Wherein, N2 is the leakage risk coefficient of the second branch pipeline, P max is the maximum pressure value of the second branch pipeline risk period, P min is the minimum pressure value of the second branch pipeline risk period, q i is the total flow of the i-th third branch pipeline in the risk period in the gas supply point grouping where the second branch pipeline is located; Q jThe total flow of the jth secondary branch pipeline in the risk period in the department partition where the primary branch pipeline is located, i∈(1, 2, i, n), and n is the total number of gas supply points in the gas supply point grouping.

[0095] Specifically, due to the use of terminal pipelines, the pressure in the pipeline fluctuates, and when the pipeline flow surges, it takes time for the gas to flow to the next level of pipeline, resulting in a small error between the pipeline flow of one type and the total flow of the next level of pipeline, and the pipeline pressure also fluctuates. The above formula can consider the increase in the number of terminal pipelines used, the impact on the pressure, and the pipeline leakage risk coefficient calculated by the flow fluctuation. In the embodiment, through a large amount of data statistics, the pipeline with a leakage risk coefficient exceeding the preset threshold value is sent to the monitoring alarm module with the pipeline type and the gas pressure and gas flow data of the pipeline. The N2 preset threshold is 0.2, and when N2 is greater than 0.2, the probability of small leakage in the pipeline increases.

[0096] In one embodiment of the present application, the leakage risk coefficient of the primary branch pipeline is calculated by the following formula:

[0097]

[0098] Wherein, N1 is the leakage risk coefficient of the primary branch pipeline, R max is the maximum pressure of the primary branch pipeline in the risk period, R min is the minimum pressure of the primary branch pipeline in the risk period, G K is the total flow of the kth primary branch pipeline in the risk period in the department partition connected by the gas source pipeline, j∈(1, 2, j, m), and m is the total number of gas supply point groupings in the department partition.

[0099] In one embodiment of the present application, the leakage risk coefficient of the gas source pipeline is calculated by the following formula:

[0100]

[0101] Wherein, N1 is the leakage risk coefficient of the gas source pipeline, D max is the maximum pressure of the gas source pipeline in the risk period, D min is the minimum pressure of the gas source pipeline in the risk period, G K is the total flow of the kth primary branch pipeline in the risk period in the department partition connected by the gas source pipeline; F is the total flow of the gas source pipeline in the risk period, k∈(1, 2, j, w), and w is the total number of department partitions in the gas supply source.

[0102] The above examples are only used to illustrate the technical method of the present application but not limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present application can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present application.

Claims

1. A remote monitoring system for medical gases, characterized in that, include: Data acquisition module: used to divide all gas supply pipelines into gas source pipelines, primary branch pipelines, secondary branch pipelines and tertiary branch pipelines, and to process the data collected by the gas pressure sensor and gas flow sensor of each pipeline to obtain the characteristic spectrum data of each sensor. Template matching module: Used to detect the characteristic spectrum data of the three-level branch pipeline and the gas supply source through template matching algorithm, and transmit the fault signal to the monitoring and alarm module when a fault occurs in the three-level branch pipeline or the gas supply source. Historical data acquisition module: Acquires historical data on gas pressure and gas flow rate of gas source pipelines, primary branch pipelines, secondary branch pipelines and tertiary branch pipelines when there is leakage and when there is no leakage, and filters the gas pressure and gas flow rate data of different pipelines when different numbers of gas supply points are in use; Model building module: Used to train neural network models for detecting gas source pipelines, primary branch pipelines, and secondary branch pipelines based on gas pressure and gas flow data of different pipelines when different numbers of gas supply points are selected by the historical data acquisition module. Leakage monitoring module: Used to acquire real-time data from the data acquisition module and input the acquired data into the model building module to detect whether the pipeline is in normal use. Among them, the number of gas flow data of the three-level branch pipeline that are not zero represents the number of gas supply points in use. The assessment module is used to assess the leakage risk coefficients of gas source pipelines, primary branch pipelines, secondary branch pipelines, and tertiary branch pipelines based on the corresponding characteristic spectrum data of gas pressure and gas flow data. This includes the following steps: The leakage risk coefficient of the secondary branch pipeline is calculated using the following formula: Where N2 is the leakage risk coefficient of the secondary branch pipeline, P max P represents the maximum pressure during the risk period of the secondary branch pipeline. min q represents the minimum pressure during the risk period of the secondary branch pipeline. i Q represents the total flow rate of the i-th tertiary branch pipeline during the risk period within the group of gas supply points where the secondary branch pipelines are located; j Let N2 be the total flow rate of the j-th secondary branch pipeline in the departmental division where the primary branch pipeline is located during the risk period, i∈(1,2,...i,...n), where n is the total number of gas supply points in the gas supply point group, and the preset threshold of N2 is 0.

