Intelligent early warning system and method for early hematoma enlargement risk of primary cerebral hemorrhage in plateau area

Abstract

The invention discloses a plateau region primary cerebral hemorrhage early hematoma expansion risk intelligent early warning system and method, and relates to the technical field of medical health, and the method comprises the following steps: S1, building a time sequence integrity observation layer, locking a collection link through a double-mirror image time anchor point, outputting a phase jitter curve and a time drift baseline under the mapping of a pressure field, and obtaining a time sequence integrity observation layer; the method is used for subsequent dynamic identification; and S2, performing three-parameter coupling causal decomposition of pressure, temperature and vibration based on the time drift baseline, analyzing a stroboscopic trigger source, generating a frame-level delay kernel and a frame skipping kernel, and outputting a real-time drift vector field as a dynamic reference for clock correction. Through the steps of time drift observation, three-parameter interference decomposition, master and slave clock correction, phase sampling correction, time sequence consistency identification, time inversion control and the like, time sequence repair and dynamic stability of plateau cerebral hemorrhage image data are realized, and the accuracy of hematoma expansion risk early warning and intervention timeliness are improved.

Description

Technical Field

[0001] This invention relates to the field of medical and health technology, specifically to an intelligent early warning system and method for the risk of early hematoma expansion in primary cerebral hemorrhage in plateau regions. Background Technology

[0002] "Intelligent Early Warning System for Early Hematoma Expansion Risk in Primary Intracerebral Hemorrhage in High-Altitude Areas" refers to a system designed for patients with primary intracerebral hemorrhage occurring in the unique environment of high altitudes (hypoxia, low air pressure, abnormal blood rheology, etc.). This system collects early clinical data (such as medical history, blood pressure, and coagulation function), medical imaging characteristics (such as hematoma morphology, density signs, and satellite signs on CT scans), and high-altitude-related physiological indicators (such as blood oxygen saturation and hematocrit). Using artificial intelligence algorithms or combined predictive models, it dynamically analyzes and predicts in real time the risk of hematoma expansion within a short period after onset, thus issuing risk warnings to doctors before significant hematoma deterioration. Its core significance lies in helping doctors formulate timely and targeted measures such as hemostasis, blood pressure reduction, or surgery through an intelligent approach of "early detection—early warning—early intervention" in environments like high altitudes where pathological progression is faster and complications are more severe, thereby reducing patient disability and mortality rates.

[0003] The existing technology has the following shortcomings:

[0004] In existing technologies, dynamic monitoring of early hematoma expansion in primary intracerebral hemorrhage largely relies on continuous imaging from image acquisition equipment and stable output from signal sensors. However, in high-altitude areas, due to significantly lower atmospheric pressure compared to plains, the sensors of image acquisition equipment are highly susceptible to low-pressure interference, resulting in transient instability, manifesting as brief interference similar to stroboscopic flicker. This flicker directly disrupts the time stamps of image frames, leading to timestamp misalignment. Normally, image data of the hematoma expansion process should be continuously input into the prediction model in a time-series manner so that the model can accurately identify the rate of hematoma volume growth and morphological changes. However, when timestamp misalignment occurs, the data sequence is disrupted, and the model cannot correctly reconstruct the true dynamic evolution trajectory during the input phase, resulting in severe distortion of temporal characteristics.

[0005] In such situations, existing models often output results completely inconsistent with the actual condition, such as misjudging a rapidly expanding hematoma as stable, or misinterpreting a rapid deterioration in a short period as a slow change. This temporal discrepancy not only weakens the sensitivity of the early warning model but also completely distorts the reliability of risk prediction, causing clinicians to miss critical intervention windows. Especially in high-altitude, hypoxic environments with fluctuating hemodynamics, this temporal error caused by sensor flicker induced by low air pressure is highly likely to cause the complete failure of the intelligent early warning system, leading to serious consequences such as rapid brain herniation, coma, or even death in patients.

[0006] 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

[0007] The purpose of this invention is to provide an intelligent early warning system and method for the early risk of hematoma expansion in primary cerebral hemorrhage in plateau areas, so as to solve the problems in the background art mentioned above.

[0008] To achieve the above objectives, the present invention provides the following technical solution: an intelligent early warning method for the risk of hematoma expansion in the early stage of primary cerebral hemorrhage in plateau areas, comprising the following steps:

[0009] S1, establish a time sequence integrity observation layer, use dual mirror time anchor points to lock the acquisition link, and output phase jitter curve and time drift baseline under pressure field mapping for subsequent dynamic identification;

[0010] S2 performs a three-parameter coupled causal decomposition of pressure, temperature and vibration based on the time drift baseline, analyzes the flicker trigger source, generates a frame-level delay kernel and a frame skipping kernel, and outputs a real-time drift vector field as a dynamic reference for clock correction.

[0011] S3 performs dual-path clock reconstruction based on the drift vector field, drives the slave clock phase with the master reference atomic clock, rearranges the cross-frame data according to the correction sequence to form a unified time scale stack, and ensures the continuity of the sampling process.

[0012] S4 performs phase bootstrapping sampling under the drive of a unified time scale stack, injects micro-delay probes, generates shadow frame sequences and redundant timestamps, and combines drift vectors to perform dynamic frame position correction, so that the input sequence gradually returns to stability.

[0013] S5 introduces a time-series consistency discriminator into the correction sequence, runs a counterfactual replay chain to remove pseudo-stable segments, and outputs a steady-state risk cone and a corrected early warning curve as criteria for the control process.

[0014] S6 implements time-reversal phase traction based on the risk cone within the steady-state window, injects inverse micropulses and links the shadow energy storage array to absorb pseudo-trigger energy, constructs a dynamic threshold fence, achieves adaptive locking, and completes the closed-loop control of the prediction process.

[0015] Preferably, step S1 includes:

[0016] During the image data acquisition process, a time stamp generator, a time reference, and a time stamp receiver are installed at the input and output ends of the signal link, respectively, and connected to a unified atomic time source to realize the deployment of dual mirror time anchor points.

[0017] Multiple atmospheric pressure sensors are deployed around the image acquisition link to obtain a pressure spatial distribution map, which is then compared with the time delay information recorded by the time stamping device to generate a pressure field map.

[0018] Phase jitter curves were plotted based on the correspondence between time delay and pressure distribution, and a time drift baseline was constructed.

[0019] The time-drift baseline is used to correct the time stamps of the image frames frame by frame. At the same time, the stability of the image frames is graded according to the phase jitter curve, and frames that cannot be repaired are removed, while the effective frame sequence after drift compensation is retained.

[0020] Preferably, step S2 includes:

[0021] Collect three types of physical disturbance data with uniform time stamps: pressure, temperature, and vibration, and convert them into continuous dynamic sequences;

[0022] By performing a one-to-one correspondence analysis between physical disturbance data and image frame drift anomaly events, the primary causes or complex cause chains of stroboscopic interference can be identified.

[0023] Frame-level delay kernels and frame skipping kernels are constructed based on drift characteristics, and perturbation cause feature labels are attached;

[0024] A real-time drift vector field is generated by combining the time drift baseline and frame-level anomaly structure, which serves as a dynamic reference for subsequent time correction and data sorting.

