Early screening system and method for moyamoya disease based on brain functional connectivity features

Abstract

The application discloses a system and method for early screening of moyamoya disease by fusing brain function connection characteristics, and relates to the technical field of biological information analysis, and comprises the following steps: constructing a unified time anchor record band in the whole process of collecting multi-source brain function signals for early screening of moyamoya disease, generating a time anchor record list by recording the time information of all sampling nodes, and taking the time anchor record list as a time reference for subsequent sampling error tracing. The application introduces a unified time anchor record band, a staggered trace band and a cross isolation seam to structurally constrain the time stagger and energy cross of multi-source brain function signals, reduce the interference of false peaks on brain function connection characteristics, and continuously adjust the time fusion process by constructing a non-cross alignment sequence and a dynamic rolling regulation mechanism, so that the time structure is stable, and the recognition reliability of weak brain function abnormalities in early screening of moyamoya disease is improved.

Description

Technical Field

[0001] This invention relates to the field of bioinformatics analysis technology, specifically to an early screening system and method for Moyamoya disease that integrates brain functional connectivity features. Background Technology

[0002] Early screening for Moyamoya disease by integrating brain functional connectivity features involves collecting brain functional activity data from subjects (e.g., functional magnetic resonance imaging (fMRI), electroencephalography (EEG), or near-infrared spectroscopy (fNIRS)) to model and extract features of the dynamic synergistic relationships between different brain regions. During subject identification, these brain functional connectivity features are fused and analyzed with multimodal information such as anatomical structure, hemodynamics, and clinical symptoms to identify individuals with early abnormal brain functional network patterns. The core of this method lies in utilizing the strength of inter-brain connectivity, network topology characteristics, and their temporal changes to reveal the potential brain functional imbalances and compensatory mechanisms before the onset of clinical symptoms in Moyamoya disease, achieving non-invasive, quantitative, and intelligent early screening. This approach breaks through the traditional diagnostic model that relies on angiography or symptom presentation, enabling the detection of Moyamoya disease to be moved to the microscopic stage of neurofunctional changes.

[0003] Existing technologies have the following shortcomings: In existing technologies, the fusion process for multi-source brain functional signals typically relies on a unified time reference and interpolation resampling methods to achieve time synchronization. However, due to slight deviations in sampling frequency, sampling delay, and phase response among different signal sources, energy cross-resampling easily occurs within the fusion band. This phenomenon manifests as implicit energy overlap between brain functional signals during time fusion, with fluctuation peaks from different sources being incorrectly superimposed within the same time window, forming spurious peak responses. These spurious responses possess pseudo-high consistency and high intensity characteristics numerically, making them easily misidentified by existing models as genuine brain functional coordination activities, thus solidifying into erroneous connection patterns during model training. As the model learns, this erroneous pattern gradually amplifies, causing the early screening results for Moyamoya disease to continuously deviate from the true brain functional state, ultimately leading to a decrease in the sensitivity of early screening models to focal abnormalities, resulting in serious technical consequences such as misdiagnosis and missed diagnosis. Summary of the Invention

[0004] This invention addresses the technical problems existing in the prior art by proposing an early screening system and method for Moyamoya disease that integrates brain functional connectivity features, thereby solving the problems mentioned in the background art.

[0005] The technical solution of this invention to solve the above-mentioned technical problems is as follows: an early screening method for moyamoya disease integrating brain functional connectivity features, comprising the following steps: In the entire process of acquiring multi-source brain function signals for early screening of Moyamoya disease, a unified time anchor band was constructed. A time anchor list was generated by recording the time information of all sampling nodes, and the time anchor list was used as the time reference for subsequent sampling error tracing. Based on the time anchor list, the time series of multi-source brain functional signals are compared window by window, the difference in the sampling starting point of each window is calculated, and the misalignment trace band containing time misalignment information is generated. Cross-energy dense regions are identified in the misalignment trace band to form cross-hotspot columns. Based on the cross-hotspot column, a silent mezzanine window is inserted into the time series. The peak trailing caused by time misalignment is stripped out within the silent mezzanine window to form a cross isolation seam to separate different energy segments, and the cross isolation seam is used as the boundary for time rearrangement. By rearranging the time series of multi-source brain functional signals in conjunction with cross-isolation seams, time particles are shifted sequentially along the peak order of each signal to generate a non-cross-aligned sequence with a single time path, and a dynamic regulation entry point is established in the non-cross-aligned sequence. Dynamic control of the time-nest roll disk is performed based on the non-cross-aligned sequence. The time-nest roll disk automatically rotates according to the cross energy density. By adjusting the window width and time particle step size in real time, the cross energy is kept under control and the time fusion is kept stable.

[0006] Preferably, the steps for generating the time anchor list are as follows: Before the multi-source brain function signal acquisition is started, a unified time anchor tape is established, and a time trigger signal is sent to each acquisition device through a unified time trigger control unit so that each sampling channel records the synchronous time zero point. Under the constraint of a unified time anchor band, the time information of all sampling nodes generated during the acquisition process is continuously recorded, the timestamps of each sampling node are extracted and a list of continuous sampling nodes is formed in chronological order. A time distribution map is constructed based on a list of continuous sampling nodes. The time information of each sampling node is uniformly encoded and sequentially mapped to generate a time anchor list covering the entire collection process. During signal fusion and analysis, the time anchor list is continuously called as a time reference to track the temporal position of multi-source brain functional signals, which is used to trace the source of sampling errors and maintain the uniformity and continuity of the time series.

[0007] Preferably, after generating the time anchor list, the time offset information of each sampling node is associated with the corresponding signal type, channel identifier and sampling time segment, so that the time anchor list serves as a unified time reference and continuously participates in the time position mapping of multi-source brain functional signals throughout the signal fusion process, so as to realize the continuous tracking of the source of sampling error and avoid energy superposition caused by time misalignment.

