Medical gas monitoring and intelligent adjusting method, system, equipment and medium

Abstract

The invention relates to the technical field of medical gas monitoring, in particular to a medical gas monitoring and intelligent adjusting method, system and equipment and a medium. The method comprises the following steps: firstly, acquiring real-time data such as pressure, flow and temperature through multiple sensors, transmitting the data to a cloud for deep analysis, and extracting system operation state characteristics; then similarity analysis is carried out based on a pre-established historical fault case library, and risk level evaluation is realized; comparing with a preset target parameter to obtain a deviation value, and finally formulating a scientific adjustment strategy according to the deviation value and the risk level; and the reliability and the safety of system operation are obviously improved.

Description

Technical Field

[0001] The present application relates to the technical field of medical gas monitoring, and in particular to medical gas monitoring and intelligent regulation methods, systems, equipment and media. Background Art

[0002] Medical gas systems are important infrastructure in modern hospitals, providing the necessary gas medium for various medical equipment and treatment processes. With the development of medical technology and the expansion of hospital scale, higher requirements are placed on the safety, reliability and intelligence level of medical gas systems. Especially in key departments such as intensive care units and operating rooms, the stability of gas supply is directly related to patient safety.

[0003] The current medical gas monitoring system mainly adopts fixed threshold alarm and single parameter monitoring, and performs simple status monitoring by setting upper and lower pressure limits, flow warning values, etc. The adjustment of system operating parameters mainly relies on regular inspections and experience judgment, lacking intelligent analysis and prediction capabilities; this situation needs to be further improved. Summary of the invention

[0004] In order to solve the problem that the existing medical gas monitoring system lacks intelligent analysis and prediction capabilities, the present application provides a medical gas monitoring and intelligent regulation method, system, device and medium, which adopts the following technical solutions: In a first aspect, the present application provides a medical gas monitoring and intelligent regulation method, comprising the following steps: Collect pressure, flow and temperature data of medical gas system to obtain real-time operating parameters; Transmitting the real-time operating parameters to a cloud data center for data analysis to obtain system operating status characteristics; According to the system operation status characteristics, combined with a pre-established database containing historical failure cases, the current system risk level is evaluated; Based on the comparison between the system operation state characteristics and the preset target parameters, a parameter deviation value is calculated; According to the parameter deviation value and the current system risk level, a system adjustment strategy is determined to obtain a control instruction.

[0005] By adopting the above technical solutions, the medical gas system has problems such as insufficient multi-parameter collaborative analysis and delayed fault warning during operation. For example, during the peak period of gas consumption in the operating room of a tertiary hospital, the correlation between abnormal pressure fluctuations and flow changes could not be discovered in time, resulting in gas supply interruption and affecting the operation process. The present application first collects real-time data such as pressure, flow, and temperature through multiple sensors, transmits these data to the cloud for in-depth analysis, and extracts system operation status characteristics. Then, similarity analysis is performed based on a pre-established historical fault case library to achieve risk level assessment. Then, the deviation value is obtained by comparing with the preset target parameters, and finally a scientific adjustment strategy is formulated according to the deviation value and risk level. The reliability and safety of the system operation are significantly improved.

[0006] Optionally, the real-time operating parameters are transmitted to a cloud data center for data analysis to obtain system operating status characteristics, which specifically includes the following steps: Performing time series decomposition on the real-time operating parameters to obtain periodic fluctuations, seasonal changes and random disturbance components; Determine the daily fluctuation range and peak period of each operating parameter; According to a preset medical scenario type, identifying a usage mode of the real-time operating parameter, the usage mode including a conventional gas usage mode, an emergency gas usage mode, and an equipment maintenance mode; Calculate the matching degree between the usage pattern and the fluctuation range of the current time period; Based on the matching degree and the usage mode, a system operation status feature is obtained.

[0007] By adopting the above technical solution, when the number of day surgeries is large and emergency rescue is frequent, if the system cannot accurately distinguish between routine gas use and emergency gas use, it will lead to unreasonable gas allocation and affect the quality of gas supply; this application first performs a time series decomposition of the real-time operating parameters, and innovatively separates the parameter changes into three dimensions: periodic fluctuations, seasonal changes, and random disturbances; then determines the dynamic fluctuation law of the parameters through statistical analysis; then, based on the preset medical scenario feature library, establishes a discrimination mechanism for the three typical modes of routine gas use, emergency gas use, and equipment maintenance; then, achieves accurate scene identification through matching degree calculation; and finally obtains accurate system operation status characteristics, thereby improving the adaptability of the scene.

[0008] Optionally, obtaining system operation status characteristics based on the matching degree and the usage mode specifically includes the following steps: Determining characteristic indicators according to the usage mode, wherein the characteristic indicators include pressure stability, flow uniformity and temperature fluctuation rate; Calculating a weight coefficient of each characteristic index based on the matching degree, and performing weighted calculation on the characteristic index using the weight coefficient to obtain a quantized value; The quantized values ​​are combined to construct a system state vector, and the system state vector is standardized to obtain a system operation state feature.

