Multi-modal monitoring method, system and equipment based on hardware collaboration and storage medium

Abstract

The invention relates to the field of medical monitoring, and discloses a multi-modal monitoring method, system and device based on hardware collaboration and a storage medium, and the method comprises the steps: quickly starting core monitoring after power-on, and supporting hot plug and plug-and-play access of medical equipment; multi-source access is carried out, a unified time axis is constructed through clock synchronization and resampling, and disconnection merging is supported; motion features are extracted to drive adaptive anti-interference, and abnormal authenticity is identified; based on redundancy observation and space-time alignment, weighting fusion parameters through consistency analysis, and intelligently judging and shielding low-quality channels; capturing an event trigger point, packaging a holographic structured event packet and establishing a bidirectional index; and constructing a full-time-sequence evolution view, executing association attribution and trend early warning, and feeding back an optimization algorithm through playback and redisk. According to the invention, the problems of low data quality, unexplainable alarm and traceability deficiency in an extreme dynamic environment are solved, and the requirements of pre-hospital first aid high-quality monitoring and rapid decision making are met.

Description

Technical Field

[0001] This invention relates to the field of medical monitoring, and in particular to a hardware-based multimodal monitoring method, system, device, and storage medium. Background Technology

[0002] Pre-hospital emergency care is a crucial link in medical rescue, emphasizing the "golden ten minutes" and "golden hour." Although existing portable devices can collect vital signs such as electrocardiogram and blood oxygenation, they lack hardware-level anti-motion artifact mechanisms in extreme dynamic environments such as the absence of nets in the wild, stretcher transport, and vehicle bumps. This makes the raw data susceptible to noise contamination, and the unstable signal leads to a high false alarm rate, seriously interfering with clinical judgment.

[0003] Existing intelligent diagnosis and treatment technologies, such as multimodal AI analysis (CN120913802A), multidimensional health status assessment (CN120998475A), and blockchain evidence storage (CN121034585A), perform well in static or networked environments, but are difficult to adapt to pre-hospital scenarios. Emergency scenes often cannot obtain complete historical data in real time to build accurate models, and weak network environments cause data flow gaps. Simultaneously, blockchain consensus delays lead to decision-making lags, centralized storage poses a risk of tampering, and pre-hospital and in-hospital data are fragmented, resulting in the loss of patient trajectory during transport.

[0004] In summary, existing technologies have significant limitations in terms of data quality under dynamic interference, real-time decision-making response, and end-to-end security traceability. Alarms lack contextual evidence chains, leading to inexplicability; AI-assisted diagnostic responses are delayed, failing to meet the urgent needs for high-quality monitoring and rapid, reliable decision-making in emergency scenarios. Summary of the Invention

[0005] This invention provides a hardware-based multimodal monitoring method, system, computer device, and storage medium to address the problems of existing technologies in extreme dynamic environments, which suffer from low data quality, unexplainable alarms, delayed AI decision-making, and lack of full-process safety traceability due to the lack of anti-interference mechanisms and efficient collaborative architecture. These issues make it difficult to meet the urgent needs of pre-hospital emergency care for high-quality monitoring and rapid, reliable decision-making.

[0006] A hardware-coordinated multimodal monitoring method includes: After the system is powered on, it completes self-test initialization, prioritizes the collection of core vital signs and builds a basic monitoring view, and supports hot-swapping and plug-and-play access of imaging diagnostic equipment to achieve seamless data integration. By connecting heterogeneous medical devices in parallel through multiple interface aggregation nodes, standardized data frames are parsed for unified management; a unified time axis is established by using a hardware clock to perform time base assignment and transmission delay compensation; then, through clock deviation correction and resampling technology, multi-source data is accurately mapped to the same continuous time axis, and data is automatically and seamlessly merged after the device disconnection is restored. Based on the unified time axis, multidimensional motion features are extracted and the transport environment status is identified to construct a physiological motion coupled data system; the generated motion tags are shared to the signal processing module in real time to drive the adaptive anti-interference algorithm to dynamically switch filtering strategies, and the motion features are used to identify the authenticity of physiological abnormalities; A heterogeneous redundant observation architecture is used to acquire multiple physical quantity observations of the same physiological indicator in parallel, and the motion tags are combined for spatiotemporal alignment. A dynamic consistency analysis strategy is used to perform real-time comparison and confidence determination of multi-source data to obtain comparison results and confidence determination results. Based on the comparison results and the confidence determination results, the multiple physical quantity observations are weighted and fused to obtain physiological parameters. When an anomaly is detected in the comparison results, an anomaly intelligent adjudication mechanism is activated to trace the source of interference and dynamically block low-quality channels. Deploy a multi-dimensional event monitoring engine to receive abnormal physiological parameter signals and the confidence level determination results in real time; when an event trigger point is captured, lock the full waveform, image and metadata within the dynamic time window, encapsulate them into an immutable multi-dimensional holographic structured event package, and establish a bidirectional index association; Based on holographic data containing the multidimensional holographic structured event package, a full-time clinical evolution view is constructed, multidimensional data time-series correlation attribution is performed, and pathological changes and environmental interference are accurately identified; macro-trend early warning and stage assessment are implemented to upgrade from instantaneous alarm to continuous perception; full-process quality control and review are empowered by visualization playback tools, and the analysis results are fed back to the front-end algorithm to optimize anti-interference and adjudication strategies.

[0007] Optionally, after power-on, the system completes self-test initialization, prioritizes the acquisition of core vital signs and constructs a basic monitoring view, and simultaneously supports hot-swapping and plug-and-play access of imaging diagnostic devices to achieve seamless data fusion, including: After the system is powered on, it automatically completes the edge terminal self-test and core module initialization; The system prioritizes driving fast-response sensors, which immediately initiate continuous data acquisition and real-time calculation of key parameters upon signal access; these fast-response sensors include ECG sensors, blood oxygen sensors, and respiration sensors. Based on the core vital sign data that is already in place, dynamic monitoring curves are rendered in real time and basic data recording services are started to quickly build a visualized basic monitoring view. During basic monitoring operations, the system supports on-demand dynamic access of the imaging diagnostic devices and automatically completes device identification and time-series association.

[0008] Optionally, the process involves parallel access to heterogeneous medical devices via multi-interface aggregation nodes, parsing standardized data frames for unified management; utilizing a hardware clock to perform time base assignment and transmission delay compensation to establish a unified time axis; and then using clock skew correction and resampling technology to accurately map multi-source data to the same continuous time axis, and automatically performing seamless data merging after device disconnection and recovery, including: Configure the edge terminal as a multi-interface aggregation node, and connect to a variety of medical sensors and devices in parallel through wired and / or wireless links to establish a multi-channel data input environment; Define and parse predefined data frames containing device identifiers, data types, acquisition serial numbers, and raw payloads to achieve unified identification and standardized management of heterogeneous data from different sources; Based on the built-in hardware clock, the system performs a receiving stamp the instant the data physically arrives, and performs reverse compensation calculations in conjunction with a preset interface transmission delay model to generate an accurate collection timestamp. The device-to-device deviation is corrected by periodic clock synchronization handshake, the data is strictly time-sequentially sorted using a global continuous time axis queue, and the time alignment problem of multi-frequency data is solved by interpolation or resampling techniques. Real-time detection of the continuity of the collected sequence number, automatic marking of packet loss or abnormal events, and activation of the local caching mechanism when device communication is interrupted to ensure that data is not lost during the disconnection period; After the device is reconnected, the cached data is seamlessly merged into the global timeline in timestamp order, ensuring the continuity and temporal consistency of multimodal monitoring data throughout its lifecycle.

[0009] Optionally, the step of extracting multidimensional motion features based on the unified time axis and identifying the transport environment state to construct a physiological motion coupled data system; sharing the generated motion tags to the signal processing module in real time to drive the adaptive anti-interference algorithm to dynamically switch filtering strategies, and using motion features to identify the authenticity of physiological abnormalities, includes: Raw data is collected using an inertial measurement unit, and multidimensional features including time domain, frequency domain, and attitude are extracted using a sliding window algorithm. Combined with pattern recognition technology, attitude changes and motion patterns are determined in real time. The identified motion states are converted into independent modal data, and synchronously fused with vital sign data based on a unified timestamp to generate a physiological motion coupling data stream with motion state labels and quality scores. Motion context information is then extracted from the physiological motion coupling data stream. The signal processing strategy is dynamically adjusted based on the motion context: under motion interference, adaptive filtering and baseline drift suppression algorithms are automatically enabled, or the weight of specific sensors is reduced and intermittent measurements are delayed, in order to eliminate motion artifacts and prevent false alarms. Establish a two-way correlation mechanism between movement patterns and clinical events, utilize movement characteristics to help identify the causes of physiological abnormalities, and execute alarm suppression, downgrading, or triggering specific emergency alarms accordingly.

