Hyperbaric oxygen chamber decompression safety control system and method based on big data

Abstract

The invention relates to the technical field of hyperbaric oxygen chamber decompression, in particular to a hyperbaric oxygen chamber decompression safety control system and method based on big data. The method comprises the following steps that when the hyperbaric oxygen chamber enters a decompression operation stage, change data of the opening degree of a valve in the decompression process are collected, gas exhaust flow data are synchronously collected, and a decompression operation data sequence is constructed; on the basis of the pressure reduction operation data sequence, a controllable adjustment interval of the valve opening change data and the gas discharge flow data is recognized, and a structural constraint control interval of gas discharge flow change is determined; according to the structural constraint control interval, limiting the change amplitude of the opening degree of the valve, calculating the change rate of the pressure in the chamber, and determining a segmented pressure change path of the hyperbaric oxygen chamber; through pressure reduction of the hyperbaric oxygen chamber, the reliability of pressure reduction operation of the hyperbaric oxygen chamber and the structural safety guarantee level are improved.

Description

Technical Field

[0001] This invention relates to the field of hyperbaric oxygen chamber decompression technology, and in particular to a safety control system and method for hyperbaric oxygen chamber decompression based on big data. Background Technology

[0002] Currently, hyperbaric oxygen chambers typically rely on experience or pre-set decompression curves to adjust valve openings during decompression operation, controlling the gradual decrease in chamber pressure. This method is unsuitable for situations with complex chamber structural stresses and significant fluctuations in oxygen discharge rates, as it struggles to ensure stress balance and structural safety during decompression. It is prone to localized overloads or excessively rapid pressure changes, increasing the risk of structural damage. Furthermore, traditional decompression control lags in responding to abnormal flow fluctuations or stress anomalies, lacking a closed-loop adjustment mechanism, making it difficult to achieve high precision, safety, and controllability in the decompression process. By acquiring real-time data on valve opening changes, gas discharge flow rates, and chamber structural stresses, and establishing dynamic correlations between these data, stress constraint control, valve adjustment optimization, and segmented, controllable pressure reduction during decompression can be achieved, effectively improving the safety, reliability, and structural load-bearing capacity of hyperbaric oxygen chamber decompression operations. Summary of the Invention

[0003] Therefore, it is necessary to provide a big data-based hyperbaric oxygen chamber decompression safety control system and method to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a big data-based method for decompression safety control in hyperbaric oxygen chambers includes the following steps:

[0005] Step S1: When the hyperbaric oxygen chamber enters the decompression operation phase, collect data on the changes in valve opening during the decompression process, and simultaneously collect data on the gas discharge flow rate to construct a decompression operation data sequence;

[0006] Step S2: Based on the pressure reduction operation data sequence, identify the controllable adjustment range of valve opening change data and gas discharge flow rate data, and determine the structural constraint control range of gas discharge flow rate change;

[0007] Step S3: Based on the structural constraint control range, limit the range of valve opening changes, calculate the rate of pressure change inside the chamber, and determine the segmented pressure change path of the hyperbaric oxygen chamber.

[0008] Step S4: After determining the segmented pressure change path of the hyperbaric oxygen chamber, continuously assess the stress state of the chamber's structural components. When the stress state enters the preset stable stress range, update the valve control parameters for the current decompression stage and enter the next decompression control cycle.

[0009] The present invention also provides a dynamic contrast optimization system for a display screen, for performing the dynamic contrast optimization method for a display screen as described above, the dynamic contrast optimization system for a display screen comprising:

[0010] The data acquisition module is used to collect data on valve opening changes during the decompression process when the hyperbaric oxygen chamber enters the decompression operation phase, and simultaneously collect gas discharge flow data to construct a decompression operation data sequence.

[0011] The control range determination module is used to identify the controllable adjustment range of valve opening change data and gas discharge flow rate data based on the pressure reduction operation data sequence, and to determine the structural constraint control range of gas discharge flow rate change.

[0012] The valve limiting module is used to limit the range of valve opening changes based on the structural constraint control range, calculate the rate of pressure change inside the chamber, and determine the segmented pressure change path of the hyperbaric oxygen chamber.

[0013] The control parameter update module is used to continuously determine the stress state of the chamber structure components after determining the segmented pressure change path of the hyperbaric oxygen chamber. When the stress state enters the preset stable stress range, the valve control parameters of the current decompression stage are updated, and the next decompression control cycle begins.

[0014] The beneficial effects of this method are as follows: By synchronously collecting valve opening change data and gas discharge flow rate data during the decompression stage and constructing a decompression operation data sequence, refined monitoring and control of the decompression process are achieved. By continuously collecting the actual valve opening value and corresponding gas discharge flow rate value, a high-precision time-synchronized data sequence is constructed, providing a reliable data foundation for subsequent analysis and control. By segmenting and organizing the decompression operation data sequence, the controllable adjustment range of valve opening change and gas discharge flow rate change is identified. Combined with the stress state of the cabin structural components, the structural constraint control range of gas discharge flow rate change is determined, thereby enabling the valve... The valve adjustment is matched with the stress state of the chamber, avoiding overload or local stress concentration in the chamber's structural components. The valve opening variation is limited based on structural constraints within the control range. Simultaneously, the rate of pressure change within the chamber is calculated by combining the steady-state of gas discharge flow changes, and segmented pressure reduction paths are planned. This ensures that pressure changes gradually advance along the stress range that the chamber's structural components can withstand, guaranteeing balanced pressure reduction and structural safety. By continuously collecting data on the stress state of the chamber's structural components and comparing it with the preset stable stress range, valve control parameters are updated in real time, achieving dynamic closed-loop control of the decompression process and ensuring that each segment of pressure change remains within the structurally permissible range. Overall, this method, through multi-dimensional data-driven coordinated control of valve opening, gas discharge flow, and chamber stress, achieves high precision, safety, and controllability in the decompression process, significantly improving the reliability and structural safety of the hyperbaric oxygen chamber's decompression operation. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the steps involved in a big data-based method for decompression safety control in a hyperbaric oxygen chamber.