[0009] By adopting the above technical solution, the present application first dynamically selects pressure stability, flow uniformity and temperature fluctuation rate according to the usage mode; then introduces the scene matching degree into the weight calculation process to realize the dynamic adjustment of the feature indicator weight; finally, through the state vector construction and standardization processing, a unified evaluation standard is established, the system state is quantified, and the accuracy of state evaluation is improved through adaptive weight allocation.

[0010] Optionally, according to the system operation status characteristics, combined with a pre-established database containing historical failure cases, the current system risk level is evaluated, specifically including the following steps: Extracting the top N fault cases with the highest feature similarity from the historical fault case database; Calculating the characteristic distance between the system operation state characteristic and each fault case; Weighting the fault cases based on the characteristic distance to obtain a probability distribution of fault types; Calculate a comprehensive risk index based on the probability distribution of the fault types and the severity of each type of fault; The comprehensive risk index is mapped to a preset risk level interval to obtain the current system risk level.

[0011] By adopting the above technical solution, the present application first uses a similarity matching algorithm to screen out N similar cases with the most reference value from the historical fault case library; then, through feature distance calculation, the degree of proximity between the current state and the historical fault is quantified; then, a fault probability distribution model is established based on the distance weight to achieve an accurate assessment of the possibility of occurrence of different types of faults; then, a comprehensive risk index is constructed in combination with the severity of the fault; finally, the risk level is determined through intelligent mapping; the accuracy and foresight of risk assessment are improved, and the safety and reliability of system operation are significantly improved.

[0012] Optionally, the parameter deviation value is calculated based on the comparison between the system operation state characteristics and the preset target parameters, which specifically includes the following steps: Obtain the gas usage priority and importance level of each department and establish a department weight matrix; Based on the department weight matrix, the dynamic adjustment coefficient of each operating parameter is calculated; Perform parameter normalization on the system operation status characteristics, calculate the deviation between the normalized parameters and their respective target values, and obtain the basic deviation value; The basic deviation value is weightedly corrected according to the dynamic adjustment coefficient to obtain a final parameter deviation value.

[0013] By adopting the above technical scheme, this application first introduces the department weight matrix to quantify the gas usage priority and importance level of each department into specific indicators; then dynamically generates the adjustment coefficient of the operating parameters based on the weight matrix, realizing differentiated management of parameter adjustment; then, through parameter normalization and target value comparison, a standardized deviation calculation system is established; finally, weighted correction is performed through dynamic adjustment coefficients; not only differentiated management of departments is realized, but also the pertinence and rationality of parameter adjustment are improved through dynamic weight adjustment.

[0014] Optionally, determining a system adjustment strategy according to the parameter deviation value and the current system risk level to obtain a control instruction specifically includes the following steps: Constructing a decision matrix according to the parameter deviation value and the current system risk level; Determine the current system state point position in the decision matrix, and select a corresponding adjustment strategy template according to the state point position; A specific parameter adjustment scheme is generated based on the adjustment strategy template, and the parameter adjustment scheme is converted into a control instruction.

[0015] By adopting the above technical solution, this application constructs a system status decision matrix based on parameter deviation values ​​and risk levels; then accurately identifies system status points through matrix positioning technology, and achieves accurate matching of adjustment strategies; finally, generates specific control instructions based on preset strategy templates, establishes a decision execution chain, and improves the scientificity and timeliness of decisions.

[0016] Optionally, the method further comprises the following steps: Establishing a hierarchical authority graphical user interface, the user interface including a real-time data visualization module, a fault diagnosis module and a remote control module; Displaying the system operation status characteristics in the form of a trend graph, wherein the trend graph includes a pressure change line graph, a flow distribution bar graph, and a temperature distribution pie chart; Invoke a fault diagnosis library according to the current system risk level to generate a maintenance guide including fault definition, cause analysis and repair suggestions; The user interface is connected to a remote monitoring terminal, and corresponding data access and control are enabled based on user authority levels.

[0017] By adopting the above technical solution, when dealing with system anomalies, the hospital's maintenance personnel need to switch between multiple independent systems to view data, and lack clear fault diagnosis guidance, resulting in inefficient problem handling. In addition, improper permission settings may cause security risks during remote management. This application first designs a graphical interface framework with hierarchical permissions, which organically integrates real-time monitoring, fault diagnosis and remote control functions. Then, through a variety of data visualization technologies, the system operation status is displayed in intuitive forms such as line graphs, bar graphs and pie charts. A fault diagnosis support system is then established to automatically generate a professional instruction manual containing fault definition, cause analysis and repair suggestions. Finally, a permission-based access control mechanism is used to ensure system security and improve the efficiency of system maintenance.

[0018] In a second aspect, the present application provides a medical gas monitoring and intelligent regulation system, comprising: Real-time operation parameter acquisition module, used to collect pressure, flow and temperature data of the medical gas system to obtain real-time operation parameters; A system operation status feature acquisition module is used to transmit the real-time operation parameters to a cloud data center for data analysis to obtain system operation status features; A current system risk level assessment module, used to assess the current system risk level based on the system operation status characteristics and in combination with a pre-established database containing historical fault cases; A parameter deviation value calculation module is used to compare the system operation state characteristics with the preset target parameters to calculate the parameter deviation value; The control instruction acquisition module is used to determine the system adjustment strategy and obtain the control instruction according to the parameter deviation value and the current system risk level.

