Oxygen inhalation terminal flow balance control system

Abstract

The invention relates to the technical field of flow control, in particular to an oxygen inhalation terminal flow balance control system which comprises a pressure distribution monitoring module, a flow regulation execution module, a terminal state judgment module, an instruction optimization adaptation module and an operation record acquisition module. According to the method, the pressure of multiple points in the oxygen supply pipeline is periodically collected, the change amplitude is analyzed, sensitive identification and state marking of flow abnormity are achieved, the accuracy and execution matching performance of flow adjustment are improved by combining the time point of equipment operation with time window matching of pressure fluctuation, and the flow adjustment accuracy is improved through a forward insertion rearrangement mechanism of a control instruction. According to the method and the system, priority issuing is guaranteed in a lagging scene, the adjustment response speed and the execution stability are enhanced, an adjustment process is recorded, accurate traceability and regulation and control quality evaluation of an oxygen supply terminal operation track are supported, and the regulation and control precision, the abnormity recognition capability and the overall controllability of system operation are effectively improved in a multi-terminal concurrent oxygen consumption scene.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of flow control, in particular to an oxygen inhalation terminal flow equalization control system. BACKGROUND

[0002] The technical field of flow control refers to the related technologies for achieving reasonable distribution and stable supply of flow through adjustment, detection and distribution methods during gas or liquid transportation and application. The core matters include monitoring of gas or liquid pressure and flow rate, precise regulation of flow size, and management of flow distribution equality when multiple users or devices are used simultaneously. This technical field is widely used in medical oxygen supply, industrial gas transportation, environmental control and other scenarios, and plays an important role in ensuring safety, stability and resource utilization. Among them, the traditional oxygen inhalation terminal flow equalization control system refers to the flow distribution device used when multiple patients are provided with oxygen inhalation in a hospital or centralized oxygen supply environment. For the problem of how to achieve equalization of oxygen supply flow among multiple terminals in centralized oxygen supply, the traditional oxygen inhalation terminal flow equalization control adjusts and distributes the oxygen supply amount by installing mechanical flow meters, flow limiting valves or differential pressure controllers on each oxygen inhalation terminal, so as to maintain the flow balance of each terminal within a certain range.

[0003] The traditional oxygen inhalation terminal flow equalization control relies on mechanical flow meters and flow limiting devices for independent adjustment, and it is difficult to realize the linkage judgment of the state between terminals. When there is oxygen fluctuation or rapid change in the number of oxygen users, it is easy to cause sudden drop or rise of the flow of some terminals. Such devices lack the ability to continuously monitor the overall state of the oxygen supply pipe network, and cannot quickly respond to short-term pressure disturbances, thereby causing adjustment lag or insufficient adjustment. For example, when multiple patients simultaneously use oxygen inhalation devices, it often leads to excessive flow in the near-end terminal and insufficient oxygen supply in the far-end, affecting the oxygen supply equality and oxygen safety. Moreover, since the operation is based on manual setting or physical adjustment, the system record of the adjustment process cannot be realized, resulting in a large blind area for fault tracking and operation quality evaluation. SUMMARY

[0004] In order to solve the technical problems existing in the prior art, the present application provides an oxygen inhalation terminal flow equalization control system. The technical solution is as follows: On the one hand, an oxygen inhalation terminal flow equalization control system is provided, which comprises: A pressure distribution monitoring module obtains the pressure values of multiple monitoring nodes in the oxygen supply pipeline, calculates the pressure change amplitude of the monitoring nodes, judges whether it exceeds the pressure fluctuation reference range, and generates a pressure distribution state set; The flow regulation execution module performs a combination matching operation of pressure values of two continuous periods according to the pressure distribution state set, sets the flow regulation state as being in regulation if both are marked as being abnormal, otherwise sets the flow regulation state as having been completed, and generates a flow regulation state label group; The terminal state discrimination module extracts an oxygen supply equipment starting time point and a current period time point based on the flow regulation state label group, combines a current state identifier and the flow regulation state, judges whether to match a time window of a running stage, marks as being able to be promoted if it is true, and generates a state promotion adaptation set; The instruction optimization adaptation module extracts control instructions according to the state promotion adaptation set according to the state promotion adaptation set, compares a current execution index and a target index, performs front insertion and rearrangement if the target lags, and generates a task control sequence mapping table.