[0016] Figure 2 A schematic diagram of a hyperbaric oxygen chamber decompression safety control system based on big data;

[0017] Figure 3 This is a schematic diagram of the external structure of a hyperbaric oxygen chamber;

[0018] Figure 4 Schematic diagram of decompression operation data curves for a hyperbaric oxygen chamber;

[0019] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0021] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0022] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0023] To achieve the above objectives, please refer to Figures 1 to 4 A big data-based method for decompression safety control in hyperbaric oxygen chambers includes the following steps:

[0024] Step S1: When the hyperbaric oxygen chamber enters the decompression operation phase, collect data on the changes in valve opening during the decompression process, and simultaneously collect data on the gas discharge flow rate to construct a decompression operation data sequence;

[0025] Step S2: Based on the pressure reduction operation data sequence, identify the controllable adjustment range of valve opening change data and gas discharge flow rate data, and determine the structural constraint control range of gas discharge flow rate change;

[0026] Step S3: Based on the structural constraint control range, limit the range of valve opening changes, calculate the rate of pressure change inside the chamber, and determine the segmented pressure change path of the hyperbaric oxygen chamber.

[0027] Step S4: After determining the segmented pressure change path of the hyperbaric oxygen chamber, continuously assess the stress state of the chamber's structural components. When the stress state enters the preset stable stress range, update the valve control parameters for the current decompression stage and enter the next decompression control cycle.

[0028] All specific values ​​involved in this embodiment are exemplary parameters used to clearly illustrate the technical operation process and are not the only limitation of the present invention.

[0029] In one embodiment, after the hyperbaric oxygen chamber completes pressurization and enters the depressurization phase, the depressurization control unit initiates the depressurization safety control process. During the depressurization phase, real-time monitoring is performed on the exhaust valves involved in depressurization regulation. Valve opening change data is acquired according to a preset sampling period, with the valve opening expressed as a percentage value corresponding to the actual stroke of the valve core. Simultaneously, gas discharge flow rate data is collected at the exhaust pipeline location, expressed as the volume or mass of oxygen discharged per unit time. The aforementioned valve opening change data and gas discharge flow rate data are recorded according to a unified time base to construct a depressurization operation data sequence, which reflects the correspondence between valve regulation behavior and gas discharge status during the depressurization process.

[0030] After obtaining the decompression operation data sequence, the valve opening change data and gas discharge flow rate data in the sequence are jointly analyzed to identify the sections where the gas discharge flow rate changes continuously and stably during valve adjustment. These sections are determined as the controllable adjustment range of valve opening change and gas discharge flow rate change. Within the controllable adjustment range, the structural constraint control range corresponding to the gas discharge flow rate change is further determined by combining the stress bearing parameters of the cabin structural components. This is used to limit the allowable flow rate change range during decompression.

[0031] After the structural constraint control range is determined, the valve opening change range is constrained according to the range, and the single adjustment amount of the valve is limited to the corresponding upper and lower limits. Under the condition that the valve opening change is restricted, the rate of change of the chamber pressure with time is calculated by combining the current gas discharge flow rate and the real-time pressure value in the chamber, and the segmented pressure change path of the hyperbaric oxygen chamber in the current decompression stage is determined accordingly, so that the decompression process is executed sequentially according to multiple continuous pressure segments.

[0032] During the execution of the segmented pressure change path, the stress state data of the cabin structural components is continuously collected. The stress state data includes the strain value or equivalent stress value at the key locations of the structure. By comparing the current stress state with the preset stable stress range, when the determination result shows that the stress state of the cabin structural components has entered the stable stress range, the valve control parameters corresponding to the current decompression stage are updated, and the control process is switched to the next decompression control cycle to continue the subsequent decompression operation.

[0033] In another embodiment, to improve the executability and stability of the pressure reduction control process, the sampling period for the pressure reduction operation data sequence is set between 0.5 seconds and 2 seconds. The valve opening change data uses the actual opening feedback value of the electric regulating valve, and the gas discharge flow rate data is obtained by a mass flow meter installed in the exhaust pipeline. During the controllable adjustment range identification process, the correspondence between the valve opening change rate and the gas discharge flow rate change rate is used as an auxiliary judgment condition. When both maintain a consistent change trend within a continuous sampling period, the corresponding segment is included in the controllable adjustment range.

[0034] When the structural constraint control range is determined, the stable stress range of the cabin structural components is set in advance based on the structural design parameters or historical decompression operation data. For example, the equivalent stress change of the structural components is kept within a preset proportion of the rated bearing capacity.

[0035] During the segmented pressure change path execution phase, each pressure segment corresponds to an independent valve control parameter group. When the stress state of the cabin structure is detected to have stabilized and entered the stable stress range of the corresponding segment, the system switches to the next parameter group to complete the sequential advancement of the pressure segments.

[0036] Please refer to [link / reference needed] for further information. Figure 3 The exhibition showcased a hyperbaric oxygen chamber device. The tilted monitoring screen above displayed real-time images of the chamber, and instruments and valves were arranged on it. Its core technology is to dynamically adjust the decompression process by collecting real-time data on valve opening, gas flow, and chamber stress.

[0037] Please refer to [link / reference needed] for further information. Figure 4This demonstrates the dynamic changes in valve opening and gas discharge flow rate during the decompression phase of a hyperbaric oxygen chamber, as well as the structural constraint control process: the horizontal axis represents sampling time, and the vertical axis represents gas discharge flow rate; the blue curve represents changes in valve opening, the green curve corresponds to the linkage between flow rate and opening, and the orange curve represents the flow range under structural constraints.

[0038] Of particular importance, step S1 includes:

[0039] When the hyperbaric oxygen chamber switches from steady-state operation to decompression operation, a decompression start identifier is acquired, and the time corresponding to the decompression start identifier is used as the data acquisition start time of the decompression stage.

[0040] During the pressure reduction phase, the actual opening value of the pressure reducing valve is continuously collected according to the preset sampling period, and the valve opening status corresponding to each sampling time is recorded to form valve opening change data.

[0041] While collecting data on valve opening changes, the gas discharge flow rate during the decompression process is also collected synchronously at the same sampling period as the valve opening, forming gas discharge flow rate data that is time-aligned with the valve opening change data.

