Medical intelligent multilayer protective gas delivery system, method and device and electronic equipment

Abstract

The invention relates to the field of medical equipment, and discloses a medical intelligent multi-layer protective gas conveying system, method and device and electronic equipment, and the system comprises a data collection module which is used for collecting gas flow, pressure, temperature and leakage state information in real time; the path optimization module is used for dynamically optimizing a gas conveying path; the control execution module adjusts the opening degree of the valve and the pumping power according to the optimization result; the feedback updating module feeds back the execution effect after path switching to the optimization model, and dynamically adjusts optimization parameters; the monitoring platform module visually displays the operation state of the system and provides alarming and fault positioning functions under abnormal conditions. Through closed-loop optimization and intelligent regulation and control, the energy consumption and leakage risk are reduced, the gas conveying efficiency and stability are improved, meanwhile, the real-time requirement in a complex medical scene is met, and the system is suitable for conveying scenes of medical gas such as oxygen and anesthetic gas.

Description

Technical Field

[0001] The present invention relates to the technical field of medical equipment, and specifically to a medical intelligent multi-layer protective gas delivery system, method, device and electronic equipment. Background Art

[0002] Gas delivery systems are an indispensable infrastructure in modern medical facilities, especially in hospitals and other medical places, where they are used to supply medical gases such as oxygen and anesthetic gases to meet the needs of different areas such as operating rooms and wards. However, in the existing technology, medical gas delivery systems have many deficiencies in terms of safety, efficiency and intelligence, and these defects are particularly prominent in complex medical scenarios.

[0003] At present, most medical gas delivery systems rely mainly on a single pipeline structure for gas transmission, and achieve flow control through basic pressure sensors and mechanical valves. However, in complex and dynamic usage scenarios, such systems are difficult to effectively respond to real-time changes in gas demand. For example, when the demand for oxygen in a certain area of ​​the hospital increases sharply, the existing system is usually unable to quickly adjust the gas flow distribution, which may result in insufficient gas supply in key areas and waste of gas resources in other areas. In addition, the traditional system lacks an intelligent optimization mechanism for the gas delivery path, and its delivery path is usually fixed, and gas is delivered along a static path regardless of how the current environment changes. This design not only leads to high energy consumption, but also increases the risk of leakage or other safety accidents under complex conditions.

[0004] Another significant problem is the lack of safety assurance in existing medical gas delivery systems. For possible leaks in pipelines, existing technologies usually rely on manual inspections or static alarm devices, lacking real-time detection capabilities and rapid response mechanisms. Once a leak occurs, the gas may continue to leak out, not only endangering the safety of patients and medical staff, but also polluting the environment. Moreover, due to the lack of adaptive optimization capabilities of the system, it is impossible to effectively balance the operating efficiency and safety of the system in complex scenarios (such as frequent changes in gas supply demand in multiple regions or dynamic changes in pipeline status).

[0005] In addition, at the system operation and maintenance level, existing technologies usually lack comprehensive monitoring of the operating status and intuitive display of abnormal situations, resulting in operation and maintenance personnel often having to rely on experience when dealing with faults, and having difficulty locating problems in a timely and accurate manner. As a result, the operation and maintenance efficiency is low, further increasing the management cost and use risk of the system.

[0006] In summary, the shortcomings of existing medical gas delivery systems in terms of dynamic optimization capabilities, safety assurance levels, intelligence, and operation and maintenance efficiency make it difficult to meet the needs of complex medical scenarios. Summary of the invention

[0007] In view of the deficiencies in the prior art, the present invention provides a medical intelligent multi-layer protective gas delivery system, method, device and electronic equipment, which solve the problems of the existing medical gas delivery system in terms of dynamic optimization capability, safety, resource utilization efficiency and intelligence.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a medical intelligent multi-layer protective gas delivery system, comprising: Data acquisition module, used to collect gas flow, pressure, temperature and leakage status information in the gas transmission pipeline in real time; The path optimization module is connected to the data acquisition module. Based on the real-time collected data, the Markov chain model and multi-objective optimization algorithm are used to dynamically adjust the gas delivery path to minimize energy consumption, leakage risk and response time; A control execution module is connected to the path optimization module, and adjusts the valve opening and pumping power of the gas delivery path according to the optimization result of the path optimization module to execute path switching; A feedback update module is connected to the control execution module and dynamically adjusts the optimization parameters in the path optimization module based on the actual execution results of the control execution module; The monitoring platform module is connected to the data acquisition module and the path optimization module, and is used to visually monitor the operating status of the gas delivery system and provide abnormal alarms and operating status diagrams.

[0009] Preferably, the Markov chain model in the path optimization module includes: State space, each gas transport path is defined as a state; The state transition matrix represents the probability of switching the gas transmission path, which is calculated based on the comprehensive cost of the path, and the comprehensive cost includes gas energy consumption, leakage risk and response time.

[0010] Preferably, the multi-objective optimization algorithm of the path optimization module is optimized based on the following objective function: Minimize the path energy consumption, which is calculated based on the gas flow rate and path length; Minimize the leakage risk, which is calculated based on the historical leakage probability of the path and the environmental sensitivity weight; Minimize path response time, which is determined by gas delivery velocity and path length.

[0011] Preferably, the path optimization module dynamically updates the path state transition probability in the following manner: The state transition probability of each path is inversely proportional to the comprehensive cost of the path. The lower the comprehensive cost of the path, the higher the transition probability; The comprehensive cost includes the weighted sum of gas energy consumption, leakage risk and response time, and the weights are adjusted according to actual needs.