2. When N2 is greater than 0.2, the probability of a minor leak in the pipeline increases. For pipelines whose leakage risk factor exceeds the preset threshold, the pipeline type, gas pressure, and gas flow data are sent to the monitoring and alarm module. Monitoring and alarm module: Used to alarm for abnormal gas source pipelines, primary branch pipelines and secondary branch pipelines detected by the leakage monitoring module, alarm for fault signals of the template matching module, and display the evaluation data of the evaluation module.

2. The medical gas remote monitoring system according to claim 1, characterized in that, The data acquisition module includes: Pipeline division unit: Used to divide all pipelines connecting the gas supply source of the hospital. The pipeline connected to the gas supply source of the hospital is set as the gas source pipeline. The various areas of the hospital that need to use medical gas are divided into departments, and each department is further divided into different gas supply point groups according to the location of the medical gas supply point. The pipeline from the gas source pipeline to each department is the primary branch pipeline. The pipeline from the primary branch pipeline to each gas supply point group within each department is the secondary branch pipeline. The pipeline from the secondary branch pipeline to each gas supply point within each gas supply point group is the tertiary branch pipeline. Pipeline monitoring unit: Pressure sensors and gas flow sensors are installed in the gas source pipeline, primary branch pipeline, secondary branch pipeline and tertiary branch pipeline to monitor the gas pressure and gas flow in each pipeline in real time; Gas source detection unit: An tilt sensor and a gas purity sensor are installed in the electrolysis chamber and gas storage chamber of the medical electronic nebulizer that supplies the gas source, in order to monitor the tilt angle of the device and the gas purity. Data processing unit: Used to process the data collected by each sensor, extract the signal sequence, and perform power spectrum analysis to obtain the characteristic spectrum data of each sensor.

3. The medical gas remote monitoring system according to claim 2, characterized in that, The template matching module uses a template matching algorithm to detect the characteristic spectrum data of the tertiary branch pipeline and the gas supply source. When a fault occurs in the tertiary branch pipeline or the gas supply source, the fault signal is transmitted to the monitoring and alarm module, including the following steps: For tertiary branch pipelines, a template matching algorithm is applied to match the characteristic spectrum information of gas pressure and gas flow rate corresponding to the gas supply point of the tertiary branch pipeline under the usage state with the template spectrum of the preset usage state in order to determine whether there is a leak in the tertiary branch pipeline. For the gas supply source, the characteristic spectrum information corresponding to the tilt angle and gas purity data of the obtained electrolysis chamber and gas storage chamber is matched with the preset template spectrum to determine whether the electrolysis chamber and gas storage chamber are in normal use. If the three-level branch pipeline and the gas supply source are both in normal use, the other pipelines are then tested through the leak detection module. If the gas supply source is not in normal use, the gas supply source is determined to be faulty, and the fault signal is transmitted to the monitoring and alarm module. If a tertiary branch pipeline is used abnormally, the corresponding tertiary branch pipeline number will be used to generate fault information, and the fault signal will be transmitted to the monitoring and alarm module to trigger an alarm.

4. The medical gas remote monitoring system according to claim 3, characterized in that, Screening gas pressure and flow rate data for different pipelines under different usage conditions at different numbers of gas supply points includes the following steps: Filter historical data to obtain gas pressure and gas flow data of the corresponding tertiary branch pipelines of each gas supply point when it is in use and when it is not in use. Gas pressure and flow rate data for secondary branch pipelines when different numbers of gas supply points in the gas supply point group are in use; Data on gas pressure and gas flow rate of primary branch pipelines when different numbers of gas supply points in a department are in use; For all gas supply points supplied by gas sources, the gas pressure and gas flow data of the gas supply pipelines when the number of gas supply points is in use is as follows.