[0019] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned medical gas monitoring and intelligent regulation method when executing the computer program.

[0020] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned medical gas monitoring and intelligent regulation method.

[0021] In summary, the present application includes at least one of the following beneficial technical effects: This application first collects real-time data such as pressure, flow, and temperature through multiple sensors, transmits these data to the cloud for in-depth analysis, and extracts system operation status characteristics; then performs similarity analysis based on a pre-established historical fault case library to achieve risk level assessment; then obtains deviation values ​​by comparing with preset target parameters, and finally formulates scientific adjustment strategies based on deviation values ​​and risk levels; significantly improving the reliability and safety of system operation; In the case of a large number of day surgeries and frequent emergency rescues, if the system cannot accurately distinguish between routine gas use and emergency gas use, it will lead to unreasonable gas allocation and affect the quality of gas supply. This application first decomposes the real-time operating parameters in time series, and innovatively separates the parameter changes into three dimensions: periodic fluctuations, seasonal changes, and random disturbances. Then, the dynamic fluctuation law of the parameters is determined through statistical analysis. Then, according to the preset medical scene feature library, a discrimination mechanism for the three typical modes of routine gas use, emergency gas use, and equipment maintenance is established. Then, the scene is accurately identified through matching calculation. Finally, accurate system operation status characteristics are obtained, which improves the adaptability of the scene. This application first dynamically selects pressure stability, flow uniformity and temperature fluctuation rate according to the usage mode; then introduces the scene matching degree into the weight calculation process to realize the dynamic adjustment of the feature indicator weight; finally, through state vector construction and standardization processing, a unified evaluation standard is established to realize the quantification of system status, and the accuracy of status evaluation is improved through adaptive weight allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a schematic diagram of a process of a medical gas monitoring and intelligent regulation method in an embodiment of the present application. Figure 1 ; Figure 2 It is a flowchart of step S200 in a medical gas monitoring and intelligent regulation method according to an embodiment of the present application; Figure 3 It is a flowchart of step S250 in a medical gas monitoring and intelligent regulation method according to an embodiment of the present application; Figure 4 It is a flowchart of step S300 in a medical gas monitoring and intelligent regulation method according to an embodiment of the present application; Figure 5 It is a flowchart of step S400 in a medical gas monitoring and intelligent regulation method according to an embodiment of the present application; Figure 6 It is a flowchart of step S500 in a medical gas monitoring and intelligent regulation method according to an embodiment of the present application; Figure 7 This is a schematic diagram of a process of a medical gas monitoring and intelligent regulation method in an embodiment of the present application. Figure 2 ; Figure 8 This is a module schematic diagram of a medical gas monitoring and intelligent regulation system according to an embodiment of the present application; Fig. 9 It is a diagram of the internal structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0024] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0025] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0026] In the first aspect, the present application provides a medical gas monitoring and intelligent regulation method, referring to Figure 1 , including the following steps: S100, collecting pressure, flow, and temperature data of the medical gas system to obtain real-time operating parameters.

[0027] In this embodiment, the operation data is collected by arranging a sensor network at key nodes of the medical gas system. The medical gas system includes supply pipelines such as oxygen, nitrogen, and compressed air. High-precision sensors are installed on the main pipelines, department branches, and terminal gas points of each pipeline to achieve real-time monitoring of system operation parameters.

[0028] Specifically, pressure sensors, flow sensors and temperature sensors are installed at key locations in the medical gas system. Among them, the pressure sensor is arranged at the main gas supply pipeline and branches on each floor to monitor the pressure status of the pipeline network; the flow sensor is installed at the air inlet of each department to record the change in gas consumption in real time; the temperature sensor is used to monitor the gas temperature in the pipeline. These sensors transmit the collected data to the nearest data acquisition unit through the industrial bus to form a complete monitoring network.

[0029] S200: Transmit the real-time operating parameters to the cloud data center for data analysis to obtain system operating status characteristics.

[0030] In this embodiment, edge computing combined with cloud analysis is used to process data. A local processing unit is set up inside the hospital to perform preliminary processing on the collected data, and then the processed data is uploaded to the cloud for in-depth analysis, which improves data processing efficiency.

[0031] Specifically, the local processing unit first pre-processes the sensor data, and the cloud analysis module combines the pipeline network structure model to extract and analyze the system operation characteristics.

[0032] S300. Evaluate the current system risk level based on system operation status characteristics and in combination with a pre-established database containing historical failure cases.

[0033] In this embodiment, the system pre-establishes a database of medical gas system fault cases. The database records various fault conditions that occurred during historical operation, including fault characteristics, type classification, and processing methods, providing a reference for risk assessment.