[0005] As a further scheme of the application, the pressure distribution state set comprises pressure amplitude marking information, monitoring node markers and pressure change amplitude values, the flow regulation state label group comprises a flow regulation state, a state identifier and a record time point, the state promotion adaptation set comprises a task state number, a time window marker and a state matching result, and the task control sequence mapping table comprises an original instruction position index, a rearranged instruction index and per-state index variation information.

[0006] As a further scheme of the application, the pressure distribution monitoring module comprises: The pressure sampling submodule obtains continuous period sampling values reported by a plurality of pressure sensors in a networked oxygen supply pipeline, extracts a time interval of adjacent sampling points, and calculates a ratio of a difference between adjacent sampling values and a corresponding time interval to obtain a pressure change amplitude value; The amplitude judgment submodule filters amplitude values greater than a reference range of pressure fluctuation based on the pressure change amplitude value in combination with the reference range of pressure fluctuation, records corresponding sampling time points, and generates an abnormal time node sequence; The result labeling submodule counts sampling time points and pressure change amplitude values based on the abnormal time node sequence, judges whether the time points belong to any node in the corresponding sequence, labels abnormal states and normal states, integrates labeling information of the time points, and obtains a pressure distribution state set.

[0007] As a further scheme of the application, the flow regulation execution module comprises: The amplitude summary submodule obtains pressure amplitude marking states corresponding to time points in the pressure distribution state set, forms an amplitude state combination of two adjacent sampling time points, labels pressure amplitudes and stores them in time sequence, and generates a continuous amplitude identifier sequence; The state matching submodule judges whether each group of two points is abnormal at the same time according to the continuous amplitude identification sequence; if yes, it is marked as adjustment in progress, otherwise, it is marked as adjustment completed, and state records are made according to time points to obtain a flow adjustment state sequence; The label generation submodule extracts adjustment states according to time sections and generates state labels based on the flow adjustment state sequence, and obtains a flow adjustment state label group by combining time points, adjustment states and labels into a structured set.

[0008] As a further scheme of the application, the terminal state discrimination module comprises: The time matching submodule extracts start time points and current cycle time points uploaded by the networked oxygen supply equipment based on the flow adjustment state label group, calculates time differences and locates sampling points, judges whether the running stage is within a time window, and generates a running stage interval matching label; The state extraction submodule extracts state labels in the matched sampling section according to corresponding state labels and flow adjustment states in the flow adjustment state label group, and performs synchronous operation in combination with the interval matching label to obtain stage synchronization deviation data; The adaptation screening submodule performs threshold judgment operation according to the stage synchronization deviation data, screens combination time periods in which the deviation amount in the stage state combination is less than a set reference value, uniformly structures records of corresponding time points and state labels, and forms a state advancement adaptation set by summarizing.

[0009] As a further scheme of the application, the instruction optimization adaptation module comprises: The control instruction extraction submodule extracts corresponding state numbers according to state entries marked as advanceable in the state advancement adaptation set, screens and combines matching control instructions to generate a stage control instruction set; The instruction position comparison submodule screens instructions whose target position numbers are less than the current execution position based on each control instruction in the stage control instruction set, calculates the offset change amount of the control instruction, performs front insertion and records index changes to generate instruction offset change data; The control sequence mapping submodule identifies the mapping between state numbers and new index numbers of control instructions based on the control instruction front and back index numbers and corresponding state numbers recorded in the instruction offset change data, analyzes the bidirectional mapping relationship between state numbers and new index numbers, and obtains a task control sequence mapping table.