5. A medical gas remote monitoring system according to claim 4, characterized in that, The model building module trains a neural network model for detecting gas source pipelines, primary branch pipelines, and secondary branch pipelines, including the following steps: A neural network model for detecting secondary branch pipelines was established. The model was trained by taking the characteristic spectrum information corresponding to the gas pressure and gas flow data of the secondary branch pipelines, as well as the number of gas supply points in the group of gas supply points in use, as input. A neural network model for detecting primary branch pipelines was established. The model was trained by taking the characteristic spectrum information corresponding to the gas pressure and gas flow data of the primary branch pipelines, as well as the number of gas supply points in use in the departmental area, as input. A neural network model for detecting gas source pipelines is established. The model is trained by taking the characteristic spectrum information corresponding to the gas pressure and gas flow data of the gas source pipeline, as well as the number of gas supply points in use among the gas supply points supplied by the gas source, as input. Among them, the number of gas supply points in the gas supply point group that are in use is the number of pipelines with non-zero flow in the tertiary branch pipelines corresponding to the gas supply point.

6. A medical gas remote monitoring system according to claim 5, characterized in that, The leakage monitoring module inputs the collected data into the model building module to detect whether the pipeline is in normal use, including the following steps: The data acquisition module acquires real-time gas pressure and flow rate data from the gas source pipeline, primary branch pipeline, secondary branch pipeline, and tertiary branch pipeline. The data processing unit processes the data collected by the sensors to obtain real-time characteristic spectrum data of the gas pressure and flow rate data. The number of non-zero gas flow rate data in the tertiary branch pipeline is counted as the number of gas supply points in use. The real-time characteristic spectrum data of the gas pressure and flow rate data, as well as the number of gas supply points in use corresponding to the tertiary branch pipelines, are input into the model building module to detect whether the pipeline is in normal use.

7. A medical gas remote monitoring system according to claim 1, characterized in that, The assessment module evaluates the leakage risk coefficients of the gas source pipeline, primary branch pipeline, and secondary branch pipeline, including the following steps: If the leak detection module detects that the gas source pipeline, primary branch pipeline, secondary branch pipeline and tertiary branch pipeline are all in normal use within the preset evaluation interval, the gas pressure and gas flow data of the gas source pipeline, primary branch pipeline and secondary branch pipeline during the time period when the gas flow data fluctuation is greater than the preset threshold will be selected as the risk detection data for the risk period. Based on the risk detection data of the selected gas source pipeline, primary branch pipeline, and secondary branch pipeline during the risk period, the leakage risk coefficients of the gas source pipeline, primary branch pipeline, and secondary branch pipeline are calculated respectively. For primary and secondary branch pipelines, the departmental zoning and gas supply point grouping data of the pipeline location are sent to the monitoring and alarm module.

8. A medical gas remote monitoring system according to claim 7, characterized in that, The leakage risk coefficient of the primary branch pipeline is calculated using the following formula: Where N1 is the leakage risk coefficient of the primary branch pipeline, and R max R represents the maximum pressure during the risk period of the primary branch pipeline. min G represents the minimum pressure during the risk period of the primary branch pipeline. K In the departmental area connected by the gas source pipeline, the total flow rate of the kth primary branch pipeline during the risk period is j∈(1,2,j,m), where m is the total number of gas supply point groups in the departmental area.

9. A medical gas remote monitoring system according to claim 7, characterized in that, The leakage risk coefficient of the gas source pipeline is calculated using the following formula: Where N1 is the leakage risk coefficient of the gas source pipeline, and D max D represents the maximum pressure during the high-risk period of the gas pipeline. min G represents the minimum pressure during the risk period of the gas supply pipeline. K F is the total flow rate of the kth primary branch pipeline in the departmental area connected to the gas source pipeline during the risk period; F is the total flow rate of the gas source pipeline during the risk period, k∈(1,2,j,w), and w is the total number of departmental areas in the gas source.

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