[0042] The collected valve opening change data and gas discharge flow data are sorted in chronological order and packaged according to the sampling time to construct a pressure reduction operation data sequence for the pressure reduction operation stage.

[0043] In one embodiment, when the hyperbaric oxygen chamber switches from steady-state operation to depressurization operation, the chamber control unit outputs a depressurization start signal. This depressurization start signal is either a depressurization command issuance signal or the first response signal from the depressurization valve. The system uses the time corresponding to the depressurization start signal as the start time for data acquisition during the depressurization phase, and all subsequent data acquisition uses this time as the time reference.

[0044] During the pressure reduction phase, the actual opening degree of the pressure-reducing valve is continuously collected according to a pre-set sampling period, which can be set to a fixed time interval within the range of 0.1s to 1s. At each sampling moment, the actual opening degree value fed back by the pressure-reducing valve actuator is read. The valve opening degree value can be expressed as a percentage to reflect the actual open state of the valve at that moment. The valve opening degree values ​​corresponding to each sampling moment are recorded in chronological order to form a valve opening degree change data sequence.

[0045] While collecting data on valve opening changes, the gas discharge flow rate during decompression is simultaneously collected using the same sampling period as the valve opening. The gas discharge flow rate can be obtained from a flow sensor installed in the exhaust pipeline, and the unit of flow rate can be [unit missing]. or By reading the gas discharge flow rate value at the same sampling time, the gas discharge flow rate data is made to maintain a one-to-one correspondence with the valve opening change data on the time axis, thereby forming a time-aligned gas discharge flow rate change data sequence.

[0046] After data acquisition is completed, the valve opening change data and gas discharge flow rate data are organized in chronological order according to the sampling time. The valve opening value and gas discharge flow rate value corresponding to the same sampling time are encapsulated to construct a pressure reduction operation data unit consisting of "sampling time - valve opening - gas discharge flow rate". Multiple pressure reduction operation data units are arranged in chronological order to form a pressure reduction operation data sequence for the pressure reduction operation stage.

[0047] In another embodiment, after the pressure reduction start-up indicator is determined, the control unit can perform validity verification processing on the collected raw valve opening data and gas discharge flow data, and remove invalid sampling points caused by communication jitter or sensor transient abnormalities; for continuously missing sampling moments, the data of the previous valid sampling point can be used to fill in the gaps, so as to ensure the continuity of the pressure reduction operation data sequence in the time dimension.

[0048] In addition, when encapsulating the pressure reduction operation data unit, a sampling sequence number or timestamp is added to each sampling point for time synchronization and segment division processing when performing valve regulation section identification, flow change analysis and structural constraint interval determination in subsequent steps, so that the pressure reduction operation data sequence can be directly used as the basic input data for subsequent analysis steps.

[0049] Preferably, step S2 includes:

[0050] In the pressure reduction operation data sequence, the valve opening change data and gas discharge flow rate change data are segmented and organized to form candidate segments of valve regulation behavior and flow response;

[0051] Stability assessment is performed on the gas discharge flow rate change data within the candidate section, and valid sections with controlled flow rate changes are retained.

[0052] Based on the effective range, the constraints between the valve opening change data and the gas discharge flow rate change data are determined to form the controllable adjustment range corresponding to valve regulation.

[0053] Collect stress state data of cabin structural components and, in conjunction with the controllable adjustment range, determine the corresponding structural constraint control range.

[0054] In one embodiment, based on the constructed depressurization operation data sequence, the valve opening change data and gas discharge flow rate change data are segmented and organized. Specifically, the valve opening change amplitude between adjacent sampling points is used as the segmentation basis. When the valve opening change between adjacent sampling points exceeds a preset minimum change threshold, the corresponding time interval is identified as a continuous valve adjustment behavior segment. Simultaneously, gas discharge flow rate change data aligned with this time interval is extracted to form candidate segments corresponding to valve adjustment behavior and flow response. Multiple candidate segments are arranged in chronological order, covering the entire adjustment process in the depressurization operation data sequence.

[0055] After candidate segments are formed, stability is determined for the gas discharge flow rate changes within each candidate segment. Specifically, within each candidate segment, the flow rate change amplitude and rate of change are calculated based on the gas discharge flow rate values ​​of adjacent sampling points and compared with a pre-set flow rate fluctuation threshold. When both the flow rate change amplitude and rate of change within the candidate segment are within the preset threshold range, the gas discharge process within that candidate segment is determined to be under control, and the segment is retained as a valid segment. Otherwise, candidate segments with significant flow rate fluctuations and discontinuous changes are removed from subsequent processing.

[0056] After determining the effective range, constraints are established between the valve opening change data and the gas discharge flow rate change data within the effective range. Specifically, within the effective range, a one-to-one correspondence is established between the valve opening change and the gas discharge flow rate change at each sampling time. Based on multiple sets of correspondences, the proportional range between the valve opening change and the gas discharge flow rate change is calculated. When the change relationship at subsequent sampling points satisfies this proportional range, the valve adjustment behavior and flow response are determined to be in a controllable corresponding state, thus forming the controllable adjustment range corresponding to the valve adjustment.

[0057] Based on the established controllable adjustment range, stress state data of the cabin structural components are simultaneously collected. This stress state data can be acquired by strain sensors or force sensors deployed at key structural locations within the cabin, with the sampling period consistent with the decompression operation data sequence. The stress state data is mapped to the controllable adjustment range over time. When the stress state data falls within a preset stress variation range within the controllable adjustment range, this time interval is determined as the structural constraint control range corresponding to the valve adjustment behavior, serving as the constraint basis for subsequent decompression control analysis.

[0058] In another embodiment, when segmenting candidate segments, in addition to segmenting based on the valve opening change amplitude, a minimum duration is introduced as an auxiliary condition. Only when the valve opening change duration exceeds a preset time threshold is the segment identified as a candidate segment, thus avoiding the impact of instantaneous jitter on the segmentation results. Furthermore, when determining the structural constraint control interval, a sliding window averaging process can be applied to the stress state data to reduce the interference of outliers at individual sampling points on segment determination. The boundary position of the structural constraint control interval is confirmed by ensuring that multiple consecutive sampling points meet the stress change range, thereby maintaining a stable correspondence between the structural constraint control interval and the controllable adjustment interval on the time axis.

