An oxygen uptake terminal flow equalization control system

Abstract

The present application relates to the technical field of flow control, in particular to an oxygen absorption terminal flow balance control system, which comprises a pressure distribution monitoring module, a flow adjustment execution module, a terminal state discrimination module, an instruction optimization adaptation module and a running record acquisition module. In the present application, the pressure at multiple points in the oxygen supply pipeline is periodically collected and the change amplitude is analyzed to achieve sensitive identification and state marking of flow abnormalities. The time window matching of the device running point and pressure fluctuation is combined to improve the accuracy and execution matching of flow adjustment. Through the front insertion rearrangement mechanism of the control instruction, priority is ensured in the lag scenario, the adjustment response speed and execution stability are enhanced, and the adjustment process is recorded to support the accurate tracing and quality evaluation of the oxygen supply terminal running track, effectively improving the control accuracy, abnormal identification ability and overall controllability of system operation in the multi-terminal concurrent oxygen use scenario.

Description

Technical Field

[0001] This invention relates to the field of flow control technology, and in particular to a flow equalization control system for an oxygen inhalation terminal. Background Technology

[0002] Flow control technology refers to the technologies used in the transportation and application of gases or liquids to achieve reasonable distribution and stable supply of flow through regulation, detection, and allocation. Its core aspects include monitoring gas or liquid pressure and flow rate, precise control of flow rate, and management of flow distribution balance when multiple users or devices are using the system simultaneously. This technology is widely used in medical oxygen supply, industrial gas transportation, and environmental control, playing a crucial role in ensuring safety, stability, and resource utilization. Traditional oxygen terminal flow balance control systems refer to flow distribution devices used in hospitals or centralized oxygen supply environments to provide oxygen to multiple patients. Addressing the issue of balancing oxygen flow among multiple terminals in centralized oxygen supply systems, traditional oxygen terminal flow balance control adjusts and distributes the oxygen supply by installing mechanical flow meters, flow limiting valves, or differential pressure controllers at each terminal, thereby maintaining flow balance within a certain range.

[0003] Traditional oxygen therapy terminal flow equalization control relies on independent adjustment of mechanical flow meters and flow limiting devices, making it difficult to achieve coordinated judgment of the status between terminals. When there are fluctuations in oxygen consumption or rapid changes in the number of oxygen users, some terminals are prone to sudden drops or increases in flow. Such devices lack the ability to continuously monitor the overall status of the oxygen supply network and cannot respond quickly to short-term pressure disturbances, resulting in lag or under-regulation. For example, when multiple patients use oxygen therapy equipment at the same time, it often leads to excessive flow at the proximal terminal and insufficient oxygen supply at the distal terminal, affecting the balance of oxygen supply and the safety of oxygen use. Furthermore, because the operation is based on manual settings or physical adjustments, it is impossible to record the adjustment process systematically, resulting in significant blind spots in fault tracking and operational quality assessment. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide an oxygen inhalation terminal flow equalization control system. The technical solution is as follows:

[0005] On the one hand, an oxygen inhalation terminal flow equalization control system is provided, the system comprising:

[0006] The pressure distribution monitoring module acquires the pressure values ​​of multiple monitoring nodes in the oxygen supply pipeline, calculates the pressure change amplitude of the monitoring nodes, determines whether it exceeds the pressure fluctuation benchmark range, and generates a pressure distribution state set.

[0007] The flow regulation execution module performs a pressure value combination matching operation for two consecutive cycles based on the pressure distribution state set. If both are marked as abnormal, the flow regulation state is set to "in regulation"; otherwise, it is set to "regulation completed" and a flow regulation state label group is generated.

[0008] The terminal status determination module extracts the oxygen supply equipment start time and the current cycle time based on the flow regulation status tag group, and combines the current status identifier and flow regulation status to determine whether it matches the time window of the operation phase. If it does, it is marked as an advanceable state and a status advancement adaptation set is generated.

[0009] The instruction optimization and adaptation module advances the adaptation set according to the state, extracts control instructions based on the state-adaptation set, compares the current execution index with the target index, and if the target is lagging, performs insertion and rearrangement to generate a task control order mapping table.

[0010] As a further embodiment of the present invention, the pressure distribution state set includes pressure amplitude labeling information, monitoring node markers, and pressure change amplitude values; the flow regulation state label group includes flow regulation state, state identifier, and recording time point; the state advancement adaptation set includes task state number, time window marker, and state matching result; and the task control sequence mapping table includes original instruction position index, rearranged instruction index, and change information for each state index.

[0011] As a further aspect of the present invention, the pressure distribution monitoring module includes:

[0012] The pressure sampling submodule acquires continuous periodic sampling values ​​reported by multiple pressure sensors in the networked oxygen supply pipeline, extracts the time interval between adjacent sampling points, and calculates the ratio of the difference between adjacent sampling values ​​to the corresponding time interval to obtain the pressure change amplitude value.

[0013] The amplitude determination submodule, based on the pressure change amplitude value and combined with the pressure fluctuation reference range, filters out the amplitude values ​​of pressure change that are greater than the reference range, records the corresponding sampling time points, and generates an abnormal time node sequence.

[0014] The result labeling submodule, based on the abnormal time node sequence, statistically analyzes the sampling time points and pressure change amplitude values, determines whether the time point belongs to any node in the corresponding sequence, labels the abnormal state and the normal state, integrates the labeling information of the time points, and obtains a set of pressure distribution states.

[0015] As a further aspect of the present invention, the flow regulation execution module includes:

[0016] The amplitude summarization submodule obtains the pressure amplitude labeling status corresponding to the time point in the pressure distribution state set, combines two adjacent sampling time points to form an amplitude state combination, labels the pressure amplitude and stores it in chronological order, and generates a continuous amplitude identifier sequence.