[0010] Optionally, the method involves acquiring multiple physical quantity observations of the same physiological indicator in parallel using a heterogeneous redundant observation architecture, and performing spatiotemporal alignment with the motion tags; utilizing a dynamic consistency analysis strategy to perform real-time comparison and confidence determination of multi-source data to obtain comparison results and confidence determination results; weighting and fusing the multiple physical quantity observations based on the comparison results and the confidence determination results to obtain physiological parameters; and when an anomaly is detected in the comparison results, activating an intelligent anomaly adjudication mechanism to trace the source of interference and dynamically block low-quality channels, including: Configure multi-source sensing channels based on different physical sensing principles to monitor the same physiological indicator in parallel. The multimodal signal processing algorithm is run in parallel to extract feature parameters from different sensing channels and all calculation results are mapped to a global continuous time axis to form a multidimensional parameter vector for comparison. Real-time calculation of the deviation of multi-source observations and application of adaptive dynamic threshold to determine consistency; For consistent data, improve its quality level and use a signal-to-noise ratio-based weighted average algorithm for fusion output to reduce random errors and improve measurement accuracy; When the difference between multi-source data exceeds the limit, the anomaly adjudication mechanism is triggered. By combining the motion context to trace the signal quality, low-confidence channels are automatically marked, alarm weights are dynamically adjusted, or false alarms are suppressed.

[0011] Optionally, the deployed multi-dimensional event monitoring engine receives abnormal physiological parameter signals and the confidence level determination results in real time; upon capturing an event trigger point, it locks the full waveform, image, and metadata within the dynamic time window, encapsulates them into an immutable multi-dimensional holographic structured event package, and establishes a bidirectional index association, including: Based on a unified hardware clock reference, all acquired multimodal data are assigned millisecond-level timestamps and appended to the storage medium in strict timing order to form a continuous data stream that retains the complete timing topology. Real-time scanning of key physiological parameters and system status; precise location and marking of event trigger points based on multiple trigger conditions; the multiple trigger conditions include at least one of threshold exceeding limits, deterioration of nonlinear trends, abnormal multimodal consistency, and specific clinical operation instructions; At the moment an event is triggered, dynamic time window data of the preceding and following time periods are adaptively extracted according to the event type and copied from the circular buffer to a dedicated read-only event protection area; Integrate raw waveform data, high-frequency parameter trends, image keyframes / files, environmental motion tags, and operation metadata, encapsulate them into atomic structured event packages containing unique IDs and index pointers according to a predefined data structure, and store them securely. A bidirectional indexing mechanism is built in the database to link event packages with the original continuous time series data stream. This mechanism supports quick location of the original data by event ID or highlights event markers when replaying the data stream, enabling fast retrieval of massive amounts of data. Based on a unified timeline, vital sign waveforms, video images, motion postures, and / or operation logs are synchronously reproduced.

[0012] Optionally, the process involves constructing a full-time clinical evolution view based on holographic data containing the multidimensional holographic structured event package, performing multidimensional data temporal correlation attribution, accurately identifying pathological changes and environmental interference; implementing macro-trend early warning and stage assessment to upgrade from instantaneous alarm to continuous perception; and empowering full-process quality control and review through visualization playback tools, and feeding the analysis results back to the front-end algorithm to optimize anti-interference and adjudication strategies, including: By integrating discrete physiological parameters, imaging evidence, and medical operation logs on a continuous time axis, the limitations of single-point numerical values ​​are broken, and the continuous events of the patient throughout the entire transfer process are reconstructed. Automatically analyze the time lag and correlation between specific medical and nursing procedures and subsequent physiological responses to assess the effectiveness of treatment, and couple and map environmental disturbance events with fluctuations in vital signs; Based on long-term data fitting, we can identify gradual trends that have not crossed the boundary but continue to deteriorate and provide early warnings. At the same time, we divide the entire process into different treatment stages, quantify the parameter stability and event frequency of each stage, and form a complete set of data.

[0013] A hardware-coordinated multimodal monitoring system includes: The quick-start monitoring module is used to complete self-test initialization after the system is powered on, prioritize the collection of core vital signs and build a basic monitoring view, and support hot-swappable and plug-and-play access of imaging diagnostic equipment to achieve seamless data integration. The hardware collaboration and time alignment module is used to connect heterogeneous medical devices in parallel through multiple interface aggregation nodes, parse standardized data frames for unified management, use hardware clocks to perform time base assignment and transmission delay compensation to establish a unified time axis, and then use clock deviation correction and resampling technology to accurately map multi-source data to the same continuous time axis, and automatically perform seamless data merging after device disconnection and recovery. The motion interference identification module is used to extract multi-dimensional motion features based on the unified time axis and identify the transport environment status, and construct a physiological motion coupling data system; the generated motion tags are shared to the signal processing module in real time to drive the adaptive anti-interference algorithm to dynamically switch the filtering strategy, and the motion features are used to identify the authenticity of physiological abnormalities; The multimodal cross-validation module is used to acquire multiple physical quantity observations of the same physiological indicator in parallel through a heterogeneous redundant observation architecture, and perform spatiotemporal alignment with the motion tag; it uses a dynamic consistency analysis strategy to perform real-time comparison and confidence determination of multi-source data to obtain comparison results and confidence determination results, and performs weighted fusion of the multiple physical quantity observations based on the comparison results and the confidence determination results to obtain physiological parameters; when an anomaly is detected in the comparison results, an anomaly intelligent adjudication mechanism is activated to trace the source of interference and dynamically block low-quality channels; The traceable event recording module is used to receive abnormal physiological parameter signals and the confidence judgment results in real time by deploying a multi-dimensional event monitoring engine; when an event trigger point is captured, the full waveform, image and metadata within the dynamic time window are locked, encapsulated into an immutable multi-dimensional holographic structured event package, and a bidirectional index association is established. The clinical analysis and decision support module is used to construct a full-time clinical evolution view based on holographic data containing the multidimensional holographic structured event package, perform multidimensional data time-series correlation attribution, accurately identify pathological changes and environmental interference; implement macro trend early warning and stage assessment, realizing the upgrade from instantaneous alarm to continuous perception; empower the whole process quality control and review through visualization playback tools, and feed the analysis results back to the front-end algorithm to optimize anti-interference and adjudication strategies.

[0014] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the aforementioned hardware-cooperative multimodal monitoring method.

[0015] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described hardware-cooperative multimodal monitoring method.

[0016] The aforementioned hardware-coordinated multimodal monitoring method, system, computer equipment, and storage media, through hot-swappable heterogeneous device access, hardware clock synchronization, and resampling technology, construct a highly robust unified timeline, achieving seamless fusion of multi-source vital signs and imaging data and automatic merging of disconnected data. Combined with physiological motion coupling analysis and a heterogeneous redundant observation architecture, the system can dynamically switch anti-interference strategies and intelligently resolve multi-source data conflicts, significantly improving the accuracy of parameter measurements and the specificity of alarms in complex transport environments. Furthermore, based on multi-dimensional holographic structured event packages and a full-time clinical evolution view, this invention upgrades from instantaneous alarms to continuous perception, supporting accurate attribution, macro-trend early warning, and full-process quality control review, forming a closed-loop optimized intelligent monitoring system. Therefore, this invention effectively overcomes the shortcomings of existing technologies in extreme dynamic environments, such as low data quality, unexplainable alarms, lagging AI decision-making, and lack of full-process safety traceability due to the lack of anti-interference mechanisms and efficient collaborative architecture. It fully meets the urgent needs of pre-hospital emergency scenarios for high-quality monitoring data and rapid, reliable clinical decision-making. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a hardware-coordinated multimodal monitoring method according to an embodiment of the present invention; Figure 2 This is another flowchart of a hardware-coordinated multimodal monitoring method in one embodiment of the present invention; Figure 3 This is a schematic diagram of a hardware-coordinated multimodal monitoring device according to an embodiment of the present invention; Figure 4 This is a schematic diagram of a computer device according to an embodiment of the present invention. Detailed Implementation

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

[0020] In one embodiment, such as Figure 1As shown, a hardware-coordinated multimodal monitoring method is provided, including the following steps S10~S60.

[0021] S10. After the system is powered on, it completes self-test initialization, prioritizes the collection of core vital signs and builds a basic monitoring view, and supports hot-swapping and plug-and-play access of imaging diagnostic equipment to achieve seamless data integration.

[0022] Understandably, upon power-up, the system automatically performs edge terminal self-tests and core module initialization, then prioritizes driving rapid-response sensors such as ECG, blood oxygen, and respiration. Continuous data acquisition and real-time calculation of key parameters are initiated the instant a signal is received. Based on the readily available core vital sign data, the system instantly renders dynamic monitoring curves and activates basic recording services, rapidly building a visualized basic monitoring view to ensure real-time control of the patient's critical physiological state in the shortest possible time after power-on.