[0059] Preferably, a stability determination is performed on the gas discharge flow rate change data within the candidate section, and the effective sections with controlled flow rate changes include:

[0060] Within the candidate section, a time series is constructed based on the gas discharge flow rate change data to calculate the flow rate change rate, and a rate limit is set in conjunction with the oxygen medium parameters.

[0061] When the rate of change of flow rate within the sampling period exceeds the limit, the section will be identified as a discontinuous discharge section and removed.

[0062] After removing non-continuous segments, the rate fluctuation coefficient is calculated for the remaining segments, and segments that meet the preset threshold are identified as valid segments.

[0063] In one embodiment, based on the constructed depressurization operation data sequence, corresponding gas discharge flow rate change data is extracted in each candidate segment. The gas discharge flow rate change data consists of multiple flow rate sampling points collected according to a preset sampling period, which can be set to 0.1s to 1s. The gas discharge flow rate values ​​of each sampling point in the candidate segment are arranged in chronological order to construct a time series of gas discharge flow rate changes.

[0064] After constructing the time series, the flow rate change rate within each sampling period is calculated based on the difference in gas discharge flow rate between adjacent sampling points and the sampling period, resulting in a flow rate change rate sequence corresponding to the candidate segment. Combining the physical properties of the oxygen medium in the hyperbaric oxygen chamber, a rate limit is set for the flow rate change rate. This rate limit constrains the maximum allowable variation in gas discharge flow rate per unit time, and its value can be set based on the oxygen density, the initial pressure inside the chamber, and the rated flow capacity of the pressure reducing valve.

[0065] When the flow rate change rate for any sampling period within a candidate segment exceeds the rate limit, it is determined that discontinuous discharge behavior exists within that candidate segment. This segment is marked as a discontinuous discharge segment and removed from subsequent analyses to avoid including segments with abrupt flow changes or unstable discharge within the controllable analysis range. After removing discontinuous discharge segments, a rate fluctuation coefficient is calculated for the flow rate change rate sequence within the remaining segments. The rate fluctuation coefficient characterizes the dispersion of the flow rate change rate within a segment and can be calculated from the standard deviation and average value of the flow rate change rates. When the rate fluctuation coefficient is not greater than a preset fluctuation threshold, the gas discharge flow rate change within that segment is determined to be continuous and stable. This segment is identified as an effective segment with controlled flow rate change and serves as the basis segment for subsequent valve regulation relationship analysis.

[0066] In another embodiment, when setting the flow rate change limit, different limit parameters can be used for different decompression stages. For example, a smaller rate limit can be set in the early stage of decompression, and the rate limit can be appropriately relaxed in the middle and later stages of decompression to match the exhaust characteristics after the pressure inside the chamber gradually decreases. In addition, when calculating the rate fluctuation coefficient, a sliding time window method is used to segment the flow rate change sequence. Only when the rate fluctuation coefficient in multiple consecutive windows meets the preset threshold is the corresponding segment finally confirmed as a valid segment, thereby improving the continuity and reliability of the flow stability determination result in time.

[0067] Preferably, after removing non-continuous segments, the rate fluctuation coefficient is calculated for the remaining segments, and segments that meet the threshold are determined as valid segments, including:

[0068] Calculate the rate of change of gas discharge flow rate in the remaining section;

[0069] Perform cycle-by-cycle deviation calculation on the rate of change of gas discharge flow rate corresponding to each sampling period to obtain the rate deviation sequence;

[0070] Based on the rate deviation sequence, a rate of change fluctuation coefficient is generated, and the fluctuation coefficient is compared with a pre-stored stability threshold.

[0071] When the rate of change fluctuation coefficient is less than the stability threshold, the gas discharge flow rate change in this section is determined to be normal, and the section is identified as an effective section with continuous flow rate change.

[0072] In one embodiment, after eliminating discontinuous discharge segments, for each remaining segment, the corresponding gas discharge flow rate change data is extracted. This gas discharge flow rate change data consists of multiple gas discharge flow rate values ​​collected at preset sampling periods. The sampling period can be set to 0.1s to 1s and remains consistent throughout the entire segment. Based on the difference in gas discharge flow rate values ​​between adjacent sampling periods, combined with the sampling period length, the gas discharge flow rate change rate corresponding to each sampling period is calculated, forming a flow rate change rate sequence for that segment. The flow rate change rate is used to characterize the degree of change in gas discharge flow rate per unit time. After obtaining the flow rate change rate sequence, a period-by-period deviation calculation is performed on the sequence. Specifically, using the average rate of the flow rate change rate sequence within the segment as the reference rate, the deviation value between the actual flow rate change rate corresponding to each sampling period and the reference rate is calculated. All deviation values ​​are arranged in chronological order to form a rate deviation sequence.

[0073] Based on the rate deviation sequence, the rate of change fluctuation coefficient for this segment is calculated. This rate of change fluctuation coefficient characterizes the overall fluctuation level of the flow rate change rate within the segment. It can be calculated as the ratio of the standard deviation of the rate deviation sequence to the average flow rate change rate, or as the normalized mean square value of the rate deviation. Subsequently, the calculated rate of change fluctuation coefficient is compared with a pre-stored stability threshold. When the rate of change fluctuation coefficient is less than the stability threshold, the gas discharge flow rate change within this segment is determined to be in a normal and continuous state, and this segment is identified as a valid segment with continuous flow rate change, used for subsequent valve regulation relationship and structural constraint analysis. When the rate of change fluctuation coefficient does not meet the stability threshold condition, this segment is not included in the valid segment range.

[0074] In another embodiment, when calculating the average rate of flow rate change, the median rate within the segment or the average rate of the sliding window can be used as the reference rate to reduce the impact of fluctuations at individual sampling points on the rate deviation sequence. The stability threshold can be set in stages according to different decompression stages or different chamber specifications. For example, a smaller stability threshold can be used in the early stage of decompression, and the stability threshold can be appropriately relaxed in the later stage of decompression, so that the determination of the rate of change fluctuation coefficient is more in line with the actual characteristics of the exhaust process.