[0008] Preferably, the steps for generating the cross-hotspot column are as follows; A unified time index is performed on the time series of multi-source brain functional signals based on the time anchor list, and the acquisition period is divided into continuous comparison windows according to the time anchor list. Within each comparison window, the sampling start time information of each brain function signal is extracted, and the sampling start difference between different signals is calculated based on the time anchor list to form time difference data arranged by window. Based on the time difference data, a misalignment trace band containing time misalignment information is generated in chronological order to characterize the time drift relationship of multi-source brain functional signals during the acquisition process; In the misaligned trace zone, identify the time segments where time misaligned trajectories intersect or cluster, extract the areas of high energy density at intersection, and form a column of intersection hotspots in chronological order to identify the locations of concentrated time intersections.

[0009] Preferably, during the formation of the cross-hotspot column, the corresponding time start point, time end point, and participation signal identifier are recorded for each cross-energy-dense region, so that each hotspot in the cross-hotspot column maintains a time mapping relationship with the sampling nodes in the time anchor list, which provides a clear time positioning basis for subsequent time isolation and time rearrangement.

[0010] Preferably, the process for forming the cross-seam is as follows: The cross-hotspot columns are mapped to the time series of multi-source brain functional signals, the time segments corresponding to the cross-hotspots are marked, and the expanded hotspot time range is formed. Based on the extended hotspot time range, a silent mezzanine window is inserted into the time series to cause the signal in the corresponding time segment to enter an energy interruption state. Peak trailing images are stripped within the time range covered by the silent interlayer window, so that the position corresponding to the silent interlayer window forms a cross isolation seam in the time series to separate different energy segments; The resulting cross-separation seams are used as time boundaries to segment the time series for subsequent time rearrangement.

[0011] Preferably, after inserting the silent interlayer window and forming the cross isolation seam, the original sampling order of each time segment remains unchanged, so that the cross isolation seam continues to serve as a fixed boundary in the time series, thereby maintaining the independence and continuity of adjacent energy segments in the time fusion process.

[0012] Preferably, the steps for generating cross-aligned sequences are as follows: Using cross-separation seams as time boundaries, the time series of multi-source brain functional signals are segmented and organized to form isolated time segments while maintaining the sampling order within each segment. Based on the peak distribution of brain functional signals in each time segment, the peak order within the time segment is uniformly sorted, and time particles are shifted along the peak order to reconstruct the time arrangement. The time segments that have completed the time particle transfer are concatenated in chronological order to generate a non-intersecting aligned sequence containing only a single time path. A dynamic control entry point is set in the non-cross-aligned sequence to record the time position and time particle interval information to support the adjustment of the subsequent time structure.

[0013] Preferably, a time-nesting roll disk is introduced for dynamic control based on the non-cross-aligned sequence, causing the time-nesting roll disk to rotate according to the cross-energy density. The time series is then adjusted by modifying the time fusion window width and the time particle step size, as follows: Map the non-cross-aligned sequence to the temporal carrying space of the time nest roll disk, so that the time particles are distributed in the corresponding time layer in time order; The cross-energy density distribution information within each time layer is obtained along the time direction of the non-cross-aligned sequence, and an energy distribution band consistent with the time path is formed; The time nest roll disk is driven to rotate based on the change of cross energy density between time layers, while the width of the time fusion window and the time particle step size are adjusted. The spin shift and adjusted time structure are continuously applied to the non-cross-aligned sequence to keep the cross energy controlled and maintain the stability of the time fusion state.

[0014] An early screening system for Moyamoya disease that integrates brain functional connectivity features includes a time anchoring construction module, a time alignment and misalignment detection module, an energy isolation and peak stripping module, a time rearrangement and alignment generation module, and a dynamic rollover modulation module. The time anchor construction module constructs a unified time anchor band throughout the entire process of multi-source brain function signal acquisition for early screening of Moyamoya disease. It generates a time anchor list by recording the time information of all sampling nodes and uses the time anchor list as the time benchmark for subsequent sampling error tracing. The time alignment and misalignment detection module performs window-by-window alignment of the time series of multi-source brain functional signals based on the time anchor list, calculates the difference in the sampling start point of each window, generates misalignment trace bands containing time misalignment information, and identifies cross-energy dense regions in the misalignment trace bands to form cross-hotspot columns. The energy isolation and peak stripping module inserts a silent interlayer window into the time series based on the cross hotspot column. Within the silent interlayer window, it strips the peak trailing caused by time misalignment, forming a cross isolation seam to separate different energy segments, and uses the cross isolation seam as the boundary for time rearrangement. The time rearrangement and alignment generation module rearranges the time series of multi-source brain functional signals in conjunction with the cross-isolation seam, shifts time particles along the peak order of each signal, generates a non-cross-aligned sequence with a single time path, and establishes a dynamic regulation entry point in the non-cross-aligned sequence. The dynamic roll control module performs dynamic control operations on the time nest roll disk based on the non-cross-aligned sequence. The time nest roll disk automatically rotates according to the cross energy density. By adjusting the window width and time particle step size in real time, it keeps the cross energy continuously under control and maintains a stable state of time fusion.

[0015] The beneficial effects of this invention are: This invention, by introducing the synergistic effect of a unified temporal anchor band, misalignment trace band, and cross-isolating seam throughout the entire process of multi-source brain functional signal acquisition and fusion, continuously constrains the problems of temporal misalignment and energy crossover at the temporal structure level. Through window-by-window characterization of sampling start-point differences and precise localization of energy-dense crossover regions, this invention effectively reduces the superposition of spurious peaks caused by temporal shifts before signal fusion. This ensures that multi-source brain functional signals maintain a clear, independent, and traceable structural relationship in the temporal dimension, thereby providing a more realistic and stable temporal basis for constructing brain functional connectivity features and avoiding interference from temporal artifacts in screening results.