[0010] As a further scheme of the application, the offset change amount of the control instruction adopts the formula: ; Wherein, represents the offset change amount of the control instruction, represents the first The target position number of the instruction, The current execution position number, The adjustment coefficient associated with the first The instruction, The total number of instructions in the control instruction set.

[0011] As a further scheme of the present application, the system further comprises a running record acquisition module: The running record acquisition module performs instruction issuing operation on the task according to the current state sequence based on the task control sequence mapping table, and records the issuing time point, instruction number and current flow regulation state in combination to generate the oxygen supply terminal running record.

[0012] As a further scheme of the present application, the oxygen supply terminal running record comprises the issuing time point, instruction number and flow regulation state record.

[0013] As a further scheme of the present application, the running record acquisition module comprises: The task instruction issuing submodule reads the task state number in the current scheduling queue according to the state number and control instruction index position recorded in the task control sequence mapping table, performs sorting operation on the instruction number in the control queue according to the mapping table index information, issues the corresponding control instruction to the lower oxygen supply terminal control end in sequence, records the time node and control number of each issued instruction operation in combination to generate the task instruction response number sequence; The control data recording submodule monitors the current flow regulation state of the oxygen supply terminal based on each control number recorded in the task instruction response number sequence, collects the issuing time point, control number and flow state data in the corresponding time period, and acquires the instruction control state combination data; The running record generation submodule extracts the time sequence data and flow regulation state identifier of the control number according to the time point and flow regulation state corresponding to each control number in the instruction control state combination data, constructs the structure record entry sorted in time sequence, integrates all control data structures, and outputs the oxygen supply terminal running record.

[0014] The technical scheme provided by the embodiment of the present application has at least the following beneficial effects: Through continuous periodic acquisition and change amplitude analysis of the pressure of multiple monitoring nodes in the oxygen supply pipeline, high sensitivity identification of abnormal flow fluctuation is realized, and then the oxygen supply flow state can be dynamically marked and judged in real time. Combined with the time window matching mechanism between the device running point and the flow fluctuation state, the accuracy and execution adaptability of flow regulation in the running stage are effectively improved. At the same time, by using the dynamic front insertion and rearrangement strategy of the control instruction execution sequence, it is ensured that the instruction can be issued preferentially under the condition of lag, so as to optimize the response efficiency and execution stability of the flow regulation process. Through the generation of regulation records in the whole process, the precise traceability of the oxygen supply terminal running track and the closed-loop evaluation of the regulation quality are realized, which effectively enhances the regulation accuracy, abnormal perception ability and operation controllability of the centralized oxygen supply system in the multi-terminal concurrent operation scene. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creating labor.

[0016] Figure 1 is a schematic diagram of an oxygen absorption terminal flow equalization control system provided by the embodiment of the present application; Figure 2 is a system framework schematic diagram of the present application; Figure 3 is a pressure distribution monitoring module flow chart in the present application; Figure 4 is a flow regulation execution module flow chart in the present application; Figure 5 is a terminal state discrimination module flow chart in the present application; Figure 6 is an instruction optimization adaptation module flow chart in the present application; Figure 7 is a running record acquisition module flow chart in the present application. DETAILED DESCRIPTION

[0017] The technical solutions in the present application will be described below in combination with the drawings.

[0018] In the embodiments of the present application, the words such as "example", "for example" are used to represent as an example, illustration or explanation. Any embodiment or design scheme described as "example" in the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present application, the meaning expressed by "and / or" can be both, or can be one of the two.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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: 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 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. 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. 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. 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.

[0023] 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.

[0024] Specifically, such as Figure 2 , Figure 3 As shown, 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. 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.

