Intelligent intracranial pressure monitoring system

Abstract

The invention discloses an intracranial pressure intelligent monitoring system, which relates to the technical field of intracranial pressure monitoring, and comprises a continuous pressure conduction chain construction module for establishing a continuous pressure conduction chain of an intracranial pressure signal, carrying out time sequence segmentation analysis on the intracranial pressure signal acquired in real time, and obtaining a continuous pressure conduction chain of the intracranial pressure signal; identifying a section with lagging and smooth abnormity in the pressure feedback curve, and determining the section as a nonlinear response positioning range; and the elastic pressure buffer window adjusting module introduces an elastic pressure buffer window in the nonlinear response positioning range and adaptively adjusts the width of the elastic pressure buffer window according to the pressure change rate of the adjacent time slices. By constructing a continuous pressure conduction chain and introducing an elastic buffering and layering rearrangement mechanism, it is ensured that an intracranial pressure signal accurately reflects true pressure changes in the abnormal stage. Through dynamic weight band and rhythmicity balance regulation and control, self-adaptive stability of signal energy and a time structure is achieved, and continuity and early warning accuracy of intracranial pressure monitoring are improved.

Description

Technical Field

[0001] This invention relates to the field of intracranial pressure monitoring technology, and more specifically to an intelligent intracranial pressure monitoring system. Background Technology

[0002] Intelligent monitoring of intracranial pressure (ICP) refers to the process of real-time, continuous, non-invasive, or minimally invasive dynamic monitoring and intelligent assessment of changes in ICP using multi-parameter sensing, signal fusion, and intelligent analysis technologies. Its core lies in establishing a dynamic mapping model between ICP changes and cerebral blood flow and brain tissue compliance through the collaborative acquisition of multi-source physiological data, including high-sensitivity pressure sensors, electroencephalogram (EEG) signals, electrical impedance tomography (EIA), Doppler ultrasound, optical reflectance, and cerebral hemodynamic parameters. The system utilizes artificial intelligence algorithms to filter noise, predict trends, and identify anomalies in the monitoring signals, enabling early detection of abnormally high ICP trends, cerebral edema, or intracranial hemorrhage. Intelligent monitoring not only provides quantitative ICP values ​​but also allows for personalized threshold adaptive adjustment and early warning pushes based on individual patient differences and clinical background. This enables accurate, continuous, low-risk assessment and intelligent intervention support for ICP changes without the need for continuous invasive monitoring.

[0003] The existing technology has the following shortcomings: During intelligent intracranial pressure monitoring, when local blood vessels in brain tissue collapse due to momentary blood flow obstruction, vasospasm, or sudden changes in tissue stress, the originally continuous pressure feedback chain enters a nonlinear saturation state within a short period. At this time, the transmission relationship of pressure changes in the sensing path is compressed or even truncated, resulting in a significant decrease in the monitoring signal's responsiveness to the actual peak intracranial pressure. When the monitoring system makes judgments based on the saturated feedback curve, it is easy to misidentify intracranial pressure that is actually in a rapidly rising phase as a relatively stable state, thus forming a brief "false stable" interval. This phenomenon is particularly insidious in the highly dynamic pathological evolution stage, easily masking the early characteristics of a surge in intracranial pressure, delaying the triggering of key warning signals, and thus increasing the probability of serious intracranial complications.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent intracranial pressure monitoring system to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an intelligent intracranial pressure monitoring system, characterized in that it includes a continuous pressure transmission chain construction module, an elastic pressure buffer window adjustment module, a hierarchical temporal rearrangement module, a dynamic response weight band allocation module, and a rhythmic balance control module; The continuous pressure transmission chain construction module establishes a continuous pressure transmission chain for intracranial pressure signals, performs time series segmentation analysis on the real-time acquired intracranial pressure signals, identifies segments in the pressure feedback curve that exhibit sluggishness and abnormal smoothness, and determines these segments as the nonlinear response localization range for subsequent dynamic signal compensation processing. The elastic pressure buffer window adjustment module introduces an elastic pressure buffer window within the nonlinear response positioning range. The width of the elastic pressure buffer window is adaptively adjusted according to the pressure change rate of adjacent time slices, so that the signal compression caused by local vascular collapse is extended in the time dimension to restore the weakened pressure gradient structure. The hierarchical timing reordering module, based on the extended signal range output by the elastic pressure buffer window, performs hierarchical timing reordering on the pressure transmission chain, and re-expands the signal within the nonlinear response positioning range, thereby maintaining the temporal continuity and peak response integrity of the pressure transmission chain. The dynamic response weight band allocation module sets the dynamic response weight band according to the pressure transmission chain after hierarchical time-series rearrangement, and allocates weights according to the pressure response amplitude of each time period, so that the rearranged continuous signal obtains an energy ratio that matches the actual pressure change during the fusion process, thereby eliminating the false steady state caused by signal compression. The rhythmic balance regulation module establishes a rhythmic balance regulation mechanism based on the continuous pressure sequence output by the dynamic response weight band. By monitoring the instantaneous changes in pressure transmission in the pressure transmission chain, it adjusts the position of the time anchor point to keep the pressure feedback chain within the continuous transmission range, thereby achieving real-time perception and continuous tracking of intracranial pressure mutation stages.

[0007] Preferably, the steps for establishing a continuous pressure conduction chain of intracranial pressure signals include: When monitoring intracranial pressure in real time, the raw signal of intracranial pressure changes over time is continuously recorded by continuously acquiring intracranial pressure signals, and the sampling interval is kept stable. All sampling points are arranged in chronological order to form a pressure transmission chain that is continuous in time dimension. After forming a complete continuous pressure transmission chain, the pressure signal is segmented in the time dimension. The entire pressure transmission chain is divided into several continuous sub-segments according to a fixed time length or pressure change rate trend in order to capture the local dynamic characteristics of pressure changes. After completing the time segmentation, the pressure feedback curve in each segment is analyzed segment by segment. By observing the pressure change rate, peak shape and waveform smoothness, segments with sluggishness and smoothness anomalies are identified. After identifying the sections with sluggishness and smoothness anomalies, these sections are confirmed as the nonlinear response location range, and their time sequence and position correspondence are maintained in the original continuous pressure transmission chain as the processing target for subsequent dynamic signal compensation.