[0016] This invention constructs non-cross-aligned sequences and introduces a dynamic control mechanism using a time-nesting roll disk, enabling the temporal fusion process to possess continuous adaptive adjustment capabilities. By adjusting the window width and temporal particle step size in real time based on changes in cross-energy density, this invention can maintain the stability of the temporal structure during long-term fusion of multi-source brain functional signals, preventing the re-accumulation of cross-energy in local time periods, thereby ensuring the consistency and continuity of brain functional connectivity features across different time scales. This stable temporal fusion state helps improve the ability to detect weak brain functional abnormalities in the early screening of Moyamoya disease, reducing the risk of misdiagnosis and missed diagnosis. Attached Figure Description

[0017] Figure 1 This is a flowchart of the method for early screening of Moyamoya disease that integrates brain functional connectivity features, as described in this invention.

[0018] Figure 2 This is a schematic diagram of the modules of the Moyamoya disease early screening system that integrates brain functional connectivity features according to the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0021] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0022] This invention provides, for example Figure 1 The illustrated method for early screening of Moyamoya disease based on the integration of brain functional connectivity features includes the following steps: In the entire process of acquiring multi-source brain function signals for early screening of Moyamoya disease, a unified time anchor band was constructed. A time anchor list was generated by recording the time information of all sampling nodes, and the time anchor list was used as the time reference for subsequent sampling error tracing. A unified time anchor band was constructed throughout the entire process of multi-source brain function signal acquisition. A time anchor list was generated by recording the time information of all sampling nodes, and this time anchor list was used as the time reference for subsequent sampling error tracing. The specific steps are as follows:

[0023] Before initiating multi-source brain function signal acquisition for early screening of Moyamoya disease, a unified time anchor band is established. To ensure that all signal sources are sampled under the same time reference, during the acquisition preparation phase, the imaging device for acquiring functional magnetic resonance imaging (fMRI) signals, the EEG acquisition device for acquiring EEG signals, and the near-infrared spectroscopy device for acquiring cerebral blood flow or blood oxygenation change signals need to be connected to a unified time trigger control unit. This time trigger control unit is equipped with a high-precision clock source capable of providing standard time pulse signals with nanosecond-level resolution. Each acquisition device receives this standard time pulse signal through a physical synchronization cable or a wireless synchronization signal receiver, using the unified time trigger signal as the acquisition start command. When the time anchor band is triggered, all acquisition channels simultaneously record the zero point of time, forming a synchronization start marker. Each sampling channel generates sampling data according to a unified time rhythm during subsequent acquisition, and a unique time anchor number is appended to the header of each sampling data packet. In this way, the entire acquisition process forms a time anchor band that runs through the entire acquisition process in the time domain, making the time markers of multi-source signals completely aligned, providing a precise starting point for subsequent time comparison and error tracking.

[0024] After the time-anchor band is constructed, all time data generated during the acquisition process is continuously recorded and nodes are extracted. During the acquisition of multi-source brain function signals, each sampling channel continuously outputs sampling frame data at a fixed sampling frequency. Simultaneously with the output of each frame of sampling data, the data recording unit automatically captures the absolute timestamp of the sampling moment and records it in the time log file. The data recording unit stores the timestamp information sequentially into the time cache table according to the sampling order and time anchor number to ensure the consistency between the order of sampling nodes and the time anchors. When the acquisition time reaches a preset period, the system exports the time data in the cache table as a time series file. This file arranges the time information of all sampling nodes in chronological order, including the sampling start time, sampling duration, time interval between adjacent sampling nodes, signal type identifier, and channel number. Subsequently, the time series file is subjected to continuous sampling node extraction, and all sampling nodes are reordered according to chronological order to form a continuous list of sampling nodes. In this way, a complete time distribution map can be obtained, which accurately describes the temporal positional relationship, sampling interval differences, and synchronization offset characteristics of different brain function signals throughout the acquisition period, providing a data foundation for the subsequent generation of the time anchor list.

[0025] After obtaining the time distribution map of continuous sampling nodes, the time distribution map is uniformly encoded and sequentially mapped to generate a time anchor list. In this process, the starting point of the global time axis is first determined based on the time zero point in the time anchor band. Then, the absolute time value of each sampling node is read sequentially, and its offset relative to the global time zero point is calculated and recorded as the time anchor offset field. The time anchor offset field of each sampling node, along with the corresponding signal type, channel number, sampling frequency, and data segment start and end times, are written into the time anchor list. The time anchor list arranges all nodes in chronological order, forming a multi-dimensional table. Each row of the table corresponds to a specific sampling node, containing complete information such as the sampling time point, relative offset, signal type, signal number, and time interval. During the generation of the time anchor list, a loop is used to sequentially input all sampling nodes in chronological order, ensuring that each modal signal has a unified index identifier within the same time system. The generated time-anchor list covers the entire acquisition period, preserving both the complete time trajectory of the acquisition process and the temporal interval relationships between different signal channels, thus constructing a full-process temporal mapping framework for multi-source brain functional signals. This time-anchor list can be repeatedly referenced in subsequent signal fusion processes to determine the temporal correspondence between different signal segments.

[0026] After the time-anchor list is generated, it serves as the unified time reference for subsequent sampling error tracing and is used throughout the entire signal fusion and analysis process. In the early screening of Moyamoya disease, all preprocessing, time alignment, feature extraction, and multimodal fusion of brain functional signals use the time information in the time-anchor list as the time reference. When fusing multi-source signals, the system first reads the time offset field of each sampling node in the time-anchor list and determines the position of each signal segment on the global time axis based on the offset field. When time differences or sampling delays occur, the source of the deviation can be located by comparing the time offset recorded in the time-anchor list, thus distinguishing between time drift caused by device sampling delay and true time differences caused by changes in brain functional state. Throughout the screening process, the time-anchor list always serves as the core of time control, participating in the timing tracking and error tracing of each sampled signal. By maintaining consistency with the time-anchor band, it ensures that all multi-source brain functional signals remain unified and continuous in the time dimension, enabling each signal segment to accurately correspond to a unique time position and avoiding false peak superposition or functional connection misjudgment caused by time misalignment.