[0025] The amplitude judgment submodule filters the amplitude values greater than the reference range from the pressure change amplitude values based on the pressure change amplitude values in combination with the pressure fluctuation reference range, records the corresponding sampling time points, and generates an abnormal time node sequence; Based on the pressure change amplitude values, such as [0.03, -0.01, 0.03] MPa / s, in combination with the preset pressure fluctuation reference range, the pressure fluctuation reference range is determined according to the normal operation characteristics of the oxygen supply pipeline, the clinical oxygen stability requirements, and the industry safety standards, for example, in the hospital oxygen supply scene, the pressure fluctuation reference range is set to [-0.02 MPa / s, 0.02 MPa / s], this reference range is obtained through long-term monitoring data statistical analysis of the hospital oxygen supply pipeline under normal flow and normal oxygen load, and is confirmed by clinical medical experts, to ensure the safety of oxygen use within this range, first, compare each pressure change amplitude value with this reference range, filter out the amplitude values greater than the upper limit of the reference range or less than the lower limit of the reference range, for example, the amplitude value 0.03 MPa / s is greater than the upper limit of the reference range 0.02 MPa / s, while the amplitude value -0.01 MPa / s is within the reference range, and the amplitude value 0.03 MPa / s is also greater than the upper limit of the reference range, for the filtered amplitude values exceeding the threshold, record the corresponding accurate sampling time points, for example, the sampling time point corresponding to 0.03 MPa / s is and , record the time point, arrange the recorded sampling time points 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 of abnormal fluctuation of the oxygen supply pipeline pressure.

[0026] The result labeling submodule counts the sampling time points and the pressure change amplitude values based on the abnormal time node sequence, judges whether the time points belong 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 pressure distribution state set; Based on the abnormal time node sequence, for example , and the pressure change amplitude values calculated before, count each sampling time point and its corresponding pressure change amplitude value, first, traverse all the sampling time points, and judge whether each time point belongs to any node in the abnormal time node sequence, for example, for the time point , judge that it does not belong to , for the time point , judge that it belongs to , for the time point , judge that it does not belong to , for the time point , judge that it belongs to According to the judgment result, each sampling time point is marked with the pressure state to which it belongs. If the time point belongs to the abnormal time node sequence, it is marked as "abnormal state", otherwise it is marked as "normal state". For example, time point is marked as "normal state", time point is marked as "abnormal state", time point is marked as "normal state", and time point is marked as "abnormal state". At the same time, the corresponding pressure change amplitude value is attached when marking, for example, for the "abnormal state" of time point , the amplitude value 0.03 MPa / s is attached, and for the "abnormal state" of time point , the amplitude value 0.03 MPa / s is attached. Then, the sampling time points with marking information are integrated to form a structured pressure distribution state set, which clearly presents the fluctuation state of the oxygen supply pipeline pressure at each time point within a specific monitoring period. For example, the set contains similar entries: "time point , state: normal, amplitude: none", "time point , state: abnormal, amplitude: 0.03 MPa / s", "time point , state: normal, amplitude: none", and "time point , state: abnormal, amplitude: 0.03 MPa / s".

[0027] Specifically, as shown in Figure 2 , Figure 4 , the flow regulation execution module includes: The amplitude summary submodule obtains the pressure amplitude marking state corresponding to each time point in the pressure distribution state set, forms an amplitude state combination with two adjacent sampling time points, marks the pressure amplitude and stores it in time sequence, and generates a continuous amplitude identification sequence. The amplitude summary submodule obtains the pressure amplitude marking state corresponding to each time point in the pressure distribution state set, for example, for time point , the state is "normal", and for time point , the state is "abnormal" and the amplitude is 0.03 MPa / s. An amplitude state combination is formed with two adjacent sampling time points and their respective amplitude marking states, for example, combination one is ( , normal) and ( , abnormal), combination two is ( , abnormal) and ( , normal), and combination three is ( , normal) and ( , abnormal), in each amplitude state combination, mark its pressure amplitude information, if one of the time points is marked as "abnormal", mark the pressure amplitude of this combination as the amplitude value of the abnormal point, if both points are abnormal, mark the amplitude value of the two abnormal points, 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, then, store these marked pressure amplitude combinations in strict time sequence to generate a continuous amplitude identification sequence, which reflects the evolution of pressure fluctuation 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)].