[0034] Specifically, the system evaluates the current operation risk through feature matching. The real-time operation features are compared and analyzed with historical cases, and the risk level is determined based on the degree of similarity and fault level. For example, when the system detects that the operation features of a certain area are similar to the historical gas outage features, an early warning signal is issued in a timely manner.

[0035] S400: Compare the system operation status characteristics with the preset target parameters to calculate the parameter deviation value.

[0036] In this embodiment, the system pre-sets differentiated target parameters for different departments. Key departments such as intensive care units and operating rooms adopt higher control standards, while ordinary areas adopt conventional standards.

[0037] Specifically, the system compares the real-time operating parameters with the department's target values. By calculating the difference between the actual value and the target value, a standardized deviation index is obtained. For example, when the gas supply pressure in the operating room deviates, the system can promptly calculate the degree of deviation from the target value and provide a reference for adjustment.

[0038] S500: Determine a system adjustment strategy based on the parameter deviation value and the current system risk level, and obtain a control instruction.

[0039] In this embodiment, a hierarchical adjustment strategy is adopted to formulate a control plan. According to the combination of parameter deviation degree and risk level, corresponding adjustment measures are selected to ensure the smooth operation of the system.

[0040] Specifically, the system presets a multi-level strategy of basic regulation, moderate regulation and emergency regulation. It automatically selects the appropriate regulation scheme and generates control instructions according to the real-time status. For example, when the gas supply in a department is abnormal, the system can select the appropriate regulation scheme according to the specific situation, such as adjusting the gas supply parameters or activating the backup pipeline.

[0041] In one embodiment, referring to Figure 2 In step S200, the real-time operating parameters are transmitted to the cloud data center for data analysis to obtain system operating status characteristics, which specifically includes the following steps: S210, performing time series decomposition on the real-time operation parameters to obtain periodic fluctuations, seasonal changes and random disturbance components.

[0042] In this embodiment, a time series analysis method is used to process the operating parameters of the medical gas system, and the change characteristics on different time scales are identified by decomposing the continuously collected data.

[0043] Specifically, the system uses a sliding time window to process the data in segments, identifying hourly periodic fluctuations, daily and monthly seasonal changes, and irregular random disturbances. For example, after decomposing the pressure data of a hospital's oxygen supply system, it is possible to clearly see the gas usage patterns during the day and night, as well as the seasonal changes during holidays.

[0044] S220: Determine the daily fluctuation range and peak occurrence period of each operating parameter.

[0045] In this embodiment, the system establishes a dynamic parameter fluctuation range management mechanism. The system calculates the statistical characteristics of parameters in different time periods based on historical operation data and establishes a scientific fluctuation range determination standard.

[0046] Specifically, the system counts the changing patterns of various parameters according to time periods, and determines the normal fluctuation range and peak occurrence patterns. For example, by analyzing the gas usage data of a hospital's emergency department, it was found that gas usage peaks often occur between 9 and 11 a.m. on weekdays, and a more targeted gas supply strategy can be formulated based on this.

[0047] S230. According to a preset medical scenario type, identify a usage mode of the real-time operating parameters, where the usage mode includes a conventional gas usage mode, an emergency gas usage mode, and an equipment maintenance mode.

[0048] In this embodiment, the system presets a variety of typical gas usage modes for medical scenarios. By analyzing the real-time operating parameter characteristics, the current gas usage status is classified into the corresponding usage mode.

[0049] Specifically, the system identifies the current gas usage mode based on parameter change characteristics. The conventional gas usage mode is characterized by stable parameter changes; the emergency gas usage mode has the characteristics of sudden rapid increases; and the equipment maintenance mode presents regular periodic changes. For example, when the system detects a sudden increase in the flow rate of a department, it can quickly determine whether to enter the emergency gas usage mode.

[0050] S240: Calculate the matching degree between the usage pattern and the fluctuation range of the current time period.

[0051] In this embodiment, the system pre-establishes a pattern matching evaluation system, which can accurately judge the system operation status by calculating the matching degree between the current operation parameters and various usage pattern characteristics.

[0052] Specifically, the system will conduct a correlation analysis between the real-time parameters and the preset mode characteristics and calculate the matching score. For example, when the gas usage parameters in a certain area have a high degree of similarity with the characteristics of the emergency mode, the system will respond according to the emergency gas usage mode first.

[0053] S250: Based on the matching degree and the usage mode, obtain system operation status characteristics.

[0054] Specifically, the system considers the matching score and the type of usage mode to construct a characteristic description of the system's operating status. The dominant operating mode is determined based on the matching score, and a system operating status characteristic report is generated based on the specific performance of each parameter. For example, in a hospital's operating area during the morning routine surgery period, the system can accurately identify the regular gas usage mode and give the corresponding operating status evaluation results.

[0055] In one embodiment, referring to Figure 3 In step S250, based on the matching degree and the usage mode, the system operation status characteristics are obtained, which specifically includes the following steps: S251. Determine characteristic indicators according to the usage mode, the characteristic indicators including pressure stability, flow uniformity and temperature fluctuation rate.