[0023] While ensuring stable operation of basic monitoring, the system supports on-demand dynamic access (hot-swappable) of imaging diagnostic devices. When imaging devices are connected, the system automatically completes device identification, protocol handshake, and association mapping with time-series data, seamlessly integrating the image stream into the existing monitoring timeline. This achieves a smooth expansion from core vital signs to multimodal images, enriching diagnostic dimensions without interrupting the current monitoring process.

[0024] Optionally, step S10, which involves the system completing self-test initialization after power-on, prioritizing the acquisition of core vital signs and constructing a basic monitoring view, while simultaneously supporting hot-swappable and plug-and-play access of imaging diagnostic devices to achieve seamless data fusion, includes: S101. After the system is powered on, it automatically completes the edge terminal self-test and core module initialization. S102. Prioritize driving the fast-response sensor to immediately start continuous data acquisition and real-time calculation of key parameters upon signal access; the fast-response sensor includes an electrocardiogram sensor, a blood oxygen sensor, and a respiration sensor. S103. Based on the core vital signs data that are ready, render dynamic monitoring curves in real time and start basic data recording services to quickly build a visualized basic monitoring view. S104. During basic monitoring operation, the system supports on-demand dynamic access of the imaging diagnostic equipment and automatically completes equipment identification and time-series association.

[0025] Understandably, upon power-up, the edge monitoring terminal immediately and automatically executes a self-test procedure and completes the initialization of core components such as the clock module and data cache module. This process requires no manual intervention or additional configuration, allowing the system to quickly transition from the startup state to the monitoring preparation state, laying a stable operational foundation for subsequent data acquisition.

[0026] After initialization, the system prioritizes driving vital sign sensors with rapid response capabilities, including ECG, blood oxygen, and / or respiratory monitoring modules. Once the electrodes, probes, or cuffs of these sensors are physically connected, the system immediately initiates a continuous data acquisition process and calculates key parameters such as heart rate and blood oxygen saturation in real time, ensuring that effective physiological indicators can be obtained the instant the signal is received.

[0027] Based on readily available core vital sign data, the system can immediately render dynamic monitoring curves and initiate basic data recording services without waiting for all peripherals to connect. This mechanism can quickly build a visualized basic monitoring view, enabling medical staff to grasp the patient's basic vital signs at the first moment, realizing an efficient "establish basic monitoring first" process.

[0028] During the stable operation of basic monitoring, the system supports the on-demand dynamic access of imaging diagnostic equipment such as handheld wireless ultrasound or portable digital radiography (DR) systems. When a new device is detected, the system automatically identifies the device and correlates it with the current monitoring data in a time sequence, without interrupting the collection of existing vital signs. This forms a progressive monitoring mode of "gradual supplementary examinations," improving the flexibility and continuity of clinical emergency care and diagnosis.

[0029] This embodiment significantly shortens system startup time through automatic power-on self-test and core module initialization, ensuring that monitoring equipment can quickly enter a ready state; it prioritizes driving fast-response sensors and performs data acquisition and calculation in real time, achieving "zero-delay" startup of vital sign monitoring and effectively capturing key physiological changes at the moment of patient access; combined with real-time rendered dynamic monitoring curves and the on-demand dynamic access mechanism of imaging equipment, it not only constructs a visualized basic monitoring view, but also improves the flexibility and diagnostic efficiency of multimodal data fusion, saving valuable time for clinical emergency care.

[0030] S20. Through parallel access to heterogeneous medical devices via multi-interface aggregation nodes, standardized data frames are parsed for unified management; a unified time axis is established by using a hardware clock to perform time base assignment and transmission delay compensation; then, through clock deviation correction and resampling technology, multi-source data is accurately mapped to the same continuous time axis, and data is automatically and seamlessly merged after the device disconnection is restored.

[0031] Understandably, the system configures edge terminals as multi-interface aggregation nodes, connecting to various heterogeneous medical devices such as ECG and imaging equipment via wired and wireless links in parallel, thus constructing a multi-channel data input environment. The system defines and parses predefined data frames containing device identifiers, data types, and raw payloads, enabling unified identification and standardized management of heterogeneous data from different sources, laying the data foundation for multimodal fusion.

[0032] In terms of time synchronization, the system uses a built-in high-precision hardware clock to perform a receiving stamp the instant the data physically arrives, and combines this with a preset interface transmission delay model to perform reverse compensation calculations to generate accurate acquisition timestamps. Through periodic clock synchronization handshakes to correct device-to-device deviations, the system utilizes a global continuous timeline queue to strictly sort the data in time sequence, and employs interpolation or resampling techniques to solve the time alignment problem for multi-frequency data, ensuring that all data is accurately mapped to the same continuous timeline.

[0033] Furthermore, the system monitors the continuity of the collected sequence numbers in real time and automatically marks packet loss or abnormal events. When device communication is interrupted, a local caching mechanism is immediately activated to save the data during the disconnection period; after the device reconnects, the system automatically and seamlessly merges the cached data into the global timeline in timestamp order. This mechanism effectively ensures the continuity and temporal consistency of multimodal monitoring data throughout its entire lifecycle, avoiding data gaps caused by network fluctuations.

[0034] Optionally, step S20, namely, parallel access to heterogeneous medical devices through multi-interface aggregation nodes, parsing standardized data frames for unified management; using a hardware clock to perform time base assignment and transmission delay compensation to establish a unified time axis; and then using clock skew correction and resampling technology to accurately map multi-source data to the same continuous time axis, and automatically performing seamless data merging after device disconnection and recovery, includes: S201. Configure the edge terminal as a multi-interface aggregation node, and connect to a variety of medical sensors and devices in parallel through wired links and / or wireless links to establish a multi-channel data input environment; S202. Define and parse predefined data frames containing device identifier, data type, acquisition sequence number and original payload to achieve unified identification and standardized management of heterogeneous data from different sources; S203: Based on the built-in hardware clock, it performs a receiving stamp the moment the data physically arrives, and performs reverse compensation calculations in combination with the preset interface transmission delay model to generate an accurate acquisition timestamp. S204. Correct the deviation between devices by periodically synchronizing the handshake with a clock, strictly sort the data in time using a global continuous time axis queue, and use interpolation or resampling techniques to solve the time alignment problem of multi-frequency data. S205: Real-time detection of the continuity of the collected sequence number, automatic marking of packet loss or abnormal events, and activation of the local caching mechanism when device communication is interrupted to ensure that data is not lost during the disconnection period; S206. After the device is reconnected, the cached data is seamlessly merged into the global timeline in timestamp order to ensure the continuity and temporal consistency of multimodal monitoring data throughout its lifecycle.

[0035] Understandably, the edge monitoring terminal is configured as a core node for multi-interface convergence. By integrating wired communication interfaces such as USB and serial ports, as well as wireless links such as Wi-Fi and Bluetooth, it constructs a parallel, multi-channel data input environment. This architecture allows the system to simultaneously connect to various heterogeneous medical sensors and devices, such as ECG, pulse oximetry, and imaging equipment, enabling real-time parallel input of multi-source data and laying a solid hardware foundation for multimodal collaborative acquisition.

[0036] At the data transmission level, the system defines and parses a standardized predefined data frame structure, which is required to include a device identifier, data type identifier, acquisition sequence number, and original data payload. Through this mechanism, regardless of the complexity of the data source, the edge terminal can uniformly identify and standardize its management, effectively distinguishing between vital signs, image streams, or event records, ensuring the consistency and traceability of heterogeneous data during the parsing phase.

[0037] To ensure accuracy in the time dimension, the system relies on a built-in high-precision hardware clock module to immediately lock the count value and generate a receiving timestamp the instant the data frame physically arrives at the interface. Subsequently, the system performs reverse compensation calculations based on a preset interface transmission delay model, deducting fixed delays from different communication links and internal device processing, thereby deriving a collection timestamp that accurately reflects the moment the physiological event occurred, eliminating time errors caused by transmission lag.

[0038] Building upon this foundation, the system proactively corrects clock deviations between access devices and edge terminals through a periodic clock synchronization handshake protocol, unifying the dispersed local sampling times into the system time domain. Utilizing a constructed global continuous timeline queue, the system rigorously sorts the corrected data according to the acquisition timestamp. For differences in sampling frequencies (such as high-frequency ECG and low-frequency blood pressure), time window interpolation or resampling techniques are employed to achieve precise alignment of multimodal data on the logical time grid, forming multi-parameter snapshots at the same physical moment.