[0012] Preferably, the control execution module executes the path switching through the following steps: Receive optimization results provided by the path optimization module, including optimal path selection and adjustment instructions; Adjust valve opening and pumping power to make gas flow through the optimal path first and reduce the gas delivery volume of non-preferred paths; Detect the flow, pressure and temperature changes after path switching, and transmit the detection data to the feedback update module.

[0013] Preferably, the feedback updating module adjusts the optimization parameters in the path optimization module, including: Update the transition probabilities in the path state transfer matrix, and adjust based on real-time changes in gas flow, pressure, and leakage status; Update the weight parameters in the multi-objective optimization algorithm to dynamically balance energy consumption, response time and leakage risk according to the actual needs of system operation.

[0014] Preferably, the monitoring platform module includes: Data visualization unit, used to display the status information of the gas delivery system in real time, including flow, pressure, temperature, and path selection results; Abnormal alarm unit, used to send an alarm to the operation and maintenance personnel when the pressure or flow of the gas transmission path deviates from the set range; The fault location unit is used to quickly locate the path node where the abnormality occurs based on the status information provided by the path optimization module.

[0015] The present invention also provides a medical intelligent multi-layer protective gas delivery method, comprising the following steps: Real-time collection of gas flow, pressure, temperature and leakage status information in the gas delivery system; Based on the collected data, a Markov chain model is constructed to define the path state and calculate the path state transition probability; Using a multi-objective optimization algorithm, the gas delivery path is dynamically optimized based on the comprehensive cost of energy consumption, leakage risk and response time; According to the optimization results, the valve opening and pumping power of the gas delivery path are adjusted to achieve optimal path switching; Collect the execution results after the path is switched, feed the execution data back to the optimization model, and dynamically adjust the optimization parameters; The gas delivery status is visually displayed and an alarm is triggered in case of abnormal conditions.

[0016] The present invention also provides a medical intelligent multi-layer protective gas delivery device, comprising: A data acquisition unit, used to collect real-time data of the gas delivery system; Optimization computing unit, used to build Markov chain models and perform multi-objective optimization; A control execution unit, used for adjusting the gas delivery path according to the optimization calculation result; A feedback adjustment unit, used for feeding back the execution result to the optimization calculation unit to adjust the optimization parameters; The display unit is used to display the operating status of the gas delivery system.

[0017] The present invention also provides an electronic device, comprising 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 above method is implemented.

[0018] The present invention also provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0019] The present invention provides a medical intelligent multi-layer protective gas delivery system, method, device and electronic equipment. It has the following beneficial effects: 1. The present invention uses the multi-objective optimization algorithm of the path optimization module and combines real-time data to dynamically adjust the gas delivery path, so that the gas is preferentially run through the path with the lowest comprehensive cost, effectively reducing energy consumption and avoiding resource waste. In medical scenarios, this optimization design can ensure the efficient supply of key resources such as oxygen and anesthetic gas to meet the changing needs of different regions.

[0020] 2. The present invention monitors the gas flow, pressure, temperature and leakage status in real time through the data acquisition module, and combines the feedback update module to quickly respond to abnormal situations and adjust parameters, which can significantly reduce the risk of leakage and the probability of pipeline failure. At the same time, the control execution module monitors the execution effect in real time when the path is switched, avoiding system instability caused by operating errors and ensuring the continuous and safe operation of the transportation system.

[0021] 3. The present invention uses the Markov chain model and state transfer matrix, combined with a multi-objective optimization algorithm, to achieve rapid dynamic adjustment of the gas delivery path. When the system detects a sudden increase in gas demand in a certain area, it can quickly optimize the path allocation, increase the gas supply priority in key areas, and limit gas delivery in non-emergency areas, thereby ensuring that emergency needs are met in a timely manner.

[0022] 4. The monitoring platform module of the present invention uses visualization technology to display the operating status of the gas delivery system in real time, including flow distribution, pressure changes and path selection results, and triggers alarms and provides fault location information under abnormal conditions. This intuitive display and efficient alarm design can help operation and maintenance personnel quickly understand the system operation status and handle abnormalities in a timely manner, thereby improving operation and maintenance efficiency and system controllability.

[0023] 5. The present invention dynamically adjusts the parameters of the optimization model through the feedback update module, thus realizing the closed-loop optimization control of the system. Regardless of the deviation caused by external environmental changes or internal operation errors, the feedback update module can timely correct the path optimization parameters to ensure that the system is always in the best operating state, thereby meeting the high reliability and high flexibility requirements in complex medical scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic diagram of the system architecture of the present invention; Figure 2 It is a schematic diagram of the method flow of the present invention; Figure 3 It is a schematic diagram of the structure of the device of the present invention; Figure 4 It is a schematic diagram of the structure of the electronic device of the present invention.

[0025] Among them, 100, data acquisition unit; 200, optimization calculation unit; 300, control execution unit; 400, feedback adjustment unit; 500, display unit; 40, electronic device; 41, processor; 42, memory; 43, storage medium. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] Please refer to the attached Figure 1 The present invention provides a medical intelligent multi-layer protective gas delivery system, which aims to achieve efficient, safe and intelligent delivery of medical gases (such as oxygen, anesthetic gas, etc.) through real-time acquisition, dynamic optimization and closed-loop feedback. The system includes a data acquisition module, a path optimization module, a control execution module, a feedback update module and a monitoring platform module, and each module completes the intelligent management of gas delivery through logical collaboration.

[0028] The specific implementation of the system will be described in detail below, including its core principles, module functions, and the interaction mechanism between modules.