[0027] Based on the time anchor list, the time series of multi-source brain functional signals are compared window by window, the difference in the sampling starting point of each window is calculated, and the misalignment trace band containing time misalignment information is generated. Cross-energy dense regions are identified in the misalignment trace band to form cross-hotspot columns. Based on a time-anchored list, time series of multi-source brain functional signals are compared window by window. The difference in sampling starting point for each window is calculated to generate a misalignment trace band containing temporal misalignment information. Cross-energy-dense regions are then identified within the misalignment trace band to form a cross-hotspot column. The specific steps are as follows:

[0028] After obtaining a complete list of time-anchored data, all multi-source brain functional signals acquired during the early screening for Moyamoya disease were selected as input data. These multi-source brain functional signals included brain region oxygenation level-dependent signals acquired via functional magnetic resonance imaging (fMRI), neural electrical activity signals acquired via electroencephalography (EEG), and cerebral hemodynamic signals acquired via near-infrared spectroscopy. To ensure these signals could be compared under a unified time reference, the entire acquisition cycle was divided into several continuous and fixed-length comparison windows based on the time markers in the time-anchored data list. Each comparison window used the time number in the time-anchored data list as its starting index and covered the corresponding intervals of all signals with a fixed time span. For example, when the time nodes recorded in the time-anchored data list had a millisecond-level resolution, each comparison window could correspond to a preset sampling time period, thus accommodating sampled data from different signal sources within the same time range. During window division, it was ensured that the time span of each window was sufficient to cover the sampling nodes of all signal channels within that time period, so that the time boundaries at both ends of the window corresponded to the actual time nodes in the time-anchored data list. In this way, a set of comparison windows that are continuous in time and do not overlap with each other are formed, providing a strict time division basis for subsequent time difference calculations.

[0029] After dividing the comparison window, for each comparison window, the sampling start time information of all brain function signals within that window is extracted. Specifically, at the starting boundary of each window, the sampling start time value recorded in the time anchor list for the corresponding signal channel is read. This time value is the timestamp of the first sampling node of the sampling device in that window. Using the same comparison window as a unit, the start time values ​​of all signal channels are compared, and the sampling start difference between each channel is calculated. The sampling start difference refers to the time offset of the sampling start time of two or more signals within the same window. This offset reflects the delay or advance characteristics of different sampling devices in time response. During the calculation, the global reference time of the time anchor list is maintained as a unified reference, so that the start difference of each signal corresponds to a unified time axis. By repeating the above operation for each comparison window, a set of sampling start difference data arranged in chronological order can be obtained. These difference data reflect the time offset changes of multi-source brain function signals in different sampling intervals, and can reveal subtle timing inconsistencies in the acquisition process, such as start offsets caused by sampling delay, sampling frequency drift, or signal response lag. Through continuous calculation, a time difference sequence covering the entire collection period can be obtained, providing data support for the subsequent generation of misaligned trace bands.

[0030] After calculating the sampling start point difference, the sampling start point difference data for all time windows are arranged chronologically to generate a misalignment trace band containing time misalignment information. The misalignment trace band uses time as the horizontal axis and the sampling start point difference value as the vertical change index. By continuously recording the time offset of each time window, a set of trajectories reflecting the time drift characteristics of multi-source signals is formed. When constructing the misalignment trace band, the time difference curve of each signal channel is independently plotted within the same time coordinate frame, allowing for a visual representation of the time offset trend of different signals throughout the entire acquisition period. The relative position of each time trajectory indicates the degree of time misalignment between different signals, and the rise or fall of the trajectory reflects changes in the direction of time offset. By continuously stitching all trajectories together on the time axis, multiple time drift curves reflecting the sampling time relationship can be obtained, distinguished by color or number. In the misalignment trace band, temporal continuity is maintained, and the difference data of each time window are sequentially connected, creating a visually smooth temporal change structure for the time drift process. This misalignment trace band not only describes the temporal stability of a single signal channel but also reveals the relative temporal evolution patterns between multi-source signals. Through this trajectory processing, the misalignment of multi-source brain functional signals in the temporal dimension is accurately expressed, providing a continuous temporal reference for subsequent extraction of cross-energy-dense regions.

[0031] After obtaining the misalignment trace band containing temporal misalignment information, the temporal intersection characteristics between different signal trajectories within the misalignment trace band are identified to determine densely populated areas of intersection energy and form a cross-hotspot column. During the identification process, the relative positional relationships between multiple signal trajectories are analyzed window by window along the time axis of the misalignment trace band. When misalignment trajectories of different signals are found to intersect, overlap, or converge within a certain time interval, that interval is identified as a potential area of ​​concentrated intersection energy. Further, the segments with the densest distribution of time offsets are extracted from these regions, and the duration, number of participating signals, mean time offset, and offset range of these segments are calculated to determine the precise boundaries of the densely populated areas of intersection energy. All identified densely populated areas of intersection energy are numbered chronologically, and the time start, time end, duration, participating signal identifiers, and average time offset data for each area are summarized to form a complete cross-hotspot column. The cross-hotspot column clearly reflects the temporal intersection characteristics of multi-source brain functional signals throughout the acquisition process, with each hotspot representing a specific time period in which a concentration of temporal misalignment occurs. In this way, the periods most prone to spurious time overlaps during multi-source signal acquisition can be identified, providing precise reference locations for subsequent time correction and energy isolation. The entire cross-hotspot list maintains a one-to-one correspondence with the time anchor list, ensuring that each hotspot location can be traced back to a specific time anchor and sampling node, thereby enabling precise time alignment and cross-suppression operations in the subsequent signal fusion stage.