[0017] The status matching submodule determines whether two points in each group are simultaneously abnormal based on the continuous amplitude identifier sequence; if so, it marks them as being in adjustment, otherwise it marks them as being in adjustment, and records the status according to the time point to obtain the flow adjustment status sequence.

[0018] The tag generation submodule extracts the adjustment status according to the time segment based on the flow adjustment status sequence and generates status identifiers. It then merges the time point, adjustment status and identifier into a structured set to obtain the flow adjustment status tag group.

[0019] As a further aspect of the present invention, the terminal state determination module includes:

[0020] The time matching submodule extracts the start time point and the current cycle time point uploaded by the networked oxygen supply equipment based on the flow regulation status tag group, calculates the time difference and locates the sampling point, determines whether the operation phase is within the time window, and generates an operation phase interval matching identifier.

[0021] The status extraction submodule extracts status tags in the matching sampling segment based on the status identifier and flow adjustment status in the flow adjustment status tag group, and performs synchronization calculations in combination with the interval matching identifier to obtain stage synchronization deviation data.

[0022] The adaptation filtering submodule performs threshold judgment based on the stage synchronization deviation data, filters the time period of the stage state combination where the deviation is less than the set benchmark value, records the corresponding time point and state identifier in a unified structure, and summarizes them to form a state advancement adaptation set.

[0023] As a further aspect of the present invention, the instruction optimization and adaptation module includes:

[0024] The control instruction extraction submodule extracts the corresponding state number based on the state entries marked as advanceable in the state advancement adaptation set, filters the matching control instructions, merges and compares them, and generates a stage control instruction set.

[0025] The instruction position comparison submodule, based on each control instruction in the stage control instruction set, filters instructions whose target position number is less than the current execution position, calculates the offset change of the control instruction, performs pre-insertion and records the index change, and generates instruction offset change data.

[0026] The control sequence mapping submodule identifies the mapping between the status number and the new index number of the control instruction recorded in the instruction offset change data, identifies the index number before and after the control instruction and the corresponding status number, analyzes the bidirectional mapping relationship between the status number and the new index number, and obtains the task control sequence mapping table.

[0027] As a further aspect of the present invention, the offset change of the control command is determined by the following formula:

[0028] ;

[0029] in, This represents the amount of offset change in the control command. Representing the The target location number of the instruction. Represents the current execution location number. Representative and the Adjustment factor associated with each instruction. To control the total number of instructions in the instruction set.

[0030] As a further aspect of the present invention, the system also includes a running record acquisition module:

[0031] The operation record acquisition module, based on the task control sequence mapping table, issues instructions to tasks according to the current state sequence, and records the issuance time, instruction number, and current flow adjustment status to generate an oxygen supply terminal operation record.

[0032] As a further aspect of the present invention, the oxygen supply terminal operation record includes the issuance time, instruction number, and flow regulation status record.

[0033] As a further aspect of the present invention, the operation record acquisition module includes:

[0034] The task instruction issuance submodule reads the task status number in the current scheduling queue according to the status number and control instruction index position recorded in the task control sequence mapping table, performs a sorting operation on the instruction number in the control queue according to the index information of the mapping table, and issues the corresponding control instructions to the lower oxygen supply terminal control terminal in sequence. It records the time node of each issued instruction operation and the control number to generate a task instruction response number sequence.

[0035] The control data recording submodule monitors the current flow regulation status of the oxygen supply terminal based on each control number recorded in the task instruction response number sequence, collects the issuance time, control number and flow status data within the corresponding time period, and obtains instruction control status combination data.

[0036] The operation record generation submodule extracts the time series data of the control number and the flow regulation status identifier according to the time point and flow regulation status corresponding to each control number in the instruction control status combination data, constructs a structured record entry sorted by time series, integrates all control data structures, and outputs the oxygen supply terminal operation record.

[0037] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0038] By continuously collecting and analyzing the pressure changes at multiple monitoring nodes within the oxygen supply pipeline, highly sensitive identification of abnormal flow fluctuations is achieved. This allows for dynamic marking and real-time adjustment of the oxygen supply flow status. Combined with a time window matching mechanism between equipment operating points and flow fluctuation states, the accuracy and adaptability of flow regulation during operation are effectively improved. Furthermore, by utilizing a dynamic pre-emption and rearrangement strategy for control command execution order, priority is ensured for commands even when they are delayed, thereby optimizing the response efficiency and execution stability of the flow regulation process. Through the generation of full-process regulation records, precise tracing of the oxygen supply terminal's operating trajectory and closed-loop evaluation of regulation quality are achieved, effectively enhancing the regulation accuracy, anomaly detection capability, and operational controllability of the centralized oxygen supply system in multi-terminal concurrent operation scenarios. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a schematic diagram of an oxygen inhalation terminal flow equalization control system provided in an embodiment of the present invention;

[0041] Figure 2 This is a schematic diagram of the system framework of the present invention;

[0042] Figure 3 This is a flowchart of the pressure distribution monitoring module in this invention;

[0043] Figure 4 This is a flowchart of the flow regulation execution module in this invention;

[0044] Figure 5 This is a flowchart of the terminal status determination module in this invention;

[0045] Figure 6 This is a flowchart of the instruction optimization and adaptation module in this invention;

[0046] Figure 7This is a flowchart of the operation record acquisition module in this invention. Detailed Implementation

[0047] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0048] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0049] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0050] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0051] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0052] This invention provides an oxygen inhalation terminal flow equalization control system, such as... Figures 1-2 The diagram shown illustrates a flow equalization control system for an oxygen inhalation terminal. This system includes:

[0053] The pressure distribution monitoring module acquires the pressure values ​​of multiple monitoring nodes in the oxygen supply pipeline, calculates the pressure change amplitude of the monitoring nodes, determines whether it exceeds the pressure fluctuation benchmark range, and generates a pressure distribution state set.