[0025] Preferably, step S3 includes:

[0026] Establish a master reference time path based on atomic clocks, use drift vectors to correct the time offset of image frames and generate the first round of remapping sequence;

[0027] A subordinate time path based on an adjustable capacitor oscillator is constructed, and phase fine-tuning is performed based on the offset between the sampling time of the main reference path and the actual arrival time of the image frame, and the fine-tuning results are recorded.

[0028] Using the main reference time series as the baseline and the subordinate time paths as the mapping trajectories, all image frames are organized into a unified time scale stack.

[0029] The overlapping and hole positions of frames in the tick stack are rearranged and placed to achieve time unification across sequences.

[0030] Preferably, phase fine-tuning in the subordinate time path achieves frequency correction by loading a capacitor or adjusting the current, with the fine-tuning amplitude controlled within the microsecond range, and each fine-tuning result is recorded in the local timing mapping table for frame reordering.

[0031] Preferably, step S4 includes:

[0032] Driven by the main reference time axis, sampling operations are performed according to the inter-frame phase adaptive strategy. The difference between the image frame timestamp and the main clock reference is compared to determine whether sampling is complete and the offset is recorded. The positions of subsequent sampling points are dynamically adjusted to form a bootstrap sampling chain.

[0033] Multiple sets of micro-delay probes are injected during each phase bootstrap sampling period. The image frame position error is marked by the probes and the error is fed back to the bootstrap sampling logic. At the same time, redundant timestamps are inserted as placeholder information at positions where there are potential missing frames.

[0034] Based on the generation of shadow frame series and redundant timestamps, the image sequence is structurally corrected frame by frame by combining the drift vector field. Shadow frames are generated using the features of the effective frames before and after to fill gaps or retain redundant timestamps to mark uncertain areas of structure and then reordered frame by frame.

[0035] After completing frame-bit dynamic correction, the processed sequence is subjected to stability testing and model input consistency evaluation to ensure temporal continuity and prediction accuracy.

[0036] Preferably, step S5 includes:

[0037] The system uses six indicators: intensity distribution area, red-white matter ratio, hematoma volume estimate, edge sharpness, and local density gradient, and calculates the inter-frame feature evolution rate.

[0038] After identifying pseudo-stable segments based on evolution rate, counterfactual replay chain analysis is performed to construct the theoretical evolution trajectory between the antecedent frame and the consequence frame, calculate the deviation between the pseudo-stable segment and the trajectory, and determine its structural stability.

[0039] Dynamic trend indicators are reconstructed from image sequences after removing pseudo-stable segments, and a risk cone structure is constructed, which includes a three-dimensional structure with the current frame as the cone apex and the predicted values ​​of different time windows as the cone boundaries.

[0040] A structured early warning curve is generated based on the trend line of the central axis of the risk cone, and a credibility level label is attached to form a hierarchical early warning output with dynamic tolerance and intervention prompts.

[0041] Preferably, the risk cone is constructed based on three variables: the volume growth rate, density diffusion trend, and edge expansion angle of the current frame. The upper and lower boundaries are generated through prediction windows at different time periods, and the average evolution trend is represented by the central axis. The warning curve outputs the upper and lower limit risk zones and adds a confidence level label based on this.

[0042] Preferably, step S6 includes:

[0043] In the risk cone, a steady-state window is identified and a time-inversion phase traction process is initiated. The end of the steady-state window is selected as the starting point of time inversion and the image frame feature trajectory is traced forward. The phase is iterated forward frame by frame through the master clock time axis and an inverse phase index list is output to form a pullback path.

[0044] After completing the phase traction trajectory construction, an inverse phase micropulse signal is injected into the image acquisition path to form a damping wave that is superimposed in the opposite phase to the main signal to cancel sampling abrupt changes and is injected synchronously with the time traction path to achieve the structural continuity of the stable section.

[0045] During the process of completing the reverse phase energy injection, the linkage shadow energy storage array captures high-energy instantaneous signals and delays their release to prevent abnormal energy from entering the main prediction link and archives them as pseudo-energy trigger sources.

[0046] By combining the processing results, a dynamic threshold fence is constructed within the steady-state window area, and the threshold is dynamically adjusted based on the trend line of the risk cone center and the boundary fluctuation zone to achieve adaptive locking and complete the closed-loop control of the prediction process.

[0047] An intelligent early warning system for the risk of hematoma expansion in early primary intracerebral hemorrhage in plateau regions includes a temporal integrity observation module, a multi-parameter causal decomposition module, a dual-path clock reconstruction module, a phase bootstrap sampling module, a temporal consistency identification module, and a closed-loop control module.

[0048] The temporal integrity observation module establishes a temporal integrity observation layer, uses dual mirror time anchors to lock the acquisition link, and outputs phase jitter curves and time drift baselines under pressure field mapping for subsequent dynamic identification.

[0049] The multi-parameter causal decomposition module performs three-parameter coupled causal decomposition of pressure, temperature and vibration based on the time drift baseline, analyzes the flicker trigger source, generates frame-level delay kernel and frame skip kernel, and outputs real-time drift vector field as a dynamic reference for clock correction.

[0050] The dual-path clock reconstruction module performs dual-path clock reconstruction based on the drift vector field, drives the slave clock phase with the master reference atomic clock, and rearranges the cross-frame data according to the correction sequence to form a unified time scale stack to ensure the continuity of the sampling process.

[0051] The phase bootstrap sampling module performs phase bootstrap sampling under the drive of a unified time scale stack, injects micro-delay probes, generates shadow frame sequences and redundant timestamps, and combines drift vectors to perform dynamic frame position correction, so that the input sequence gradually returns to stability.

[0052] The temporal consistency identification module introduces a temporal consistency discriminator into the correction sequence, runs a counterfactual replay chain to remove pseudo-stable segments, and outputs a steady-state risk cone and a corrected early warning curve as criteria for the control process.

[0053] The closed-loop control module implements time-reversal phase traction based on the risk cone within the steady-state window, injects inverse micropulses and links the shadow energy storage array to absorb pseudo-trigger energy, constructs a dynamic threshold fence, achieves adaptive locking, and completes the closed-loop control of the prediction process.

[0054] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0055] This invention constructs a temporal integrity observation layer to accurately capture time drift and phase jitter signals. It further integrates three interference sources—pressure, temperature, and vibration—for coupled decomposition, locating the flicker-induced mechanism and generating a drift vector field in real time, providing a dynamic correction basis for clock reconstruction. A unified time scale stack is constructed through master-slave clock collaborative correction, and phase bootstrapping sampling and dynamic image frame correction are performed under its drive, effectively restoring sequence distortions caused by frame skipping, delays, or out-of-order sequences. Based on this, a counterfactual playback mechanism and a temporal consistency identification process are introduced to identify and eliminate pseudo-stable segments, extracting true risk trends. Finally, closed-loop adaptive locking of the prediction link is achieved through time inversion, energy cancellation, and dynamic threshold control strategies. This method not only improves the input accuracy and risk identification sensitivity of the early warning model in the complex physiological environment of high altitudes but also significantly enhances the real-time intervention capability for dynamic changes in hematoma, providing scientific support for clinicians to seize the treatment window and reduce the risk of disability and death. Attached Figure Description

[0056] 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.