[0039] Regarding data integrity assurance, the system monitors the continuity of acquisition sequence numbers in data frames in real time. Once a sequence number interruption or abnormal jump is detected, the system automatically marks it as a packet loss or delay event and provides a notification in subsequent analysis. Simultaneously, when a device is detected to be temporarily offline or communication is interrupted, the edge terminal immediately activates a local caching mechanism to temporarily store received data, preventing the loss of critical monitoring information due to network fluctuations.

[0040] Finally, once the offline device reconnects, the system automatically reads the data from the local cache and seamlessly merges it into the correct position on the global continuous timeline based on its precise timestamps. This mechanism not only fills the data gaps during the disconnection period but also ensures the continuity and temporal consistency of multimodal monitoring data throughout its entire lifecycle, providing a complete and reliable time-series basis for subsequent clinical diagnosis and data analysis.

[0041] This embodiment breaks down communication barriers between different medical devices by using parallel access through multiple interfaces and unified parsing of heterogeneous data frames, achieving efficient aggregation and standardized management of multi-source vital sign data. By utilizing hardware-level stamping, latency reverse compensation, and global timing alignment technologies, it effectively eliminates latency differences in multi-channel data transmission, ensuring strict synchronization of multimodal data with millisecond-level accuracy. Furthermore, by combining local caching after disconnection with a seamless merging mechanism after reconnection, it completely solves the problems of data loss and timing discontinuity caused by network fluctuations, ensuring the integrity and continuity of monitoring data throughout the entire life cycle, and providing a highly reliable data foundation for subsequent accurate diagnosis.

[0042] S30. Based on the unified time axis, extract multidimensional motion features and identify the transport environment status to construct a physiological motion coupling data system; share the generated motion tags to the signal processing module in real time to drive the adaptive anti-interference algorithm to dynamically switch filtering strategies, and use motion features to identify the authenticity of physiological abnormalities.

[0043] Understandably, the system uses an inertial measurement unit to collect raw data, extracts multidimensional features covering the time domain, frequency domain, and posture through a sliding window algorithm, and combines pattern recognition technology to determine the patient's posture changes and movement patterns in real time. The system converts the identified movement states into independent modal data, and synchronously fuses them with vital sign data based on a unified timestamp to generate a physiological motion coupling data stream with movement state labels and quality scores. It also extracts key movement context information from the data, constructing a complete physiological motion coupling data system.

[0044] Based on this, the system dynamically adjusts its signal processing strategy according to the extracted motion context. When motion interference is detected, it automatically activates adaptive filtering and baseline drift suppression algorithms, or intelligently reduces the weight of the interfered sensor and delays intermittent measurements, thereby effectively eliminating motion artifacts and preventing false alarms. Simultaneously, the system establishes a two-way correlation mechanism between motion patterns and clinical events, using motion features to help identify the causes of physiological abnormalities (such as distinguishing between true arrhythmias and motion artifacts). Based on this, it performs alarm suppression, downgrade processing, or triggers specific emergency alarms, significantly improving the accuracy of monitoring data and the reliability of clinical decisions.

[0045] Optionally, step S30, namely, extracting multidimensional motion features based on the unified time axis and identifying the transport environment state to construct a physiological motion coupling data system; sharing the generated motion tags to the signal processing module in real time to drive the adaptive anti-interference algorithm to dynamically switch filtering strategies, and using motion features to identify the authenticity of physiological abnormalities, includes: S301. Raw data is collected using an inertial measurement unit, and multi-dimensional features including time domain, frequency domain and attitude are extracted through a sliding window algorithm. Combined with pattern recognition technology, attitude changes and motion patterns are determined in real time. S302. The identified motion state is converted into independent modal data, and synchronously fused with vital sign data based on a unified timestamp to generate a physiological motion coupling data stream with motion state labels and quality scores, and motion context information is extracted from the physiological motion coupling data stream. S303. Dynamically adjust the signal processing strategy according to the motion context: automatically enable adaptive filtering and baseline drift suppression algorithm under motion interference, or reduce the weight of specific sensors and delay intermittent measurements to eliminate motion artifacts and prevent false alarms. S304. Establish a two-way correlation mechanism between movement patterns and clinical events, use movement characteristics to help identify the causes of physiological abnormalities, and execute alarm suppression, downgrading or triggering specific emergency alarms accordingly.

[0046] Understandably, the edge monitoring terminal utilizes an integrated high-precision six-axis inertial measurement unit (IMU) to acquire raw triaxial acceleration and angular velocity data of the device or patient in real time. The system performs in-depth processing on the data within a continuous time window using a sliding window algorithm, extracting multi-dimensional motion features including root mean square acceleration, spectral energy distribution, and pitch / roll angle variation amplitude. Combined with a pre-trained pattern recognition algorithm, the system can accurately determine the current motion pattern, such as distinguishing between stationary micro-movements, low-frequency vibrations caused by continuous transport, sudden posture changes caused by falls, and rhythmic shaking caused by shivering or convulsions, providing precise motion context for subsequent anti-interference processing.

[0047] The system transforms identified motion states into independent modal data, which is then synchronously fused with vital signs data such as ECG and blood oxygenation based on a unified timestamp to generate a physiological-motor coupled data stream with motion state labels and motion quality scores. This mechanism not only records physiological parameters but also fully preserves the motion environment information at the time the parameters were generated. The extracted motion context information is pushed to the signal processing and alarm module in real time through a standardized interface, serving as the core basis for dynamically adjusting monitoring strategies, thus achieving a leap from simple data collection to "perception-cognition" collaborative processing.

[0048] Based on the real-time motion context, the system dynamically switches signal processing strategies to eliminate motion artifacts and prevent false alarms. When "continuous transport" or high-amplitude vibration is detected, the system automatically activates adaptive filters or wavelet transform algorithms to suppress baseline drift of the ECG signal, while reducing the calculation weight of blood oxygen data or marking it as a reference value. For intermittent measurements such as non-invasive blood pressure, the system intelligently delays the measurement cycle or prompts medical staff to repeat the measurement after the patient has stabilized. This adaptive mechanism significantly improves the purity and reliability of vital sign data in dynamic environments.

[0049] Furthermore, the system establishes a two-way correlation mechanism between movement patterns and clinical events, using movement characteristics to help identify the causes of physiological abnormalities, thereby implementing intelligent alarm management. If a sudden increase in heart rate is accompanied by a "sudden change in posture," the system determines it as a postural change and automatically suppresses or downgrades the alarm; if the abnormal waveform matches the characteristics of "rhythmic jerking," it triggers a specific convulsion or chills warning; if the system detects a patient instantly transitioning from a state of motion to "sudden stillness" accompanied by a sudden drop in heart rate, it immediately determines a risk of syncope or cardiac arrest and triggers the highest level of emergency alarm. This deep correlation analysis not only reduces false alarms but also provides crucial differential diagnostic evidence for clinical decision support.

[0050] This embodiment achieves real-time and accurate identification of motion states through inertial measurement units and multidimensional feature extraction technology, and deeply integrates it with vital sign data to construct a physiological motion coupled data stream with quality scores. Based on the dynamic adjustment of signal processing strategies according to motion context, motion artifact interference is effectively eliminated, significantly reducing baseline drift and false alarm rates caused by patient activity. At the same time, a two-way correlation mechanism between motion patterns and clinical events is established, which not only improves the ability to identify the causes of abnormalities, but also intelligently distinguishes between real critical situations and motion interference, thereby optimizing alarm grading strategies and improving the clinical reliability and response accuracy of the monitoring system.

[0051] S40. Obtain multi-physical quantity observation values ​​of the same physiological indicator in parallel through a heterogeneous redundant observation architecture, and perform spatiotemporal alignment with the motion tag; use a dynamic consistency analysis strategy to perform real-time comparison and confidence determination of multi-source data to obtain comparison results and confidence determination results; perform weighted fusion of the multi-physical quantity observation values ​​based on the comparison results and the confidence determination results to obtain physiological parameters; when an anomaly is detected in the comparison results, activate the anomaly intelligent adjudication mechanism to trace the source of interference and dynamically block low-quality channels.

[0052] Understandably, the system is configured with multi-source sensing channels based on different physical sensing principles to monitor the same physiological indicator in parallel, and performs strict spatiotemporal alignment with the motion tags generated in the aforementioned steps. By running multimodal signal processing algorithms in parallel, the system extracts feature parameters from each heterogeneous channel, maps them uniformly to a global continuous time axis, forms a multidimensional parameter vector for real-time comparison, and constructs a highly reliable heterogeneous redundant observation architecture.

[0053] During the data fusion phase, the system calculates the deviation of multi-source observations in real time and applies an adaptive dynamic threshold to determine their consistency. For data that passes the consistency test, the system automatically improves its quality level and uses a signal-to-noise ratio-based weighted average algorithm for fusion output, effectively reducing random errors and significantly improving the measurement accuracy of the final physiological parameters. At the same time, the system generates real-time comparison results and confidence level judgment results, providing quantitative basis for subsequent decision-making.