[0054] The flow regulation execution module performs a combination matching operation of pressure values ​​for two consecutive cycles based on the pressure distribution status set. If both are marked as abnormal, the flow regulation status is set to "in regulation"; otherwise, it is set to "regulation completed" and a flow regulation status label group is generated.

[0055] The terminal status discrimination module extracts the oxygen supply equipment start time and the current cycle time based on the flow regulation status label group. It combines the current status identifier and the flow regulation status to determine whether it matches the time window of the operation phase. If it does, it is marked as a state that can be advanced and a status advancement adaptation set is generated.

[0056] The instruction optimization and adaptation module advances the adaptation set based on the state, extracts control instructions based on the advancement of the adaptation set based on the state, compares the current execution index with the target index, and if the target is lagging, it performs insertion and rearrangement to generate a task control order mapping table.

[0057] The operation record acquisition module issues instructions to tasks according to the task control sequence mapping table and the current status sequence, and records the issuance time, instruction number and current flow adjustment status to generate the oxygen supply terminal operation record.

[0058] The pressure distribution status set includes pressure amplitude labeling information, monitoring node markers, and pressure change amplitude values. The flow regulation status label group includes flow regulation status, status identifier, and recording time point. The status advancement adaptation set includes task status number, time window marker, and status matching result. The task control sequence mapping table includes the original instruction location index, the rearranged instruction index, and change information for each status index. The oxygen supply terminal operation record includes the issuance time point, instruction number, and flow regulation status record.

[0059] Specifically, such as Figure 2 , Figure 3 As shown, the pressure distribution monitoring module includes:

[0060] The pressure sampling submodule acquires continuous periodic sampling values ​​reported by multiple pressure sensors in the networked oxygen supply pipeline, extracts the time interval between adjacent sampling points, and calculates the ratio of the difference between adjacent sampling values ​​to the corresponding time interval to obtain the pressure change amplitude value.

[0061] Accurately acquire continuous periodic sampling values ​​reported by multiple pressure sensors in the networked oxygen supply pipeline. These sampling values ​​originate from sensors deployed at multiple points from the hospital's central oxygen supply station to the terminals in each ward. For example, a pressure sensor connected to an oxygen terminal in a certain ward continuously transmits pressure readings at a frequency of one data point per second. Specifically, during the monitoring period, the sensor transmits pressure readings at specific time points. Report pressure values The following time point Report pressure values Time point Report Time point Report Similarly, the time interval between adjacent sampling points is extracted. This time interval is a fixed sampling period preset by the sensor hardware. After preliminary testing and verification, to ensure data real-time performance and stability, this interval is fixed at 1.0 second. Then, the difference between adjacent sampled values ​​is calculated. For example, for... and The difference is ,for and The difference is ,for and The difference is Next, the difference is compared with the corresponding 1.0 second time interval to obtain the pressure change amplitude value. For example, , , The pressure change amplitude value reflects the instantaneous rate and direction of pressure change in the oxygen supply pipeline. A positive value indicates that the pressure is increasing and a negative value indicates that the pressure is decreasing. The pressure change amplitude value is generated, for example, [0.03, -0.01, 0.03] MPa / s, which is used for subsequent pressure fluctuation analysis.

[0062] The amplitude judgment submodule, based on the pressure change amplitude value and combined with the pressure fluctuation benchmark range, filters out the amplitude values ​​of pressure change that are greater than the benchmark range, records the corresponding sampling time points, and generates an abnormal time node sequence.

[0063] Based on the pressure change amplitude, such as [0.03, -0.01, 0.03] MPa / s, and in conjunction with a preset pressure fluctuation benchmark range, a judgment is made. This benchmark range is determined jointly based on the normal operating characteristics of the oxygen supply pipeline, clinical oxygen stability requirements, and industry safety standards. For example, in a hospital oxygen supply scenario, the pressure fluctuation benchmark range is set to [-0.02 MPa / s, 0.02 MPa / s]. This benchmark range was determined through statistical analysis of long-term monitoring data of the hospital's oxygen supply pipeline under normal flow and normal oxygen load conditions, and was approved by clinical medical professionals. After expert review and confirmation to ensure safe oxygen use within this range, the process begins by comparing each pressure change amplitude with this baseline range. Amplitudes exceeding the upper limit or falling below the lower limit of the baseline range are identified. For example, an amplitude of 0.03 MPa / s exceeds the upper limit of the baseline range (0.02 MPa / s), while an amplitude of -0.01 MPa / s falls within the baseline range. Similarly, an amplitude of 0.03 MPa / s also exceeds the upper limit. For these threshold-exceeding amplitudes, their precise sampling time points are recorded. For instance, the sampling time point corresponding to 0.03 MPa / s is... and Then, the recorded sampling time points are arranged in chronological order to generate an abnormal time node sequence. For example, based on the above data, the abnormal time node sequence is: This sequence clearly indicates the time points when the pressure in the oxygen supply pipeline experienced abnormal fluctuations.

[0064] The result labeling submodule is based on the abnormal time node sequence, statistically analyzes the sampling time points and pressure change amplitude values, determines whether the time point belongs to any node in the corresponding sequence, labels abnormal and normal states, integrates the labeling information of the time points, and obtains a set of pressure distribution states.