[0029] Data acquisition module In this embodiment, the data acquisition module is used to collect the gas flow, pressure, temperature and leakage status information in the pipeline of the medical gas delivery system in real time. The data acquisition module is the basis for the operation of the system of the present invention, and is responsible for providing accurate and real-time system operation parameters for the path optimization, control execution and feedback update modules.

[0030] Specifically, the data acquisition module achieves comprehensive data acquisition through a variety of sensors arranged in the gas delivery pipeline. These sensors include but are not limited to flow sensors, pressure sensors, temperature sensors and leakage sensors. Various sensors are installed at key nodes of the system, such as gas sources, pipeline intersections, terminal interfaces and monitoring areas.

[0031] As an option, these sensors are connected to the route optimization module via wired or wireless means, and send the collected data signals to the route optimization module in real time for processing and analysis.

[0032] The flow sensor is mainly used to monitor the real-time changes of gas flow and provide the current gas distribution ratio on each path. The flow sensor is arranged at the entrance or key node of each main transmission path and describes the gas flow with the following formula: ;in: Indicates the flow rate of gas; Indicates the cross-sectional area of ​​the pipeline; Indicates the average flow rate of the gas, measured directly by the sensor.

[0033] It should be noted that the flow sensor can calculate the gas flow value of each path in real time by combining the geometric characteristics of the pipeline and the gas flow rate. These flow data will be transmitted to the path optimization module for subsequent path status judgment.

[0034] In some embodiments, pressure sensors are installed in the pipeline to monitor the pressure distribution of the system in real time. Specifically, the installation locations of the pressure sensors include the source end, intersection point and terminal node of the gas delivery pipeline to ensure that the pressure distribution of the entire system can be fully captured. Through these data, the occurrence point of pressure anomalies, such as possible leaks or blockages, can be identified.

[0035] For example, the pressure value collected by the pressure sensor is It can be expressed according to the following formula: ; in: is the density of the gas; is the acceleration due to gravity; is the height of the gas in the pipeline; is the reference pressure.

[0036] It can be understood that by collecting pressure data at multiple locations, the system can calculate the pressure gradient, identify possible leaks or abnormal points, and provide auxiliary judgment basis for the path optimization module.

[0037] In another possible implementation, temperature sensors are arranged on the main paths and terminal nodes to monitor the changes in gas temperature in real time. Accurate temperature monitoring is crucial to the safety and effectiveness of certain medical gases (such as anesthetic gases).

[0038] Specifically, the temperature value measured by the temperature sensor It can be used to calculate the state parameters of the gas and analyze it in combination with the following gas state equation: ; in: is the pressure (obtained by the pressure sensor); is the pipeline volume; is the number of moles of gas; is the gas constant; is the absolute temperature of the gas.

[0039] It can be understood that by combining pressure and temperature data, the system is able to model the gas delivery status more accurately and provide more detailed operating parameters for subsequent path optimization.

[0040] As an option, the leakage sensor in this embodiment is mainly installed in the inert gas filling layer or vacuum layer outside the pipeline to detect pipeline leakage. The leakage sensor determines whether there is a leak and the location of the leak by real-time monitoring of the inert gas concentration or vacuum change.

[0041] Specifically, the leak sensor can determine the leak rate using the following formula: ; in: is the leakage rate; is the pressure change of the vacuum layer or the inert gas layer; is the time interval.

[0042] It should be noted that the arrangement of the leakage sensor can quickly detect small leaks in the pipeline and provide accurate abnormal alarm signals to the control execution module, thereby triggering a rapid response mechanism.

[0043] In a possible implementation, the data acquisition module forms a complete data structure through the collected flow, pressure, temperature and leakage information, and the data structure includes but is not limited to the following fields: Timestamp, used to mark the data collection time; Node location, used to mark the spatial location of data collection; Flow value, pressure value, temperature value and leakage status are used to describe the real-time operating status of the system.

[0044] These data are transmitted to the path optimization module in a structured manner, and the next optimization plan is determined through dynamic calculation of the path optimization module.

[0045] It should be noted that the sensor type, installation location and acquisition frequency in the data acquisition module can be adjusted according to different medical scenarios. For example, for the oxygen supply scenario in the operating room, a high-frequency sampling method (for example, once per second) can be used; while for the oxygen supply in the general ward, the sampling frequency can be appropriately reduced (for example, once every ten seconds) to save system resources.

[0046] It can be understood that the design of the data acquisition module not only ensures the real-time and accuracy of the data, but also takes into account the efficiency and stability of the system operation, thus laying the foundation for subsequent path optimization and execution.

[0047] Path optimization module In this embodiment, the path optimization module is the core part of the medical intelligent multi-layer protective gas delivery system. Its function is to dynamically optimize and adjust the gas delivery path based on the real-time collected gas delivery data through the Markov chain model and multi-objective optimization algorithm to achieve high efficiency, safety and stability of system operation.

[0048] As an option, the path optimization module receives real-time data from the data acquisition module, which includes key parameters such as flow, pressure, temperature and leakage status. The path optimization module determines the optimal path through modeling analysis and calculation, and transmits the optimization results to the control execution module for implementation.

[0049] In a possible implementation, the path optimization module models and dynamically adjusts the state of the gas delivery path by constructing a Markov chain model. Specifically, the core elements of the Markov chain model include state space, state transition matrix and transition cost.

[0050] It should be noted that the state space of the path optimization module consists of various paths in the gas delivery system, and the state set is expressed as: ; in, Indicates The status of the gas transport path.

[0051] State transition matrix Describes the path from Switch to Path The probability of transition Satisfies the following formula: ; in: Indicates that the path Transfer to Path The comprehensive cost; is the smoothing coefficient, which is used to adjust the sensitivity of the transition probability.