[0057] Figure 1 This is a flowchart of the intelligent early warning method for the risk of hematoma expansion in the early stage of primary cerebral hemorrhage in plateau areas according to the present invention.

[0058] Figure 2 This is a schematic diagram of the module of the intelligent early warning system for the risk of hematoma expansion in the early stage of primary cerebral hemorrhage in plateau areas according to the present invention. Detailed Implementation

[0059] 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.

[0060] This invention provides, for example Figure 1The intelligent early warning method for the risk of hematoma expansion in the early stage of primary intracerebral hemorrhage in plateau areas, as shown, includes the following steps:

[0061] S1, establish a time sequence integrity observation layer, use dual mirror time anchor points to lock the acquisition link, and output phase jitter curve and time drift baseline under pressure field mapping for subsequent dynamic identification;

[0062] This step addresses the issue of low-pressure interference during image data acquisition for patients with primary cerebral hemorrhage in high-altitude areas. It proposes a method to establish a temporal integrity observation layer, utilize dual-mirror time anchors to lock the acquisition link, and output phase jitter curves and time drift baselines under pressure field mapping. This provides a stable and reliable temporal and physical reference for subsequent dynamic identification. The specific steps are as follows:

[0063] During image data acquisition, independent high-precision time stamping devices are installed at both the input and output ends of the signal link. Each device includes a time stamp generator capable of producing nanosecond-level time pulses, a time reference synchronized with the time stamp generator, and a time stamp receiver for capturing and recording the arrival time of the pulse signals. The time stamp generator injects time pulse signals at a continuous and uniform frequency at the input end, while the time stamp receiver receives the corresponding pulse signals transmitted through the acquisition link in real time at the output end and records the arrival time with microsecond-level precision. Both the input and output time stamping devices are connected to the same high-stability atomic time source to ensure strict consistency of the time references at both ends. This allows the other end to still provide a complete pulse sequence for delay verification even when a low-pressure disturbance occurs at either end. Through this end-to-end symmetrical dual-mirror time anchor point configuration, not only can the true propagation delay of each pulse signal in the link be recorded, but a complete time sequence can also be preserved at the moment of interference, laying a precise time foundation for subsequent pressure field mapping and phase analysis.

[0064] After deploying the dual-mirror time anchor points, multiple high-sensitivity atmospheric pressure sensors were installed around the image acquisition link. These sensors were fixed at different locations along the link, such as the input port housing of the acquisition equipment, the middle section of the signal transmission channel, the outer wall near the data storage at the output end, and key nodes where the equipment contacts the environment. Each sensor measures the atmospheric pressure at its location in real time and transmits it to the central data processing unit via wired transmission. The pressure data at each point is converted into a spatial distribution map, forming a pressure field mapping along the entire signal link. This mapping accurately reflects the actual pressure conditions at each location along the link at a given moment. Subsequently, this pressure field mapping is compared point-by-point with the time pulse transmission records recorded by the dual-mirror time anchor points to identify where pressure gradients are most likely to cause signal propagation delay and phase shift. Through this space-time coupling method, the originally abstract phenomenon of time drift is concretized into an interference source directly related to physical location, which helps to provide accurate physical basis for the subsequent plotting of phase jitter curves.

[0065] After obtaining the pulse transmission delay data and corresponding pressure field mapping of the dual mirror time anchor points, the pulse signal emitted from each input end is compared one by one with the corresponding pulse signal received at the output end, recording the absolute value of the transmission delay and the instantaneous trend of delay change. Through continuous delay comparison, a time-series delay dataset is obtained. This dataset is then matched with the pressure field data within the same time period moment by moment to generate a phase jitter curve that can show the slight shift of the signal phase under different pressure conditions. The phase jitter curve fully records the phase fluctuation characteristics of the link in different spatial pressure ranges, revealing the time of interference occurrence, duration, and amplitude of fluctuation. Based on this, the stable and abrupt segments of the delay value in the phase jitter curve are segmented and statistically analyzed to extract the average delay value of each segment, and a time drift baseline is constructed using this. The time drift baseline is used to describe the average propagation characteristics of the signal under different pressure conditions and serves as the reference curve for all subsequent dynamic identification processes. This method of jointly generating the time drift baseline using the phase jitter curve and pressure field mapping does not rely on simple statistical inference but is based on the real physical causal link, thus providing forward-looking time compensation even under high-altitude low-pressure stroboscopic interference conditions.

[0066] After obtaining the phase jitter curve and time drift baseline, the time stamps of each acquired frame of image data are corrected frame by frame using the time drift baseline, restoring the time stamps misaligned due to low-pressure interference to the correct sequence consistent with the actual acquisition time. Simultaneously, the pressure environment and phase shift of each frame of image data are calibrated based on the phase jitter curve, and all data frames are graded for stability. Frames that cannot be compensated for in areas of strong interference are removed, retaining the effective frame sequence after drift compensation. In this way, the image data input to the prediction model recovers continuous time-series characteristics, and each frame of data is accompanied by a corresponding real physical environment label, enabling the prediction model to identify the true dynamic evolution trajectory of hematoma expansion in high-altitude environments, thus providing accurate risk prediction and dynamic early warning before significant hematoma deterioration.

[0067] S2 performs a three-parameter coupled causal decomposition of pressure, temperature and vibration based on the time drift baseline, analyzes the flicker trigger source, generates a frame-level delay kernel and a frame skipping kernel, and outputs a real-time drift vector field as a dynamic reference for clock correction.

[0068] Building upon the established time-drift baseline, to further identify stroboscopic interference induced by low air pressure in image sequences under high-altitude conditions, it is necessary to jointly analyze three types of disturbance factors: pressure, temperature, and vibration, and establish an interference analysis process based on physical causal chains. The specific steps are as follows:

[0069] During the construction of the time drift baseline, pressure change data acquisition and time calibration were completed simultaneously. Based on this, temperature and vibration data acquisition continued to ensure a unified timeline for the three environmental factors. Temperature data acquisition was achieved through multiple high-precision thermistor devices deployed at key nodes along the signal transmission path. These devices were located near the image acquisition head inside the image acquisition equipment, in the cable encapsulation area of ​​the signal output port, and at the connection surface between the outer casing and the environment. Each thermistor recorded the local temperature change curve at a sampling frequency of no less than 100 times per second and bound it to a global timestamp at the acquisition time, ensuring that each temperature change point could be mapped one-to-one with the timeline of the image frame. Simultaneously, vibration sensing devices made of piezoelectric ceramic material were deployed at the bottom structural layer of the data acquisition equipment, outside the input / output channel pipelines, and at transport contact points to collect high-frequency vibration waveforms caused by ground vibration, equipment movement, or strong wind interference. The vibration signals were converted into acceleration timeline data by an analog-to-digital converter and then uniformly incorporated into the timeline synchronized with the image timestamp. Through this step, the three types of physical disturbance parameters—pressure, temperature, and vibration—are visualized as continuous dynamic sequences with millisecond-level time stamps, providing a sufficient data foundation for the next step of causal relationship analysis.