[0008] Preferably, the step of introducing an elastic pressure buffer window within the nonlinear response positioning range includes: After determining the nonlinear response positioning range, the time distribution characteristics within this range are continuously analyzed. Each time slice is marked as an independent time unit, and the initial boundary and initial width of the elastic pressure buffer window are set according to the start and end times. After forming the initial elastic pressure buffer window, the pressure change rate of adjacent time slices within the coverage area of ​​the buffer window is continuously analyzed. The width of the buffer window is adaptively adjusted according to the difference and trend of the change rate, and the extension direction of the buffer window is adjusted according to the direction of pressure change. After completing the adaptive adjustment of the buffer window width, the pressure transmission chain within the buffer window is subjected to time extension processing. The concentrated pressure changes in a short period of time are redistributed according to the buffer window width and boundary transition is performed to restore the pressure gradient structure. After the elastic pressure buffer window is extended, the extended signal output by the buffer window is re-embedded into the original continuous pressure transmission chain. The balance adjustment is made according to the pressure difference between the time slices on both sides of the buffer window, so that the extended signal can be smoothly connected with the adjacent normal section.

[0009] Preferably, during the adaptive adjustment of the elastic pressure buffer window, the width of the buffer window is adjusted according to the difference in the pressure change rate between adjacent time slices. When the pressure change rate shows a continuous upward trend, the buffer window extends backward along the time axis; when the pressure change rate shows a slowing trend, the buffer window extends forward along the time axis, so as to ensure that the pressure signal is continuously distributed in the time dimension and is consistent with the actual physiological pressure transmission direction.

[0010] Preferably, the step of hierarchically rearranging the pressure transmission chain based on the extended signal range output by the elastic pressure buffer window includes: After the elastic pressure buffer window completes the time extension, the extended signal range output by the buffer window is used as the basic input for signal rearrangement. Each time slice is time-calibrated, and the boundary of the extended signal range is time-aligned with the adjacent normal segment of the original pressure transmission chain to establish the time reference for rearrangement. After completing time calibration and boundary alignment, the time slice sequence within the extended signal range is hierarchically divided into multiple time response layers based on the difference in pressure change rate, in order to form a hierarchical pressure transmission chain. After forming a hierarchical structure, the timing is rearranged according to the characteristic sequence of each time response layer. The upper and lower layer signals are interleaved and continuously adjusted at the boundary according to the pressure change trend, so that the rearranged signals transition naturally. After completing the hierarchical time-series rearrangement, the rearrangement results are integrated as a whole, arranged sequentially according to the time index, and boundary fusion processing is performed to ensure that the pressure transmission chain remains consistent in terms of temporal continuity and peak response integrity.

[0011] Preferably, during the hierarchical time-series rearrangement process, when the upper and lower time response layers are staggered, the insertion order is determined according to the pressure change trend of adjacent time slices, and the signal is smoothly connected by linear insertion or merging in the boundary fusion process, so that the rearranged pressure transmission chain remains continuous on the time axis and the pressure change of each time slice is without abrupt jumps.

[0012] Preferably, the step of setting the dynamic response weight band based on the pressure transmission chain after hierarchical temporal rearrangement includes: After the hierarchical time sequence is rearranged, the rearranged pressure transmission chain is used as the basis for weight allocation. The pressure change amplitude in each time period of the entire pressure transmission chain is continuously measured, and a set of pressure response amplitudes with uniform time distribution is established. After obtaining the pressure response amplitude for each time period, dynamic response weight bands are set according to the relative magnitude of the pressure change amplitude and its distribution characteristics on the time axis, and a continuous weight distribution structure is formed with the average pressure change amplitude as the reference baseline. After the dynamic response weight band is set, the weight band is merged with the rearranged pressure conduction chain to make the signal energy distribution of each time period consistent with the corresponding weight value, and to perform smooth transition processing between adjacent time periods. After signal fusion is completed, the energy balance of the fused pressure conduction chain is adjusted. The energy distribution is fine-tuned by referring to the weight distribution of adjacent time periods, so that the pressure conduction chain remains smooth and continuous in time and eliminates the spurious stable state.

[0013] Preferably, the dynamic response weight band performs a continuous and smooth transition during the fusion process based on the difference between the pressure change amplitude and the weight distribution in adjacent time segments, so that the fused pressure transmission chain forms a gradually connected energy distribution structure on the time axis, and the dynamic characteristics of pressure change are restored by fine-tuning the signal amplitude in the smooth area during energy balance adjustment, thereby ensuring that the continuity of the pressure transmission chain is consistent with the real response.

[0014] Preferably, the steps for establishing a rhythmic balance control mechanism based on a continuous pressure sequence with dynamic response weighted output include: After the continuous pressure sequence is output by the dynamic response weighted band, the overall time rhythm characteristics of the continuous pressure sequence are determined. By analyzing the direction, amplitude and rate of pressure change in time slices, a set of pressure change cycles is established and a rhythm baseline is formed. Based on the establishment of the rhythm baseline, the instantaneous change characteristics in the continuous pressure sequence are identified in real time. The rhythm baseline is divided into several dynamic observation areas, and rhythm abnormalities that exceed the average value of the rhythm baseline or show the opposite trend are recorded. After identifying rhythmic anomalies, the time anchors are dynamically adjusted based on their location and distribution characteristics to ensure that the time anchors are consistent with the dynamic rhythm of the pressure transmission chain and to maintain the continuous transmission state of the pressure feedback chain. After completing the dynamic adjustment of the time anchors, a rhythmic balance control structure is established based on the updated time anchors. The adjusted pressure transmission chain is compared on a time-by-time basis and extended and smoothed to restore the pressure feedback chain to the continuous transmission interval and achieve continuous tracking of the intracranial pressure mutation stage.

[0015] Preferably, in the process of establishing the rhythmic balance control structure, the extension and smoothing process includes smoothing the time slices that deviate from the rhythm curve in the continuous pressure sequence according to the rhythm baseline corresponding to the time anchor point, so that the pressure change amplitude maintains a continuous transition within the rhythm curve range, thereby achieving adaptive recovery and dynamic balance maintenance of the pressure feedback chain when rhythm deviation occurs.