[0032] Based on the cross-hotspot column, a silent mezzanine window is inserted into the time series. The peak trailing caused by time misalignment is stripped out within the silent mezzanine window to form a cross isolation seam to separate different energy segments, and the cross isolation seam is used as the boundary for time rearrangement. This study focuses on the energy superposition problem caused by temporal misalignment. Through refined utilization of cross-hotspot columns, structured isolation methods are introduced within the time domain, thereby providing clear boundaries for subsequent time rearrangement. Specifically, the steps include:

[0033] After generating the cross-hotspot column, it is mapped back to the time series of the original multi-source brain function signals to determine the precise location of each cross-hotspot on the time axis. Specifically, based on the start and end time information recorded in the cross-hotspot column, the corresponding time segments are marked one by one in the unified time series of the multi-source brain function signals, so that each hotspot in the cross-hotspot column corresponds to a clear time range. During this process, the time indexing rules are maintained in accordance with the time anchor list and misalignment trace bands to ensure accurate alignment of the time positions of the cross-hotspots in different signals. Subsequently, each marked cross-hotspot time segment is expanded, reserving buffer time periods before and after the original hotspot time range to cover the energy extension area caused by sampling start misalignment and peak trailing. In this way, it can be ensured that the subsequently inserted silent mezzanine window can completely cover the energy crossover area caused by time misalignment without omitting potential trailing effects. After mapping and expansion, a set of clearly identified cross-hotspot time intervals will be formed in the original time series, providing an accurate temporal positioning basis for the insertion of silent mezzanine windows.

[0034] Based on clearly defined cross-hotspot time intervals, a silent interlayer window is inserted into the time series of multi-source brain functional signals. The silent interlayer window refers to an artificially set energy suppression segment within the time interval corresponding to the cross-hotspot, its function being to interrupt the energy continuity of different signals within that time range. In specific implementation, within each cross-hotspot time interval, the signal amplitude changes within the corresponding time period are silenced, preventing the signals within that time period from participating in subsequent temporal superposition and energy accumulation processes. The start and end times of the silent interlayer window are strictly set according to the cross-hotspot extension interval determined in the previous step, ensuring complete correspondence with the cross-hotspot on the time axis. During the insertion process, the overall structure of the time series is maintained, introducing energy interruption markers only within the designated segments, ensuring logical continuity on the time axis while creating clear separation at the energy level. In this way, without changing the original sampling order, the problem of different signal peaks superimposing within the same time period due to time misalignment can be effectively prevented, creating conditions for subsequent peak blurring removal.

[0035] After the silent interlayer window is inserted, peak trailing is stripped from the signal within the window, creating a cross-separation seam in the time series. Peak trailing refers to the extension effect of signal peaks in adjacent time periods caused by time misalignment and sampling delay. This effect causes energy belonging to different time positions to produce a continuous tail in the time series. To remove this trailing effect, the temporal trajectory of each brain function signal is truncated within the time segment covered by the silent interlayer window, clearly separating the energy boundaries of adjacent signals before and after the silent interlayer window, so that the silent interlayer window no longer carries any continuous energy change information. Through this processing, the energy curves that were originally continuously distributed in the time series are artificially divided into multiple independent energy segments. After stripping, a stable time separation band is formed at the position corresponding to the silent interlayer window in the time series; this time separation band is the cross-separation seam. The cross-separation seam appears as a clear break boundary on the time axis, used to distinguish different energy segments before and after the cross hotspot, preventing signals in different time periods from having energy cross-influences through temporal continuity.

[0036] After forming the cross-separation seams, these seams serve as boundaries for subsequent temporal rearrangement, structurally segmenting the time series of multi-source brain functional signals. Specifically, each cross-separation seam divides the complete time series into several independent time segments, each corresponding to a temporally continuous and energy-isolated signal interval. This segmentation ensures that the signal energy changes within each time segment originate from the actual sampling relationships within the same time range, without being affected by energy superposition caused by cross-hotspots. In the subsequent temporal rearrangement process, all time segments are arranged with the cross-separation seams as the starting or ending boundary, ensuring that the temporal rearrangement operation is carried out in an orderly manner under the constraints of the cross-separation seams. By using the cross-separation seams as fixed boundaries, the energy crossover problem caused by temporal misalignment can be avoided during the temporal rearrangement process, thus guaranteeing the structural stability and energy independence of the time series after rearrangement.

[0037] By rearranging the time series of multi-source brain functional signals in conjunction with cross-isolation seams, time particles are shifted sequentially along the peak order of each signal to generate a non-cross-aligned sequence with a single time path, and a dynamic regulation entry point is established in the non-cross-aligned sequence. Using the formed cross-seam as the core constraint, the multi-source brain functional signals are restored to a non-crossing, controllable single-path structure in the time dimension through the reconstruction of the time series structure and the orderly transfer of time particles. The specific steps are as follows;

[0038] Based on the established and stable embedding of multi-source brain functional signals in the cross-seam isolation seams, the original time series is segmented and organized using each cross-seam isolation seam as a boundary marker of the temporal structure. Specifically, the complete time series is divided into multiple continuous but isolated time segments according to the location of the cross-seam isolation seams. Each time segment is located between two adjacent cross-seam isolation seams or between the start of the time series and the nearest cross-seam isolation seam. Each time segment contains only signal content unaffected by cross-energy interference, and the original sampling order remains unchanged. During the segmentation process, each time segment is labeled with its start time, end time, corresponding cross-seam isolation seam number, and the types of multi-source brain functional signals it contains, giving each time segment a clear structural position on the time axis. In this way, the originally continuous but potentially cross-risk time series is decomposed into multiple structurally clear and energy-independent time segments, laying the foundation for subsequent temporal rearrangement.

[0039] After dividing the time segments, the temporal sequence within each segment is reconstructed based on the peak distribution of multi-source brain functional signals. Specifically, the peak positions of each brain functional signal are extracted within each time segment; these peak positions reflect the moments of concentrated energy within that segment. Then, using the time sequence recorded in the time anchor list as a reference, the peak positions of different signals are uniformly sorted, forming a sequential arrangement of all peaks in the time dimension. Based on this, the concept of time particles is introduced, viewing the time series as composed of continuous time particles, each corresponding to a signal state within a minimum time unit. Following the aforementioned sorted peak order, the time particles are shifted, rearranging the peaks that were originally scattered across different signals onto the same time path according to their actual occurrence order. This shifting method ensures that the main energy activities of multi-source brain functional signals form a unified sequential relationship on the time axis, preventing overlap of different signal peaks at the same time position.