[0065] Based on abnormal time node sequences, for example In addition to the previously calculated pressure change amplitude values, statistics are performed on each sampling time point and its corresponding pressure change amplitude value. First, all sampling time points are traversed, and it is determined whether each time point belongs to any node in the abnormal time node sequence. For example, for time point It is determined that it does not belong to Regarding the time point To determine if it belongs to Regarding the time point It is determined that it does not belong to Regarding the time point To determine if it belongs to Based on the judgment results, the pressure state of each sampling time point is labeled. If the time point belongs to an abnormal time node sequence, it is labeled as "abnormal state"; otherwise, it is labeled as "normal state". For example, time point Marked as "normal state", time point Marked as "abnormal state", time point Marked as "normal state", time point It is marked as "abnormal state," and the corresponding pressure change value is included with the label. For example, for The "abnormal state" is accompanied by an amplitude value of 0.03 MPa / s, for The "abnormal state" was indicated by an amplitude of 0.03 MPa / s. Then, the sampling time points with labeled information were integrated to form a structured pressure distribution set. This set clearly shows the pressure fluctuations in the oxygen supply pipeline at various time points within a specific monitoring period. For example, the set contains entries like: "Time Point..." Status: Normal, Amplitude: None, Time Point Status: Abnormal, Amplitude: 0.03 MPa / s, Time Point Status: Normal, Amplitude: None, Time Point Status: Abnormal, Amplitude: 0.03 MPa / s.

[0066] Specifically, such as Figure 2 , Figure 4 As shown, the flow regulation execution module includes:

[0067] The amplitude summarization submodule obtains the pressure amplitude labeling status corresponding to the time point in the pressure distribution status set, combines two adjacent sampling time points to form an amplitude status combination, labels the pressure amplitude and stores it in chronological order, and generates a continuous amplitude identifier sequence.

[0068] Obtain the pressure amplitude label status corresponding to each time point in the pressure distribution state set. For example, for time point... The status is "normal" for the given time point. The status is "abnormal" and the amplitude is 0.03 MPa / s. Two adjacent sampling time points and their respective amplitude labeling statuses are combined to form an amplitude status combination. For example, combination one is ( (normal) and ( (abnormal), combination two is ( (abnormal) and ( (Normal), combination three is ( (normal) and ( In each amplitude state combination, its pressure amplitude information is marked. If any time point is marked as "abnormal", the pressure amplitude of the combination is marked as the amplitude value of the abnormal point. If two points are abnormal, the amplitude values ​​of the two abnormal points are marked. For example, combination one is marked as 0.03 MPa / s, combination two is marked as 0.03 MPa / s, and combination three is marked as 0.03 MPa / s. Subsequently, these marked pressure amplitude combinations are stored in strict chronological order to generate a continuous amplitude identification sequence. This sequence reflects the evolution of pressure fluctuations on the time axis. For example, the sequence is represented as [( normal, Abnormal, 0.03 MPa / s), ( abnormal, Normal, 0.03 MPa / s), normal, [Abnormal, 0.03 MPa / s], this sequence clearly shows the specific fluctuations in the oxygen supply pipeline pressure from one sampling point to the next.

[0069] The status matching submodule determines whether two points in each group are simultaneously abnormal based on the continuous amplitude identifier sequence; if so, it marks them as being in adjustment, otherwise it marks them as being in adjustment, and records the status according to the time point to obtain the flow adjustment status sequence.

[0070] Identify the sequence based on continuous amplitude, for example [( normal, Abnormal, 0.03 MPa / s), ( abnormal, Normal, 0.03 MPa / s), normal, [Abnormal, 0.03 MPa / s], determine whether two adjacent time points in each group of the sequence are simultaneously in an abnormal state. The judgment criterion is to check whether both sampling points in each combination are marked as "abnormal state" in the pressure distribution state set. For example, for combination one ( normal, (Abnormal), the two points are not abnormal at the same time, for combination two ( abnormal, (Normal), and the two points are not both abnormal at the same time. Assume there is a combination ( abnormal, If two points are simultaneously abnormal, they are marked as "adjusting" state, indicating that the oxygen supply pressure is actively adjusting or in an unstable adjustment period. If the abnormality is not simultaneous, it is marked as "adjustment completed" state, indicating that the pressure fluctuation has stabilized or the adjustment action has ended. Subsequently, the state is precisely recorded according to time points to obtain the flow regulation state sequence. For example, the sequence is [( Normal - (Abnormality, adjustment complete) Exceptions - Normal, adjustment complete. Normal - [Abnormal, adjustment complete], this sequence clearly reflects the flow regulation status of the oxygen supply pipeline at different time periods.

[0071] The tag generation submodule extracts the adjustment status according to the time segment based on the flow adjustment status sequence and generates status identifiers. It then merges the time point, adjustment status and identifier into a structured set to obtain the flow adjustment status tag group.

[0072] Based on the flow regulation state sequence, for example [( Normal - (Abnormality, adjustment complete) Exceptions - Normal, adjustment complete. Normal - [Abnormal, adjustment complete], accurately extract the corresponding adjustment status according to time segment and generate status identifiers. The division of time segments is based on consecutive identical adjustment states. For example, if arrive If all values ​​are in the "Adjustment Complete" state, then this is extracted into a time segment, and a unique "Adjustment Complete" status identifier is generated, such as "STAT_COMPLETE_001". This identifier is generated by encoding the status type and appending a sequence number. Each time point, its corresponding adjustment status, and the generated status identifier are merged into a structured set. For example, for time point... Its adjustment status is "adjustment complete", and the corresponding status identifier is "STAT_COMPLETE_001". These three data items are combined into a single structured entry, such as ( Adjustment complete (STAT_COMPLETE_001), for The same merging operation is performed at the same time points. Finally, the traffic adjustment status label group is obtained. This label group provides a standardized reference for subsequent terminal status determination. For example, the label group is [(status)]. Adjustment complete, marked as STAT_COMPLETE_001), (Status) Adjustment complete, marked as STAT_COMPLETE_001), (Status) Adjustment complete, marked as STAT_COMPLETE_001), (Status) Once the adjustment is complete, it will be marked as STAT_COMPLETE_001. If the "adjusting" status is detected in another time period, a new status identifier will be generated, such as "STAT_ADJUSTING_002".