[0028] The state matching sub-module determines whether each group of two points is abnormal at the same time according to the continuous amplitude identification sequence; if so, it is marked as "adjusting", otherwise, it is marked as "adjustment completed", and the state is recorded according to the time point to obtain a flow regulation state sequence; According to the continuous amplitude identification sequence, for example, [( normal, abnormal, 0.03 MPa / s), abnormal, normal, 0.03 MPa / s), normal, abnormal, 0.03 MPa / s)], determine whether each group of two adjacent time points in the sequence is in an abnormal state at the same time, the determination basis is to check whether the two sampling points in each combination are both 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), the two points are also not abnormal at the same time, and assuming that there is a combination ( abnormal, abnormal), it is determined as abnormal at the same time, if the result of the determination is that the two points are abnormal at the same time, it is marked as "adjusting" state, which indicates that the oxygen supply pressure is being actively adjusted or is in an unstable adjustment period, if the result of the determination is not abnormal at the same time, it is marked as "adjustment completed" state, which indicates that the pressure fluctuation has tended to be stable or the adjustment action has ended, then, the state is accurately recorded according to the time point to obtain a flow regulation state sequence, for example, the sequence is [( Normal - Abnormal, adjustment completed), Abnormal - Normal, adjustment completed), Normal - Abnormal, adjustment completed)], this sequence clearly reflects the flow regulation state of the oxygen supply pipeline at different time periods.

[0029] The label generation submodule extracts the regulation state according to the time segment based on the flow regulation state sequence, and generates a state identifier. The time point, regulation state, and identifier are combined into a structured set to obtain a flow regulation state label group; Based on the flow regulation state sequence, for example, Normal - Abnormal, adjustment completed), Abnormal - Normal, adjustment completed), Normal - Abnormal, adjustment completed)], the corresponding regulation state is accurately extracted according to the time segment, and a state identifier is generated. The division of the time segment is based on continuous identical regulation states, for example, if to are all in the "adjustment completed" state, they are extracted as a time segment, and a unique "adjustment completed" state identifier is generated, for example, "STAT_COMPLETE_001". Each time point, its corresponding regulation state, and the generated state identifier are combined into a structured set, for example, for the time point , the regulation state is "adjustment completed", and the corresponding state identifier is "STAT_COMPLETE_001". The three data are combined into a structured entry, such as ( , adjustment completed, STAT_COMPLETE_001), for and other time points, the same merging operation is performed. Finally, the flow regulation state label group is obtained, which provides a standardized reference basis for subsequent terminal state discrimination, for example, the label group is [(state , adjustment completed, identifier STAT_COMPLETE_001), (state , adjustment completed, identifier STAT_COMPLETE_001), (state , adjustment completed, identifier STAT_COMPLETE_001), (state , adjustment completed, identifier STAT_COMPLETE_001)], if the "adjusting" state is identified in another time period, a new state identifier will be generated, such as "STAT_ADJUSTING_002".

[0030] Specifically, as shown in Figure 2 , Figure 5 , the terminal state determination module comprises: The time matching submodule extracts the starting time point and the current cycle time point uploaded by the networked oxygen supply equipment based on the flow regulation state label group, calculates the time difference and locates the sampling point, determines whether the running stage is within the time window, and generates a running stage interval matching identifier; Based on the flow regulation state label group, the label group contains the corresponding regulation state and state identifier of the oxygen supply pipeline at each time point, for example, , regulation complete, STAT_COMPLETE_001), the starting time point and the current cycle time point uploaded by the networked oxygen supply equipment (such as terminal equipment such as a respirator in a ward and an anesthetic machine) are accurately extracted, for example, a certain respirator starts at , and the current cycle sampling time point is , the time difference between the starting time point and the current cycle time point is calculated, for example, the time difference is 5 seconds, according to the time difference, the corresponding sampling point is accurately traced back and located, for example, the time difference of 5 seconds corresponds to the 5th sampling point from the starting time, it is determined whether the current running stage is within the preset time window, the time window is set according to the characteristics of the normal starting and stable running of the medical equipment, for example, the stable running time window of the respirator is set to 10 seconds to 30 minutes after starting, the setting of this window is based on a large amount of clinical data and the recommended stable period parameters of the equipment manufacturer, for example, if the time difference is 5 seconds, it is determined that it is within the time window of the "starting stage", if the time difference is 15 seconds, it is determined that it is within the time window of the "stable running stage", a running stage interval matching identifier is generated, for example, if the time difference is 5 seconds, a "starting stage" matching identifier is generated, if the time difference is 15 seconds, a "stable running stage" matching identifier is generated, finally, the identifier will be used as the key basis for subsequent determination of the terminal state.