[0040] After completing the temporal particle shift, the reconstructed temporal particle sequences within each time segment are concatenated to generate a non-overlapping sequence with a single time path. Specifically, while maintaining the position of the cross-seam, the temporal particle sequences in adjacent time segments are spliced ​​together in chronological order, ensuring that the end time of each time segment is logically continuous with the start time of the next. During the splicing process, it is ensured that there is no overlap or backtracking of temporal particles between different time segments, allowing the entire time series to progress monotonically in the same direction. The resulting non-overlapping sequence contains only one clear time path, with all temporal particles of multi-source brain functional signals arranged according to this time path. In this non-overlapping sequence, any time position corresponds to only one temporal particle state, eliminating the possibility of multiple signals simultaneously occupying the same time position from a temporal structure perspective, giving the time series a single-path, non-overlapping structural characteristic.

[0041] After the non-cross-aligned sequence is formed, dynamic control entry points are set within it to allow for flexible adjustments to the time structure based on changes in temporal cross-density. Specifically, several key time nodes are selected as control entry points within the non-cross-aligned sequence. These time nodes are typically located near the original cross-seam isolation points or at the connection points between time segments. Each dynamic control entry point records its time position, corresponding time particle number, and the interval information between adjacent time particles. When adjustments to the time structure are needed during subsequent processing, these dynamic control entry points can be used to redistribute the arrangement order or time interval of local time particles without disrupting the single-path structure of the entire non-cross-aligned sequence. By pre-establishing dynamic control entry points within the non-cross-aligned sequence, flexible adjustment space is provided for the subsequent time fusion stage, enabling the time series to maintain its non-cross-aligned characteristics while possessing scalability and adjustability, thus adapting to the time variation requirements of different acquisition stages and signal states.

[0042] Dynamic control of the time-nest roll disk is performed based on the non-cross-aligned sequence. The time-nest roll disk automatically rotates according to the cross energy density. By adjusting the window width and time particle step size in real time, the cross energy is kept under control and the time fusion is kept stable. Based on the premise that non-cross-aligned sequences have already been formed and the temporal structure has a clear single path, this paper introduces a temporal control structure with spatial mapping characteristics to continuously, progressively, and responsively control the temporal fusion process. The specific steps are as follows:

[0043] After the non-cross-aligned sequence is constructed, it is mapped to the time-carrying space of the time-nest roll disk. Specifically, the non-cross-aligned sequence is considered as a time carrier continuously unfolding along a single time path, on which time particles are distributed in chronological order. The time-nest roll disk is constructed as a ring-shaped time-control structure unfolding around this time path, its interior divided into several concentric time layers in chronological order, each time layer corresponding to a continuous time segment in the non-cross-aligned sequence. During the mapping process, each time particle in the non-cross-aligned sequence falls into its corresponding time layer position, while maintaining the relative order of the time particles within the time layer. Through this mapping method, the originally linearly arranged non-cross-aligned sequence is introduced into a rollable and rotatable time-carrying structure, enabling subsequent dynamic control operations to unfold around the time path. This step ensures a stable time mapping relationship between the time-nest roll disk and the non-cross-aligned sequence, providing a structural basis for subsequent spin control based on cross-energy density.

[0044] After the non-cross-aligned sequence is mapped to the time-nesting roll disk, the cross-energy density distribution within each time layer is continuously sensed along the temporal direction of the non-cross-aligned sequence. Specifically, within each time layer, the energy states of the multi-source brain functional signals carried by the time particles are converged and described, forming a cross-energy density representation corresponding to that time layer. Cross-energy density reflects the degree of energy concentration and its changing trend during the temporal fusion process of different brain functional signals within the same time layer. By continuously acquiring the cross-energy density information of each time layer along the time path, a time-varying energy distribution band can be formed within the time-nesting roll disk. This energy distribution band maintains strict temporal consistency with the non-cross-aligned sequence, ensuring that the energy state at each time point corresponds to a specific time particle segment. During this process, the temporal order of the non-cross-aligned sequence is not changed; only its energy distribution characteristics are continuously marked, thus providing a clear energy reference for subsequent roll modulation.

[0045] After obtaining the cross-energy density distribution band, the time-nest roll disk is automatically rotated based on the changes in cross-energy density between time layers, and the width of the time fusion window and the time particle step size are adjusted simultaneously. Specifically, when the cross-energy density between adjacent time layers shows a clustering trend, the time-nest roll disk rotates along the time path, causing the high-energy-density region to shift outward or inward in spatial position, thereby changing the bearing mode of this region in time fusion. During the rotation process, for time layers with high cross-energy density, the width of the corresponding time fusion window is appropriately reduced, making the distribution of time particles in this time segment denser, thus limiting the diffusion of energy in the time dimension; for time layers with low cross-energy density, the width of the time fusion window is correspondingly widened, making the distribution of time particles smoother, in order to maintain overall time continuity. At the same time, by adjusting the step size of the time particles, the time interval between adjacent time particles is refined or stretched according to the changes in cross-energy density, thereby dynamically buffering the energy distribution on the time axis. Through this coordinated operation of rotation and adjustment, the time nest roll disk can adaptively adjust the morphology of the time structure according to changes in cross energy density without disrupting the single-path characteristics of the non-cross-aligned sequence.

[0046] After the time-nesting roll disk completes its rotation and simultaneously adjusts the window width and time particle step size, the adjusted time structure continues to act on the non-cross-aligned sequence to keep the cross energy under control and maintain the stability of the time fusion. Specifically, the rotated and adjusted time layer structure is reapplied to the non-cross-aligned sequence, allowing the time particles in the non-cross-aligned sequence to continue advancing along the time path according to the new time window configuration and step size distribution. During this process, the time-nesting roll disk maintains a dynamic linkage with the non-cross-aligned sequence, continuously updating the cross energy density distribution as time progresses, and performing rotation and adjustment operations accordingly. Through this continuous action, the time fusion process remains under control throughout the entire acquisition and processing cycle, preventing the re-accumulation of cross energy in local time periods. Simultaneously, since all control operations revolve around the non-cross-aligned sequence and are constrained by a single time path structure, the time fusion remains stable and continuous overall, without introducing new time misalignment problems due to local adjustments, thus maintaining the long-term stability of the time fusion state.