[0016] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention constructs a continuous pressure transmission chain during intracranial pressure monitoring and combines it with an elastic pressure buffer window and a hierarchical temporal rearrangement mechanism. This allows the intracranial pressure signal to maintain temporal continuity and complete dynamic response even when affected by local vascular collapse or tissue compliance mutations. It can extend the temporal dimension and restore the structure when the signal is compressed or the transmission is interrupted, so that the true fluctuation trend of intracranial pressure changes can be reproduced. This improves the monitoring signal's response to pressure peaks during rapid rises, effectively avoids the formation of false stable intervals, and enables early characteristics of pressure abnormalities to be identified and tracked in a timely manner.

[0017] This invention achieves adaptive balance adjustment of energy distribution and time anchor points in the pressure transmission chain through the linkage of dynamic response weighting bands and rhythmic balance regulation mechanisms. This ensures that the continuous signal maintains consistency with the actual changes in intracranial pressure in both energy proportion and temporal structure. This rhythmic regulation method enables the monitoring system to continuously maintain signal transmission stability and pressure rhythm synchronization during highly dynamic phases, achieving continuous tracking and real-time perception of intracranial pressure mutations, thereby significantly improving the reliability of monitoring results and the accuracy of early warning. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0019] Figure 1 This invention relates to an intelligent intracranial pressure monitoring system. Detailed Implementation

[0020] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0021] This invention provides, for example Figure 1 The intracranial pressure intelligent monitoring system shown includes a continuous pressure transmission chain construction module, an elastic pressure buffer window adjustment module, a hierarchical temporal rearrangement module, a dynamic response weight band allocation module, and a rhythmic balance control module. The continuous pressure transmission chain construction module establishes a continuous pressure transmission chain for intracranial pressure signals, performs time series segmentation analysis on the real-time acquired intracranial pressure signals, identifies segments in the pressure feedback curve that exhibit sluggishness and abnormal smoothness, and determines these segments as the nonlinear response localization range for subsequent dynamic signal compensation processing. The specific implementation method for this step is as follows: In real-time monitoring of intracranial pressure (ICP) in subjects, raw signals of ICP changes over time are continuously recorded by acquiring ICP signals. A stable sampling interval is maintained during acquisition, ensuring that each pressure sampling point corresponds to a specific timestamp, thus constructing a continuous pressure data sequence over time. This data sequence includes pressure change characteristics under the combined effects of local blood flow in brain tissue, cerebrospinal fluid flow, and changes in brain tissue compliance. By arranging these raw signals in chronological order and connecting all sampling points sequentially, a continuous pressure transmission chain reflecting the dynamic changes in ICP is formed. During signal acquisition, to prevent errors caused by transient noise or sampling drift, point-by-point smoothing transitions are performed between temporally adjacent sampling points to ensure the continuity of the pressure curve matches actual physiological changes. The pressure changes within each time period not only reflect the pulsating characteristics of intracranial blood flow but also the response delay of brain tissue to pressure transmission. Therefore, the continuous pressure transmission chain established at this stage provides a complete, continuous, and physically meaningful signal basis for subsequent dynamic analysis.

[0022] After forming a complete continuous pressure transmission chain, the pressure signal is segmented in the time dimension to capture the local dynamic characteristics of pressure changes. Specifically, the entire pressure transmission chain is divided into several continuous sub-segments according to a fixed time length or the trend of pressure change rate. Each sub-segment contains a relatively complete pressure change cycle, including the rise, peak, fall, and recovery phases. During the segmentation process, the pressure change rate between adjacent sampling points is first calculated. By comparing the change trends of the rates between consecutive sampling points, the segment boundaries are determined. When the pressure change rate shows a stable trend over several consecutive time segments, it is considered the end of an independent response cycle, and the segment boundary is set at that point. For regions with abrupt changes in the rate of change, the segment boundary automatically expands to both sides of the change point, ensuring the continuity of the internal changes within each segment. In this way, the entire pressure transmission chain is divided into several segments that are continuous in time and relatively independent in physiological response. The start and end points of each segment have clear time markers to maintain precise correspondence with the original time axis during subsequent identification.

[0023] After time segmentation, the pressure feedback curve within each segment is analyzed segment by segment, focusing on identifying potential lag and smoothing anomalies. Specifically, morphological features are extracted from each pressure feedback curve segment, and the rate of increase, peak shape, and downward trend of pressure change are observed point by point. When the pressure feedback curve shows a sustained flattening during the rising phase or a significant slowdown in the slope within the time interval where a rapid increase should occur, it indicates that the pressure transmission chain in that segment may be affected by local blood flow obstruction, vasoconstriction, or changes in brain tissue elasticity, leading to energy compression and feedback lag during signal transmission. Further observation of the waveform characteristics within this segment reveals that when the pressure peak fails to form within the normal time range or the curve apex becomes smooth without obvious spikes, a nonlinear saturation state can be identified. Based on this, the pressure change amplitudes of adjacent time slices within the segment are compared. If the pressure difference between adjacent time slices continuously decreases and the waveform shows a slow changing trend, it can be confirmed that the signal response of that segment has entered the compression phase. In this way, specific segments with lag and smoothing anomalies can be accurately identified in the entire pressure transmission chain, providing a reliable basis for determining the range of nonlinear response.

[0024] After identifying segments exhibiting sluggishness and smoothness anomalies, these segments are designated as nonlinear response localization ranges. Their temporal and positional correspondences are maintained within the existing continuous pressure conduction chain structure to facilitate subsequent targeted dynamic signal compensation. The specific process for determining the nonlinear response localization range includes: first, recording the start and end times of the segment and marking its temporal range within the entire pressure conduction chain; then, comparing the pressure change characteristics within the segment with those of adjacent normal segments to clarify the relative position and duration of the nonlinear response segment within the entire conduction chain; finally, using this nonlinear response localization range as the processing target for the subsequent dynamic compensation stage, allowing for targeted adjustments to pressure changes within this range during signal recovery. Through this process, the established continuous pressure conduction chain not only maintains continuity and integrity in the temporal dimension but also physically reflects the true dynamic process of intracranial pressure transmission. Based on this, the determined nonlinear response localization range accurately corresponds to the abnormal state of local intracranial tissues during changes in blood flow or abrupt changes in elastic response, thus laying a precise temporal and spatial foundation for subsequent dynamic signal compensation.

[0025] By implementing the above steps, the continuous pressure transmission chain of intracranial pressure signals can achieve refined analysis of time segments and accurate identification of abnormal segments while maintaining data integrity, thereby timely detecting and identifying nonlinear response intervals in the pressure transmission path during continuous monitoring.