[0054] When an anomaly is detected due to excessive differences in multi-source data, the system immediately activates the intelligent anomaly adjudication mechanism. This mechanism combines motion context information to trace the signal quality, automatically identifies and marks low-confidence channels, dynamically adjusts alarm weights, or directly suppresses false alarms caused by single-channel failures. If necessary, the system will also prompt medical staff to perform retesting to ensure accurate and reliable monitoring data is output even in complex interference environments.

[0055] Optionally, step S40 involves acquiring multiple physical quantity observations of the same physiological indicator in parallel using a heterogeneous redundant observation architecture, and performing spatiotemporal alignment with the motion tag; using a dynamic consistency analysis strategy to perform real-time comparison and confidence determination of multi-source data to obtain comparison results and confidence determination results; weighting and fusing the multiple physical quantity observations based on the comparison results and the confidence determination results to obtain physiological parameters; and when an anomaly is detected in the comparison results, activating an intelligent anomaly adjudication mechanism to trace the source of interference and dynamically block low-quality channels, including: S401. Configure multi-source sensing channels based on different physical sensing principles to monitor the same physiological indicator in parallel. S402. Parallel execution of multimodal signal processing algorithms, extracting feature parameters from different sensing channels respectively, and mapping all calculation results to a global continuous time axis to form a multidimensional parameter vector for comparison; S403. Real-time calculation of the deviation of multi-source observations and application of adaptive dynamic threshold to determine consistency. S404. For consistent data, improve its quality level and use a signal-to-noise ratio-based weighted average algorithm for fusion output to reduce random errors and improve measurement accuracy. S405. When the difference between multi-source data exceeds the limit, the abnormal adjudication mechanism is triggered. By combining the motion context to trace the signal quality, low-confidence channels are automatically marked, alarm weights are dynamically adjusted, or false alarms are suppressed.

[0056] Understandably, the system first configures multi-source sensing channels based on different physical sensing principles to construct a heterogeneous redundant observation architecture for key vital signs. By deploying electrophysiological (e.g., ECG), optical (e.g., PPG), and pressure sensing devices in parallel, the system can independently and synchronously monitor the same physiological indicators such as heart rate and respiration. This design ensures that when one type of sensor fails due to poor contact, motion interference, or inherent limitations, other sensing channels can still provide effective observations, laying a solid hardware foundation for multimodal cross-validation.

[0057] Building upon this foundation, the system executes multimodal signal processing algorithms in parallel, extracting feature parameters from different sensing channels. For example, it simultaneously calculates the RR interval of the electrocardiogram and the peak-to-peak interval of the pulse wave to obtain both "electrical" and "optical" heart rates, or combines impedance respiration and plethysmography to extract dual respiratory frequencies. All calculation results are uniformly mapped to a global continuous time axis, forming a multidimensional parameter vector at the same moment, providing standardized data input for subsequent consistency comparison and confidence fusion.

[0058] The system calculates the absolute and relative deviation percentages between multi-source observations in real time and applies an adaptive dynamic threshold to determine data consistency. This threshold is not fixed but dynamically adjusted based on the motion state identified in step S30: strict deviation limits are applied in a static state, while the threshold is appropriately relaxed in a high-motion state to distinguish between real pathological changes and fluctuations caused by environmental interference, thereby accurately determining whether the observation results of each channel are in a consistent state.

[0059] For observational data deemed consistent, the system automatically upgrades its quality level (e.g., marking it as high confidence) and uses a signal-to-noise ratio (SNR)-based weighted averaging algorithm for fusion output. By dynamically allocating the weights of each channel, the system effectively reduces the random error of a single sensor, significantly improves the accuracy and stability of the final measurement parameters, and ensures that the output vital sign data has extremely high clinical reference value.

[0060] When the difference between multi-source data exceeds a preset range, the system immediately triggers an anomaly adjudication mechanism. This mechanism combines motion context information to trace the signal quality and diagnose whether the problem stems from sensor malfunction or motion artifacts. The system automatically marks the interfered channel as "low confidence" or "interference state" and provides a prompt in the user interface; simultaneously, it dynamically reduces or blocks the weight of abnormal channels in the alarm logic to avoid false alarms caused by a single data source error.

[0061] Furthermore, to address abnormal discrepancies in key indicators, the system implements an intelligent fault-tolerance strategy: if only a single valid source remains, the alarm triggering time threshold is increased to prevent momentary false alarms; if all sources are unreliable, a "signal monitoring insufficient" message is output instead of a pathological alarm. For intermittent measurements such as non-invasive blood pressure that significantly deviate from continuous monitoring trends, the system will proactively prompt for retesting. This series of mechanisms effectively eliminates interference noise in complex environments, ensuring the authenticity and reliability of the monitoring data.

[0062] This embodiment significantly improves the robustness and anti-interference capability of single physiological indicator monitoring by using parallel monitoring of multi-physics principle sensing channels and multi-dimensional parameter vector construction, and by utilizing a redundant measurement mechanism. Based on the consistency judgment of adaptive dynamic threshold and the signal-to-noise ratio weighted fusion algorithm, random errors are effectively reduced, and measurement accuracy is maximized when the data is consistent. When abnormal differences in multi-source data are detected, the system can intelligently trace the source and perform hierarchical adjudication in combination with motion context, automatically suppressing false alarms caused by low-confidence channels, and ensuring the high specificity of critical alarms and the reliability of clinical decision-making.

[0063] S50. Deploy a multi-dimensional event monitoring engine to receive abnormal physiological parameter signals and the confidence level determination results in real time; when an event trigger point is captured, lock the full waveform, image and metadata within the dynamic time window, encapsulate them into an immutable multi-dimensional holographic structured event package, and establish a bidirectional index association.

[0064] Understandably, the system deploys a multi-dimensional event monitoring engine to receive abnormal physiological parameter signals and confidence level judgment results from preceding steps in real time. Based on a unified hardware clock benchmark, the engine continuously scans key physiological indicators and system status. Once it detects triggering conditions such as threshold exceeding limits, deterioration of nonlinear trends, abnormal multimodal consistency, or specific clinical operation instructions, it immediately and accurately locates and marks the event trigger point, ensuring a millisecond-level response to critical moments.

[0065] At the moment an event is triggered, the system adaptively extracts dynamic time window data containing preceding and following time periods based on the event type, seamlessly copying it from the circular buffer to a dedicated read-only event protection area. Subsequently, the system integrates the raw full waveform data, high-frequency parameter trends, image keyframes, environmental motion tags, and operational metadata, encapsulating them according to a predefined data structure into an atomic, tamper-proof, multi-dimensional holographic structured event package containing a unique ID and index pointer, and securely storing it to fully preserve the spatiotemporal topology of the event.

[0066] Finally, the system constructs a bidirectional indexing mechanism in the database that links event packets to the original continuous time-series data stream. This mechanism not only supports quickly locating the physical location of the original data by event ID, but also highlights event markers when replaying the full data stream, enabling efficient retrieval of massive amounts of data. Based on this, the system can synchronously reproduce vital sign waveforms, video images, motion postures, and operation logs on a unified timeline, providing panoramic data support for clinical review and diagnosis.

[0067] Optionally, step S50, namely deploying the multi-dimensional event monitoring engine, receives abnormal physiological parameter signals and the confidence level determination results in real time; when an event trigger point is captured, the full waveform, image, and metadata within the dynamic time window are locked, encapsulated into an immutable multi-dimensional holographic structured event package, and a bidirectional index association is established, including: S501, based on a unified hardware clock reference, assigns millisecond-level timestamps to all acquired multimodal data and appends them to the storage medium in strict timing order to form a continuous data stream that retains the complete timing topology. S502. Real-time scanning of key physiological parameters and system status, and precise location and marking of event trigger points based on multiple trigger conditions; the multiple trigger conditions include at least one of threshold exceeding the limit, nonlinear trend deterioration, multimodal consistency abnormality, and specific clinical operation instructions; S503. At the moment the event is triggered, dynamically extract the time window data of the preceding and following time periods according to the event type, and copy it from the circular buffer to a dedicated read-only event protection area. S504 integrates raw waveform data, high-frequency parameter trends, image keyframes / files, environmental motion tags, and operation metadata, and encapsulates them into atomic structured event packages containing unique IDs and index pointers according to a predefined data structure, and stores them securely. S505. Construct a bidirectional indexing mechanism between event packages and original continuous time series data streams in the database, supporting quick location of original data positions by event ID, or highlighting event markers when replaying data streams, to achieve fast retrieval of massive amounts of data; S506. Synchronously reproduce vital sign waveforms, video images, motion postures, and / or operation logs based on a unified timeline.