[0047] This invention, by introducing the synergistic effect of a unified temporal anchor band, misalignment trace band, and cross-isolating seam throughout the entire process of multi-source brain functional signal acquisition and fusion, continuously constrains the problems of temporal misalignment and energy crossover at the temporal structure level. Through window-by-window characterization of sampling start-point differences and precise localization of energy-dense crossover regions, this invention effectively reduces the superposition of spurious peaks caused by temporal shifts before signal fusion. This ensures that multi-source brain functional signals maintain a clear, independent, and traceable structural relationship in the temporal dimension, thereby providing a more realistic and stable temporal basis for constructing brain functional connectivity features and avoiding interference from temporal artifacts in screening results.

[0048] This invention constructs non-cross-aligned sequences and introduces a dynamic control mechanism using a time-nesting roll disk, enabling the temporal fusion process to possess continuous adaptive adjustment capabilities. By adjusting the window width and temporal particle step size in real time based on changes in cross-energy density, this invention can maintain the stability of the temporal structure during long-term fusion of multi-source brain functional signals, preventing the re-accumulation of cross-energy in local time periods, thereby ensuring the consistency and continuity of brain functional connectivity features across different time scales. This stable temporal fusion state helps improve the ability to detect weak brain functional abnormalities in the early screening of Moyamoya disease, reducing the risk of misdiagnosis and missed diagnosis.

[0049] This invention provides, for example Figure 2 The illustrated early screening system for Moyamoya disease, which integrates brain functional connectivity features, includes a time anchoring construction module, a time alignment and misalignment detection module, an energy isolation and peak stripping module, a time rearrangement and alignment generation module, and a dynamic rollover modulation module. The time anchor construction module constructs a unified time anchor band throughout the entire process of multi-source brain function signal acquisition for early screening of Moyamoya disease. It generates a time anchor list by recording the time information of all sampling nodes and uses the time anchor list as the time benchmark for subsequent sampling error tracing. The time alignment and misalignment detection module performs window-by-window alignment of the time series of multi-source brain functional signals based on the time anchor list, calculates the difference in the sampling start point of each window, generates misalignment trace bands containing time misalignment information, and identifies cross-energy dense regions in the misalignment trace bands to form cross-hotspot columns. The energy isolation and peak stripping module inserts a silent interlayer window into the time series based on the cross hotspot column. Within the silent interlayer window, it strips the peak trailing caused by time misalignment, forming a cross isolation seam to separate different energy segments, and uses the cross isolation seam as the boundary for time rearrangement. The time rearrangement and alignment generation module rearranges the time series of multi-source brain functional signals in conjunction with the cross-isolation seam, shifts time particles along the peak order of each signal, generates a non-cross-aligned sequence with a single time path, and establishes a dynamic regulation entry point in the non-cross-aligned sequence. The dynamic roll control module performs dynamic control operations on the time nest roll disk based on the non-cross-aligned sequence. The time nest roll disk automatically rotates according to the cross energy density. By adjusting the window width and time particle step size in real time, it keeps the cross energy continuously under control and maintains a stable state of time fusion.

[0050] The method for early screening of Moyamoya disease that integrates brain functional connectivity features provided in this embodiment of the invention is implemented through the aforementioned early screening system for Moyamoya disease that integrates brain functional connectivity features. For details of the specific methods and procedures of the early screening system for Moyamoya disease that integrates brain functional connectivity features, please refer to the embodiments of the method for early screening of Moyamoya disease that integrates brain functional connectivity features, which will not be repeated here.

[0051] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0052] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0053] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0054] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0055] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0056] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0057] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An early screening method for Moyamoya disease that integrates brain functional connectivity features, characterized in that, Includes the following steps: In the entire process of acquiring multi-source brain function signals for early screening of Moyamoya disease, a unified time anchor band was constructed. A time anchor list was generated by recording the time information of all sampling nodes, and the time anchor list was used as the time reference for subsequent sampling error tracing. Based on the time anchor list, the time series of multi-source brain functional signals are compared window by window, the difference in the sampling starting point of each window is calculated, misaligned trace bands are generated, and cross-energy dense regions are identified in the misaligned trace bands to form cross-hotspot columns. Based on the cross-hotspot column, a silent mezzanine window is inserted into the time series. Within the silent mezzanine window, the peak trailing caused by time misalignment is stripped away to form a cross isolation seam, which is used as the boundary for time rearrangement. By rearranging the time series of multi-source brain functional signals in conjunction with cross-isolation seams, shifting time particles along the peak order of each signal, generating non-cross-aligned sequences, and establishing dynamic regulation entry points in the non-cross-aligned sequences; Dynamic control of the time-nest roll disk is performed based on the non-cross-aligned sequence. The time-nest roll disk automatically rotates according to the cross energy density. By adjusting the window width and time particle step size in real time, the cross energy is kept under control and the time fusion is kept stable.

2. The method for early screening of Moyamoya disease based on the fusion of brain functional connectivity features according to claim 1, characterized in that, The steps to generate the time anchor list are as follows: Before the multi-source brain function signal acquisition is started, a unified time anchor tape is established, and a time trigger signal is sent to each acquisition device through a unified time trigger control unit so that each sampling channel records the synchronous time zero point. Under the constraint of a unified time anchor band, the time information of all sampling nodes generated during the acquisition process is continuously recorded, the timestamps of each sampling node are extracted and a list of continuous sampling nodes is formed in chronological order. A time distribution map is constructed based on a list of continuous sampling nodes. The time information of each sampling node is uniformly encoded and sequentially mapped to generate a time anchor list covering the entire collection process. During signal fusion and analysis, the time anchor list is continuously used as a time reference to track the temporal location of multi-source brain functional signals.