[0056] In this embodiment, according to the characteristics of different usage modes, corresponding characteristic indicators are selected for status evaluation. In the conventional gas usage mode, the focus is on system stability, in the emergency gas usage mode, the focus is on response speed, and in the equipment maintenance mode, the focus is on monitoring the system regulation ability.

[0057] Specifically, the system sets a corresponding combination of characteristic indicators for each usage mode. Pressure stability reflects the fluctuation of gas supply pressure, flow uniformity represents the balance of gas distribution, and temperature fluctuation rate reflects the temperature change characteristics of the system. For example, in the conventional gas usage mode of a hospital operating room, the system mainly monitors the pressure stability indicator to ensure the stability of gas supply pressure during surgery.

[0058] S252: Calculate the weight coefficient of each characteristic index based on the matching degree, and use the weight coefficient to perform weighted calculation on the characteristic index to obtain a quantized value.

[0059] In this embodiment, a dynamic weight allocation mechanism is adopted to adaptively adjust the importance of each indicator according to the pattern matching degree. The higher the matching degree of the pattern, the higher the weight of the corresponding feature indicator.

[0060] Specifically, the system designs a weight calculation formula through the mode matching degree and performs weighted operations on various characteristic indicators. When the matching degree of a certain usage mode is high, the characteristic indicators related to the mode will obtain a larger weight coefficient. For example, in the emergency gas mode, the weight coefficient of flow uniformity will be increased accordingly to ensure the stable supply of each gas point.

[0061] S253, combining the quantized values ​​to construct a system state vector, performing standardization processing on the system state vector, and obtaining system operation state characteristics.

[0062] In this embodiment, the system establishes a multi-dimensional state vector to characterize the system operation state. By combining various quantitative indicators according to preset rules, a feature vector reflecting the overall state of the system is constructed, and normalized to make it comparable.

[0063] Specifically, the system organizes the weighted characteristic indicators into a state vector according to a predetermined format, and uses a standardized method to map each component to a unified interval.

[0064] In one embodiment, referring to Figure 4 In step S300, according to the system operation status characteristics, combined with the pre-established database containing historical fault cases, the current system risk level is evaluated, which specifically includes the following steps: S310: extract the top N fault cases with the highest feature similarity from the historical fault case database.

[0065] In this embodiment, a feature similarity retrieval method is used to filter relevant cases from the historical fault database. The system retrieves fault records with similar features in the historical database based on the current operating status features.

[0066] Specifically, the system uses a multi-dimensional feature matching algorithm to calculate the similarity score and selects several cases with the highest similarity for analysis.

[0067] S320: Calculate the characteristic distance between the system operation status characteristic and each fault case.

[0068] Specifically, the distance between the current state and the historical case in the feature space is calculated to quantify the similarity between the two.

[0069] S330 . Weight the fault cases based on the feature distance to obtain a fault type probability distribution.

[0070] In this embodiment, the case with a smaller feature distance obtains a larger weight, thereby playing a more important role in the probability distribution calculation.

[0071] Specifically, the system designs a weight calculation function based on the characteristic distance and calculates the weighted probability of each type of fault. For example, when an abnormality occurs in a gas supply system, historical gas outage fault cases with a similar characteristic distance to the current state will receive a higher weight, and the probability of the corresponding fault type will also increase accordingly.

[0072] S340. Calculate the comprehensive risk index based on the probability distribution of fault types and the severity of each type of fault.

[0073] Specifically, the system multiplies the probability of the fault type and the severity level and sums them up to obtain a comprehensive index that reflects the overall risk. For example, when the system detects that a gas supply interruption fault may occur in a certain area, considering the high severity of this type of fault, a higher risk index will be obtained even if the probability is low.

[0074] S350: Map the comprehensive risk index to a preset risk level range to obtain the current system risk level.

[0075] In this embodiment, the system pre-sets a hierarchical risk assessment standard. According to the numerical range of the risk index, the system operation status is divided into different risk levels.

[0076] Specifically, the system presets multiple risk levels such as low risk, medium risk and high risk, and determines the current risk level through risk index mapping.

[0077] In one embodiment, referring to Figure 5 In step S400, the parameter deviation value is calculated based on the comparison between the system operation state characteristics and the preset target parameters, which specifically includes the following steps: S410. Obtain the gas usage priority and importance level of each department and establish a department weight matrix.

[0078] In this embodiment, the departments of the hospital are divided into different priority levels according to the characteristics of the medical tasks of each department and the importance of gas demand, forming a differentiated management strategy among departments.

[0079] Specifically, the system evaluates the importance of gas use in each department and establishes a weight matrix that includes priority and importance levels. For example, operating rooms and intensive care units are given the highest priority, while general wards are set to a lower priority, thereby ensuring the gas demand of key departments when allocating resources.

[0080] S420. Calculate the dynamic adjustment coefficient of each operating parameter based on the department weight matrix.

[0081] In this embodiment, a dynamic weight allocation mechanism is adopted to calculate the adjustment coefficient of the operating parameters according to the department weight matrix.