[0073] Specifically, such as Figure 2 , Figure 5 As shown, the terminal status determination module includes:

[0074] The time matching submodule extracts the start time point and the current cycle time point uploaded by the networked oxygen supply equipment based on the flow regulation status label group, calculates the time difference and locates the sampling point, determines whether the operation phase is within the time window, and generates an operation phase interval matching identifier.

[0075] Based on the flow regulation status label group, this label group contains the regulation status and status indicator of the oxygen supply pipeline at various points in time, for example, ( Adjustment complete (STAT_COMPLETE_001), precisely extract the start time and current cycle time uploaded by networked oxygen supply devices (e.g., ventilators, anesthesia machines, etc. in wards). For example, a ventilator in... Started, current period sampling time point is The system calculates the time difference between the start time and the current cycle time. For example, if the time difference is 5 seconds, it accurately traces back and locates the corresponding sampling point. For example, a 5-second time difference corresponds to the 5th sampling point counted from the start time. It then determines whether the current operating phase is within a preset time window. The time window is set based on the characteristics of normal start-up and stable operation of medical equipment. For example, the stable operation time window for a ventilator is set to 10 seconds to 30 minutes after start-up. This window setting is based on a large amount of clinical data and the stabilization period parameters recommended by the equipment manufacturer. For example, if the time difference is 5 seconds, it is determined that it is within the "start-up phase" time window; if the time difference is 15 seconds, it is determined that it is within the "stable operation phase" time window. It generates an operating phase interval matching identifier. For example, if the time difference is 5 seconds, a "start-up phase" matching identifier is generated; if the time difference is 15 seconds, a "stable operation phase" matching identifier is generated. Finally, the identifier will serve as a key basis for subsequent determination of the terminal status.

[0076] The status extraction submodule extracts status tags from the matching sampling segment based on the status identifier and flow adjustment status in the flow adjustment status tag group, and performs synchronization calculations in conjunction with the interval matching identifier to obtain stage synchronization deviation data.

[0077] Based on the corresponding status identifier and flow adjustment status in the flow adjustment status label group, for example, if the label group contains the identifier "STAT_COMPLETE_001" corresponding to the "adjustment complete" status, within the matched sampling segment, accurately extract the status labels synchronized with the matching identifier of the operation phase interval. For example, if the matching identifier of the operation phase interval is "stable operation phase," then search in the flow adjustment status label group for all status labels within the time range of the "stable operation phase." arrive If all samples are within the "stable operation phase" time window and their adjustment status is "adjustment completed" and marked as "STAT_COMPLETE_001", then extract "STAT_COMPLETE_001" and the "adjustment completed" status. At the same time, perform synchronization calculations by combining the operation phase interval matching identifiers. The purpose of the synchronization calculation is to confirm whether the operation phase of the oxygen supply terminal is consistent with the flow adjustment status of the oxygen supply pipeline. For example, if the operation phase is "stable operation phase" and the flow adjustment status is "adjustment completed", then it is considered to be synchronized. If the operation phase is "stable operation phase" but the flow adjustment status is "adjustment in progress", then it is considered to have a deviation. This synchronization calculation is completed through a logical AND operation, for example, (operation phase = stable phase) AND (adjustment status = adjustment completed). Finally, obtain the phase synchronization deviation data. This data includes the operation phase, adjustment status, and the synchronization consistency judgment result between the two for each sampling segment. For example, the data is [(time period 1, stable operation, adjustment completed, synchronization), (time period 2, start-up phase, adjustment in progress, synchronization), (time period 3, stable operation, adjustment in progress, deviation)].

[0078] The adaptation filtering submodule performs threshold judgment based on the stage synchronization deviation data, filters the time period of the stage state combination where the deviation is less than the set benchmark value, records the corresponding time point and state identifier in a unified structure, and summarizes them to form a state advancement adaptation set.

[0079] Threshold judgment is performed based on the phase synchronization deviation data. This data includes the operating phase, adjustment status, and synchronization status of each time period. For example, the data contains entries (Time Period 3, Stable Operation, Adjustment, Deviation). For each phase state combination, the deviation is compared with a set benchmark value. The deviation is quantified here as the duration or frequency of the "asynchronous" state. The set benchmark value is determined based on the clinical quality requirements and safety margin of oxygen supply. For example, the maximum allowable duration of "asynchronous" state is set at 5 seconds. This benchmark value is determined through clinical observation and data analysis of fluctuations in patients' physiological indicators under different deviation durations. Phase state combinations with deviations less than the set benchmark value are selected. Combined time periods, for example, if the "asynchronous" state of time period 3 lasts for 8 seconds, it exceeds the set benchmark value of 5 seconds and is not filtered. If the "asynchronous" state of time period 4 lasts only for 2 seconds, it is less than the set benchmark value and will be filtered. The corresponding time points and status identifiers are recorded in a structured manner. For example, the start and end times of time period 4, as well as its corresponding flow regulation status identifier and operating stage identifier, are recorded. The records are then summarized to form a status advancement adaptation set. This set clearly indicates which time periods the operating status of the oxygen supply terminal and the flow regulation status of the oxygen supply pipeline maintain good consistency or an acceptable deviation range. For example, the status advancement adaptation set might be [time period 4, start time, end ... ,Finish Status indicator Operation phase identifier ].

[0080] Specifically, such as Figure 2 , Figure 6 As shown, the instruction optimization and adaptation module includes:

[0081] The control instruction extraction submodule extracts the corresponding state number from the state entries marked as advanceable in the state advancement adaptation set, filters the matching control instructions, merges and compares them, and generates a stage control instruction set.