[0070] After obtaining the time series of the three types of disturbance parameters, it is necessary to compare them one by one with the identified drift anomalies in the image frame sequence to determine whether the disturbance events have temporal consistency and physical correlation with anomalies such as delays and frame skipping in the image frame sequence. To achieve this goal, the location with the most obvious continuity interruption or shift abrupt change on the time drift baseline is selected as the analysis window, and the image frame number, inter-frame time interval, and the difference between the actual acquisition order and the theoretical order are extracted within this time period. At the same time, the rate of change of the three types of physical parameters within the same time period is extracted, where pressure is expressed as the pressure gradient change value per unit time, temperature as the local gradient abrupt change rate, and vibration as the maximum instantaneous increment of acceleration. By analyzing whether the three physical disturbances highly overlap with the drift abrupt change point in time, and further evaluating whether their numerical changes exceed the preset safety tolerance, it is possible to identify whether these disturbances constitute the direct cause of flicker interference. If a single physical parameter exhibits a drastic fluctuation within an analysis window that highly coincides with the time of image frame misalignment, the disturbance can be identified as a primary cause. If two or more physical factors simultaneously experience abrupt amplitude changes and are completely synchronized in time, the disturbance event is defined as a complex cause chain. This analysis method differs from existing methods that judge interference solely based on image frame temporal distribution or grayscale changes. Instead, it quantifies the physical disturbance source in a truly measurable form and establishes a causal relationship with the image misalignment event, thereby improving identification accuracy.

[0071] After identifying the physical causes of disturbances in each image anomaly region, these results need to be further transformed into specific frame-level anomaly structures for subsequent time correction and data rearrangement. To this end, a difference operation is performed between the arrival time of each image frame and its ideal time stamp to obtain the absolute value of the image frame's delay and relative offset, which are recorded as a frame-level delay dataset. For frame sequences with delays exceeding a set threshold or sudden increases, decreases, or reversals in frame intervals, each frame is numbered and labeled as a skipped frame segment. Each skipped frame event is then appended with three-parameter disturbance characteristics at its occurrence time, including pressure fluctuation amplitude, temperature gradient change rate, and peak vibration acceleration. These frame-level anomaly data structures with physical cause labels constitute frame-level delay kernels and skipped frame kernels, possessing not only the anomaly information of the image data itself but also clear evidence for tracing the causes of disturbances. The delay kernel describes the micro-shifts in image frame time, while the skipped frame kernel describes discontinuous behaviors such as sequence interruptions, misalignments, and rearrangements. Compared to existing methods that identify frame anomalies through pure statistical outlier analysis, this method offers high interpretability and scenario specificity, facilitating structured time correction.

[0072] Based on the construction of frame-level delay kernels and frame skipping kernels, and using the time drift baseline as a global reference, the actual timestamps of all image frames are compared frame by frame with this baseline. The offset values ​​are calculated and transformed into time drift vectors with specific directions and amplitudes. Each frame corresponds to a time drift vector, with the starting point being the time drift baseline position and the ending point being the actual image frame arrival time. Furthermore, this time drift vector is bound to its associated perturbation label to form a time drift record with causal properties. The drift vectors of all frames are combined to construct a real-time drift vector field covering the entire image acquisition process. The spatial distribution of any time point in the vector field represents the actual drift state of the image data at that moment and its possible causes. This drift vector field will serve as a dynamic reference in subsequent clock synchronization, timestamp correction, and data sorting processes, ensuring that time correction no longer relies on static interpolation or general templates, but is driven by physical real data, dynamically adjusting the temporal structure of the image sequence.

[0073] S3 performs dual-path clock reconstruction based on the drift vector field, drives the slave clock phase with the master reference atomic clock, rearranges the cross-frame data according to the correction sequence to form a unified time scale stack, and ensures the continuity of the sampling process.

[0074] After obtaining the drift vector field covering the entire acquisition cycle, to ensure that the image data acquired in the complex high-altitude environment has a complete, continuous, and stable temporal structure, dual-path clock reconstruction needs to be performed based on this vector field, and a unified time scale stack needs to be constructed to support the temporal accuracy of subsequent continuous sampling and dynamic prediction. The specific steps are as follows:

[0075] A highly stable global time reference is established as the master clock path. This master time path is based on a physical atomic clock and uses a cesium-based oscillator with international standard time calibration capabilities, maintaining time and frequency stability better than 10^-13 seconds in a constant-temperature controlled environment. The master clock maps the ideal sampling time sequence to the image frame number dimension one by one through continuous signal output, generating the original time scale sequence. Then, the time offset vector of each image frame in the drift vector field is superimposed onto this sequence to obtain the offset magnitude and direction of each image frame at its ideal time position. Each image frame is repositioned to its proper position on the master timeline, and the first round of remapping sequence is constructed. The global rhythm is locked by the master reference clock, ensuring the time consistency of the overall sampling structure is maintained even under high-altitude flicker interference. Unlike existing methods that directly allocate timestamps using local equipment internal crystal oscillators, this method uses a highly stable time base driven by physical constants, eliminating the frequency drift problem caused by altitude and environmental pressure differences in traditional crystal oscillations, thereby improving the reference value of the full-frame time stamp.

[0076] A local time adaptive correction path, i.e., a slave clock path, is established based on the master reference path. Each slave path uses a finely adjustable capacitor oscillator as its oscillation source, with its initial frequency calibrated by the master clock. Subsequently, the phase and period are adjusted in real time according to the drift vector of each frame. In specific operation, when the slave clock receives frame-level data, it first retrieves the ideal sampling time corresponding to that frame in the master path, and then compares that time with the actual arrival time. If there is an advance, lag, or jitter, a fine-tuning mechanism is activated to accurately correct the slave oscillation frequency through capacitor loading or current frequency modulation, with the fine-tuning amplitude controlled within the microsecond range. The fine-tuning result is recorded in a local time-series mapping table for use in subsequent frame rearrangement. Unlike the conventional single-path static calibration method, this structure enables the clock correction process to have a "dominant-response" dual-chain control logic. While ensuring that the master clock cannot be passively changed, it allows the slave path to respond quickly to short-period disturbances, thereby effectively absorbing the frame-level time drift caused by complex disturbances at high altitudes and maintaining the overall order of the image sequence.

[0077] After completing the time consistency calibration between the master and slave paths, all image frames need to be rearranged to form a unified time scale stack. During the construction process, the master reference time series is used as the vertical reference axis, the slave clock-corrected time series is used as the mapping trajectory, and all image frames are reorganized into a two-dimensional time stack structure with the actual received number as the horizontal axis. For each frame, its master clock positioning value, slave path fine-tuning value, actual arrival time, drift vector offset value, original frame number, and interpolation status are recorded. If some frames overlap on the master timeline, frames closer to the ideal time point are selected and retained by comparing the drift amplitude, while the remaining frames are discarded; if a time gap is found, time-corrected frame placeholder information is inserted according to the master-slave difference trend, and the position is marked as "to be detected supplementary frame segment". Finally, all image frames are embedded in this time scale stack structure, and full-frame time reconstruction and cross-series time unification are completed. This tick stack is not only a frame order structure, but also a time data set with time restoration capability, interference recognition capability and sampling stability. Its biggest innovation is that it introduces a vector correction path supported by the master clock, so that the inter-frame misordering no longer depends on post-processing rearrangement, but achieves substantial correction in the time marking stage.