[0031] The state extraction submodule extracts the state label in the matched sampling section according to the corresponding state identifier and flow regulation state in the flow regulation state label group, and performs synchronous operation combined with the interval matching identifier to obtain stage synchronization deviation data; According to the corresponding state identifier and flow regulation state in the flow regulation state label group, for example, the label group contains the identifier "STAT_COMPLETE_001" corresponding to the "regulation complete" state, in the matched sampling section, the state label synchronized with the running stage interval matching identifier is accurately extracted, for example, if the running stage interval matching identifier is "stable running stage", all state labels within the "stable running stage" time range in the flow regulation state label group are searched, for example, if to If the time window is in the "steady running stage", and the adjustment state is "adjustment complete" and the identifier is "STAT_COMPLETE_001", the "STAT_COMPLETE_001" and "adjustment complete" state are extracted, and the running stage interval matching identifier is combined for synchronous operation. The purpose of the synchronous operation is to confirm whether the running stage of the oxygen supply terminal and the flow adjustment state of the oxygen supply pipeline are consistent. For example, if the running stage is "steady running stage" and the flow adjustment state is "adjustment complete", it is considered to be synchronous. If the running stage is "steady running stage" but the flow adjustment state is "adjustment in progress", it is considered to be deviated. This synchronous operation is completed through logical AND operation. For example, (running stage = steady stage) and (adjustment state = adjustment complete). Finally, the stage synchronization deviation data is obtained, which contains the running stage, adjustment state and synchronization consistency judgment result of each sampling section. For example, the data is [(time period 1, steady running, adjustment complete, synchronization), (time period 2, start stage, adjustment in progress, synchronization), (time period 3, steady running, adjustment in progress, deviation)].

[0032] The adaptive screening sub-module performs threshold judgment operation according to the stage synchronization deviation data, screens the combined time period of the stage state combination with a deviation less than a set reference value, and records the corresponding time point and state identifier in a unified structure, and forms a state advancement adaptation set by summarizing. The threshold judgment operation is performed according to the stage synchronization deviation data, which contains the running stage, adjustment state and synchronization of each time period. For example, there is an entry (time period 3, steady running, adjustment in progress, deviation) in the data. For the deviation amount in each stage state combination, compare it with the set reference value. The deviation amount is quantified as the duration or frequency of "asynchronous" state. The set reference value is determined according to the clinical quality requirements and safety margin of oxygen supply. For example, the maximum allowed "asynchronous" state duration is 5 seconds. This reference value is determined by clinical observation and data analysis of patient physiological indicators under different deviation durations. The combined time period of the stage state combination with a deviation less than the set reference value is screened. For example, the "asynchronous" state of time period 3 lasted for 8 seconds, which exceeded the set reference value of 5 seconds, and was not screened. If the "asynchronous" state of time period 4 only lasted for 2 seconds, it was less than the set reference value and was screened out. The corresponding time point and state identifier are recorded in a unified structure. For example, the start and end time points of time period 4, as well as its corresponding flow adjustment state identifier and running stage identifier are recorded. The records are summarized to form a state advancement adaptation set, which clearly indicates which time periods the running state of the oxygen supply terminal and the flow adjustment state of the oxygen supply pipeline maintain good consistency or acceptable deviation range. For example, the state advancement adaptation set may be [time period 4, start end state identifier running phase identifier ].