[0026] The elastic pressure buffer window adjustment module introduces an elastic pressure buffer window within the nonlinear response positioning range. The width of the elastic pressure buffer window is adaptively adjusted according to the pressure change rate of adjacent time slices, so that the signal compression caused by local vascular collapse is extended in the time dimension to restore the weakened pressure gradient structure. The specific implementation method for this step is as follows: After determining the nonlinear response localization range, the temporal distribution characteristics of the identified sluggish and smoothing anomaly segments within this range are continuously analyzed. Specifically, each time slice within the nonlinear response localization range is marked as an independent time unit, and the pressure change amplitude and rate of change between adjacent time slices are recorded. This time-slice marking clearly reflects the local non-uniformity of pressure change over time within the nonlinear response localization range. Subsequently, an initial boundary for an elastic buffer is set on the time axis of this range. This initial boundary is determined based on the start and end times of the nonlinear response segment, ensuring that the center of the buffer window is aligned with the midpoint of the anomaly segment, thereby guaranteeing that the buffer completely covers the anomaly response segment in time. At this point, the initial width of the elastic pressure buffer window is set according to the duration of the anomaly segment, typically covering the entire time range of the anomaly segment, allowing subsequent adaptive adjustments to be implemented on a complete time scale.

[0027] After establishing the initial elastic pressure buffer window, the pressure change rate of adjacent time slices within the buffer window's coverage area is continuously analyzed to determine the basis for adjusting the buffer window width. Specifically, by comparing the pressure change rate of adjacent time slices within the nonlinear response localization range point by point, when the difference in pressure change rate between adjacent time slices is small and the trend is gentle, it indicates that the pressure transmission in this interval is still in a relatively stable state, and the original width of the buffer window can be maintained unchanged. When the difference in pressure change rate between adjacent time slices increases and shows a continuous upward or downward trend, it indicates that the pressure transmission in this time period has experienced local compression or abrupt change. To prevent the signal energy from being excessively concentrated and lost in a short period, the width of the buffer window needs to be expanded so that the buffer time interval can cover a longer time period, thereby extending the signal response process within this segment. The direction of expansion of the buffer window width is dynamically adjusted according to the trend of pressure change rate. When the pressure change rate increases significantly in the latter half of the time axis, the buffer window is extended backward; when the pressure change slows down in the first half, the buffer window is extended forward, to ensure that the temporal expansion direction of the signal is consistent with the actual physiological response pressure transmission direction. By adaptively adjusting the width of the buffer window, the pressure signal within the nonlinear response positioning range is redistributed on the time axis, thereby reducing the signal compression effect caused by collapse.

[0028] After adaptively adjusting the width of the buffer window, the pressure transmission chain within the buffer window undergoes time-stretching processing to achieve a smooth transition of the signal in the time dimension. Specifically, within the range of the elastic pressure buffer window, the average pressure change rate between adjacent time slices is selected as a reference value for the local change trend. The pressure signal, which originally changed intensively in a short period of time, is redistributed according to the width of the buffer window, so that the originally dense pressure changes are smoothly expanded over a longer time range. Through this time-stretching method, the pressure feedback curve can transition from its original steep shape to a slowly rising shape, thereby restoring the compressed pressure gradient structure. To ensure that the extended pressure changes still reflect the true physiological response characteristics, boundary transition processing is applied to the time slices at both ends of the buffer window, so that the buffer window is smoothly connected with the adjacent normal pressure transmission section, avoiding abrupt changes or discontinuities during the time-stretching process. In this way, the signal changes within the nonlinear response localization range are not only extended in the time dimension, but also maintain continuity in amplitude changes, ensuring the physical continuity and physiological rationality of the entire pressure transmission chain.

[0029] After the elastic pressure buffer window is extended, the extended signal output from the buffer window is structurally reorganized so that the recovered pressure gradient can seamlessly connect with the normal signal outside the nonlinear response localization range. Specifically, the signal range output from the buffer window is re-embedded into the original continuous pressure conduction chain, ensuring that the extended time series maintains the correct order on the global time axis. To prevent abrupt changes in energy distribution between the extended signal within the buffer window and the original signal, a balancing adjustment is performed during the embedding process based on the pressure difference between the time slices on both sides of the buffer window, ensuring a natural transition in amplitude between the output of the buffer and the adjacent normal segment. After embedding, the signal compression and peak attenuation phenomena that originally appeared within the nonlinear response localization range are significantly alleviated, and the pressure feedback curve recovers its complete gradient change characteristics. At this point, the extended pressure conduction chain maintains a smooth transition in temporal continuity and undergoes a redistribution in the pressure gradient structure, allowing the signal originally affected by local collapse to reflect a dynamic process that more closely approximates the actual intracranial pressure changes. In this way, the elastic pressure buffer window not only effectively extends the signal in time, but also physically repairs the problem of weakened pressure response caused by local collapse, providing a continuous and stable signal foundation for subsequent hierarchical time-series rearrangement and dynamic weight allocation.

[0030] Through the execution of the above steps, the signal within the nonlinear response localization range no longer exists in a transient compressed form, but is smoothly expanded through the time extension of the elastic pressure buffer window, thereby forming a complete, continuous, and time-level pressure change structure throughout the entire pressure transmission chain. This method of introducing an adaptively adjustable elastic pressure buffer window within the nonlinear response range effectively expands the temporal distribution of the intracranial pressure signal, eliminates signal energy concentration and peak distortion caused by local vascular collapse, and ensures the authenticity of the response to dynamic changes in intracranial pressure during monitoring.

[0031] The hierarchical timing reordering module, based on the extended signal range output by the elastic pressure buffer window, performs hierarchical timing reordering on the pressure transmission chain, and re-expands the signal within the nonlinear response positioning range, thereby maintaining the temporal continuity and peak response integrity of the pressure transmission chain. The specific implementation method for this step is as follows: After the elastic pressure buffer window completes its time extension, the extended signal interval output by the buffer window is used as the basic input for signal rearrangement. This extended signal interval contains a complete time-slice sequence obtained by extending the signal within the nonlinear response positioning range. Its time span is longer than the original signal interval, and the pressure changes between time slices are smoother. To maintain the accuracy of the time sequence in subsequent rearrangement, each time slice in the extended signal interval is first time-calibrated, giving each time slice a clear time index value. Simultaneously, the start and end boundaries of the extended signal interval are time-aligned with the adjacent normal segments of the original pressure conduction chain to ensure the relative position of the extended interval remains continuous on the global time axis. During this process, the extended interval of the elastic pressure buffer window formed in the previous stage is considered an independent time layer, serving as the core reference layer for subsequent time-series rearrangement, allowing the entire pressure conduction chain rearrangement to unfold around this time layer. Through these operations, seamless temporal connection between the extended signal interval and the original pressure conduction chain is ensured, establishing an accurate time reference for subsequent hierarchical time-series rearrangement.