[0082] Based on the state advancement adaptation set, the state entries marked as advanceable are used. For example, if there are state entries in the set ( , Status indicator Operation phase identifier The entry indicates that the oxygen supply status is good or the deviation is controllable. The corresponding status number is precisely extracted, for example, from the status identifier. The system parses out the status number "001", which typically corresponds to a preset oxygen supply status code table. For example, "001" represents "stable oxygen supply", and "002" represents "low flow oxygen supply". Then, it filters out control commands that match this status number. These control commands are predefined operations used to adjust oxygen supply flow or pressure. For example, the control command matching status number "001" (stable oxygen supply) might be "maintain current flow", while the command matching status number "002" (low flow oxygen supply) might be "increase pressure by 0.1 MPa" or "increase flow by 0.5 L / min". The matching rules for control commands are defined through analysis of historical operating data of the oxygen supply pipeline and comprehensive consideration of clinical needs, and are validated through multiple rounds of simulation. The filtered matching control commands are then compared with the currently executed commands. For example, if the matching command is "maintain current flow" while the currently executed command is "increase pressure by 0.05 MPa", a comparison is made to generate a set of stage control commands. This set contains optimized control commands that should be executed within each suitable time period.

[0083] The instruction position comparison submodule filters instructions with target position numbers less than the current execution position based on each control instruction in the stage control instruction set, calculates the offset change of the control instruction, performs pre-insertion and records the index change, and generates instruction offset change data.

[0084] The offset change of the control command is calculated using the following formula:

[0085] ;

[0086] in, This represents the amount of offset change in the control command. This represents the target location number of the j-th instruction. Represents the current execution location number. The adjustment factor associated with the j-th instruction is represented by n, where n is the total number of instructions in the control instruction set.

[0087] Based on each control instruction in the stage control instruction set, for example, instruction 1 in the set, its target position number is... , and instruction 2, whose target location number Set the current execution position number ,this The value represents the real-time average pressure measurement of each oxygen supply terminal in the networked oxygen supply pipeline, and is continuously reported through the sensor network to accurately filter out target location numbers that are less than the current execution location number. The instruction, for example, for instruction 1, its target location Not less than Therefore, it is not filtered. For instruction 2, its target location is... Less than Therefore, instruction 2 is selected, and then the offset change of the control instruction is calculated. This offset change quantifies the overall deviation between the target position of all instructions to be executed and the current execution position. A larger value indicates a greater deviation between the current state and the desired state. The value is weighted by an adjustment factor, and the formula is expressed as follows: ,in, To control the offset change of instructions, this represents the comprehensive quantization value of all related instructions deviating from the current execution state. Representing the The target location number of the instruction specifically refers to the oxygen supply pipeline pressure value that the instruction expects to achieve, for example... , This represents the current execution location number, i.e., the real-time monitoring pressure value of the oxygen supply pipeline. , Representative and the The adjustment factor associated with each instruction ranges from 0.0 to 5.0, for example, 0.1 or 0.5. This factor is set according to the clinical importance of the instruction and its level of impact on patient safety. For example, an oxygen supply pressure instruction for the intensive care unit (ICU) is set relatively low because it has a significant impact on patients' vital signs. A value of 0.1 is used to ensure that its offset is within... The 0.1 value, which carries a higher weight in the calculation and prompts priority adjustment, was determined based on observations of physiological changes in simulated ICU patients when the oxygen supply pressure deviated from 0.02 MPa for more than 5 seconds. For oxygen supply instructions in general wards, a higher value was set because its impact is relatively smaller. A value of 0.5 makes its offset within The weighting in the calculation is relatively low. This set value of 0.5 was determined by observing the impact on patient comfort in general wards when the oxygen supply pressure deviates from 0.05 MPa for more than 10 seconds. To control the total number of instructions in the instruction set, here ;

[0088] The calculation logic of this formula is as follows: first, calculate the target location of each instruction. With the current execution position The difference This directly reflects the absolute amount and direction of the deviation, which is then multiplied by a weighting factor. This weighting factor adjusts for bias, with higher importance instructions ( The smaller the value, the larger the weighting factor, making its deviation have a greater impact on the total offset. Contribute more, while low-importance instructions ( Larger values ​​have smaller weighting factors, so reducing their bias has an effect on... The contribution is then summed, and finally all weighted absolute deviations are summed. The total offset change is obtained. This formula introduces an adjustment factor. This achieves differentiated weighting of instruction deviations of varying importance, resulting in a more accurate calculation of the total offset. It can more accurately reflect the potential mismatch problems that need to be addressed first in the current system, thereby guiding the subsequent instruction pre-insertion optimization, avoiding blindly handling all deviations, and improving the intelligence level of instruction scheduling.

[0089] Instruction 1 ( ) and instruction 2 ( Substitute the parameters into the formula for calculation:

[0090] ;

[0091] The result indicates that the total weighted offset change of the current oxygen supply system is 0.0315 MPa. This value reflects the overall mismatch between all relevant commands and the current actual oxygen supply status. This offset change is subsequently used to determine whether to perform a pre-insertion operation, i.e., to prioritize the execution of certain commands. If the pressure exceeds a preset threshold (e.g., 0.025 MPa), a pre-insertion operation is triggered, indicating a significant difference between the current and desired system state, requiring immediate optimization of the instruction sequence. For example, for the selected instruction 2, a pre-insertion operation is performed, inserting it before the current execution position in the instruction queue and recording its new index change in the queue, generating instruction offset change data. This data includes the offset, original index, and new index of each instruction. For example, the instruction offset change data is: [Instruction 2, =0.0133, original index X, new index Y].

[0092] The control sequence mapping submodule identifies the mapping between the status number and the new index number of the control instruction recorded in the instruction offset change data, identifies the index number before and after the control instruction and the corresponding status number, analyzes the bidirectional mapping relationship between the status number and the new index number, and obtains the task control sequence mapping table.