[0052] It can be understood that, through the above formula, the path optimization module can dynamically adjust the transfer probability according to the comprehensive cost of each path, so that the system preferentially selects the path with the lowest comprehensive cost.

[0053] Specifically, the comprehensive cost It is the core calculation indicator of the path optimization module, which consists of three parts: energy consumption, leakage risk and response time. The comprehensive cost calculation formula is as follows: ; in: , , is the weight coefficient of the cost function, which is used to dynamically balance the priorities of different optimization objectives; The cost of energy consumption; The cost of leakage risk; The cost of response time.

[0054] In one embodiment, the energy cost Calculated by the following formula: ; in: Indicates the path Previous Pumping power of the pipeline section; Indicates the path Previous The length of the pipeline segment.

[0055] Leakage risk cost It is determined by the historical leakage probability of the path and the environmental sensitivity, and the calculation formula is: ; in: Indicates the path Previous Historical leakage probability of pipeline section; Indicates the path Previous Environmental sensitivity weight of the pipeline segment.

[0056] Response time cost It represents the time required for path switching, and the calculation formula is: ; in: Indicates the path Previous Length of the pipeline segment; Indicates the path Previous The conveying speed of the pipeline section.

[0057] As an option, the multi-objective optimization algorithm in the path optimization module calculates the optimal path based on the above comprehensive cost function through the weighted sum method or the Pareto optimization method. Specifically, the weighted sum method converts the comprehensive cost into a single-objective optimization problem, and the formula is as follows: ; in, Represents a collection of optimization paths.

[0058] In some embodiments, in order to dynamically adjust the priority of the optimization target in different scenarios, the path optimization module adjusts the weight coefficient according to the real-time collected data , , For example, in emergencies, priority is given to ensuring response time. Set to a higher value; in normal operation, pay more attention to energy consumption and increase The weight of .

[0059] In a possible implementation, the optimization process of the path optimization module includes the following steps: Receive flow, pressure, temperature and leakage status data provided by the data acquisition module; Update the state transition matrix based on real-time data and cost function parameters; Calculate the current optimal path and pass the path selection result to the control execution module.

[0060] It should be noted that the path optimization module realizes dynamic optimization of the gas delivery path through the above process, ensuring that the system can always select the optimal path in different operating scenarios.

[0061] It can be understood that the path optimization module, by combining the Markov chain model and the multi-objective optimization algorithm, not only realizes the dynamic adjustment of the gas delivery path, but also can respond quickly when the system parameters change, thus providing a guarantee for the efficiency and safety of the system operation.

[0062] Control execution module In this embodiment, the control execution module is used to perform a dynamic adjustment operation of the gas delivery path according to the optimization result provided by the path optimization module. Specifically, the control execution module realizes the switching of the optimal path and the redistribution of the gas flow rate by adjusting the valve opening and pumping power in the gas delivery pipeline, thereby ensuring the efficient operation of the gas delivery system.

[0063] It should be noted that the control execution module is the key to the implementation of path optimization results, and its accuracy directly affects the optimization effect and operation stability of the system.

[0064] In a possible implementation, the control execution module receives the optimal path selection result and corresponding control parameters output by the path optimization module. The control parameters include: the valve opening adjustment value of the target path, the pumping power adjustment value, and the flow limit parameter of the non-target path.

[0065] As an option, the control execution module can implement the execution of the optimization result through the following steps: 1. According to the flow demand of the target path, adjust the opening of the valve of the path.

[0066] 2. At the same time, reduce the valve opening of non-target paths and limit the gas flow of non-critical paths.

[0067] 3. Adjust the power output of the pumping equipment to ensure that the gas supply pressure meets the delivery requirements of the target path.

[0068] Specifically, the adjustment of the valve opening can be completed by an electronically controlled valve. Exemplarily, the valve opening adjustment follows the following formula: ; in: is the gas flow rate; is the valve flow coefficient, which is proportional to the valve opening; is the pressure difference across the valve, provided by the pressure sensor.

[0069] It is understandable that by adjusting ,The system can accurately control the gas flow of the target path and dynamically match the flow distribution instructions provided by the path optimization module.

[0070] As an option, the pumping power regulation in the control execution module can be realized by a variable frequency pump. The adjustment is based on the flow demand and pressure maintenance target of the target path, and the formula is as follows: ; in: is the power output of the pump; is the delivery flow of the pump; is the pressure difference between the pump outlet and inlet.

[0071] It should be noted that when the gas demand of the target path increases, the control execution module increases the flow rate by increasing the speed of the pump while maintaining the pressure stable to meet the delivery demand of the system. Conversely, when the gas demand decreases, the power output of the pump is reduced to save energy.

[0072] In a possible implementation, in order to ensure the smoothness of the path switching, the control execution module monitors the pipeline status in real time during the execution process, including the dynamic changes of pressure, flow and temperature. These status information are provided to the feedback update module through the feedback mechanism to verify the accuracy of the execution result.

[0073] Specifically, the control execution module implements dynamic adjustment of the execution process through the following monitoring steps: Real-time monitoring of flow changes after valve adjustment: The actual flow values ​​of the target path and non-target path are obtained through flow sensors to ensure that the flow distribution complies with the optimization instructions.

[0074] Real-time monitoring of pressure fluctuations: Monitor pressure changes in the pipeline through pressure sensors to avoid excessive pressure fluctuations caused by too fast valve adjustment.

[0075] Real-time monitoring of pumping power adjustment effects: The built-in power monitoring device records the pump power output to ensure that the pump operation complies with the instructions of the route optimization module.