[0032] After time calibration and boundary alignment, the time-slice sequences within the extended signal interval are hierarchically divided to form a layered pressure conduction chain. Specifically, the extended signal interval is divided into multiple time response layers according to different pressure change rates. The upper layer represents signal segments with higher pressure change rates and faster responses, while the lower layer represents signal segments with lower pressure change rates and slower responses. During the division, the pressure change amplitude and its continuous trend between adjacent time slices are used as the basis for hierarchical distinction. When the pressure change rate within a certain interval remains in a high-amplitude fluctuation state, it is classified as the upper time response layer; when the pressure change rate is low and the change is gradual, it is classified as the lower time response layer. Through this hierarchical division method, the originally mixed signal change intervals can be separated in the time dimension, allowing signal segments with different response rates to be presented independently in the time structure. In this way, the signal compression caused by collapse within the nonlinear response localization range is decomposed into multiple time layers with different response characteristics, forming a multi-layered pressure conduction structure that overlaps in time but is logically separated. This hierarchical structure provides the physical basis for subsequent timing rearrangement, enabling the signal unfolding process to proceed sequentially between different time layers without disrupting the overall temporal logic of the original pressure transmission chain.

[0033] After forming the hierarchical structure, the pressure conduction chain is rearranged temporally according to the characteristic sequence of each time response layer. Specifically, starting from the upper time response layer of the extended signal range, the original arrangement order of each time slice within that layer is gradually restored, ensuring temporal continuity with the preceding and following segments of the original pressure conduction chain. Then, the signals from the lower time response layers are sequentially inserted into the time gaps of the upper time layers, causing high-response and low-response layers to be staggered along the time axis, thus constructing a pressure conduction chain with cascading characteristics in the time dimension. During this process, to ensure a smooth signal transition, continuity adjustments are made at the connections between different time layers. That is, at the boundaries of adjacent time layers, the arrangement order of the time slices is adjusted according to the connection relationship of pressure change trends, making the transition of pressure fluctuations natural and without abrupt changes. After the rearrangement is completed, the signal, originally compressed within the nonlinear response localization range, is re-expanded, and its temporal structure changes from a single linear sequence to a hierarchical conduction structure with multi-layered temporal characteristics. At this point, the signal is redistributed temporally, allowing the pressure change peaks to recover at appropriate time positions, thereby compensating for the response loss caused by signal compression in the collapse region.

[0034] After hierarchical temporal rearrangement, the rearranged pressure conduction chain is integrated as a whole to ensure the temporal continuity of the signal and the integrity of the peak response. Specifically, all rearranged time slices are re-integrated into a continuous pressure conduction chain, arranged sequentially according to time index, so that each time slice has a unique and non-overlapping position on the time axis. To prevent gaps or overlaps between time slices after hierarchical rearrangement, boundary fusion processing is performed on the rearrangement results. Linear insertion or merging is performed between adjacent time slices according to their pressure change trends, so that the pressure change curves remain smooth and continuous at the connection points. The fused pressure conduction chain is restored to a continuous time series on a macroscopic level, but its internal signal distribution has been re-unfolded at the microscopic level. The originally compressed pressure fluctuations are re-stretched and smoothly connected with adjacent segments. In this process, the overall rhythm of the original pressure conduction chain is not destroyed, while the signal characteristics of local nonlinear response intervals are effectively restored, so that the entire pressure conduction chain remains intact in terms of temporal continuity, and at the same time, the true intracranial pressure change trend is restored in terms of peak response.

[0035] Through the implementation of the above steps, the hierarchical temporal rearrangement of the pressure conduction chain, based on the extended signal range output by the elastic pressure buffer window, not only achieves the re-expansion of the signal within the nonlinear response localization range, but also maintains the continuity of the pressure conduction chain in the temporal dimension and the integrity of the physical response. Through this combination of hierarchical and rearrangement, the signal compression originally caused by local vascular collapse is redistributed in time, allowing the peak value of pressure changes to return to a reasonable physiological time window.

[0036] The dynamic response weight band allocation module sets the dynamic response weight band according to the pressure transmission chain after hierarchical time-series rearrangement, and allocates weights according to the pressure response amplitude of each time period, so that the rearranged continuous signal obtains an energy ratio that matches the actual pressure change during the fusion process, thereby eliminating the false steady state caused by signal compression. The specific implementation method for this step is as follows: After the hierarchical temporal rearrangement is completed, the rearranged pressure conduction chain is obtained as the basis for weight allocation, and the pressure change amplitude in each time period of the entire pressure conduction chain is continuously measured. Specifically, the rearranged pressure conduction chain is re-divided into several continuous time segments according to time sequence, with each time segment corresponding to a specific pressure response cycle. Within each segment, the amplitudes of the rising, peak, and falling segments of pressure change are extracted, and the maximum pressure value, minimum pressure value, and change amplitude are recorded for each time period. To ensure the accuracy of amplitude measurement, the hierarchical structure of the previous stage is maintained during the extraction process, so that the measurement range of each time segment corresponds to its corresponding temporal layer. In this way, a set of pressure response amplitudes with uniform temporal distribution and clear physical meaning can be established on the complete pressure conduction chain. This set can reflect the dynamic energy distribution of intracranial pressure changes in different time periods, providing an accurate basis for subsequent dynamic weight setting.