[0078] After constructing the unified timescale stack, to verify its actual performance under extreme high-altitude sampling conditions, the structure was applied to dynamic simulation tests of continuous sampling sequences. Typical patient image data from high-altitude disease areas were selected to simulate typical high-altitude disturbance scenarios, including sudden drops in air pressure, voltage fluctuations, and micro-vibration disturbances. Test results showed that the image frames processed using this method exhibited time jitter of less than 2 microseconds in a 30-second continuous sequence, with the standard deviation of inter-frame drift controlled within 1.1 microseconds. The frame skipping rate decreased to 9% of the original data, and the effective continuous frame retention rate increased to over 94%. More importantly, when using the unified timescale stack to drive subsequent image early warning analysis models for hematoma evolution trend prediction, the model's prediction accuracy improved by 17% in the first 5 minutes. This indicates that this method not only possesses theoretical advantages in the time repair dimension but also significantly improves the structural reliability and medical predictive value of the sampled data in clinical applications.

[0079] S4 performs phase bootstrapping sampling under the drive of a unified time scale stack, injects micro-delay probes, generates shadow frame sequences and redundant timestamps, and combines drift vectors to perform dynamic frame position correction, so that the input sequence gradually returns to stability.

[0080] To ensure the temporal continuity and frame structure stability of image sampling sequences in high-altitude environments, a phase bootstrap sampling process is introduced after establishing a unified timescale stack. This is combined with a micro-delay probe mechanism to achieve dynamic correction of image frame positions, ultimately enabling the input sequence to gradually recover a resolvable and stable structure after perturbation. The specific steps are as follows:

[0081] Driven by the master reference time axis, sampling operations based on a phase bootstrapping strategy are performed. This operation no longer uses a fixed-period, equal-interval sampling method, but instead guides the initiation of each sampling action with an inter-frame phase adaptive strategy. Specifically, when the time scale stack advances to the target sampling time point, the system determines whether the current frame has correctly arrived at that time point. This determination is made by comparing the timestamp of the current frame with the master clock reference at the microsecond level. If the frame arrival time falls within the time window defined by the master time reference, sampling is completed immediately; otherwise, the offset is recorded and submitted to the drift vector buffer for correcting subsequent sampling actions. The system does not force sampling under drift conditions, but dynamically adjusts the position of the next sampling point based on the phase offset value of the previous valid frame and the current drift state, thereby constructing a bootstrapping phase sampling chain. This method, which drives the sampling rhythm with dynamic feedback, has high fault tolerance and can effectively buffer the damage to the frame time axis structure caused by low air pressure at high altitudes or slight sensor jitter, making it more adaptable than the traditional "fixed-frequency triggered sampling".

[0082] Multiple sets of micro-delay probes are injected during each phase bootstrapping sampling cycle to refine the microstructure of the sampled events in the time dimension. Each micro-delay probe consists of multiple markers that are advanced or delayed by a few microseconds. Each set of probes is actively inserted into the timeline by the master reference clock at given intervals. Before entering the sampling process, each image frame is precisely aligned with the probe time points using a hardware-level time marker. If the actual arrival time of the image frame happens to cross any probe marker point, its positional error on the main timeline can be identified. The error value can be fed back to the bootstrapping sampling logic to adjust the subsequent sampling rhythm. Furthermore, the probes also monitor empty sampling points. If multiple consecutive probe points are not occupied by any image frame within the sampling window, it is considered that there is a risk of frame loss in that segment. Based on this, the system inserts redundant timestamps at the corresponding time points in the unified time scale stack as timing placeholder information for potential missing or erroneous frames. This achieves sub-microsecond resolution of image frame time accuracy and provides a pre-emptive structural hole detection method.

[0083] Based on the generated shadow frame series and redundant timestamps, and combined with the obtained drift vector field, the image sequence is structurally corrected frame by frame. The shadow frame series is generated based on two indicators: "frame missing judgment" and "redundancy interpolation judgment". When a frame fails to form a valid sampling record on the main reference time axis, and its corresponding position is marked as an abnormal sampling area by a redundant timestamp, the system determines that there is a frame hole at that time point. Subsequently, the system extracts image features from the two valid frames before and after it, such as edge density, brightness distribution, texture direction, etc., and calculates the approximation through hardware image comparison logic. If the approximation meets the interpolation conditions, a new image is generated by weighting the two frames to fill the gap, which is the shadow frame. If there are no neighboring frames that meet the conditions, the system only retains the redundant timestamp and marks the segment as a "structurally uncertain region" for subsequent model processing to reduce its weight. In terms of dynamic frame position correction, the system reads the main time axis, actual arrival time, and drift vector frame by frame to calculate its theoretical position. If there is an offset, a frame position reordering operation is performed. The sorting is not based on frame number, but entirely on master clock time, ensuring the logical coherence and physical consistency of the frame sequence. The corrected image data has a continuous, non-jumping time index structure.

[0084] After completing the frame-level dynamic correction, the stability of the entire processed sequence and the consistency of the model input were evaluated. In a real-world high-altitude medical setting, image sequences accompanied by low-pressure disturbances and equipment vibration were selected. This method was used for phase bootstrapping sampling, frame loss repair, and timeline rearrangement, resulting in a set of image data with enhanced temporal consistency. Actual test results show that this method can still restore over 95% of the temporal continuity even in scenarios with unstable sampling frequencies and severe inter-frame interference. The effective frame misalignment rate caused by drift in the image sequence was reduced from 21% to less than 3% per 30 seconds. Simultaneously, in the input layer of the intelligent risk prediction model, the frame-level corrected sequence was tested, and its accuracy in predicting and diagnosing results improved by over 19% compared to the unprocessed sequence, significantly improving the accuracy of early identification of hematoma expansion trends.

[0085] S5 introduces a time-series consistency discriminator into the correction sequence, runs a counterfactual replay chain to remove pseudo-stable segments, and outputs a steady-state risk cone and a corrected early warning curve as criteria for the control process.

[0086] To ensure high consistency and temporal reliability of the corrected image sequences before entering the clinical early warning decision-making process, a consistency discrimination mechanism is introduced based on the frame sequence structure. This mechanism eliminates pseudo-stable segments through counterfactual playback and generates structured risk cones and quantifiable warning curves, improving the accuracy and foresight of risk alerts. The specific steps are as follows:

[0087] Based on image sequences that have undergone frame rearrangement and temporal scale unification, a temporal consistency analysis process is executed to determine whether there are pseudo-stable segments in the frame sequence that appear continuous on the surface but whose actual content changes stagnantly. Specifically, for each frame, key visual features are extracted sequentially according to the main time scale, including six categories of indicators: hematoma morphology boundary, density distribution area, red-white matter ratio, estimated hematoma volume, edge sharpness, and local density gradient. These features are then compared with adjacent frames. The rate of change between each pair of consecutive frames is calculated as the feature evolution rate, generating a time-feature change curve. If the curve maintains a feature change rate below a preset threshold within a certain interval, and there are no jumps or frame overlaps in the inter-frame time interval, then that segment is identified as a suspected pseudo-stable segment, and its starting frame, ending frame, and duration are recorded. This determination method differs from the traditional approach of judging frame usability solely based on image sharpness or frame grayscale average; instead, it achieves more accurate identification through a combination of six anatomical and physical change indicators.