[0068] Understandably, the system uses a unified, high-precision hardware clock reference to assign millisecond-level timestamps to all acquired multimodal data (including waveforms, parameters, images, and operation logs). This data is appended to a circular buffer and persistent storage area in strict chronological order, forming a continuous and uninterrupted time-series data stream. This mechanism not only records the numerical values ​​themselves but also fully preserves the temporal topology of the data, ensuring that the system state at any historical moment can be accurately reconstructed, laying a solid foundation for subsequent event backtracking.

[0069] The system deploys a multi-dimensional event monitoring engine to continuously scan key vital signs parameters and system operating status in real time. Based on preset multiple trigger conditions, the system can accurately locate and mark event trigger points. These conditions cover threshold exceedances of physiological parameters (confirmed by anti-shake), deterioration of nonlinear trends (such as sudden increase in heart rate or ST segment depression), abnormal consistency of multimodal cross-observations, and specific clinical operation instructions performed by medical staff (such as defibrillation, drug administration, or manual marking), ensuring that all kinds of critical clinical moments can be automatically captured.

[0070] At the moment an event is triggered, the system immediately initiates a "data snapshot and locking" mechanism. Depending on the event type, the system adaptively extracts data from a dynamic time window centered on the trigger point (e.g., a longer period before and after an arrhythmia event, and a shorter period for a fall event). Subsequently, the system copies this critical data from the regular circular overwrite buffer and locks it in a dedicated "event protection zone." This area is marked as read-only and has the highest storage priority, strictly prohibiting it from being overwritten by new data, thus ensuring the integrity and immutability of the chain of evidence.

[0071] The generated event logs constitute a multi-dimensional holographic dataset, integrating raw waveform data, high-frequency parameter trends, image keyframes or complete DICOM files, environmental and motion state labels, and detailed operation metadata logs. The system encapsulates this rich information according to a predefined standardized data structure, forming an atomic "structured event package" containing a unique event ID, trigger timestamp, cause description, and data index pointer. This package is then securely stored locally or in the cloud, ensuring the integrity and consistency of the writing process.

[0072] To support efficient data management, the system has built a bidirectional indexing mechanism in the database, linking event packets to the raw continuous time-series data stream. This mechanism allows users to quickly locate specific millisecond-level positions in the raw data stream using event IDs, or to automatically highlight relevant event markers when replaying long-term raw data streams. This bidirectional association design enables millisecond-level rapid retrieval of massive amounts of monitoring data, greatly improving the efficiency of clinical review and data analysis.

[0073] Finally, the system provides visualized event playback and debriefing analysis functions based on a unified timeline. The interface can synchronously reproduce vital sign waveforms, video streams, motion animations, and operation logs, enabling doctors to intuitively observe the temporal causal relationships between physiological changes, environmental interference, and medical procedures. Through this multimodal synchronous playback, medical staff can clearly distinguish between real pathological changes and motion artifacts, trace the inflection points of disease progression, and assess the response time of treatment measures, thereby providing objective and detailed evidence for medical quality control, incident debriefing, and clinical training.

[0074] This embodiment constructs a continuous data stream that preserves the complete topological relationship through a millisecond-level unified timestamp and a strict time-series writing mechanism, ensuring precise synchronization of multimodal vital signs, imaging, and motion data in the time dimension. Combined with adaptive capture of dynamic windows under multiple trigger conditions and encapsulation into atomic event packages, it not only achieves panoramic black-box recording of key clinical moments but also effectively prevents the loss or tampering of important data. In addition, the bidirectional indexing mechanism and unified timeline playback function support second-level retrieval and synchronous reproduction of multi-source information under massive data, greatly improving the efficiency and accuracy of clinical post-event retrospective, dispute evidence collection, and complex case analysis.

[0075] S60. Construct a full-time clinical evolution view based on holographic data containing the multidimensional holographic structured event package, perform multidimensional data time-series correlation attribution, accurately identify pathological changes and environmental interference; implement macro-trend early warning and stage assessment to achieve an upgrade from instantaneous alarm to continuous perception; empower full-process quality control and review through visualization playback tools, and feed the analysis results back to the front-end algorithm to optimize anti-interference and adjudication strategies.

[0076] Understandably, the system integrates discrete physiological parameters, imaging evidence, and medical operation logs across a continuous timeline based on holographic data containing multidimensional holographic structured event packages. This breaks through the limitations of traditional single-point numerical values, reconstructing the coherent events throughout the patient's transport process and constructing a panoramic, full-time-series view of clinical evolution. On this basis, the system performs in-depth multidimensional data temporal correlation attribution analysis, automatically calculating the time lag and correlation between specific medical operations and subsequent physiological responses to assess treatment effectiveness. It also couples and maps environmental disturbances (such as strenuous exercise) with fluctuations in vital signs, thereby accurately identifying true pathological changes and external environmental interferences, and restoring the clinical truth.

[0077] Furthermore, the system utilizes long-term data fitting technology to identify gradual trends that, while not yet exceeding the limits, exhibit continuous deterioration and issue early warnings, thus upgrading from instantaneous alerts to continuous perception. Simultaneously, the system automatically divides the entire process into different treatment stages, quantifies the parameter stability and event frequency of each stage, and generates detailed comprehensive data reports. Through visualization playback tools, medical staff can simultaneously recreate waveforms, images, and operation logs, enabling quality control and debriefing analysis throughout the entire process, and intuitively displaying the evolution of the condition and the effectiveness of interventions.

[0078] Finally, the system establishes a closed-loop optimization mechanism, feeding back the interference characteristics, false alarm causes, and decision biases obtained from in-depth backend analysis to the frontend signal processing and anomaly decision-making algorithms in real time. This feedback loop drives the frontend algorithm to dynamically adjust adaptive filtering strategies, optimize multi-source data fusion weights, and correct anomaly discrimination thresholds, continuously improving the system's anti-interference capabilities and decision accuracy in complex transport environments, ensuring that the monitoring system iterates and improves with the evolution of clinical scenarios.

[0079] In steps S10-S60, a highly robust unified timeline is constructed through hot-swappable access of heterogeneous devices, hardware clock synchronization, and resampling technology, achieving seamless fusion of multi-source vital signs and imaging data and automatic merging of disconnected data. Combined with physiological motion coupling analysis and heterogeneous redundant observation architecture, the system can dynamically switch anti-interference strategies and intelligently adjudicate multi-source data conflicts, significantly improving the accuracy of parameter measurement and the specificity of alarms in complex transport environments. In addition, based on multi-dimensional holographic structured event packages and full-time clinical evolution views, this embodiment achieves an upgrade from instantaneous alarms to continuous perception, supporting accurate attribution, macro-trend early warning, and full-process quality control review, forming a closed-loop optimized intelligent monitoring system.

[0080] Optionally, step S60, namely, constructing a full-time clinical evolution view based on holographic data containing the multidimensional holographic structured event package, performing multidimensional data time-series correlation attribution, accurately identifying pathological changes and environmental interference; implementing macro-trend early warning and stage assessment to achieve an upgrade from instantaneous alarm to continuous perception; empowering full-process quality control and review through visualization playback tools, and feeding the analysis results back to the front-end algorithm to optimize anti-interference and adjudication strategies, includes: S601 integrates discrete physiological parameters, imaging evidence, and medical operation logs on a continuous time axis, breaking the limitations of single-point values ​​and reconstructing the continuous events of the patient throughout the entire transfer process. S602. Automatically analyze the time lag and correlation between specific medical and nursing operations and subsequent physiological responses to assess the effectiveness of treatment, and couple and map environmental disturbance events with fluctuations in vital signs. S603. Based on long-term data fitting, identify gradual trends that have not crossed the boundary but continue to deteriorate and provide early warnings. At the same time, divide the entire process into different treatment stages, quantify and statistically analyze the parameter stability and event frequency of each stage, and form a complete set of data.

[0081] Understandably, the system integrates discrete physiological parameters, imaging evidence, and medical operation logs across a continuous timeline, completely breaking away from the single-point limitations of traditional monitoring that focuses only on current values. By utilizing full-time data, the system reconstructs the entire process of a patient's transfer, from on-site emergency care to in-hospital handover, into a coherent story of the patient's condition's evolution. This panoramic view allows doctors to make comprehensive judgments based on dynamic trajectories, rather than relying on a single static snapshot upon admission, thus more accurately grasping the true progression of the patient's condition.