[0037] After obtaining the pressure response amplitude for each time period, a dynamic response weight band is set for the pressure transmission chain based on the relative magnitude of the pressure change amplitude and its distribution characteristics on the time axis. Specifically, the average pressure change amplitude of the entire pressure transmission chain is used as a reference baseline. Time periods above the average are classified as high-response zones, and those below the average are classified as low-response zones. Then, a continuously distributed weight band is established on the time axis of the pressure transmission chain, with higher weight values ​​corresponding to high-response zones and lower weight values ​​corresponding to low-response zones, thus forming a continuous weight distribution structure that changes over time. This weight band covers the entire pressure transmission chain and maintains a one-to-one correspondence with each time period on the time axis. During the setting process, to ensure that the weight band remains synchronized with the time-layered structure, the stratified time-series results of the previous stage are used as a time reference. The weights of the high-response layer are preferentially allocated to the high-value areas of the weight band, while the weights of the low-response layer are allocated to the low-value areas of the weight band, thus forming a hierarchical and response-matched dynamic weight distribution pattern. In this way, the dynamic response weight band can reflect the energy proportion of different pressure change segments in the time dimension, and realize the energy recovery of real physiological pressure fluctuations.

[0038] After setting the dynamic response weighting band, the weighting band is fused with the rearranged pressure conduction chain to ensure that the signal energy distribution in each time period is consistent with its corresponding weight value, thus matching the signal energy proportion with the actual pressure changes. Specifically, during the fusion process, the signal intensity is proportionally adjusted based on the pressure response amplitude of each time period and its corresponding weight value in the dynamic response weighting band. When the pressure response amplitude of a certain time period is high and the corresponding weight value is large, the energy proportion of the signal in that time period is increased to highlight the true peak response; when the pressure response amplitude of a certain time period is low and the corresponding weight value is small, the energy allocation of the signal in that time period is appropriately reduced to avoid the interference of weak signals on the overall trend. Through this fusion method, the pressure signal, which originally had an uneven energy distribution due to nonlinear compression, is redistributed, so that its energy proportion on the time axis matches the actual dynamic process of intracranial pressure changes. To maintain temporal continuity, a smooth transition is performed between adjacent time periods, that is, the signal intensity is gradually connected according to the weight difference between adjacent periods, so that the fused signal forms a continuous and gradual change in energy distribution, avoiding signal jumps caused by abrupt weight changes.

[0039] After signal fusion, the fused pressure conduction chain undergoes energy balance adjustment to eliminate spurious stable states caused by signal compression. Specifically, the fused continuous signal is scanned throughout to identify regions where pressure changes are relatively flat over time. For these regions, the energy distribution is fine-tuned by referencing the weight distribution of adjacent time periods, slightly increasing the energy value in these flat regions to restore their original dynamic characteristics within the overall trend. Simultaneously, for excessively concentrated high-energy regions, their energy is moderately reduced based on the weight band distribution to prevent excessively prominent signal peaks and energy imbalances. Through these adjustments, the entire pressure conduction chain remains smooth and continuous over time, and maintains a balanced energy distribution, ensuring that the trend of pressure changes aligns with the rhythm of actual intracranial pressure changes. Once the energy balance adjustment is complete, the rearranged pressure conduction chain achieves a continuous transition over time and restores the true peak characteristics in amplitude response, effectively eliminating spurious stable regions caused by nonlinear responses and fully presenting the true dynamic characteristics of intracranial pressure.

[0040] Through the above steps, a dynamic response weight band is set according to the pressure conduction chain after hierarchical temporal rearrangement, and weight allocation and signal fusion are performed to match the signal energy proportion of each time period of the pressure conduction chain with the actual intracranial pressure changes. This process not only achieves signal rebalancing in the time and energy dimensions, but also eliminates the false stationary state caused by signal compression by dynamically adjusting the distribution of the weight band, so that the rearranged pressure signal restores its dynamic change characteristics.

[0041] The rhythmic balance control module establishes a rhythmic balance control mechanism based on the continuous pressure sequence output by the dynamic response weight band. By monitoring the instantaneous changes in pressure transmission in the pressure transmission chain, the time anchor point position is adjusted to keep the pressure feedback chain within the continuous transmission range, thereby realizing real-time perception and continuous tracking of intracranial pressure mutation stages. The specific implementation method for this step is as follows: After outputting a continuous pressure sequence via the dynamic response weighted band, the overall temporal rhythm characteristics of this continuous pressure sequence are determined to establish a time baseline for rhythmic balance regulation. Specifically, the pressure conduction chain adjusted by the dynamic response weighted band in the previous stage is used as input data, and its fluctuation trend in the time dimension is continuously tracked. By analyzing the direction, amplitude, and rate of pressure change at each time slice, the alternating cycles of pressure rise, stability, and decline are identified. In this process, the start and end points of each cycle are time-marked to form a complete set of pressure change cycles. This set of cycles reflects the natural rhythmic variation of intracranial pressure, namely the dynamic balance between brain tissue, cerebral blood volume, and cerebrospinal fluid. By establishing this set of cycles, a rhythmic baseline for intracranial pressure changes can be formed, enabling subsequent rhythmic balance regulation to be dynamically adjusted based on this baseline. Simultaneously, to ensure the continuity of the rhythmic baseline in the time dimension, the set of cycles is aligned with the time index of the dynamic response weighted band, ensuring that the pressure rhythmic baseline corresponds to the energy distribution of the signal across the entire time axis. In this way, the entire rhythm regulation process can be carried out under the premise of continuous time, realizing continuous monitoring and dynamic response of stress rhythm.

[0042] Based on the established rhythm baseline, the instantaneous change characteristics in continuous pressure sequences are identified in real time to monitor the dynamic response of the pressure transmission chain over different time periods. Specifically, the aforementioned rhythm baseline is divided into several dynamic observation zones, each containing multiple continuous time slices representing the instantaneous fluctuations of intracranial pressure over a short period. Within each observation zone, the rate and direction of pressure change in adjacent time slices are continuously tracked. When the rate of pressure change significantly exceeds the average rate of change of the rhythm baseline or shows the opposite trend, a rhythm imbalance is identified in that region. Such imbalances are usually caused by abnormal local blood flow regulation, mutations in brain tissue compliance, or restricted cerebrospinal fluid flow, manifesting as abrupt changes or delays in the pressure fluctuation rhythm. To capture such abrupt changes, the time point of the instantaneous change and its corresponding pressure value are recorded on the time axis and marked as a "rhythm abnormality point" in the pressure transmission chain. In this way, precise monitoring of instantaneous changes in pressure transmission can be achieved in continuous pressure sequences, providing a basis for subsequent time anchor point adjustments.