[0076] As an example, the operation process of the control execution module in a medical scenario can be described as follows: When the path optimization module detects a sudden increase in oxygen demand in a certain ward area, the control execution module will perform the following steps after receiving the optimization result: Increase the opening of the target pathway valve to deliver more oxygen to that ward area; Reduce the opening of valves in non-target paths to reduce gas delivery to areas with lower demand; Increase pumping power to ensure adequate delivery pressure when flow increases in the target path.

[0077] During this process, the control execution module verifies whether the flow of the target path meets the optimization requirements by monitoring the flow distribution after the valve adjustment; and ensures the operational stability of the pipeline system by monitoring pressure fluctuations.

[0078] It should be noted that the control execution module can not only perform path switching operations under normal circumstances, but also trigger emergency control measures when abnormal conditions (such as leakage or excessive pressure) are detected. For example, when a path is marked as a non-preferred path due to leakage, the control execution module will quickly close the valve of the relevant path and compensate for the insufficient gas supply by increasing the flow of other paths.

[0079] As an option, the control execution module can also provide real execution data to the feedback update module by calibrating the operating parameters after the execution path switching, thereby further improving the dynamic adjustment capability of the system.

[0080] It is understandable that the control execution module can accurately and efficiently implement the calculation results of the path optimization module through valve opening regulation, pumping power adjustment and real-time status monitoring.

[0081] Feedback Update Module In this embodiment, the feedback update module is used to dynamically adjust the optimization parameters in the path optimization module according to the actual operation results of the control execution module and the changes in the system state, so as to realize the closed-loop optimization control of the system. Through the feedback update module, the system can optimize the accuracy of path selection according to real-time operation data and enhance adaptability and stability.

[0082] It should be noted that the feedback update module plays a bridging role in the entire system, combining the actual operation data with the optimization calculation results to ensure the continuous effectiveness of the path optimization module in a complex dynamic environment.

[0083] In a possible implementation, the operation logic of the feedback update module includes the following main steps: data reception, error analysis, parameter adjustment, and result output. Specifically, the feedback update module receives feedback data from the control execution module, including the flow, pressure, temperature, and power change values ​​after actual execution. By comparing these data with the optimization target, the deviation between the actual operation and the optimization result is identified.

[0084] As an option, the feedback update module first calculates the error between the actual running state and the output result of the path optimization module. The formula for error calculation is as follows: ; in: is the error value; is the actual operating parameter value (such as actual flow, pressure, etc.); The target parameter value calculated for the path optimization module.

[0085] It should be noted that, by calculating the error value in real time, the feedback update module can accurately identify deviations in system operation, especially target deviations caused by external disturbances or internal execution deviations.

[0086] In some embodiments, the feedback update module classifies the error values ​​to determine the impact of different types of errors on the optimization parameters. Exemplarily, the feedback update module can process according to the following logic: Traffic error processing: When the actual traffic of the target path is lower than the target value, the feedback update module adjusts the state transfer matrix in the path optimization module to reduce the priority probability of the path. At the same time, by updating the weight parameter in the cost function , give priority to optimizing traffic-related cost items.

[0087] Pressure error processing: When the pressure fluctuation of a certain path exceeds the set threshold, the feedback update module sends a pressure warning signal to the path optimization module, indicating possible pipeline abnormalities or leakage risks, and giving priority to optimizing the leakage risk cost. .

[0088] Power error processing: When the actual consumption of pumping power is higher than the predicted value, the feedback update module readjusts the pumping power parameters of the target path to optimize the working efficiency of the pump.

[0089] Specifically, the feedback update module dynamically adjusts the core parameters of the path optimization module through error analysis. As an example, the adjustment process includes the following: State transfer matrix adjustment: The feedback update module updates the state transfer matrix The transfer probability in can be used to achieve real-time optimization of path priority. The adjustment formula is as follows: ; in: For time Time Path To Path The transition probability of For path To Path The comprehensive error of It is an adjustment coefficient used to control the influence of feedback on the state transfer matrix.

[0090] It can be understood that by dynamically adjusting the state transfer matrix, the feedback update module can reduce the priority of the path with larger error, thereby enhancing the system's adaptability to dynamic changes.

[0091] Objective function weight adjustment: When the error analysis shows that a certain optimization target has a greater impact on the actual result, the feedback update module adjusts the weight parameter of the objective function , , Reassign optimization priorities. For example: ; in: is the energy consumption error; is the weight adjustment coefficient.

[0092] This adjustment mechanism can ensure that the system responds preferentially to key optimization objectives under different operating conditions.

[0093] As an option, after completing the parameter adjustment, the feedback update module passes the updated optimization parameters to the path optimization module to form a closed-loop optimization. Through this mechanism, the path optimization module can recalculate based on the latest operating data, thereby continuously improving the system operation effect.

[0094] In one possible implementation, the feedback update module can also perform trend analysis and prediction based on long-term operation data. For example, by analyzing the historical change trend of pressure and flow, possible pipeline blockage or leakage risks can be predicted, so as to adjust the state transfer matrix and objective function weight of the path optimization module in advance.

[0095] It should be noted that the design of the feedback update module can significantly improve the robustness and adaptability of the system. In some complex medical scenarios, such as a sudden increase in oxygen demand during surgery or a sudden drop in pipeline pressure in the ward area, the feedback update module can quickly identify these abnormal situations and ensure the stable operation of the system by dynamically adjusting the optimization parameters.

[0096] It can be understood that the real-time analysis and dynamic adjustment mechanism of the feedback update module enables the present invention to maintain efficient, safe and flexible operation effects in multi-objective optimization scenarios.