[0082] Building upon this foundation, the system establishes a multi-dimensional temporal correlation analysis logic to assist clinical attribution and decision-making. On one hand, the system automatically performs "operation-effect" correlation analysis, demonstrating the time lag relationship and correlation between specific medical and nursing operations (such as medication administration and postural adjustment) and subsequent improvements in physiological parameters, helping doctors quantify and assess the effectiveness of treatment measures. On the other hand, the system conducts "environment-physiology" coupling analysis, mapping environmental disturbances such as vehicle bumps and sudden stops to concurrent fluctuations in vital signs, assisting doctors in accurately distinguishing between pathological deterioration and environmental artifacts, significantly improving diagnostic accuracy.

[0083] Furthermore, the system provides a macro-trend early warning function based on long-term data, compensating for the shortcomings of instantaneous threshold alarms. Through trend fitting algorithms, the system can identify parameters that have not yet crossed the alarm threshold but show signs of continuous deterioration (such as a slow, linear decline in blood pressure), issuing an early "trend deterioration" warning to gain an intervention window. Simultaneously, the system divides the entire process into different stages such as on-site emergency care, transport, and in-hospital handover, automatically calculating the parameter stability and event frequency at each stage to generate quantitative, phased status assessment reports, providing objective, holistic data support for emergency quality control, process optimization, and clinical teaching.

[0084] This embodiment reconstructs a coherent event chain throughout the entire patient transport process by integrating multimodal discrete data and operation logs, breaking through the limitations of traditional single-point numerical monitoring and achieving a leap from fragmented recording to panoramic disease course reconstruction. Utilizing time lag analysis of operation and physiological response and environmental coupling mapping technology, it can quantitatively assess the effectiveness of treatment measures and accurately distinguish between pathological fluctuations and environmental interference, providing causal evidence for clinical decision-making. Furthermore, based on the gradual trend identification and phased quantitative statistics of long-term data, it not only achieves early warning of risks of deterioration before exceeding the risk threshold but also forms a complete data asset covering the entire life cycle, significantly improving the quality control and scientific research analysis capabilities of critical care transport.

[0085] like Figure 2 As shown, Figure 2 This is another flowchart of the hardware-coordinated multimodal monitoring method in this embodiment. The core concept of this embodiment is to build a deeply coupled closed-loop monitoring system integrating "perception - fusion - decision - backtracking". This embodiment is not a simple stacking of functional steps, but rather uses a unified timeline as the framework, and through real-time interaction and logical locking of multidimensional data, links the six steps (S10-S60) into an indispensable organic whole.

[0086] Specifically, the system utilizes the high-precision hardware clock in S20 to establish a unique unified timeline. The basic vital signs collected in S10 (fast start), the environmental status labels generated in S30 (motion interference recognition), the heterogeneous physiological data acquired in S40 (multimodal cross-validation), and the operational metadata in S50 (retrospective event recording) must all be strictly mapped to this timeline. S20 is the system's timing foundation; without this time base to provide clock deviation correction, subsequent cross-modal data comparisons will fail due to timing misalignment, making true "multi-source fusion" impossible.

[0087] Using "motion" as a priori filter, a strong coupling is achieved between S30 and S40. In emergency dynamic environments, raw physiological signals are often overwhelmed by noise. The multidimensional motion features extracted by S30 are not displayed independently, but are transformed into real-time motion labels, which are directly injected into S40 as "prior knowledge." This coupling mechanism drives the adaptive algorithm to dynamically switch filtering strategies, enabling S40 to accurately distinguish between "pathological abnormalities" and "motion artifacts." Without the motion context input of S30, the dynamic consistency analysis of S40 would lose its anti-interference basis, inevitably leading to a high false alarm rate.

[0088] Using "confidence level" as the decision-making benchmark, a strong coupling is achieved between S40 and S50. S40 performs multi-physical quantity comparisons through a heterogeneous redundant observation architecture, outputting not only physiological parameters but also crucial confidence level determinations. This confidence level directly determines the sensitivity of event triggering and the priority of data locking in S50: only when the confidence level is determined to be high-risk will the system lock the full waveform and image into a holographic package. Without the intelligent adjudication and confidence assessment of S40, S50 would be unable to distinguish between "real critical events" and "false interference," resulting in invalid data filling the retrospective records and wasting storage resources.

[0089] Using "holographic retrospection" as a logical closed loop, strong coupling and feedback between S50 and S60 are achieved. The tamper-proof, multi-dimensional, holographic structured event package generated by S50 is the sole source of evidence for S60's clinical attribution analysis. S60 not only reconstructs the clinical evolution view using full-time data, but also feeds back the analysis results (such as anti-interference effect assessment and adjudication accuracy) to the front-end algorithm through visualization review tools. Figure 2(As shown by the dashed line in the middle), it is used to optimize the feature extraction threshold of S30 and the decision strategy of S40. This feedback mechanism realizes the leap from "passive recording" to "active evolution", ensuring the continuous adaptability of the system in complex environments.

[0090] Therefore, this embodiment connects the entire process through a unified time reference in S20, enhances the robustness of real-time monitoring by utilizing the motion-physiological coupling mechanism in S30 and S40, and finally achieves intelligent decision-making through the holographic recording and feedback loop in S50 and S60. Strict data dependencies and logical support exist between each step, collectively forming a highly reliable monitoring system adapted to extreme emergency environments.

[0091] Application Example 1: Earthquake Disaster Site Rescue Scenario In this example, facing challenges such as complex multiple injuries caused by earthquakes, the risk of internal bleeding, and severe jolting and network instability during transport, the multimodal monitoring system aims to provide continuous, robust monitoring and traceable recording. Upon discovering an injured person, rescuers quickly don portable monitoring equipment, and the system immediately completes self-test initialization, prioritizing the collection of core vital signs such as ECG, blood oxygen, blood pressure, and respiratory and temperature measurements to build a basic monitoring view. Simultaneously, handheld ultrasound and portable DR imaging diagnostic devices are seamlessly connected via hot-swappable technology. Rapid scans and imaging data of the injured person's abdomen, chest, or fracture sites are automatically analyzed and fused into a unified timeline, enabling the immediate establishment of a multidimensional initial screening from single vital signs to "vital signs + imaging."

[0092] During the dynamic transfer of injured personnel from stretchers to ambulances, the system utilizes hardware clock synchronization and resampling technology to accurately map multi-source data collected by heterogeneous devices onto a continuous time axis. Even in the face of frequent aftershocks and severe shaking caused by rugged road surfaces, it ensures that data is not lost or out of order. Based on a unified time axis, the system extracts multi-dimensional motion features in real time, identifies the current state of severe transport, and shares the generated "high motion load" label to the signal processing module in real time. This mechanism drives an adaptive anti-interference algorithm to dynamically switch filtering strategies, effectively filtering out artifacts caused by turbulence. Simultaneously, it uses motion features to distinguish between genuine and false physiological abnormalities, preventing false alarms caused by environmental interference and ensuring the accuracy of parameter measurements in extreme dynamic environments.

[0093] To address the common risks of occult blood loss or shock among earthquake victims, the system employs a heterogeneous redundant observation architecture to acquire multiple physical quantity observations of the same physiological indicator in parallel, combining these with motion tags for spatiotemporal alignment and dynamic consistency analysis. When a gradual trend is detected, such as a sustained increase in heart rate, a gradual decrease in blood pressure, and a slow decline in blood oxygen, the system does not simply trigger an instantaneous alarm. Instead, it activates an intelligent anomaly adjudication mechanism to trace the source of interference and confirm the fact that the condition is deteriorating. Once a genuine risk event is determined, the system immediately locks the full waveform, ultrasound image segments, and metadata within the dynamic time window, encapsulates them into an immutable multidimensional holographic structured event package, and marks it as "suspected risk of hemorrhagic shock," providing a reliable decision-making basis for on-site triage and treatment.

[0094] In complex network environments such as mountainous areas with signal blind spots or unstable communication at temporary command points, the system demonstrates strong data continuity assurance capabilities. When the network is interrupted, the multi-dimensional event monitoring engine continues to receive abnormal physiological parameter signals and confidence level judgment results in real time, packaging and storing all vital signs, operation logs, image indexes, and event packages locally in strict accordance with a unified timestamp strategy. Upon transfer to a network-restored area or arrival at the receiving point, the system automatically performs seamless data merging, uploading the locally stored data to the cloud or command center in chronological order. This mechanism ensures the integrity of the data chain from on-site emergency care to transfer, preventing the loss of critical information on the evolution of the patient's condition.

[0095] When injured patients arrive at the hospital emergency department for handover, doctors no longer rely solely on the results of a single examination upon admission. Instead, they quickly grasp the full picture of the injury through a comprehensive time-series clinical evolution view. Doctors can trace back along the timeline to view the continuous changes in vital signs during transport, accurately pinpoint the specific time points of on-site ultrasound examinations and any abnormal signs found, review DR images, and analyze detailed data before and after the triggering of risk events. The system's multi-dimensional data time-series correlation and attribution analysis helps doctors accurately identify which fluctuations are caused by pathological changes and which are caused by interference from the transport environment, thereby quickly formulating subsequent surgical or treatment plans and significantly reducing the time spent on pre-hospital and in-hospital information coordination.