3. The method for early screening of Moyamoya disease based on the fusion of brain functional connectivity features according to claim 2, characterized in that, After generating the time anchor list, the time offset information of each sampling node is associated with the corresponding signal type, channel identifier and sampling time segment, so that the time anchor list can continuously participate in the temporal position mapping of multi-source brain functional signals as a unified time reference throughout the entire signal fusion process.

4. The method for early screening of Moyamoya disease based on the fusion of brain functional connectivity features according to claim 2, characterized in that, The steps to generate the cross-hotspot column are as follows; A unified time index is performed on the time series of multi-source brain functional signals based on the time anchor list, and the acquisition period is divided into continuous comparison windows according to the time anchor list. Within each comparison window, the sampling start time information of each brain function signal is extracted, and the sampling start difference between different signals is calculated based on the time anchor list to form time difference data arranged by window. Based on the time difference data, generate misalignment trace bands containing time misalignment information in chronological order; Identify time segments where time-displaced trajectories intersect or cluster in the misaligned trace zone, extract areas of dense cross-energy, and form cross-hotspot columns in chronological order.

5. The method for early screening of Moyamoya disease based on the fusion of brain functional connectivity features according to claim 4, characterized in that, During the formation of the cross-hotspot column, the corresponding time start point, time end point and participation signal identifier are recorded for each cross-energy dense region, so that each hotspot in the cross-hotspot column maintains a time mapping relationship with the sampling nodes in the time anchor list.

6. The method for early screening of Moyamoya disease based on the fusion of brain functional connectivity features according to claim 4, characterized in that, The process of forming a cross-seam isolation joint is as follows: The cross-hotspot columns are mapped to the time series of multi-source brain functional signals, the time segments corresponding to the cross-hotspots are marked, and the expanded hotspot time range is formed. Based on the extended hotspot time range, a silent mezzanine window is inserted into the time series to cause the signal in the corresponding time segment to enter an energy interruption state. Peak trailing images are stripped within the time range covered by the silent interlayer window, so that the position corresponding to the silent interlayer window forms a cross isolation seam in the time series to separate different energy segments; The resulting cross-separation seams are used as time boundaries to segment the time series for subsequent time rearrangement.

7. The method for early screening of Moyamoya disease based on the fusion of brain functional connectivity features according to claim 6, characterized in that, After inserting a silent interlayer window and forming a cross-separation seam, the original sampling order of each time segment remains unchanged, so that the cross-separation seam continues to serve as a fixed boundary in the time series, thereby maintaining the independence and continuity of adjacent energy segments in the time fusion process.

8. The method for early screening of Moyamoya disease based on the fusion of brain functional connectivity features according to claim 6, characterized in that, The steps for generating a cross-aligned sequence are as follows: Using cross-separation seams as time boundaries, the time series of multi-source brain functional signals are segmented and organized to form isolated time segments while maintaining the sampling order within each segment. Based on the peak distribution of brain functional signals in each time segment, the peak order within the time segment is uniformly sorted, and time particles are shifted along the peak order to reconstruct the time arrangement. The time segments that have completed the time particle transfer are concatenated in chronological order to generate a non-intersecting aligned sequence containing only a single time path. Set dynamic control entry points in sequences without cross-alignment.

9. The method for early screening of Moyamoya disease based on the fusion of brain functional connectivity features according to claim 8, characterized in that, Based on the non-cross-aligned sequence, a time-nesting roll disk is introduced for dynamic control, causing the time-nesting roll disk to rotate according to the cross-energy density. The time series is then adjusted by modifying the time fusion window width and the time particle step size. The steps are as follows: Map the non-cross-aligned sequence to the temporal carrying space of the time nest roll disk, so that the time particles are distributed in the corresponding time layer in time order; The cross-energy density distribution information within each time layer is obtained along the time direction of the non-cross-aligned sequence, and an energy distribution band consistent with the time path is formed; The time nest roll disk is driven to rotate based on the change of cross energy density between time layers, while the width of the time fusion window and the time particle step size are adjusted. The spin shift and adjusted time structure are continuously applied to the non-cross-aligned sequence to keep the cross energy controlled and maintain the stability of the time fusion state.

10. An early screening system for Moyamoya disease incorporating brain functional connectivity features, used to implement the early screening method for Moyamoya disease incorporating brain functional connectivity features as described in any one of claims 1-9, characterized in that, It includes a time anchor construction module, a time comparison and misalignment detection module, an energy isolation and peak stripping module, a time rearrangement and alignment generation module, and a dynamic roll control module: The time anchor construction module constructs a unified time anchor band throughout the entire process of multi-source brain function signal acquisition for early screening of Moyamoya disease. It generates a time anchor list by recording the time information of all sampling nodes and uses the time anchor list as the time benchmark for subsequent sampling error tracing. The time alignment and misalignment detection module performs window-by-window alignment of time series of multi-source brain functional signals based on the time anchor list, calculates the difference in the sampling starting point of each window, generates misalignment trace bands, and identifies cross-energy dense regions in the misalignment trace bands to form cross-hotspot columns. The energy isolation and peak stripping module inserts a silent interlayer window into the time series based on the cross hotspot column. Within the silent interlayer window, it strips the peak trailing caused by time misalignment, forming a cross isolation seam, and uses the cross isolation seam as the boundary for time rearrangement. The time rearrangement and alignment generation module rearranges the time series of multi-source brain functional signals in conjunction with the cross-isolation seam, shifts time particles along the peak order of each signal to generate a non-cross-aligned sequence, and establishes a dynamic regulation entry point in the non-cross-aligned sequence. The dynamic roll control module performs dynamic control operations on the time nest roll disk based on the non-cross-aligned sequence. The time nest roll disk automatically rotates according to the cross energy density. By adjusting the window width and time particle step size in real time, it keeps the cross energy continuously under control and maintains a stable state of time fusion.

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