[0097] Monitoring platform module In this embodiment, the monitoring platform module is used to perform real-time monitoring, data visualization, abnormal alarm and status analysis on the operation status of the medical intelligent multi-layer protective gas delivery system, and provide timely operation guidance and fault handling information for operation and maintenance personnel. The monitoring platform module is connected with the data acquisition module and the path optimization module to realize the comprehensive display and dynamic management of the system operation information.

[0098] It should be noted that the monitoring platform module is not only an interface for displaying the system's operating status, but also an important means for operation and maintenance personnel to interact with the system. It can provide fault location and processing suggestions under abnormal circumstances, thereby improving the system's security and operating efficiency.

[0099] In one possible implementation, the core functions of the monitoring platform module include real-time data visualization, abnormal alarm, and fault location. Specifically, the real-time data visualization function is used to display the operating status of the gas delivery system, including gas flow, pressure, temperature, and path selection results.

[0100] As an option, the monitoring platform module generates the following main operation charts by acquiring real-time data from the data acquisition module: Flow distribution diagram: used to display the real-time flow value of each gas delivery path; Pressure distribution diagram: used to show the pressure changes of each node and path; Temperature status diagram: used to monitor the dynamic changes of gas temperature; Path optimization diagram: used to mark the paths in the current system that are in the preferred state and their operating indicators.

[0101] It is understandable that through the above data visualization function, operation and maintenance personnel can clearly understand the current operating status of the system and quickly identify possible abnormal points.

[0102] Specifically, the abnormal alarm function of the monitoring platform module is used to send an alarm to the operation and maintenance personnel when the system operating parameters exceed the set range. As an option, the abnormal alarm function includes pressure alarm, flow alarm and leakage alarm.

[0103] Pressure alarm: When the real-time pressure value of a path Out of setting range , triggers the alarm.

[0104] Flow alarm: When the actual flow of the target path Lower than the target flow calculated by the optimization module , and the deviation exceeds the preset threshold , triggering an alarm.

[0105] Leakage alarm: When the leakage sensor detects that the inert gas concentration outside the path or the pressure change of the vacuum layer exceeds the threshold , triggering an alarm.

[0106] It is understandable that the abnormal alarm function can quickly detect abnormal conditions during operation and transmit the alarm information to the operation and maintenance personnel through a visual interface and sound / text prompts.

[0107] In another possible implementation, the monitoring platform module provides a fault location function by linking with the path optimization module and the feedback update module. Specifically, when an abnormality occurs, the monitoring platform module can locate the specific path node or pipeline section where the abnormality occurs by combining the abnormal alarm information and the operation record of the path optimization module.

[0108] As an example, when the pressure value of a certain path fluctuates abnormally, the monitoring platform module identifies the upstream and downstream paths associated with the path through the state transfer records of the path optimization module, and generates a path fault chain diagram to help operation and maintenance personnel quickly identify possible causes of the fault.

[0109] It should be noted that the fault location function can not only mark abnormal nodes, but also generate a list of possible fault causes by calling historical operation data. For example, for a certain pressure abnormality, possible causes include valve failure, pipeline leakage or insufficient pumping power. The monitoring platform module will combine real-time data and historical trend analysis to provide operation and maintenance personnel with priority sorting of fault causes.

[0110] As an option, the monitoring platform module can also record and store historical data of system operation for long-term trend analysis and system optimization. Specifically, the monitoring platform module stores the following in the form of structured data: Time series data of flow, pressure, and temperature for each path; The optimization results of the path optimization module, including the state transfer matrix and the objective function weight; The triggering time, location and alarm type of the abnormal alarm.

[0111] Exemplarily, the monitoring platform module can generate the following trend graph: Traffic trend chart: shows the traffic trend of a certain path over a period of time; Pressure change graph: a historical curve showing the pressure change over time for a certain path or node; Alarm statistics chart: Statistics of the occurrence frequency and distribution of different types of alarms.

[0112] It is understandable that through these historical data and trend graphs, operation and maintenance personnel can discover potential problems in the system and take preventive maintenance measures, thereby improving the overall reliability of the system.

[0113] In one possible implementation, the monitoring platform module also has a user interaction function, allowing the operation and maintenance personnel to adjust the system's alarm threshold, data collection frequency, and the display method of the visualization chart through the interface. For example, when the gas demand in a certain ward area suddenly increases, the operation and maintenance personnel can manually adjust the flow priority of the area through the monitoring platform module to temporarily override the calculation results of the path optimization module.

[0114] It can be understood that the monitoring platform module in this embodiment significantly improves the system's operation management capability and fault handling efficiency through real-time data visualization, abnormal alarm, fault location and user interaction functions. At the same time, the linkage between the monitoring platform module and the path optimization module and the feedback update module enables the present invention to maintain an efficient and safe operation state in a dynamically changing operating environment.

[0115] In general, the present invention provides a medical intelligent multi-layer protective gas delivery system, which includes a data acquisition module, a path optimization module, a control execution module, a feedback update module and a monitoring platform module, and realizes real-time monitoring, dynamic optimization and efficient management of gas delivery through collaborative work. Among them, the data acquisition module obtains gas flow, pressure, temperature and leakage status in real time, the path optimization module uses the Markov chain model and multi-objective optimization algorithm to dynamically adjust the gas delivery path, the control execution module accurately implements the path switching, the feedback update module optimizes the algorithm parameters according to the actual operation results, and the monitoring platform module visualizes the system operation status and provides abnormal alarm and fault location functions. Through closed-loop optimization and intelligent regulation, the present invention significantly improves the safety, stability and resource utilization efficiency of the medical gas delivery system to meet the needs of complex medical scenarios.