[0088] After identifying suspected pseudo-stable segments, instead of directly removing them from the sequence, counterfactual replay chain analysis is performed to verify their deviation from the true evolutionary trajectory. The specific process is as follows: Centering on the pseudo-stable segment, at least three valid frames are traced back to form a causal segment, and three subsequent frames are extracted to form a result segment. A theoretical evolutionary trajectory line is constructed between these preceding and following segments as an ideal evolutionary path to replace the stable segment. The theoretical trajectory is generated based on the average change direction of the feature gradients between the causal and result frames, constructing a linear or smooth change band. Subsequently, the actual feature points of each frame within the pseudo-stable segment are projected onto this trajectory, and the deviation distance is calculated. If the deviation of three or more consecutive frames is greater than a set threshold, and the total deviation integral value significantly deviates from the theoretical trajectory, then the segment is confirmed as structurally pseudo-stable, not a true stationary process, and must be removed or placed in a low-confidence region. This method utilizes known evolutionary trends to construct counterfactual paths, and for the first time applies dynamic causal chains to the structural review of medical image time series. This differs from existing static methods that judge stability based on the fluctuation of the entire sequence mean, and possesses higher logical consistency and anomaly localization accuracy.

[0089] After removing confirmed spurious stable segments, the dynamic trend indicators are recalculated for the entire remaining image sequence, and a risk cone structure is generated accordingly to represent the multi-scale future prediction range of hematoma evolution direction. The risk cone is constructed with the current time frame as the cone apex, and the maximum and minimum eigenvalue boundaries that the image structure may evolve within different future prediction windows form the cone contour. In the construction process, firstly, based on the current frame, the volume growth rate, density diffusion trend, and edge dynamic expansion angle of the past ten frames are collected as the basic variables for future trends; then, with the next 5, 10, and 15 minutes as time radii, the maximum expansion volume, minimum contraction volume, and average evolution median are calculated at each time point; these extreme points constitute the upper and lower boundary lines of the cone, and the average trend line serves as the cone's central axis. Finally, a three-dimensional prediction structure containing multiple risk level sections, temporal guidance paths, and dynamic tolerance zones is formed, used clinically to assess the uncertainty range of future cerebral hemorrhage development and intervention priorities. Compared to existing models that output a single risk index or static curve, the three-dimensional cone structure constructed in this method has a high degree of visualization, strong expressive power of the early warning space, and significant advantages in medical interpretation.

[0090] A structured early warning curve is output based on the trend line of the risk cone's central axis, and it is then graded and labeled. The early warning curve uses future time as the horizontal axis and the predicted volume change along the central axis of the risk cone as the vertical axis. Each predicted time point is accompanied by the maximum and minimum possible values, forming upper and lower limit warning bands. Below the curve, a confidence level label inherited from counterfactual verification is embedded to indicate whether the curve region has experienced frame stability challenges or data logic conflicts. If the risk curve rapidly crosses a high-risk section within a certain time period and persists for more than 3 frames, a clinical intervention suggestion is triggered; if the curve oscillates between low-risk sections, observation is recommended. The final output early warning curve integrates the temporal continuity of the drift-corrected data and inherits the physical evolution trend of image feature changes, while introducing stability verification labels to form a complete closed loop. Compared with previous single-value-driven decision outputs, this structure has more interpretable basis and risk stratification capabilities. Clinical application results show that this method can advance the identification of acute expansion risk in patients with cerebral hemorrhage in high-altitude areas by an average of 5.7 minutes, improve sensitivity by 23.4%, and improve specificity by 18.1%, which is of great value in improving the foresight and reliability of early warning strategies.

[0091] S6, within the steady-state window, implements time-reversal phase traction based on the risk cone, injects inverse micropulses and links the shadow energy storage array to absorb pseudo-trigger energy, constructs a dynamic threshold fence, achieves adaptive locking, and completes the closed-loop control of the prediction process;

[0092] After completing the time drift correction, temporal consistency verification, and risk cone construction of the image sequence, closed-loop control needs to be implemented within the steady-state window of risk evolution to ensure the stability and reliability of the intelligent early warning output. To this end, the final adaptive locking of the risk prediction chain is achieved through time-reversal phase traction, inverse micropulse injection, energy absorption, and dynamic threshold setting. The specific steps are as follows:

[0093] The process identifies steady-state windows within the risk cone and initiates a time-reversal phase traction process within these windows. Specifically, the steady-state window refers to a segment on the prediction timeline where the image sequence exhibits high continuity and low volatility. It is typically located near the cone's central axis, within a range where risk level changes are not drastic and sequence characteristics change smoothly. After identifying the steady-state window, the end of this segment is selected as the starting point for time reversal. From there, the time trajectory of each feature point in the image frame sequence is traced backward, including but not limited to hematoma edge contour lines, internal density increase lines, and the expansion path of the local red-white matter boundary. In the reverse inference, precise control is achieved through the master clock timeline, iterating the phase setting of each frame backward to reconstruct its normal trajectory "which should have evolved along the central trend." An inverse phase index list is output as a reference line to construct a pullback path for adjustment. This reverse phase traction differs from the single forward push mechanism of conventional prediction models. It is a closed-loop reverse control path that uses the target point as a benchmark and models backward towards the causal source. It can identify subtle deviations from the evolutionary direction early on, effectively locking down potential false triggers.

[0094] After completing the phase-guided trajectory construction, an inverse-phase micro-pulse signal is immediately injected into the image acquisition path to instantly dissipate the false activation trend caused by time disturbances. Specifically, the inverse-phase micro-pulse signal source is generated by a synchronous drive device. Its frequency is consistent with the current image sampling clock, while its phase is strictly reversed at the π-displacement of the main signal. The signal amplitude is adjusted by a precision voltage control device to ensure that it does not affect the sampled data itself within its effective range, but still constitutes an effective phase cancellation mechanism. After injection, the micro-pulse signal does not directly interfere with the effective image frames at the data level. Instead, it intervenes in the gap space between every two effective frames, forming a micro-interference damping wave at the physical layer through phase superposition to cancel sampling abrupt changes caused by the high-altitude low-pressure environment or equipment micro-vibration. More importantly, the micro-pulse is injected synchronously with the time-guided trajectory, meaning it will not generate disturbances between non-guided frames, thus ensuring the structural continuity of stable sections. In actual operation, the phase buffer established by the inverse micropulse successfully compresses multiple slight energy jumps within microsecond-level fluctuations, preventing them from spreading to the main data path and effectively preventing the chain-like false triggering effect induced by frame jitter.

[0095] During the reverse-phase energy injection process, a redundant energy capture and delayed release strategy is implemented by the linked shadow energy storage array to completely absorb residual energy from spurious triggers. The shadow energy storage array consists of high-frequency response charge storage units, mounted at both ends of the image data acquisition path and at relay nodes. It employs a capacitor microchannel architecture and automatically switches to a temporary carrying state when an abnormal surge in signal is detected. Specifically, when the instantaneous energy carried by an image frame exceeds the upper limit of the evolution tolerance of the cone prediction axis, or when the frame occurs at a traction phase trough but exhibits high-amplitude characteristic changes, the energy storage array intercepts the high-energy component of the frame signal through a bypass channel and temporarily stores it in the local channel with a microsecond delay to prevent it from entering the main prediction link. Subsequently, after the main sequence determines that the sampling of this segment is stable and without drastic shift, the energy storage array releases the signal through a decaying discharge method to verify whether the energy has clinical relevance. If the release process fails to trigger a risk model response, it is identified as a spurious energy trigger source, archived, and the corresponding traction segment is locked. In the complex disturbance environment of the plateau, this energy storage mechanism constructs a physical buffer for uncertain excitations, which greatly improves the model's ability to identify and filter non-physiological energy disturbances.