[0075] Preferably, based on the effective range, the constraints between the valve opening change data and the gas discharge flow rate change data are determined to form the controllable adjustment range corresponding to valve adjustment, including:

[0076] Based on the valve opening change sequence and gas discharge flow rate change sequence within the effective section, a correspondence between each sampling point is established, and the valve opening change and flow rate change between adjacent sampling points are calculated.

[0077] The ratio of the valve opening change rate to the flow rate change rate is determined based on the change amount, and the ratio is used as a consistency constraint.

[0078] Within the section that meets the consistency constraint, corresponding verification is performed on the valve opening change and flow rate change. The section that meets the deviation constraint is determined as the valve flow rate controlled section.

[0079] The controlled sections are merged and their lengths are verified to form the controllable adjustment range corresponding to the valve adjustment.

[0080] In one embodiment, after filtering the effective segments, for each effective segment, valve opening change data and gas discharge flow rate change data consistent with the time range of that segment are extracted. The valve opening change data consists of multiple actual valve opening values ​​collected according to a preset sampling period, with the valve opening expressed as a percentage or angle; the gas discharge flow rate change data consists of gas discharge flow rate values ​​collected synchronously with the valve opening, and the two correspond one-to-one on the time axis.

[0081] Based on data within the same effective segment, a point-by-point correspondence is established between the valve opening change sequence and the gas discharge flow rate change sequence. Taking two adjacent sampling times as a calculation unit, the valve opening change and gas discharge flow rate change between corresponding sampling points are calculated respectively, thus forming the valve opening change sequence and the flow rate change sequence.

[0082] After obtaining the sequence of changes, the valve opening change rate and the gas discharge flow rate change rate are further calculated based on the sampling period length, and the ratio of the two is used as the rate correspondence ratio. This rate correspondence ratio is used to characterize the degree of matching between the valve adjustment action and the flow response, and is stored as a consistency constraint.

[0083] Subsequently, within the effective range, using the rate-to-rate ratio as a benchmark, a consistency check is performed on the valve opening change and the gas discharge flow rate change corresponding to each sampling point. When the rate-to-rate ratio and the change deviation within a certain range are both within the preset deviation range, that range is marked as a valve flow rate controlled range; when the change relationship deviates from the consistency constraint, the corresponding range is not included in the controlled range.

[0084] After marking the controlled sections, adjacent valve flow controlled sections are merged in chronological order, and the length of the merged section is checked. When the duration of the merged continuous section exceeds the preset minimum interval length, the continuous section is determined as the controllable adjustment interval corresponding to the valve adjustment, and serves as the basis for subsequent pressure reduction control parameter limits.

[0085] Preferably, the stress state data of the cabin structural components are collected, and combined with the controllable adjustment range, the corresponding structural constraint control range is determined, including:

[0086] During the decompression phase, the stress monitoring units deployed on the pressure-bearing structural components of the cabin continuously collect data to obtain the stress change sequence of the cabin structure.

[0087] Based on the stress changes in each consecutive time period in the stress change sequence, continuous sections where the structural stress remains within the preset bearing capacity are selected as stress-controllable sections.

[0088] The continuity of the stress-controllable section is checked in chronological order to obtain the preliminary structural constraint interval;

[0089] The initial structural constraint interval and the controllable adjustment interval are checked for coincidence in the time domain. The segment in which the initial constraint interval and the controllable adjustment interval remain consistent during the decompression stage is determined as the structural constraint control interval.

[0090] In one embodiment, after the hyperbaric oxygen chamber enters the decompression operation phase, multiple stress monitoring units are deployed at key stress locations on the pressure-bearing structural components of the chamber. These stress monitoring units can be strain sensors, stress sensors, or equivalent stress acquisition modules. Each unit continuously collects data on the stress state of the chamber structural components according to a preset stress sampling period, acquiring raw stress signals reflecting changes in the stress on the chamber structure. The acquired raw stress signals are then analyzed and converted to obtain the stress values ​​at each sampling point. These stress values ​​are then organized chronologically to construct a stress change sequence for the chamber structure corresponding to the decompression phase. This stress change sequence reflects the stress evolution of the pressure-bearing structural components during the decompression process.

[0091] Based on the stress change sequence, the stress changes within a continuous sampling period are analyzed, and the stress values ​​at each sampling point are compared with the pre-defined structural bearing capacity. When the stress values ​​at multiple consecutive sampling points are all within the structural bearing capacity, the corresponding time period is marked as a controllable stress segment; when the stress values ​​exceed the structural bearing capacity, the corresponding time period is not included in the controllable stress range.

[0092] After obtaining multiple controllable stress segments, the continuity of these segments is checked in chronological order. Segments that are temporally adjacent and have an interval less than a preset threshold are merged to form preliminary structural constraint intervals. The merged intervals are then further checked for duration. If the interval duration meets a preset minimum duration requirement, it is retained as a valid preliminary structural constraint interval. After determining the preliminary structural constraint intervals, a time-domain overlap check is performed between these intervals and the previously determined controllable adjustment intervals. Specifically, the time ranges of the two types of intervals during the decompression phase are compared, and only overlapping segments that fall within both the preliminary structural constraint interval and the controllable adjustment interval on the time axis are retained. These overlapping segments are then identified as the structural constraint control intervals corresponding to the decompression phase.

[0093] In another embodiment, when processing the force data of multiple force measurement points, different weights can be assigned to different measurement points according to the force distribution characteristics of the cabin structural components. The equivalent structural force value is calculated by weighting, and the controllable force segment is screened based on the equivalent structural force value to reduce the impact of local abnormal measurement points on the overall judgment. In addition, when performing time-domain coincidence verification, the boundary between the preliminary structural constraint interval and the controllable adjustment interval can be buffered. When the start and end time deviation between the two types of intervals is less than the preset time tolerance, the corresponding segment is regarded as a valid coincidence segment to improve the continuity of the structural constraint control interval in actual decompression control.