[0116] Please see attached Figure 2 The present invention also provides a medical intelligent multi-layer protective gas delivery method, which realizes data collection, path optimization, control execution, feedback adjustment and monitoring alarm functions in sequence through a systematic workflow, thereby dynamically optimizing the gas delivery path and improving the safety, stability and efficiency of the delivery system. The specific implementation method of the method is described in detail below in conjunction with the system workflow of the present invention.

[0117] Step S1: real-time collection of gas flow, pressure, temperature and leakage status information in the gas delivery system; In this embodiment, the gas delivery system realizes real-time data collection through a data collection module. Specifically, the data collection module is installed at key nodes of the pipeline, including the gas source, pipeline intersection and terminal interface, and the collected data includes gas flow, pressure, temperature and leakage status information.

[0118] As a possible implementation method, the data acquisition module measures gas flow, pressure and temperature parameters through sensors, and monitors the leakage status of the pipeline. The collected data is transmitted to the path optimization module in real time, providing accurate basic data for subsequent path optimization and control execution.

[0119] Through this step, the operating status of the gas delivery system can be understood in real time, providing support for dynamically optimizing the path and ensuring the safety of system operation.

[0120] Step S2: Based on the collected data, a Markov chain model is constructed to define the path state and calculate the path state transition probability; In this embodiment, after receiving the real-time data from the data acquisition module, the path optimization module first constructs a Markov chain model to model the state of the gas delivery path. Specifically, each path is defined as an independent state, and the transition probability between different paths is described by constructing a state transition matrix.

[0121] As an option, the path optimization module calculates the state transition probability of each path based on the collected data. The transition probability is affected by the comprehensive cost of factors such as the path's energy consumption, leakage risk, and response time. By dynamically adjusting the transition probability, the path with the lowest cost is given priority.

[0122] Through this step, the quantitative analysis of the gas delivery path can be achieved, providing a decision-making basis for path optimization.

[0123] Step S3: using a multi-objective optimization algorithm to dynamically optimize the gas delivery path based on the comprehensive cost of energy consumption, leakage risk and response time; In this embodiment, the path optimization module uses a multi-objective optimization algorithm to optimize the gas delivery path. According to the system operation requirements, the path optimization module dynamically calculates the comprehensive cost of each path by comprehensively considering three factors: energy consumption, leakage risk, and response time, and combines the Markov chain model to select the optimal path with the lowest cost.

[0124] In this step, the path optimization module outputs the optimization results, including the target path and its corresponding flow distribution, valve opening and pumping power adjustment parameters.

[0125] Through this step, dynamic optimization of the gas delivery path can be achieved, effectively reducing the energy consumption and leakage risk of system operation, while improving the response speed to sudden demands.

[0126] Step S4: According to the optimization result, adjusting the valve opening and pumping power of the gas delivery path to achieve optimal path switching; In this embodiment, after receiving the optimization result of the path optimization module, the control execution module dynamically adjusts the gas delivery system according to the optimization instruction. Specifically, the control execution module adjusts the valve opening of the target path so that the gas preferentially passes through the optimized selected path while limiting the flow of non-target paths.

[0127] As a possible implementation method, the control execution module also ensures that the pressure and flow of the target path are maintained at an optimal state by adjusting the pumping power to match the delivery demand of the target path.

[0128] Through this step, the path optimization results can be converted into specific execution actions to achieve dynamic switching and flow optimization of the gas delivery path.

[0129] Step S5: Collect the execution effect after the path switching, feed the execution data back to the optimization model, and dynamically adjust the optimization parameters; In this embodiment, the feedback update module monitors the execution effect of the control execution module and compares and analyzes the actual operation data with the optimization results of the path optimization module. By analyzing the errors between the actual flow, pressure and pumping power and the target parameters, the feedback update module dynamically adjusts the state transfer matrix and the objective function weight of the optimization model.

[0130] As an option, the feedback update module returns the adjusted optimization parameters to the path optimization module, so that the path optimization model can recalculate the path selection plan based on the latest operating data.

[0131] Through this step, the adaptive adjustment of the path optimization model can be achieved, further improving the dynamic response capability and operation accuracy of the system.

[0132] Step S6: Visually display the gas delivery status and trigger an alarm in abnormal situations; In this embodiment, the monitoring platform module is used to display the system operation status and abnormal alarm information. Specifically, the monitoring platform module generates a real-time status diagram of the system operation, including a flow distribution diagram, a pressure distribution diagram, and a path optimization diagram, by receiving data from the data acquisition module and the path optimization module.

[0133] In abnormal situations, such as when the pressure or flow of a path exceeds the set range, the monitoring platform module will trigger an alarm and quickly mark the abnormal node through the fault location function, prompting the operation and maintenance personnel to handle it in time.

[0134] Through this step, the system's operation and maintenance efficiency and safety can be improved, ensuring the stable operation of the gas delivery system.

[0135] The medical intelligent multi-layer protective gas delivery method of the present invention realizes real-time optimization and safety management of the gas delivery path through the collaborative work of data collection, path optimization, dynamic execution, feedback adjustment and visual monitoring. Through closed-loop optimization control, the system energy consumption and leakage risk are significantly reduced, the response capability to emergencies is improved, and a clear display of operating status and efficient exception handling capabilities are provided. This method can be applied to complex medical scenarios and provides an effective solution for the intelligent upgrade of medical gas delivery systems.

[0136] The medical intelligent multi-layer protective gas delivery device described below and the medical intelligent multi-layer protective gas delivery method described above can be referenced to each other.