[0043] After identifying rhythmic anomalies in the pressure transmission chain, time anchors are dynamically adjusted based on their location and distribution characteristics to maintain the continuous transmission state of the pressure feedback chain. Specifically, time anchors are defined as key time markers reflecting the balance of the time structure of the pressure transmission chain, with each time anchor corresponding to the start or peak position of a cycle in the rhythmic baseline. When instantaneous changes in the pressure transmission chain cause a shift in the rhythmic baseline, the original position of the time anchor will no longer correspond to the actual pressure rhythm. In this case, adjacent time anchors are repositioned according to the distribution of the anomalies on the time axis. If a pressure mutation occurs in the first half of the cycle, the time anchor is slightly adjusted forward to align with the new pressure change starting point; if the mutation occurs in the second half of the cycle, the time anchor is extended backward to match the new pressure fluctuation peak position. Through this adjustment method, the time anchors are always consistent with the dynamic rhythm of the pressure transmission chain, thereby ensuring that the pressure feedback chain maintains a continuous transmission path in the time dimension. Each fine-tuning of the time anchor point will cause the time structure of its subsequent cycles to be rearranged, so that the overall rhythm of the pressure transmission chain can quickly return to equilibrium after local disturbances, avoiding distortion or delay of the monitoring signal caused by rhythm drift.

[0044] After dynamically adjusting the time anchors, a rhythmic balance control structure for the pressure feedback chain is re-established based on the updated time anchors to achieve real-time sensing and continuous tracking of intracranial pressure abrupt changes. Specifically, all adjusted time anchors are reconnected on the time axis to form a continuous rhythmic balance curve, which represents the main dynamic response line of intracranial pressure throughout the monitoring period. Subsequently, the adjusted pressure conduction chain is compared with this rhythmic balance curve time-slice by time slice to compare the deviation of the pressure value from the rhythmic curve in each time slice. When the pressure deviation in a certain time slice exceeds the set range, the time slice is automatically classified into the rhythm abnormality interval, and the signal change trend in the interval is extended and smoothed to re-fit the rhythmic balance curve. In this way, the pressure feedback chain can self-adjust and recover to the continuous conduction interval after rhythm deviation, achieving continuous tracking of dynamic changes in intracranial pressure. At the same time, the rhythmic balance control mechanism enables the system to respond instantly when intracranial pressure abrupt changes are detected, avoiding signal interruption or false instability during the abrupt change phase, thereby ensuring the observability and continuity of response throughout the entire process of intracranial pressure changes.

[0045] Through the implementation of the above steps, the rhythmic balance regulation mechanism established based on the continuous pressure sequence with dynamic response weighted output can achieve real-time perception and dynamic correction of rhythmic changes in the pressure conduction chain during intracranial pressure monitoring. This implementation method, through continuous adjustment of the time anchor point, ensures that the pressure feedback chain maintains temporal continuity and energy balance during abrupt changes, thereby eliminating monitoring errors caused by rhythm deviations and enabling real-time capture and continuous tracking of intracranial pressure abrupt change signals.

[0046] This invention constructs a continuous pressure transmission chain during intracranial pressure monitoring and combines it with an elastic pressure buffer window and a hierarchical temporal rearrangement mechanism. This allows the intracranial pressure signal to maintain temporal continuity and complete dynamic response even when affected by local vascular collapse or tissue compliance mutations. It can extend the temporal dimension and restore the structure when the signal is compressed or the transmission is interrupted, so that the true fluctuation trend of intracranial pressure changes can be reproduced. This improves the monitoring signal's response to pressure peaks during rapid rises, effectively avoids the formation of false stable intervals, and enables early characteristics of pressure abnormalities to be identified and tracked in a timely manner.

[0047] This invention achieves adaptive balance adjustment of energy distribution and time anchor points in the pressure transmission chain through the linkage of dynamic response weighting bands and rhythmic balance regulation mechanisms. This ensures that the continuous signal maintains consistency with the actual changes in intracranial pressure in both energy proportion and temporal structure. This rhythmic regulation method enables the monitoring system to continuously maintain signal transmission stability and pressure rhythm synchronization during highly dynamic phases, achieving continuous tracking and real-time perception of intracranial pressure mutations, thereby significantly improving the reliability of monitoring results and the accuracy of early warning.

[0048] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. An intelligent intracranial pressure monitoring system, characterized in that, It includes a continuous pressure transmission chain construction module, an elastic pressure buffer window adjustment module, a hierarchical time sequence rearrangement module, a dynamic response weight band allocation module, and a rhythmic balance control module. The continuous pressure transmission chain construction module establishes a continuous pressure transmission chain for intracranial pressure signals, performs time series segmentation analysis on the real-time acquired intracranial pressure signals, identifies segments in the pressure feedback curve that exhibit sluggishness and abnormal smoothness, and determines these segments as the nonlinear response localization range. The elastic pressure buffer window adjustment module introduces an elastic pressure buffer window within the nonlinear response positioning range. The width of the elastic pressure buffer window is adaptively adjusted according to the pressure change rate of adjacent time slices, so that the signal compression is extended in the time dimension. The hierarchical timing rearrangement module, based on the extended signal range output by the elastic pressure buffer window, performs hierarchical timing rearrangement on the pressure transmission chain, and re-expands the signal within the nonlinear response positioning range; The dynamic response weight band allocation module sets the dynamic response weight band according to the pressure transmission chain after hierarchical time sequence rearrangement, and allocates weights according to the pressure response amplitude of each time period. The rhythmic balance control module establishes a rhythmic balance control mechanism based on the continuous pressure sequence output by the dynamic response weight band. By monitoring the instantaneous changes in pressure transmission in the pressure transmission chain, it adjusts the position of the time anchor point to keep the pressure feedback chain within the continuous transmission range.

2. The intelligent intracranial pressure monitoring system according to claim 1, characterized in that, The steps to establish a continuous pressure conduction chain for intracranial pressure signals include: When monitoring intracranial pressure in real time, the raw signal of intracranial pressure changes over time is continuously recorded by continuously collecting intracranial pressure signals, and all sampling points are arranged in chronological order to form a continuous pressure transmission chain in the time dimension. After forming a complete continuous pressure transmission chain, the pressure signal is segmented in the time dimension. The entire pressure transmission chain is divided into several continuous sub-segments according to a fixed time length or pressure change rate trend to capture the local dynamic characteristics of pressure changes. After completing the time segmentation, the pressure feedback curve in each segment is analyzed segment by segment. By observing the pressure change rate, peak shape and waveform smoothness, segments with sluggishness and smoothness anomalies are identified. After identifying the sections with sluggishness and smoothness anomalies, these sections are confirmed as the nonlinear response location range, and the temporal sequence and positional correspondence are maintained in the original continuous pressure transmission chain.