[0094] Preferably, during the decompression phase, the stress monitoring units deployed on the pressure-bearing structural components of the cabin continuously collect data to obtain the stress change sequence of the cabin structure's stress state, including:

[0095] Stress monitoring points were set up in the hyperbaric oxygen chamber, and strain sensors were used to collect the stress change signals of the structural components during the decompression process.

[0096] The signals collected from each stress measurement point are synchronously processed to form a structural stress monitoring sequence for the stress distribution of the pressure-bearing structure of the cabin.

[0097] Based on the force transmission direction of the cabin structure, the measurement point data in the structural force monitoring sequence are screened for directional consistency, and the load-bearing path measurement point data consistent with the decompression direction are extracted as characterization data of the cabin structure's stress state during the decompression stage.

[0098] The changes in the characterization data during the decompression process are organized to form a stress state sequence.

[0099] In one embodiment, multiple stress monitoring points are arranged at the locations of the hyperbaric oxygen chamber's shell, reinforcing ribs, and pressure-bearing structural components directly related to changes in internal pressure. Each stress monitoring point is equipped with a strain sensor to collect strain signals generated in the chamber's structural components during decompression. The strain sensor can be a resistive strain gauge or an equivalent strain acquisition element, and its range is preset according to the chamber's structural design load, for example, covering 1.2 times the maximum design bearing stress of the chamber.

[0100] After the decompression operation phase begins, each stress monitoring point synchronously collects data according to a unified stress sampling period. This sampling period can be consistent with or proportional to the sampling period of valve opening and gas discharge flow rate. The raw strain signals collected from each monitoring point are synchronously processed and time-aligned, and the strain signals are converted into corresponding stress values ​​to form a structural stress monitoring sequence reflecting the overall stress distribution of the pressure-bearing structure of the cabin.

[0101] After obtaining the structural stress monitoring sequence, the measurement point data in the structural stress monitoring sequence are screened for directional consistency based on the direction of force transmission in the cabin structure and the direction of pressure release in the cabin during decompression. According to the main load-bearing direction predetermined in the cabin structure design, the measurement point data whose force change direction is consistent with the direction of pressure release in the cabin are selected, and the data corresponding to this type of measurement point are determined as load-bearing path measurement point data, which are used to characterize the main stress state of the cabin structure during decompression.

[0102] The selected load-bearing path measurement point data are organized in chronological order, and the force values ​​corresponding to each sampling time are combined to form a force state sequence for the decompression stage. This force state sequence reflects the force changes of the pressure-bearing structure of the cabin throughout the decompression process and serves as the basis for subsequent determination of the structural constraint control range.

[0103] Preferably, step S3 includes:

[0104] Based on the structural constraint control range, the upper and lower limit thresholds of the valve opening change range are determined, and the thresholds are introduced into the valve regulation parameters in the current pressure reduction stage to limit the valve opening change range.

[0105] Under the condition that the valve opening is restricted, monitor the actual change of gas discharge flow rate and determine the steady state of the gas discharge flow rate change;

[0106] Based on the steady state of the gas discharge flow rate change, the rate of pressure change inside the chamber is calculated, and the segmented pressure change path is determined.

[0107] In one embodiment, after obtaining the structural constraint control range corresponding to the decompression stage, the adjustment capability of the decompression valve is reverse-mapped based on the stress variation range of the cabin structural components within this structural constraint control range. Specifically, according to the gas discharge flow variation range corresponding to the structural constraint control range, the maximum and minimum allowable variation range of the valve opening per unit time are determined, and this variation range is defined as the upper and lower limit thresholds of the valve opening variation range. These upper and lower limit thresholds of the valve opening variation range are introduced into the valve adjustment parameters of the current decompression stage. During the execution of decompression control, the valve control command is constrained in real time, ensuring that the valve opening variation within each sampling period is within the range of the upper and lower limit thresholds, thereby forming a valve adjustment process limited by the structural constraint control range.

[0108] Under the condition that the range of valve opening variation is limited, the actual change of gas discharge flow rate during the pressure reduction process is continuously monitored according to the same sampling period as the valve opening, and a gas discharge flow rate change sequence is obtained. Based on the gas discharge flow rate change sequence, the flow rate change in adjacent sampling periods is determined. When the flow rate change remains within the aforementioned controllable adjustment range, the corresponding time period is determined as the stable state segment of the gas discharge flow rate change.

[0109] After confirming that the gas discharge flow rate change is within a stable range, the pressure change over time is calculated using the chamber pressure value at the current sampling time to obtain the chamber pressure change rate. Based on the pressure change rate corresponding to different stable ranges, the decompression process is divided into multiple continuous pressure change segments, and the segmented pressure change path of the hyperbaric oxygen chamber in the current decompression stage is determined accordingly.

[0110] Preferably, based on the steady state of the gas discharge flow rate change, the calculation of the chamber pressure change rate and the determination of the segmented pressure change paths include:

[0111] Based on the steady state of the gas discharge flow rate change, the current gas discharge flow rate value is obtained, and combined with the current pressure value inside the chamber, the rate of pressure change inside the chamber is calculated.

[0112] Based on the rate of pressure change and the stress range that the cabin structural components can withstand, determine the segmented descent path of the pressure change inside the cabin;

[0113] During the segmented descent path, the actual stress state of the cabin structural components is continuously monitored, and the pressure change rate is adjusted according to the actual stress state so that the pressure change inside the cabin gradually advances along the corresponding stress-bearing range.

[0114] In one embodiment, during the decompression phase, the gas discharge flow rate value corresponding to the current sampling point is acquired. This value comes from a flow sensor installed at the outlet of the pressure reducing valve. The flow sensor samples once per second, generating a gas discharge flow rate time series. Simultaneously, the current pressure value inside the chamber is acquired. This pressure value is collected by an internal pressure sensor and forms a pressure time series. The gas discharge flow rate value and the internal pressure value are synchronized over time to calculate the current internal pressure change rate, which is recorded as pressure change rate data. Based on the calculated pressure change rate and the load-bearing capacity range of the chamber structure, the internal pressure decrease path is segmented. Specifically, the decompression phase is divided into several continuous pressure segments, and the pressure change amplitude of each segment does not exceed the safe load-bearing capacity of the chamber structure. Each segment corresponds to a segmented pressure change path, including the starting pressure value, ending pressure value, and allowable pressure change rate range of that segment.