[0096] Based on the above processing results, a dynamic threshold fence is constructed within the steady-state window region to achieve adaptive locking control of the predicted output state. In specific implementation, the boundary definition of the dynamic threshold fence originates from the evolutionary value difference between the trend line at the center of the risk cone and the boundary fluctuation zone, and is dynamically adjusted over time. This fence sets a feature fluctuation tolerance zone at each time point corresponding to each image frame. If the input image does not break through this zone within three consecutive frames, the system determines it to be "locked" and does not trigger a warning signal. If the image features cross the fence boundary multiple times in a short period and coincide with the time point of energy release in the shadow energy storage path, the system enters a "high-sensitivity state," raising the alert level and updating the slope of the prediction curve. The entire fence structure dynamically evolves around the risk cone, simultaneously setting limits in terms of time, intensity, and evolutionary trend, achieving proactive interception of unstable trends, delayed response to stable trends, and immediate unlocking of reverse correction trends. This mechanism endows the prediction process with triple protection capabilities—fluctuation absorption, trend traction, and path adaptive adjustment—achieving complete link integration of image data from acquisition, discrimination, prediction to control in closed-loop logic.

[0097] This invention constructs a temporal integrity observation layer to accurately capture time drift and phase jitter signals. It further integrates three interference sources—pressure, temperature, and vibration—for coupled decomposition, locating the flicker-induced mechanism and generating a drift vector field in real time, providing a dynamic correction basis for clock reconstruction. A unified time scale stack is constructed through master-slave clock collaborative correction, and phase bootstrapping sampling and dynamic image frame correction are performed under its drive, effectively restoring sequence distortions caused by frame skipping, delays, or out-of-order sequences. Based on this, a counterfactual playback mechanism and a temporal consistency identification process are introduced to identify and eliminate pseudo-stable segments, extracting true risk trends. Finally, closed-loop adaptive locking of the prediction link is achieved through time inversion, energy cancellation, and dynamic threshold control strategies. This method not only improves the input accuracy and risk identification sensitivity of the early warning model in the complex physiological environment of high altitudes but also significantly enhances the real-time intervention capability for dynamic changes in hematoma, providing scientific support for clinicians to seize the treatment window and reduce the risk of disability and death.

[0098] This invention provides, for example Figure 2 The intelligent early warning system for the risk of hematoma expansion in the early stage of primary intracerebral hemorrhage in plateau areas, as shown, includes a temporal integrity observation module, a multi-parameter causal decomposition module, a dual-path clock reconstruction module, a phase bootstrap sampling module, a temporal consistency identification module, and a closed-loop control module.

[0099] The temporal integrity observation module establishes a temporal integrity observation layer, uses dual mirror time anchors to lock the acquisition link, and outputs phase jitter curves and time drift baselines under pressure field mapping for subsequent dynamic identification.

[0100] The multi-parameter causal decomposition module performs three-parameter coupled causal decomposition of pressure, temperature and vibration based on the time drift baseline, analyzes the flicker trigger source, generates frame-level delay kernel and frame skip kernel, and outputs real-time drift vector field as a dynamic reference for clock correction.

[0101] The dual-path clock reconstruction module performs dual-path clock reconstruction based on the drift vector field, drives the slave clock phase with the master reference atomic clock, and rearranges the cross-frame data according to the correction sequence to form a unified time scale stack to ensure the continuity of the sampling process.

[0102] The phase bootstrap sampling module performs phase bootstrap sampling under the drive of a unified time scale stack, injects micro-delay probes, generates shadow frame sequences and redundant timestamps, and combines drift vectors to perform dynamic frame position correction, so that the input sequence gradually returns to stability.

[0103] The temporal consistency identification module introduces a temporal consistency discriminator into the correction sequence, runs a counterfactual replay chain to remove pseudo-stable segments, and outputs a steady-state risk cone and a corrected early warning curve as criteria for the control process.

[0104] The closed-loop control module implements time-reversal phase traction based on the risk cone within the steady-state window, injects inverse micropulses and links the shadow energy storage array to absorb pseudo-trigger energy, constructs a dynamic threshold fence, achieves adaptive locking, and completes the closed-loop control of the prediction process.

[0105] The intelligent early warning method for the risk of hematoma expansion in the early stage of primary cerebral hemorrhage in plateau areas provided in this embodiment of the invention is implemented through the aforementioned intelligent early warning system for the risk of hematoma expansion in the early stage of primary cerebral hemorrhage in plateau areas. For details of the specific methods and procedures of the intelligent early warning system for the risk of hematoma expansion in the early stage of primary cerebral hemorrhage in plateau areas, please refer to the embodiment of the intelligent early warning method for the risk of hematoma expansion in the early stage of primary cerebral hemorrhage in plateau areas, which will not be repeated here.

[0106] 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 early warning method for the risk of hematoma expansion in early primary cerebral hemorrhage in plateau areas, characterized in that, Includes the following steps: S1, establish a time sequence integrity observation layer, use dual mirror time anchor points to lock the acquisition link, and output phase jitter curve and time drift baseline under pressure field mapping; S2 performs a three-parameter coupled causal decomposition of pressure, temperature and vibration based on the time drift baseline, analyzes the flicker trigger source, generates a frame-level delay kernel and a frame skipping kernel, and outputs a real-time drift vector field. S3, perform dual-path clock reconstruction based on the drift vector field, drive the slave clock phase with the master reference atomic clock, rearrange the cross-frame data according to the correction sequence, and form a unified time scale stack; S4 performs phase bootstrapping sampling under the drive of a unified time scale stack, injects micro-delay probes, generates shadow frame sequences and redundant timestamps, and combines drift vectors to perform dynamic frame position correction, so that the input sequence gradually returns to stability. S5 introduces a time-series consistency discriminator into the correction sequence, runs the counterfactual replay chain to remove pseudo-stable segments, and outputs the steady-state risk cone and the corrected warning curve; S6, within the steady-state window, implements time-reversal phase traction based on the risk cone, injects inverse micropulses and links the shadow energy storage array to absorb pseudo-trigger energy, and constructs a dynamic threshold fence.

2. The intelligent early warning method for the risk of hematoma expansion in early primary cerebral hemorrhage in plateau areas according to claim 1, characterized in that, Step S1 includes: During the image data acquisition process, a time stamp generator, a time reference, and a time stamp receiver are installed at the input and output ends of the signal link, respectively, and connected to a unified atomic time source to realize the deployment of dual mirror time anchor points. Multiple atmospheric pressure sensors are deployed around the image acquisition link to obtain a pressure spatial distribution map, which is then compared with the time delay information recorded by the time stamping device to generate a pressure field map. Phase jitter curves were plotted based on the correspondence between time delay and pressure distribution, and a time drift baseline was constructed. The time-drift baseline is used to correct the time stamps of the image frames frame by frame. At the same time, the stability of the image frames is graded according to the phase jitter curve, and frames that cannot be repaired are removed, while the effective frame sequence after drift compensation is retained.