3. The intelligent intracranial pressure monitoring system according to claim 2, characterized in that, The steps for introducing an elastic pressure buffer window within the nonlinear response positioning range include: After determining the nonlinear response positioning range, the time distribution characteristics within this range are continuously analyzed. Each time slice is marked as an independent time unit, and the initial boundary and initial width of the elastic pressure buffer window are set according to the start and end times. After forming the initial elastic pressure buffer window, the pressure change rate of adjacent time slices within the coverage area of ​​the buffer window is continuously analyzed. The width of the buffer window is adaptively adjusted according to the difference and trend of the change rate, and the extension direction of the buffer window is adjusted according to the direction of pressure change. After completing the adaptive adjustment of the buffer window width, the pressure transmission chain within the buffer window is subjected to time extension processing. The concentrated pressure changes in a short period of time are redistributed according to the buffer window width and boundary transition is performed to restore the pressure gradient structure. After the elastic pressure buffer window is extended, the extended signal output by the buffer window is re-embedded into the original continuous pressure transmission chain, and the balance is adjusted according to the pressure difference between the time slices on both sides of the buffer window.

4. The intelligent intracranial pressure monitoring system according to claim 3, characterized in that, During the adaptive adjustment of the elastic pressure buffer window, the width of the buffer window is adjusted according to the difference in the pressure change rate between adjacent time slices. When the pressure change rate shows a continuous upward trend, the buffer window extends backward along the time axis, and when the pressure change rate shows a slowing trend, the buffer window extends forward along the time axis.

5. The intelligent intracranial pressure monitoring system according to claim 3, characterized in that, The steps for hierarchical timing rearrangement of the pressure conduction chain based on the extended signal range output by the elastic pressure buffer window include: After the elastic pressure buffer window completes the time extension, the extended signal range output by the buffer window is used as the basic input for signal rearrangement. Each time slice is time-calibrated, and the boundary of the extended signal range is time-aligned with the adjacent normal segment of the original pressure transmission chain to establish the time reference for rearrangement. After completing time calibration and boundary alignment, the time slice sequence within the extended signal range is hierarchically divided into multiple time response layers based on the difference in pressure change rate, in order to form a hierarchical pressure transmission chain. After forming a hierarchical structure, the timing is rearranged according to the characteristic sequence of each time response layer, and the upper and lower layer signals are interleaved in sequence and continuously adjusted at the boundary according to the pressure change trend. After completing the hierarchical time-series rearrangement, the rearrangement results are integrated as a whole, arranged sequentially according to the time index, and boundary fusion processing is performed.

6. The intelligent intracranial pressure monitoring system according to claim 5, characterized in that, During the hierarchical timing reordering process, when the upper and lower time response layers are interleaved, the insertion order is determined according to the pressure change trend of adjacent time slices, and the signal is smoothly connected by linear insertion or merging in the boundary fusion process.

7. The intelligent intracranial pressure monitoring system according to claim 5, characterized in that, The steps for setting dynamic response weight bands based on the pressure transmission chain after hierarchical temporal rearrangement include: After the hierarchical time sequence is rearranged, the rearranged pressure transmission chain is used as the basis for weight allocation. The pressure change amplitude in each time period of the entire pressure transmission chain is continuously measured, and a set of pressure response amplitudes with uniform time distribution is established. After obtaining the pressure response amplitude for each time period, dynamic response weight bands are set according to the relative magnitude of the pressure change amplitude and its distribution characteristics on the time axis, and a continuous weight distribution structure is formed with the average pressure change amplitude as the reference baseline. After the dynamic response weight band is set, the weight band is merged with the rearranged pressure conduction chain to make the signal energy distribution of each time period consistent with the corresponding weight value, and to perform smooth transition processing between adjacent time periods. After signal fusion is completed, the energy balance of the fused pressure transmission chain is adjusted by referring to the weight distribution of adjacent time periods to keep the pressure transmission chain smooth and continuous in time.

8. The intelligent intracranial pressure monitoring system according to claim 7, characterized in that, During the fusion process, the dynamic response weight band performs a continuous and smooth transition based on the difference between the pressure change amplitude and the weight distribution in adjacent time segments, so that the fused pressure transmission chain forms a gradually connected energy distribution structure on the time axis, and restores the dynamic characteristics of pressure change by fine-tuning the signal amplitude in the smooth area during energy balance adjustment.

9. The intelligent intracranial pressure monitoring system according to claim 7, characterized in that, The steps for establishing a rhythmic balance control mechanism based on a continuous pressure sequence with dynamic response weighted output include: After the continuous pressure sequence is output by the dynamic response weighted band, the overall time rhythm characteristics of the continuous pressure sequence are determined. By analyzing the direction, amplitude and rate of pressure change in time slices, a set of pressure change cycles is established and a rhythm baseline is formed. Based on the establishment of the rhythm baseline, the instantaneous change characteristics in the continuous pressure sequence are identified in real time. The rhythm baseline is divided into several dynamic observation areas, and rhythm abnormalities that exceed the average value of the rhythm baseline or show the opposite trend are recorded. After identifying rhythmic anomalies, the time anchors are dynamically adjusted based on their location and distribution characteristics to ensure that the time anchors are consistent with the dynamic rhythm of the pressure transmission chain and to maintain the continuous transmission state of the pressure feedback chain. After completing the dynamic adjustment of the time anchor point, a rhythmic balance control structure is established based on the updated time anchor point. The adjusted pressure transmission chain is compared on a time-by-time basis and extended smoothing is performed to restore the pressure feedback chain to the continuous transmission range.

10. The intelligent intracranial pressure monitoring system according to claim 9, characterized in that, In the process of establishing a rhythmic balance control structure, the extension and smoothing process includes smoothing the time slices that deviate from the rhythm curve in the continuous pressure sequence based on the rhythm baseline corresponding to the time anchor point, so that the pressure change amplitude maintains a continuous transition within the range of the rhythm curve.

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