[0033] Specifically, as shown in Figure 2 , Figure 6 , the instruction optimization adaptation module includes: The control instruction extraction submodule extracts the corresponding state number according to the state entry marked as advanceable in the state advance adaptation set, filters and merges the matched control instructions, and generates a phase control instruction set; According to the state entry marked as advanceable in the state advance adaptation set, for example, there is a state entry (e.g. , state identifier running phase identifier ) in the set, which indicates that the oxygen supply state is good or the deviation is controllable, and the corresponding state number is accurately extracted, for example, the state number "001" is parsed from the state identifier , which usually corresponds to a preset oxygen supply state code table, for example, "001" represents "stable oxygen supply", and "002" represents "low flow oxygen supply". Then, the control instruction matched with this state number is filtered, which is a specific operation defined in advance for adjusting the oxygen flow or pressure, for example, the control instruction matched with the state number "001" (stable oxygen supply) may be "maintain current flow", and the control instruction matched with the state number "002" (low flow oxygen supply) may be "increase 0.1 MPa pressure" or "increase flow 0.5 L / min". The matching rule of the control instruction is defined by comprehensive analysis of historical operation data of the oxygen supply pipeline and clinical needs, and is verified through multiple rounds of simulation. The matched control instruction is merged and compared with the currently executed instruction, for example, the matched instruction is "maintain current flow", and the currently executed instruction is "increase 0.05 MPa pressure". Then, the comparison is performed to generate a phase control instruction set, which includes the optimization control instructions to be executed in each adaptation time period.

[0034] The instruction position comparison submodule filters the instructions with target position numbers less than the current execution position based on each control instruction in the phase control instruction set, calculates the offset change amount of the control instruction, performs front insertion and records the index change, and generates instruction offset change data; The offset change amount of the control instruction is calculated by the formula: ; Wherein, represents the offset change amount of the control instruction, represents the target position number of the jth instruction, represents the current execution position 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. 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 ; 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 different 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. Instruction 1 ( ) and instruction 2 ( Substitute the parameters into the formula for calculation: ; 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].

[0035] The control sequence mapping submodule identifies the mapping between the state number and the new index number of the control instruction based on the control instruction index number and the corresponding state number recorded in the instruction offset change data, analyzes the bidirectional mapping relationship between the state number and the new index number, and obtains a task control sequence mapping table. Based on the control instruction index number and the corresponding state number recorded in the instruction offset change data, for example, the data contains offset change information of instruction 1: instruction 1 (offset change amount 0.833, original index 2, new index 1), and the corresponding state number is “001” (stable oxygen supply), the mapping relationship between the state number and the new index number of the control instruction is identified, for example, a mapping from “state number 001” to “new index 1” of the control instruction is established, and the mapping is established by consulting the preset state-instruction index table, the bidirectional mapping relationship between the state number and the new index number is analyzed, for example, not only is it recorded that “state number 001” corresponds to “new index 1”, but also it is recorded that “new index 1” corresponds to “state number 001”, and such bidirectional mapping relationship facilitates quick searching for the corresponding state when the instruction is executed, and quick positioning of the related instruction when the state changes, through analysis, a task control sequence mapping table is obtained, which contains accurate bidirectional mapping between all state numbers and corresponding control instruction new indexes, and the mapping table provides a key logical basis for subsequent task instruction issuing.