[0115] During the segmented pressure reduction path execution, the actual stress state data of the cabin structural components is continuously collected. The stress monitoring unit includes strain sensors installed on key pressure-bearing components. Each sensor generates stress measurement data, and the sampling frequency is consistent with the pressure change sampling. The actual stress value at each sampling point is determined, and the stress state is compared with the preset stress bearing range. When the stress is detected to be close to the upper or lower limit, the pressure change rate is adjusted according to the actual stress state, and the pressure change rate of that segment in the segmented pressure path is updated, so that the pressure change inside the cabin gradually advances along the corresponding stress bearing range, avoiding excessive or insufficient stress on the structural components.

[0116] In another embodiment, the force values ​​at measuring points on different cabin structural components are calculated equivalently in the direction of the principal force to more accurately determine the feasibility of segmented pressure change paths. Furthermore, for abnormal sampling points, such as short-term signal jitter from sensors, averaging of consecutive sampling points can be used for correction to ensure the stability of the pressure change rate calculation. The pressure change path for each segment and its corresponding force state record can be used for subsequent decompression path optimization and safety analysis.

[0117] Of particular importance, step S4 includes:

[0118] After determining the segmented pressure change path of the hyperbaric oxygen chamber, the stress state data of the chamber's structural components are continuously collected to construct the stress state sequence corresponding to the current decompression stage.

[0119] Based on the stress state sequence, the actual stress state of the cabin structural components is determined point by point, and the current stress state is compared with the pre-set stable stress range.

[0120] When the determination result indicates that the stress state has entered the stable stress range, extract the valve control parameters corresponding to the current segment pressure change path and perform update processing on the valve control parameters;

[0121] After the valve control parameters are updated, the current pressure reduction stage is marked as ended, and the system switches to the pressure reduction control cycle corresponding to the next segment.

[0122] In one embodiment, after the pressure change path of the hyperbaric oxygen chamber is determined, continuous data is collected by stress monitoring units deployed on the pressure-bearing structural components of the chamber. The data collection period is typically set to once every 0.5 to 1 second. The monitoring units include strain sensors or force sensors installed on key pressure-bearing structural components. Each sensor corresponds to a measuring point, used to record the actual stress state of the chamber structural components during the decompression process. The stress values ​​of each measuring point are organized in chronological order to form a stress state sequence for the decompression stage. This sequence reflects the stress changes of the chamber structural components under the current segmented pressure change path. The sequence is then evaluated point-by-point, comparing the actual stress state at each sampling moment with a pre-set stable stress range. The evaluation method is that when the stress values ​​of multiple consecutive sampling points are all within the stable stress range, the chamber structural components are considered to have entered a stable state.

[0123] Once the stress state is determined to be stable, the valve control parameters corresponding to the current pressure change path are extracted, including valve opening, valve regulation rate, and chamber pressure reference value. These parameters are then updated to the pressure reduction control system to complete the valve control parameter refresh for the current pressure reduction segment. After the refresh is complete, the current pressure reduction stage is marked as ended, and the system switches to the pressure reduction control cycle corresponding to the next segment, initiating the next round of pressure reduction and stress monitoring.

[0124] The present invention also provides a dynamic contrast optimization system for a display screen, for performing the dynamic contrast optimization method for a display screen as described above, the dynamic contrast optimization system for a display screen comprising:

[0125] The data acquisition module 101 is used to collect data on the changes in valve opening during the decompression process when the hyperbaric oxygen chamber enters the decompression operation stage, and to simultaneously collect gas discharge flow data to construct a decompression operation data sequence.

[0126] The control range determination module 102 is used to identify the controllable adjustment range of valve opening change data and gas discharge flow data based on the pressure reduction operation data sequence, and to determine the structural constraint control range of gas discharge flow change.

[0127] The valve limiting module 103 is used to limit the range of valve opening changes according to the structural constraint control range, calculate the rate of pressure change in the chamber, and determine the segmented pressure change path of the hyperbaric oxygen chamber.

[0128] The control parameter update module 104 is used to continuously determine the stress state of the chamber structure components after determining the segmented pressure change path of the hyperbaric oxygen chamber. When the stress state enters the preset stable stress range, the valve control parameters of the current decompression stage are updated and the next decompression control cycle is entered.

[0129] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A hyperbaric chamber decompression safety control method based on big data, characterized in that, Includes the following steps: Step S1: When the hyperbaric oxygen chamber enters the decompression operation phase, collect data on the changes in valve opening during the decompression process, and simultaneously collect data on the gas discharge flow rate to construct a decompression operation data sequence; Step S2: Based on the pressure reduction operation data sequence, identify the controllable adjustment range of valve opening change data and gas discharge flow rate data, and determine the structural constraint control range of gas discharge flow rate change; Step S3: Based on the structural constraint control range, limit the range of valve opening changes, calculate the rate of pressure change inside the chamber, and determine the segmented pressure change path of the hyperbaric oxygen chamber. Step S4: After determining the segmented pressure change path of the hyperbaric oxygen chamber, continuously assess the stress state of the chamber's structural components. When the stress state enters the preset stable stress range, update the valve control parameters for the current decompression stage and enter the next decompression control cycle.

2. The hyperbaric chamber decompression safety control method based on big data according to claim 1, wherein, Step S2 includes: In the pressure reduction operation data sequence, the valve opening change data and gas discharge flow rate change data are segmented and organized to form candidate segments of valve regulation behavior and flow response; Stability assessment is performed on the gas discharge flow rate change data within the candidate section, and valid sections with controlled flow rate changes are retained. Based on the effective range, the constraints between the valve opening change data and the gas discharge flow rate change data are determined to form the controllable adjustment range corresponding to valve regulation. Collect stress state data of cabin structural components and, in conjunction with the controllable adjustment range, determine the corresponding structural constraint control range.