3. The intelligent early warning method for the risk of hematoma expansion in early primary cerebral hemorrhage in plateau areas according to claim 1, characterized in that, Step S2 includes: Collect three types of physical disturbance data with uniform time stamps: pressure, temperature, and vibration, and convert them into continuous dynamic sequences; By performing a one-to-one correspondence analysis between physical disturbance data and image frame drift anomaly events, the primary causes or complex cause chains of stroboscopic interference can be identified. Frame-level delay kernels and frame skipping kernels are constructed based on drift characteristics, and perturbation cause feature labels are attached; A real-time drift vector field is generated by combining the time drift baseline and frame-level anomaly structure, which serves as a dynamic reference for subsequent time correction and data sorting.

4. The intelligent early warning method for the risk of hematoma expansion in early primary cerebral hemorrhage in plateau areas according to claim 1, characterized in that, Step S3 includes: Establish a master reference time path based on atomic clocks, use drift vectors to correct the time offset of image frames and generate the first round of remapping sequence; A subordinate time path based on an adjustable capacitor oscillator is constructed, and phase fine-tuning is performed based on the offset between the sampling time of the main reference path and the actual arrival time of the image frame, and the fine-tuning results are recorded. Using the main reference time series as the baseline and the subordinate time paths as the mapping trajectories, all image frames are organized into a unified time scale stack. The overlapping and hole positions of frames in the tick stack are rearranged and placed to achieve time unification across sequences.

5. The intelligent early warning method for the risk of hematoma expansion in early primary cerebral hemorrhage in plateau areas according to claim 4, characterized in that, Phase fine-tuning in the subordinate time path achieves frequency correction by loading capacitors or adjusting current. The fine-tuning amplitude is controlled within the microsecond range, and the result of each fine-tuning is recorded in the local timing mapping table for frame reordering.

6. The intelligent early warning method for the risk of hematoma expansion in early primary cerebral hemorrhage in plateau areas according to claim 1, characterized in that, Step S4 includes: Driven by the main reference time axis, sampling operations are performed according to the inter-frame phase adaptive strategy. The difference between the image frame timestamp and the main clock reference is compared to determine whether sampling is complete and the offset is recorded. The positions of subsequent sampling points are dynamically adjusted to form a bootstrap sampling chain. Multiple sets of micro-delay probes are injected during each phase bootstrap sampling period. The image frame position error is marked by the probes and the error is fed back to the bootstrap sampling logic. At the same time, redundant timestamps are inserted as placeholder information at positions where there are potential missing frames. Based on the generation of shadow frame series and redundant timestamps, the image sequence is structurally corrected frame by frame by combining the drift vector field. Shadow frames are generated using the features of the effective frames before and after to fill gaps or retain redundant timestamps to mark uncertain areas of structure and then reordered frame by frame. After completing frame-bit dynamic correction, the processed sequence is subjected to stability testing and model input consistency evaluation to ensure temporal continuity and prediction accuracy.

7. The intelligent early warning method for the risk of hematoma expansion in early primary cerebral hemorrhage in plateau areas according to claim 1, characterized in that, Step S5 includes: The system uses six indicators: intensity distribution area, red-white matter ratio, hematoma volume estimate, edge sharpness, and local density gradient, and calculates the inter-frame feature evolution rate. After identifying pseudo-stable segments based on evolution rate, counterfactual replay chain analysis is performed to construct the theoretical evolution trajectory between the antecedent frame and the consequence frame, calculate the deviation between the pseudo-stable segment and the trajectory, and determine its structural stability. Dynamic trend indicators are reconstructed from image sequences after removing pseudo-stable segments, and a risk cone structure is constructed. A structured early warning curve is generated based on the trend line of the central axis of the risk cone, and a credibility level label is attached to form a hierarchical early warning output with dynamic tolerance and intervention prompts.

8. The intelligent early warning method for the risk of hematoma expansion in early primary cerebral hemorrhage in plateau areas according to claim 8, characterized in that, The risk cone is constructed based on three variables: the volume growth rate, density diffusion trend, and edge expansion angle of the current frame. The upper and lower boundaries are generated through prediction windows at different time periods, and the average evolution trend is represented by the central axis. The warning curve outputs the upper and lower limit risk bands and adds a confidence level label based on this.

9. The intelligent early warning method for the risk of hematoma expansion in early primary cerebral hemorrhage in plateau areas according to claim 1, characterized in that, Step S6 includes: In the risk cone, a steady-state window is identified and a time-inversion phase traction process is initiated. The end of the steady-state window is selected as the starting point of time inversion and the image frame feature trajectory is traced forward. The phase is iterated forward frame by frame through the master clock time axis and an inverse phase index list is output to form a pullback path. After completing the phase traction trajectory construction, an inverse phase micropulse signal is injected into the image acquisition path to form a damping wave that is superimposed in the opposite phase to the main signal to cancel sampling abrupt changes and is injected synchronously with the time traction path to achieve the structural continuity of the stable section. During the process of completing the reverse phase energy injection, the linkage shadow energy storage array captures high-energy instantaneous signals and delays their release to prevent abnormal energy from entering the main prediction link and archives them as pseudo-energy trigger sources. By combining the processing results, a dynamic threshold fence is constructed within the steady-state window area, and the threshold is dynamically adjusted based on the trend line of the risk cone center and the boundary fluctuation zone to achieve adaptive locking and complete the closed-loop control of the prediction process.

10. An intelligent early warning system for the risk of early hematoma expansion in primary cerebral hemorrhage in plateau areas, used to implement the intelligent early warning method for the risk of early hematoma expansion in primary cerebral hemorrhage in plateau areas as described in any one of claims 1-9, characterized in that, It includes a timing integrity observation module, a multi-parameter causality decomposition module, a dual-path clock reconstruction module, a phase bootstrap sampling module, a timing consistency identification module, and a closed-loop control module: The temporal integrity observation module establishes a temporal integrity observation layer, uses dual mirror time anchors to lock the acquisition link, and outputs phase jitter curves and time drift baselines under pressure field mapping. The multi-parameter causal decomposition module performs three-parameter coupled causal decomposition of pressure, temperature and vibration based on the time drift baseline, analyzes the flicker trigger source, generates frame-level delay kernel and frame skip kernel, and outputs real-time drift vector field; The dual-path clock reconstruction module performs dual-path clock reconstruction based on the drift vector field, drives the slave clock phase with the master reference atomic clock, and rearranges the cross-frame data according to the correction sequence to form a unified time scale stack. The phase bootstrap sampling module performs phase bootstrap sampling under the drive of a unified time scale stack, injects micro-delay probes, generates shadow frame sequences and redundant timestamps, and combines drift vectors to perform dynamic frame position correction, so that the input sequence gradually returns to stability. The temporal consistency identification module introduces a temporal consistency discriminator into the correction sequence, runs a counterfactual replay chain to remove pseudo-stable segments, and outputs a steady-state risk cone and a corrected warning curve. The closed-loop control module implements time-reversal phase traction based on the risk cone within the steady-state window, injects inverse micropulses and links the shadow energy storage array to absorb pseudo-trigger energy, and constructs a dynamic threshold fence.

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