[0093] Based on the control command index numbers and corresponding status numbers recorded in the command offset change data, for example, the data contains the offset change information of command 1: command 1 (offset change amount 0.833, original index 2, new index 1), its corresponding status number is "001" (stable oxygen supply). The mapping relationship between the status number and the new index number of the control command is identified. For example, a mapping from "status number 001" to "new index 1 of control command" is established. This mapping is established by consulting a preset status-command index lookup table. The bidirectional mapping relationship between the status number and the new index number is analyzed. For example, not only is the correspondence between "status number 001" and "new index 1" recorded, but the correspondence between "new index 1" and "status number 001" is also recorded. This bidirectional mapping relationship facilitates the quick lookup of the corresponding status when executing commands and the quick location of relevant commands when the status changes. Through analysis, a task control sequence mapping table is obtained. This table contains the precise bidirectional mapping between all status numbers and the corresponding new index of the control command. This mapping table provides a key logical basis for the subsequent issuance of task commands.

[0094] Specifically, such as Figure 2 , Figure 7 As shown, the runtime record acquisition module includes:

[0095] The task instruction issuance submodule reads the task status number in the current scheduling queue according to the status number and control instruction index position recorded in the task control sequence mapping table, performs a sorting operation on the instruction number in the control queue according to the index information of the mapping table, and issues the corresponding control instructions to the lower oxygen supply terminal control terminal in sequence. It records the time node of each issued instruction operation and the control number to generate a task instruction response number sequence.

[0096] Based on the status number and control instruction index position recorded in the task control sequence mapping table (for example, if the mapping table shows "status number 001" mapped to "control instruction index 1"), the task status numbers in the current scheduling queue are first read. For example, if the status number of the task to be executed in the current scheduling queue is "001", then the instruction numbers in the control queue are sorted according to the index information in the mapping table. For example, if the original instruction order in the control queue is [instruction 3, instruction 1, instruction 2], according to the mapping table, instruction 1 (corresponding to status 001) should be in the first position. The columns are reordered as [Instruction 1, Instruction 3, Instruction 2]. Control instructions are then sent sequentially to the next lower-level oxygen supply terminal control terminal in this order. For example, "Instruction 1" is sent first to the ward oxygen supply terminal, followed by "Instruction 3," and then "Instruction 2." The operation time node and control number of each issued instruction are combined and recorded. For example, "August 20, 08:30:05 - Instruction 1" and "August 20, 08:30:08 - Instruction 3" are recorded. A task instruction response number sequence is generated, which clearly records the execution time and specific number of all issued instructions.

[0097] The control data recording submodule monitors the current flow regulation status of the oxygen supply terminal based on each control number recorded in the task instruction response number sequence, collects the issuance time, control number and flow status data within the corresponding time period, and obtains instruction control status combination data.

[0098] Based on each control number recorded in the task instruction response number sequence, for example, the sequence contains "(08:30:05, Instruction 1)", the current flow regulation status of the oxygen supply terminal is monitored. For example, it monitors whether the actual oxygen supply flow on the ventilator in the ward changes according to the requirements of "Instruction 1". The time point of instruction issuance, control number, and flow status data within the corresponding time period are collected. For example, for Instruction 1, the time point of collection is 08:30:05, the control number is Instruction 1, and the flow status data within 30 seconds starting from 08:30:05 may be [5.0, 5.1, 5.0, ..., 5.2] L / min. The flow status data is acquired in real time through the built-in sensor of the terminal device. The combined data of instruction control status is obtained. This data associates the issued instruction with the actual flow response. This combined data provides the basis for evaluating the effect of instruction execution.

[0099] The operation record generation submodule extracts the time series data of the control number and the flow regulation status identifier based on the time point and flow regulation status corresponding to each control number in the instruction control status combination data, constructs a structured record entry sorted by time series, integrates all control data structures, and outputs the oxygen supply terminal operation record.

[0100] Based on the time point and flow regulation status corresponding to each control number in the command control status combination data, for example, for control number "Command 1", its time point is 08:30:05, and the corresponding flow regulation status data is [5.0, 5.1, ..., 5.2] L / min, the time series data of the control number is accurately extracted. This indicates that Command 1 was issued at 08:30:05, and its corresponding flow regulation status identifier, for example, the flow regulation status may be identified as "stable flow 5.0 L / min". A structured record entry ordered by time series is constructed. For example, the data for "Command 1" is... The issuance time, execution effect (flow rate change), and final status identifier are integrated into a single entry, such as (08:30:05, instruction 1, flow rate reaches 5.1 L / min, status: stable). All control data structures are integrated, and the execution records of all control instructions are archived in chronological order to output the oxygen supply terminal operation record. This record provides a complete and traceable historical view of all instruction issuance, execution effect, and flow rate adjustment status of the oxygen supply terminal within a specific time period. For example, the record will include when the instruction was issued, what instruction was issued, how the terminal device responded, and the specific trajectory of changes in the oxygen supply flow rate.