[0137] Please see attached Figure 3 The present invention also provides a medical intelligent multi-layer protective gas delivery device, comprising: A data acquisition unit 100 is used to collect real-time data of the gas delivery system; An optimization calculation unit 200, used to construct a Markov chain model and perform multi-objective optimization; A control execution unit 300 is used to adjust the gas delivery path according to the optimization calculation result; A feedback adjustment unit 400, used to feed back the execution result to the optimization calculation unit to adjust the optimization parameters; The display unit 500 is used to display the operating status of the gas delivery system.

[0138] The device of this embodiment can be used to execute the above method embodiment, and its principles and technical effects are similar, which will not be repeated here.

[0139] Please see attached Figure 4 The present invention further provides an electronic device 40, comprising: a processor 41 and a memory 42, wherein the memory 42 stores a computer program executable by the processor, and when the computer program is executed by the processor, the above method is performed.

[0140] The present invention further provides a storage medium 43 on which a computer program is stored. When the computer program is run by the processor 41, the above method is executed.

[0141] Among them, the storage medium 43 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0142] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Medical intelligent multi-layer protective gas delivery system, characterized in that: include: Data acquisition module, used to collect gas flow, pressure, temperature and leakage status information in the gas transmission pipeline in real time; The path optimization module is connected to the data acquisition module. Based on the real-time collected data, the Markov chain model and multi-objective optimization algorithm are used to dynamically adjust the gas delivery path to minimize energy consumption, leakage risk and response time; A control execution module is connected to the path optimization module, and adjusts the valve opening and pumping power of the gas delivery path according to the optimization result of the path optimization module to execute path switching; A feedback update module is connected to the control execution module and dynamically adjusts the optimization parameters in the path optimization module based on the actual execution results of the control execution module; The monitoring platform module is connected to the data acquisition module and the path optimization module, and is used to visually monitor the operating status of the gas delivery system and provide abnormal alarms and operating status diagrams.

2. The medical intelligent multi-layer protective gas delivery system according to claim 1, characterized in that: The Markov chain model in the path optimization module includes: State space, each gas transport path is defined as a state; The state transition matrix represents the probability of switching the gas transmission path, which is calculated based on the comprehensive cost of the path, and the comprehensive cost includes gas energy consumption, leakage risk and response time.

3. The medical intelligent multi-layer protective gas delivery system according to claim 2, characterized in that: The multi-objective optimization algorithm of the path optimization module is optimized based on the following objective functions: Minimize the path energy consumption, which is calculated based on the gas flow rate and path length; Minimize the leakage risk, which is calculated based on the historical leakage probability of the path and the environmental sensitivity weight; Minimize path response time, which is determined by gas delivery velocity and path length.

4. The medical intelligent multi-layer protective gas delivery system according to claim 3, characterized in that: The path optimization module dynamically updates the path state transition probability in the following manner: The state transition probability of each path is inversely proportional to the comprehensive cost of the path. The lower the comprehensive cost of the path, the higher the transition probability; The comprehensive cost includes the weighted sum of gas energy consumption, leakage risk and response time, and the weights are adjusted according to actual needs.

5. The medical intelligent multi-layer protective gas delivery system according to claim 1, characterized in that: The control execution module performs path switching by the following steps: Receive optimization results provided by the path optimization module, including optimal path selection and adjustment instructions; Adjust valve opening and pumping power to make gas flow through the optimal path first and reduce the gas delivery volume of non-preferred paths; Detect the flow, pressure and temperature changes after path switching, and transmit the detection data to the feedback update module.

6. The medical intelligent multi-layer protective gas delivery system according to claim 5, characterized in that: The feedback update module adjusts the optimization parameters in the path optimization module, including: Update the transition probabilities in the path state transfer matrix, and adjust based on real-time changes in gas flow, pressure, and leakage status; Update the weight parameters in the multi-objective optimization algorithm to dynamically balance energy consumption, response time and leakage risk according to the actual needs of system operation.

7. The medical intelligent multi-layer protective gas delivery system according to claim 1, characterized in that: The monitoring platform module includes: Data visualization unit, used to display the status information of the gas delivery system in real time, including flow, pressure, temperature, and path selection results; Abnormal alarm unit, used to send an alarm to the operation and maintenance personnel when the pressure or flow of the gas transmission path deviates from the set range; The fault location unit is used to quickly locate the path node where the abnormality occurs based on the status information provided by the path optimization module.

8. A medical intelligent multi-layer protective gas delivery method, based on the medical intelligent multi-layer protective gas delivery system according to any one of claims 1 to 7, characterized in that: The following steps are involved: Real-time collection of gas flow, pressure, temperature and leakage status information in the gas delivery system; Based on the collected data, a Markov chain model is constructed to define the path state and calculate the path state transition probability; Using a multi-objective optimization algorithm, the gas delivery path is dynamically optimized based on the comprehensive cost of energy consumption, leakage risk and response time; According to the optimization results, the valve opening and pumping power of the gas delivery path are adjusted to achieve optimal path switching; Collect the execution results after the path is switched, feed the execution data back to the optimization model, and dynamically adjust the optimization parameters; The gas delivery status is visually displayed and an alarm is triggered in case of abnormal conditions.

9. A medical intelligent multi-layer protective gas delivery device, used to perform the method according to claim 8, characterized in that: include: A data acquisition unit, used to collect real-time data of the gas delivery system; Optimization computing unit, used to build Markov chain models and perform multi-objective optimization; A control execution unit, used for adjusting the gas delivery path according to the optimization calculation result; A feedback adjustment unit, used for feeding back the execution result to the optimization calculation unit to adjust the optimization parameters; The display unit is used to display the operating status of the gas delivery system.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to claim 8 is implemented.

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