[0082] Specifically, the system designs the adjustment coefficient calculation formula based on the department weight to achieve differentiated management of parameter control. Among them, αi=Wi×Pi×Ci, αi is the adjustment coefficient of the i-th department; Wi is the department weight value (between 0 and 1); Pi is the priority coefficient (between 1 and 3, 3 indicates the highest priority); Ci is the department importance correction coefficient (between 1.0 and 1.5).

[0083] S430, performing parameter normalization processing on the system operation status characteristics, performing deviation calculation between the normalized parameters and respective target values, and obtaining a basic deviation value.

[0084] In this embodiment, the operating state characteristics are normalized to make parameters of different dimensions comparable, which facilitates the calculation of deviations from target values.

[0085] S440. Perform weighted correction on the basic deviation value according to the dynamic adjustment coefficient to obtain a final parameter deviation value.

[0086] In this embodiment, the system performs a weighted calculation on the dynamic adjustment coefficient and the basic deviation value to generate a deviation result that reflects the actual control demand. For example, when the gas supply parameters of the ICU fluctuate, due to its higher adjustment coefficient, the system will give a larger deviation value, indicating that priority adjustment is required.

[0087] In one embodiment, referring to Figure 6 In step S500, according to the parameter deviation value and the current system risk level, the system adjustment strategy is determined to obtain the control instruction, which specifically includes the following steps: S510: Construct a decision matrix according to the parameter deviation value and the current system risk level.

[0088] S520: Determine the current system state point position in the decision matrix, and select a corresponding adjustment strategy template according to the state point position.

[0089] In this embodiment, the system presets a variety of adjustment strategy templates. According to the position of the system state point in the decision matrix, the most suitable adjustment strategy template for the current situation is selected.

[0090] Specifically, the system configures corresponding strategy templates for different areas of the decision matrix, including stable maintenance, fine-tuning optimization, rapid adjustment, and emergency intervention. For example, when the state point falls in the high deviation-high risk area, the system will select the emergency intervention strategy template and take more radical adjustment measures.

[0091] S530: Generate a specific parameter adjustment plan based on the adjustment strategy template, and convert the parameter adjustment plan into a control instruction.

[0092] In this embodiment, the system pre-establishes a conversion mechanism from a policy template to a specific control instruction. The system generates a detailed adjustment plan based on the selected policy template and current operating parameters, and converts it into a control instruction that can be executed by the device.

[0093] Specifically, the system sets specific parameter adjustment targets and adjustment steps based on the strategy template, and then generates the corresponding control instruction sequence. For example, when the gas supply pressure in a certain area needs to be adjusted, the system will determine the adjustment amplitude and rate according to the template rules, and generate specific control instructions such as valve opening and flow adjustment.

[0094] In one embodiment, referring to Figure 7 , the method further comprises the steps of: S600: Establish a graphical user interface with hierarchical permissions, where the user interface includes a real-time data visualization module, a fault diagnosis module, and a remote control module.

[0095] In this embodiment, the system develops a user interaction system based on B / S architecture. Through hierarchical authority management, differentiated functional modules and data access rights are provided to users in different positions, realizing refined management of system operation.

[0096] Specifically, the system has designed a three-level authority structure, including operator level, administrator level and system level. For example, operator-level users can view real-time data and basic fault information, administrator-level users can set parameters and handle faults, and system-level users have full control permissions, including advanced functions such as remote debugging and system configuration.

[0097] S700. Display system operation status characteristics in the form of a trend chart, where the trend chart includes a pressure change line chart, a flow distribution bar chart, and a temperature distribution pie chart.

[0098] In this embodiment, the system converts the operating status characteristics into intuitive graphical displays to help users quickly understand system operating trends and abnormal situations.

[0099] Specifically, the system selects appropriate graphical expressions based on the characteristics of different parameters. For example, pressure changes are displayed using a line graph that is updated in real time to show the time trend, flow distribution in each department is displayed using a dynamic bar graph to show the usage comparison, and temperature distribution is displayed using a pie chart to reflect the temperature proportion of different areas.

[0100] S800. Call the fault diagnosis library according to the current system risk level and generate a maintenance guide manual including fault definition, cause analysis and repair suggestions.

[0101] In this embodiment, corresponding fault cases and processing solutions are automatically matched according to the current risk level to provide professional technical guidance for maintenance personnel.

[0102] Specifically, the system has designed a hierarchical fault diagnosis manual generation mechanism. For example, when the system detects abnormal gas pressure in a certain area, it will automatically generate a maintenance guide document containing the definition of abnormal pressure, possible cause analysis and specific repair steps, and recommend appropriate treatment plans based on actual conditions.

[0103] S900. Connect the user interface to the remote monitoring terminal, and enable corresponding data access and control based on the user authority level.

[0104] In this embodiment, the system allows authorized users to monitor the system operation status in real time through mobile devices through a secure data transmission channel, and perform corresponding control operations according to the authority.

[0105] Specifically, the system has developed access interfaces that are compatible with different terminal devices. For example, maintenance personnel can view the operating data of the area they are responsible for and receive fault alarm information through a mobile phone APP, while management personnel can perform remote parameter adjustments and system configurations through a tablet computer.