[0036] Specifically, as shown in Figure 2 , Figure 7 , the running record acquisition module includes: The task instruction issuing submodule reads the task state number in the current scheduling queue according to the state number and the 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 mapping table index information, sequentially issues corresponding control instructions to the lower oxygen supply terminal control end in order, and records the time node and the control number of each issued instruction operation to generate a task instruction response number sequence. According to the state number recorded in the task control sequence mapping table and the control instruction index position, for example, the mapping table shows that "state number 001" is mapped to "control instruction index 1", first read the task state number in the current scheduling queue, for example, the task state number to be executed in the current scheduling queue is "001", according to the index information in the mapping table, the instruction number in the control queue is sorted, for example, if the original instruction sequence of the control queue is [instruction 3, instruction 1, instruction 2], according to the mapping table, instruction 1 (corresponding to state 001) should be ranked first, then the queue is reordered as [instruction 1, instruction 3, instruction 2], and the corresponding control instructions are issued to the oxygen supply terminal control end in turn according to the order, for example, "instruction 1" is first issued to the oxygen supply terminal in the ward, then "instruction 3" is issued, and then "instruction 2" is issued, the operation time node of each issued instruction is combined with the control number, for example, "8 / 20 08:30:05-instruction 1", "8 / 20 08:30:08-instruction 3", a task instruction response number sequence is generated, which clearly records the execution time and specific number of all issued instructions.

[0037] The control data recording submodule monitors the current flow adjustment state of the oxygen supply terminal based on each control number recorded in the task instruction response number sequence, collects the issuance time point, control number and flow state data in the corresponding time period, and obtains instruction control state combination data; 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 adjustment state of the oxygen supply terminal is monitored, for example, whether the actual oxygen supply flow on the breathing machine in the ward changes according to the requirements of "instruction 1", the time point of issuing the instruction, the control number and the flow state data in the corresponding time period are collected, for example, for instruction 1, the time point is 08:30:05, the control number is instruction 1, and the flow state data in the 30 seconds starting from 08:30:05, which may be [5.0, 5.1, 5.0,..., 5.2] L / min, the flow state data is obtained by real-time acquisition through the built-in sensor of the terminal device, and the instruction control state combination data is obtained, which associates the issued instruction with the actual flow response, and this combination data provides a basis for evaluating the execution effect of the instruction.

[0038] The running record generation submodule extracts the time sequence data of the control number and the flow adjustment state identifier according to the time point and the flow adjustment state corresponding to each control number in the instruction control state combination data, constructs a structure record entry sorted by time sequence, integrates all control data structures, and outputs the oxygen supply terminal running record; According to the instruction control state combination data, the time point corresponding to each control number and the flow regulation state are controlled, for example, for the control number "instruction 1", the time point is 08:30:05, and the corresponding flow regulation state data is [5.0, 5.1,..., 5.2] L / min, the time sequence data of the control number is accurately extracted, which indicates that instruction 1 is issued at 08:30:05, and the corresponding flow regulation state is identified, for example, the flow regulation state may be identified as "stable flow 5.0 L / min", the structure record entries are constructed in time sequence order, for example, the issuing time, execution effect (flow change) and final state identification of "instruction 1" are integrated into an entry, such as (08:30:05, instruction 1, flow reaches 5.1 L / min, state: stable), all control data structures are integrated, the execution records of all control instructions are archived in time sequence, and the oxygen supply terminal operation record is output, which provides a complete and traceable history view of the oxygen supply terminal in a specific time period, all instruction issuing, execution effect and flow regulation state, for example, the record will contain when the instruction is issued, what instruction is issued, how the terminal device responds and the specific change trajectory of the oxygen supply flow.

[0039] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection 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 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 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 3, characterized in that: 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.

5. The oxygen inhalation terminal flow equalization control system according to claim 4, characterized in that: 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.

6. The oxygen inhalation terminal flow equalization control system according to claim 5, 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.

7. The oxygen inhalation terminal flow equalization control system according to claim 6, 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. 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.

8. 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.

9. The oxygen inhalation terminal flow equalization control system according to claim 8, characterized in that: The oxygen supply terminal operation record includes the issuance time, instruction number, and flow regulation status record.

10. The oxygen inhalation terminal flow equalization control system according to claim 8, 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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