3. The big data-based hyperbaric chamber decompression safety control method of claim 2, wherein, Stability assessment was performed on the gas discharge flow rate variation data within the candidate sections, and the valid sections with controlled flow rate variations were retained, including: Within the candidate section, a time series is constructed based on the gas discharge flow rate change data to calculate the flow rate change rate, and a rate limit is set in conjunction with the oxygen medium parameters. When the rate of change of flow rate within the sampling period exceeds the limit, the section will be identified as a discontinuous discharge section and removed. After removing non-continuous segments, the rate fluctuation coefficient is calculated for the remaining segments, and segments that meet the preset threshold are identified as valid segments.

4. The big data-based hyperbaric chamber decompression safety control method of claim 3, wherein, After removing non-continuous segments, the rate fluctuation coefficient is calculated for the remaining segments. Segments that meet the threshold are identified as valid segments, including: Calculate the rate of change of gas discharge flow rate in the remaining section; Perform cycle-by-cycle deviation calculation on the rate of change of gas discharge flow rate corresponding to each sampling period to obtain the rate deviation sequence; Based on the rate deviation sequence, a rate of change fluctuation coefficient is generated, and the fluctuation coefficient is compared with a pre-stored stability threshold. When the rate of change fluctuation coefficient is less than the stability threshold, the gas discharge flow rate change in this section is determined to be normal, and the section is identified as an effective section with continuous flow rate change.

5. The big data-based hyperbaric chamber decompression safety control method of claim 2, wherein, Based on the effective range, the constraints between valve opening change data and gas discharge flow rate change data are determined, forming the controllable adjustment range corresponding to valve regulation, including: Based on the valve opening change sequence and gas discharge flow rate change sequence within the effective section, a correspondence between each sampling point is established, and the valve opening change and flow rate change between adjacent sampling points are calculated. The ratio of the valve opening change rate to the flow rate change rate is determined based on the change amount, and the ratio is used as a consistency constraint. Within the section that meets the consistency constraint, corresponding verification is performed on the valve opening change and flow rate change. The section that meets the deviation constraint is determined as the valve flow rate controlled section. The controlled sections are merged and their lengths are verified to form the controllable adjustment range corresponding to the valve adjustment.

6. The method for safety control of decompression in a hyperbaric oxygen chamber based on big data as described in claim 2, characterized in that, Collect stress state data of cabin structural components and, in conjunction with the controllable adjustment range, determine the corresponding structural constraint control range, including: During the decompression phase, the stress monitoring units deployed on the pressure-bearing structural components of the cabin continuously collect data to obtain the stress change sequence of the cabin structure. Based on the stress changes in each consecutive time period in the stress change sequence, continuous sections where the structural stress remains within the preset bearing capacity are selected as stress-controllable sections. The continuity of the stress-controllable section is checked in chronological order to obtain the preliminary structural constraint interval; The initial structural constraint interval and the controllable adjustment interval are checked for coincidence in the time domain. The segment in which the initial constraint interval and the controllable adjustment interval remain consistent during the decompression stage is determined as the structural constraint control interval.

7. The hyperbaric oxygen chamber decompression safety control method based on big data according to claim 6, characterized in that, During the decompression phase, continuous data is collected from the stress monitoring units deployed on the pressure-bearing structural components of the cabin to obtain the stress change sequence of the cabin structure, including: Stress monitoring points were set up in the hyperbaric oxygen chamber, and strain sensors were used to collect the stress change signals of the structural components during the decompression process. The signals collected from each stress measurement point are synchronously processed to form a structural stress monitoring sequence for the stress distribution of the pressure-bearing structure of the cabin. Based on the force transmission direction of the cabin structure, the measurement point data in the structural force monitoring sequence are screened for directional consistency, and the load-bearing path measurement point data consistent with the decompression direction are extracted as characterization data of the cabin structure's stress state during the decompression stage. The changes in the characterization data during the decompression process are organized to form a stress state sequence.

8. The method for safety control of decompression in a hyperbaric oxygen chamber based on big data as described in claim 1, characterized in that, Step S3 includes: Based on the structural constraint control range, the upper and lower limit thresholds of the valve opening change range are determined, and the thresholds are introduced into the valve regulation parameters in the current pressure reduction stage to limit the valve opening change range. Under the condition that the valve opening is restricted, monitor the actual change of gas discharge flow rate and determine the steady state of the gas discharge flow rate change; Based on the steady state of the gas discharge flow rate change, the rate of pressure change inside the chamber is calculated, and the segmented pressure change path is determined.

9. The hyperbaric oxygen chamber decompression safety control method based on big data according to claim 8, characterized in that, Based on the steady-state of the gas discharge flow rate change, the rate of pressure change inside the chamber is calculated, and the segmented pressure change paths are determined, including: Based on the steady state of the gas discharge flow rate change, the current gas discharge flow rate value is obtained, and combined with the current pressure value inside the chamber, the rate of pressure change inside the chamber is calculated. Based on the rate of pressure change and the stress range that the cabin structural components can withstand, determine the segmented descent path of the pressure change inside the cabin; During the segmented descent path, the actual stress state of the cabin structural components is continuously monitored, and the pressure change rate is adjusted according to the actual stress state so that the pressure change inside the cabin gradually advances along the corresponding stress-bearing range.

10. A dynamic contrast optimization system for a display screen, characterized in that, For performing the dynamic contrast optimization method for a display screen as described in claim 1, the dynamic contrast optimization system for a display screen includes: The data acquisition module is used to collect data on valve opening changes during the decompression process when the hyperbaric oxygen chamber enters the decompression operation phase, and simultaneously collect gas discharge flow data to construct a decompression operation data sequence. The control range determination module is used to identify the controllable adjustment range of valve opening change data and gas discharge flow rate data based on the pressure reduction operation data sequence, and to determine the structural constraint control range of gas discharge flow rate change. The valve limiting module is used to limit the range of valve opening changes based on the structural constraint control range, calculate the rate of pressure change inside the chamber, and determine the segmented pressure change path of the hyperbaric oxygen chamber. The control parameter update module is used to continuously determine the stress state of the chamber structure components after determining the segmented pressure change path of the hyperbaric oxygen chamber. When the stress state enters the preset stable stress range, the valve control parameters of the current decompression stage are updated, and the next decompression control cycle begins.

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