[0106] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0107] In the second aspect, the present application provides a medical gas monitoring and intelligent regulation system. The medical gas monitoring and intelligent regulation system of the present application is described below in combination with the above-mentioned medical gas monitoring and intelligent regulation method.

[0108] Reference Figure 8 , a medical gas monitoring and intelligent regulation system, comprising: Real-time operation parameter acquisition module, used to collect pressure, flow and temperature data of the medical gas system to obtain real-time operation parameters; The system operation status feature acquisition module is used to transmit the real-time operation parameters to the cloud data center for data analysis to obtain the system operation status features; The current system risk level assessment module is used to assess the current system risk level based on the system operation status characteristics and in combination with a pre-established database containing historical fault cases; A parameter deviation value calculation module is used to compare the system operation status characteristics with the preset target parameters to calculate the parameter deviation value; The control instruction acquisition module is used to determine the system adjustment strategy and obtain the control instruction according to the parameter deviation value and the current system risk level.

[0109] In one embodiment, the system operation status feature acquisition module includes: The time series decomposition unit is used to perform time series decomposition on real-time operation parameters to obtain periodic fluctuations, seasonal changes and random disturbance components; A fluctuation range determination unit, used to determine the daily fluctuation range and peak occurrence period of each operating parameter; A usage mode recognition unit is used to identify the usage mode of the real-time operating parameters according to the preset medical scenario type, and the usage mode includes a conventional gas usage mode, an emergency gas usage mode and an equipment maintenance mode; A matching degree calculation unit, used to calculate the matching degree between the usage pattern and the fluctuation range of the current time period; The state feature acquisition unit is used to obtain the system operation state feature based on the matching degree and the usage mode.

[0110] In one embodiment, the state feature acquisition unit includes: A characteristic index determination component, used for determining characteristic indexes according to a usage mode, wherein the characteristic indexes include pressure stability, flow uniformity, and temperature fluctuation rate; The weight calculation component is used to calculate the weight coefficient of each feature index based on the matching degree, and use the weight coefficient to perform weighted calculation on the feature index to obtain a quantitative value; The state vector processing component is used to combine the quantized values ​​to construct the system state vector, standardize the system state vector, and obtain the system operation state characteristics.

[0111] In one embodiment, the current system risk level assessment module includes: A fault case extraction unit is used to extract the top N fault cases with the highest feature similarity from the historical fault case database; A characteristic distance calculation unit, used to calculate the characteristic distance between the system operation status characteristic and each fault case; A probability distribution acquisition unit, used for weighting the fault cases based on the feature distance to obtain the probability distribution of the fault type; A risk index calculation unit is used to calculate a comprehensive risk index based on the probability distribution of fault types and the severity of each type of fault; The risk level determination unit is used to map the comprehensive risk index to a preset risk level interval to obtain the current system risk level.

[0112] In one embodiment, the parameter deviation value calculation module includes: A weight matrix establishment unit is used to obtain the gas usage priority and importance level of each department and establish a department weight matrix; An adjustment coefficient calculation unit, used to calculate the dynamic adjustment coefficient of each operating parameter based on the department weight matrix; A basic deviation calculation unit is used to perform parameter normalization processing on the system operation status characteristics, and perform deviation calculation between the normalized parameters and their respective target values ​​to obtain a basic deviation value; The weighted correction unit is used to perform weighted correction on the basic deviation value according to the dynamic adjustment coefficient to obtain the final parameter deviation value.

[0113] In one embodiment, the control instruction acquisition module includes: A decision matrix construction unit is used to construct a decision matrix according to parameter deviation values ​​and current system risk level; A state point positioning unit is used to determine the current system state point position in the decision matrix and select a corresponding adjustment strategy template according to the state point position; The instruction generation unit is used to generate a specific parameter adjustment plan based on the adjustment strategy template and convert the parameter adjustment plan into a control instruction.

[0114] In one embodiment, it further includes: A user interface module, used to establish a graphical user interface with hierarchical permissions, the user interface includes a real-time data visualization module, a fault diagnosis module and a remote control module; A data display module is used to display the system operation status characteristics in the form of a trend chart, which includes a pressure change line chart, a flow distribution bar chart and a temperature distribution pie chart; The fault diagnosis module is used to call the fault diagnosis library according to the current system risk level and generate a maintenance guide containing fault definition, cause analysis and repair suggestions; The remote monitoring module is used to connect the user interface to the remote monitoring terminal and open corresponding data access and control based on the user's authority level.

[0115] In one embodiment, the present application provides an electronic device, which may be a server, and its internal structure diagram may be as follows: Fig. 9As shown. The electronic device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store data. The network interface of the electronic device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a medical gas monitoring and intelligent regulation method is implemented.