[0101] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A flow equalization control system for an oxygen inhalation terminal, characterized in that, The system includes: The pressure distribution monitoring module acquires the pressure values ​​of multiple monitoring nodes in the oxygen supply pipeline, calculates the pressure change amplitude of the monitoring nodes, determines whether it exceeds the pressure fluctuation benchmark range, and generates a pressure distribution state set. The flow regulation execution module performs a pressure value combination matching operation for two consecutive cycles based on the pressure distribution state set. If both are marked as abnormal, the flow regulation state is set to "in regulation"; otherwise, it is set to "regulation completed" and a flow regulation state label group is generated. The flow regulation execution module includes: The amplitude summarization submodule obtains the pressure amplitude labeling status corresponding to the time point in the pressure distribution state set, combines two adjacent sampling time points to form an amplitude state combination, labels the pressure amplitude and stores it in chronological order, and generates a continuous amplitude identifier sequence. The status matching submodule determines whether two points in each group are simultaneously abnormal based on the continuous amplitude identifier sequence; if so, it marks them as being in adjustment, otherwise it marks them as being in adjustment, and records the status according to the time point to obtain the flow adjustment status sequence. The tag generation submodule extracts the adjustment status according to the time segment based on the flow adjustment status sequence and generates status identifiers. It then merges the time point, adjustment status and identifier into a structured set to obtain the flow adjustment status tag group. The terminal status determination module extracts the oxygen supply equipment start time and the current cycle time based on the flow regulation status tag group, and combines the current status identifier and flow regulation status to determine whether it matches the time window of the operation phase. If it does, it is marked as an advanceable state and a status advancement adaptation set is generated. The terminal status determination module includes: The time matching submodule extracts the start time point and the current cycle time point uploaded by the networked oxygen supply equipment based on the flow regulation status tag group, calculates the time difference and locates the sampling point, determines whether the operation phase is within the time window, and generates an operation phase interval matching identifier. The status extraction submodule extracts status tags in the matching sampling segment based on the status identifier and flow adjustment status in the flow adjustment status tag group, and performs synchronization calculations in combination with the interval matching identifier to obtain stage synchronization deviation data. The adaptation filtering submodule performs threshold judgment based on the stage synchronization deviation data, filters the time period of the stage state combination where the deviation is less than the set benchmark value, records the corresponding time point and state identifier in a unified structure, and summarizes them to form a state advancement adaptation set. The instruction optimization and adaptation module advances the adaptation set according to the state, extracts control instructions based on the state-adaptation set, compares the current execution index with the target index, and if the target is lagging, performs insertion and rearrangement to generate a task control order mapping table.

2. The oxygen inhalation terminal flow equalization control system according to claim 1, characterized in that: The pressure distribution state set includes pressure amplitude labeling information, monitoring node markers, and pressure change amplitude values. The flow regulation state label group includes flow regulation state, state identifier, and recording time point. The state advancement adaptation set includes task state number, time window marker, and state matching result. The task control sequence mapping table includes original instruction position index, rearranged instruction index, and change information for each state index.

3. The oxygen inhalation terminal flow equalization control system according to claim 1, characterized in that: The pressure distribution monitoring module includes: The pressure sampling submodule acquires continuous periodic sampling values ​​reported by multiple pressure sensors in the networked oxygen supply pipeline, extracts the time interval between adjacent sampling points, and calculates the ratio of the difference between adjacent sampling values ​​to the corresponding time interval to obtain the pressure change amplitude value. The amplitude determination submodule, based on the pressure change amplitude value and combined with the pressure fluctuation reference range, filters out the amplitude values ​​of pressure change that are greater than the reference range, records the corresponding sampling time points, and generates an abnormal time node sequence. The result labeling submodule, based on the abnormal time node sequence, statistically analyzes the sampling time points and pressure change amplitude values, determines whether the time point belongs to any node in the corresponding sequence, labels the abnormal state and the normal state, integrates the labeling information of the time points, and obtains a set of pressure distribution states.

4. The oxygen inhalation terminal flow equalization control system according to claim 1, characterized in that: The instruction optimization and adaptation module includes: The control instruction extraction submodule extracts the corresponding state number based on the state entries marked as advanceable in the state advancement adaptation set, filters the matching control instructions, merges and compares them, and generates a stage control instruction set. The instruction position comparison submodule, based on each control instruction in the stage control instruction set, filters instructions whose target position number is less than the current execution position, calculates the offset change of the control instruction, performs pre-insertion and records the index change, and generates instruction offset change data. The control sequence mapping submodule identifies the mapping between the status number and the new index number of the control instruction recorded in the instruction offset change data, identifies the index number before and after the control instruction and the corresponding status number, analyzes the bidirectional mapping relationship between the status number and the new index number, and obtains the task control sequence mapping table.

5. The oxygen inhalation terminal flow equalization control system according to claim 4, characterized in that: The offset change of the control command is calculated using the following formula: ； in, This represents the amount of offset change in the control command. H represents the target location number of the j-th instruction, and H represents the current execution location number. The adjustment factor associated with instruction j is represented by n, where n is the total number of instructions in the control instruction set.

6. The oxygen inhalation terminal flow equalization control system according to claim 1, characterized in that: The system also includes a runtime log acquisition module: The operation record acquisition module, based on the task control sequence mapping table, issues instructions to tasks according to the current state sequence, and records the issuance time, instruction number, and current flow adjustment status to generate an oxygen supply terminal operation record.

7. The oxygen inhalation terminal flow equalization control system according to claim 6, characterized in that: The oxygen supply terminal operation record includes the issuance time, instruction number, and flow regulation status record.

8. The oxygen inhalation terminal flow equalization control system according to claim 6, characterized in that: The operation record acquisition module includes: The task instruction issuance submodule reads the task status number in the current scheduling queue according to the status number and control instruction index position recorded in the task control sequence mapping table, performs a sorting operation on the instruction number in the control queue according to the index information of the mapping table, and issues the corresponding control instructions to the lower oxygen supply terminal control terminal in sequence. It records the time node of each issued instruction operation and the control number to generate a task instruction response number sequence. The control data recording submodule monitors the current flow regulation status of the oxygen supply terminal based on each control number recorded in the task instruction response number sequence, collects the issuance time, control number and flow status data within the corresponding time period, and obtains instruction control status combination data. The operation record generation submodule extracts the time series data of the control number and the flow regulation status identifier according to the time point and flow regulation status corresponding to each control number in the instruction control status combination data, constructs a structured record entry sorted by time series, integrates all control data structures, and outputs the oxygen supply terminal operation record.

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