[0116] Those skilled in the art will understand that Fig. 9 The structure shown in the figure is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the electronic device to which the scheme of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0117] In one embodiment, an electronic device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0118] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the above-mentioned computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0119] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A medical gas monitoring and intelligent regulation method, characterized in that: The steps include: Collect pressure, flow and temperature data of medical gas system to obtain real-time operating parameters; Transmitting the real-time operating parameters to a cloud data center for data analysis to obtain system operating status characteristics; According to the system operation status characteristics, combined with a pre-established database containing historical failure cases, the current system risk level is evaluated; Based on the comparison between the system operation state characteristics and the preset target parameters, a parameter deviation value is calculated; According to the parameter deviation value and the current system risk level, a system adjustment strategy is determined to obtain a control instruction.

2. The medical gas monitoring and intelligent regulation method according to claim 1, characterized in that: The real-time operating parameters are transmitted to the cloud data center for data analysis to obtain system operating status characteristics, which specifically includes the following steps: Performing time series decomposition on the real-time operating parameters to obtain periodic fluctuations, seasonal changes and random disturbance components; Determine the daily fluctuation range and peak period of each operating parameter; According to a preset medical scenario type, identifying a usage mode of the real-time operating parameter, the usage mode including a conventional gas usage mode, an emergency gas usage mode, and an equipment maintenance mode; Calculate the matching degree between the usage pattern and the fluctuation range of the current time period; Based on the matching degree and the usage mode, a system operation status feature is obtained.

3. The medical gas monitoring and intelligent regulation method according to claim 2, characterized in that: Based on the matching degree and the usage mode, a system operation status feature is obtained, which specifically includes the following steps: Determining characteristic indicators according to the usage mode, wherein the characteristic indicators include pressure stability, flow uniformity and temperature fluctuation rate; Calculating a weight coefficient of each characteristic index based on the matching degree, and performing weighted calculation on the characteristic index using the weight coefficient to obtain a quantized value; The quantized values ​​are combined to construct a system state vector, and the system state vector is standardized to obtain a system operation state feature.

4. The medical gas monitoring and intelligent regulation method according to claim 1, characterized in that: According to the system operation status characteristics, combined with a pre-established database containing historical fault cases, the current system risk level is evaluated, specifically including the following steps: Extracting the top N fault cases with the highest feature similarity from the historical fault case database; Calculating the characteristic distance between the system operation state characteristic and each fault case; Weighting the fault cases based on the characteristic distance to obtain a probability distribution of fault types; Calculate a comprehensive risk index based on the probability distribution of the fault types and the severity of each type of fault; The comprehensive risk index is mapped to a preset risk level interval to obtain the current system risk level.

5. The medical gas monitoring and intelligent regulation method according to claim 1, characterized in that: Based on the comparison between the system operation state characteristics and the preset target parameters, the parameter deviation value is calculated, which specifically includes the following steps: Obtain the gas usage priority and importance level of each department and establish a department weight matrix; Based on the department weight matrix, the dynamic adjustment coefficient of each operating parameter is calculated; Perform parameter normalization on the system operation status characteristics, calculate the deviation between the normalized parameters and their respective target values, and obtain the basic deviation value; The basic deviation value is weightedly corrected according to the dynamic adjustment coefficient to obtain a final parameter deviation value.

6. The medical gas monitoring and intelligent regulation method according to claim 5, characterized in that: According to the parameter deviation value and the current system risk level, a system adjustment strategy is determined to obtain a control instruction, which specifically includes the following steps: Constructing a decision matrix according to the parameter deviation value and the current system risk level; Determine the current system state point position in the decision matrix, and select a corresponding adjustment strategy template according to the state point position; A specific parameter adjustment scheme is generated based on the adjustment strategy template, and the parameter adjustment scheme is converted into a control instruction.

7. The medical gas monitoring and intelligent regulation method according to claim 1, characterized in that: The method further comprises the steps of: Establishing a hierarchical authority graphical user interface, the user interface including a real-time data visualization module, a fault diagnosis module and a remote control module; Displaying the system operation status characteristics in the form of a trend graph, wherein the trend graph includes a pressure change line graph, a flow distribution bar graph, and a temperature distribution pie chart; Invoke a fault diagnosis library according to the current system risk level to generate a maintenance guide including fault definition, cause analysis and repair suggestions; The user interface is connected to a remote monitoring terminal, and corresponding data access and control are enabled based on user authority levels.

8. A medical gas monitoring and intelligent regulation system, characterized in that: include: Real-time operation parameter acquisition module, used to collect pressure, flow and temperature data of the medical gas system to obtain real-time operation parameters; A system operation status feature acquisition module is used to transmit the real-time operation parameters to a cloud data center for data analysis to obtain system operation status features; A current system risk level assessment module, used to assess the current system risk level based on the system operation status characteristics and in combination with a pre-established database containing historical fault cases; A parameter deviation value calculation module is used to compare the system operation state characteristics with the preset target parameters to calculate the parameter deviation value; The control instruction acquisition module is used to determine the system adjustment strategy and obtain the control instruction according to the parameter deviation value and the current system risk level.

9. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the medical gas monitoring and intelligent regulation method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the medical gas monitoring and intelligent regulation method described in any one of claims 1 to 7 are implemented.

Images