[0096] After the rescue mission concludes, management can utilize the system's visualization and playback tools for full-process quality control and debriefing. By recreating the complete data chain from the earthquake rescue site, managers can objectively assess changes in injuries and the timeliness of medical interventions, analyze the specific impact of drastic transfers on the vital signs of the injured, and verify the effectiveness of anti-interference algorithms in extreme environments. The comprehensive system report generated based on this holographic data not only provides a detailed summary of the rescue effort but also feeds the analysis results back to the front-end algorithm, optimizing future anti-interference strategies and anomaly detection thresholds, forming a closed-loop optimized intelligent monitoring system, and continuously improving the practical ability to respond to large-scale disaster relief.

[0097] Application Example 2: Mountain Rescue and Helicopter Transfer Monitoring Scenarios In this example, facing extreme challenges such as severe turbulence caused by rugged terrain, continuous low-frequency vibrations from helicopter flight, and unstable communication signal coverage, the multimodal monitoring system ensures continuous and highly reliable monitoring services in a heterogeneous device integration environment through a hardware collaboration mechanism. After rescuers discover an injured person in the field, they immediately activate the edge monitoring terminal. The system powers on, completes self-test initialization, and prioritizes the acquisition of core vital signs such as electrocardiogram (ECG), blood oxygen saturation (SpO2), and non-invasive blood pressure to build a basic monitoring view. Even when the equipment is not fully ready, the system can display waveforms in real time, ensuring that rescuers can grasp vital signs immediately. Subsequently, a portable handheld ultrasound and satellite communication module are seamlessly connected via hot-swappable technology. The system automatically parses standardized data frames, incorporating ultrasound images, satellite link status, and vital sign data into unified management, laying the foundation for subsequent multimodal fusion.

[0098] To address the need for multi-device collaboration in the field, the system utilizes a hardware clock to perform time base assignment and transmission delay compensation, establishing a unified timeline. Through clock skew correction and resampling technology, the system precisely maps ultrasound image frames, satellite data packets, and high-frequency vital sign data to the same continuous timeline. Even if equipment experiences a brief disconnection during transport, the system can automatically and seamlessly merge data after reconnection, ensuring a complete data chain from the "field site" to the "helicopter cabin." This strict time alignment mechanism guarantees the precise correspondence between changes in physiological parameters and examination actions (such as the timing of ultrasound scans) in subsequent analysis, eliminating diagnostic misjudgments caused by time asynchrony.

[0099] During the highly dynamic transport and flight phases, the system extracts multi-dimensional motion features based on a unified time axis, constructing a physiological motion coupled data system. During stretcher transport, the IMU identifies "low-frequency, large-amplitude swaying" features, generates motion tags, and shares them in real-time with the signal processing module. This drives an adaptive anti-interference algorithm to dynamically switch filtering strategies, effectively suppressing ECG baseline drift. Once in helicopter flight, the system identifies continuous vibrations at specific frequencies generated by the rotor through frequency domain analysis, labeling them as environmental noise. Utilizing motion features to distinguish between genuine and false physiological abnormalities, the system outputs a "motion interference index" in real-time, indicating the reliability of the current data to medical staff. This prevents vibration artifacts from being misjudged as serious arrhythmias such as premature ventricular contractions, significantly improving alarm specificity in extreme environments.

[0100] To ensure the accuracy of decision-making, the system acquires multiple physical quantity observations of the same physiological indicator in parallel through a heterogeneous redundant observation architecture and performs spatiotemporal alignment with motion tags. When the ECG shows an abnormally high heart rate during flight, the system uses a dynamic consistency analysis strategy to compare the PPG pulse rate and blood pressure trends in real time: if the PPG pulse rate increases synchronously and the waveform is good, it is determined to be a physiological acceleration; if the PPG signal quality decreases due to vibration, the confidence level of the ECG alarm is reduced. Furthermore, if an increase in heart rate is detected accompanied by a decrease in blood pressure, the system activates an abnormal intelligent adjudication mechanism, and after tracing and confirming non-interfering factors, triggers a "hemorrhagic shock" risk warning. This multi-source data weighted fusion and intelligent adjudication mechanism realizes an upgrade from single-parameter alarm to multi-modal cross-validation risk identification.

[0101] In the face of communication interruptions caused by blind spots in mountainous areas or signal interference, the system's deployed multi-dimensional event monitoring engine plays a crucial role. The system automatically switches to a highly reliable local caching mode, fully recording all waveforms, parameters, and operation logs. Once an event trigger point of a sudden change in condition (such as a sudden drop in blood pressure) is detected, the system immediately locks the full waveform, image, and metadata (including vibration data) within the dynamic time window, encapsulates it into an immutable multi-dimensional holographic structured event package, and establishes a bidirectional index association. This mechanism ensures that even in a completely network-free environment, critical "event snapshots" and evidence chains are not lost. Once the network is restored, they are automatically uploaded and archived in chronological order, achieving secure traceability of data throughout the entire process.

[0102] After the helicopter landed at the hospital, doctors constructed a full-time clinical evolution view based on holographic data containing multi-dimensional holographic structured event packages. Through a visualization playback tool, doctors could trace the entire process from "field scene – transport – helicopter flight – landing" with a single click, performing multi-dimensional data time-series correlation attribution. For example, doctors could clearly see that during the high-vibration period of helicopter flight, the ECG, after anti-interference processing, still showed the dynamic evolution of the ST segment, thus confirming myocardial ischemia rather than a misdiagnosis. This ability to accurately distinguish between pathological changes and environmental interference provides a decisive basis for the rapid diagnosis and surgical planning of critically injured patients, truly achieving a leap from instantaneous alarm to continuous perception.

[0103] Finally, after the rescue operation concluded, the management department utilized the system's full-process quality control and debriefing functions to conduct an in-depth analysis of the mountain rescue. By reproducing the execution of anti-interference strategies and risk assessment logic during the flight, the specific impact of the transfer process on the vital signs of the injured was evaluated, and the system's reliability under extreme vibration environments was verified. The analysis results will be fed back to the front-end algorithm to further optimize the filtering strategy and anomaly assessment threshold for helicopter vibrations at specific frequencies, forming a closed-loop optimized intelligent monitoring system to continuously improve the success rate of future rescues and the level of medical decision-making in complex wilderness environments.

[0104] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0105] In one embodiment, a hardware-coordinated multimodal monitoring system is provided, which corresponds one-to-one with the hardware-coordinated multimodal monitoring method described in the above embodiments. For example... Figure 3 As shown, the hardware-coordinated multimodal monitoring system includes: The quick-start monitoring module 10 is used to complete self-test initialization after the system is powered on, prioritize the collection of core vital signs and build a basic monitoring view, and support hot-swappable and plug-and-play access of imaging diagnostic equipment to achieve seamless data integration. The hardware collaboration and time alignment module 20 is used to connect heterogeneous medical devices in parallel through multiple interface aggregation nodes, parse standardized data frames for unified management, use hardware clock to perform time base assignment and transmission delay compensation to establish a unified time axis, and then use clock deviation correction and resampling technology to accurately map multi-source data to the same continuous time axis, and automatically perform seamless data merging after the device disconnection is restored. The motion interference identification module 30 is used to extract multi-dimensional motion features based on the unified time axis and identify the transport environment status, and construct a physiological motion coupling data system; the generated motion tags are shared to the signal processing module in real time to drive the adaptive anti-interference algorithm to dynamically switch the filtering strategy, and the motion features are used to identify the authenticity of physiological abnormalities; The multimodal cross-validation module 40 is used to acquire the observation values ​​of multiple physical quantities of the same physiological indicator in parallel through a heterogeneous redundant observation architecture, and perform spatiotemporal alignment with the motion tag; it uses a dynamic consistency analysis strategy to perform real-time comparison and confidence determination of multi-source data to obtain comparison results and confidence determination results; it performs weighted fusion of the observation values ​​of multiple physical quantities based on the comparison results and the confidence determination results to obtain physiological parameters; when an anomaly is detected in the comparison results, an anomaly intelligent adjudication mechanism is activated to trace the source of interference and dynamically block low-quality channels; The traceable event recording module 50 is used to receive abnormal physiological parameter signals and the confidence judgment results in real time by deploying a multi-dimensional event monitoring engine; when an event trigger point is captured, it locks the full waveform, image and metadata within the dynamic time window, encapsulates them into an immutable multi-dimensional holographic structured event package, and establishes a bidirectional index association. The clinical analysis and decision support module 60 is used to construct a full-time clinical evolution view based on holographic data containing the multidimensional holographic structured event package, perform multidimensional data time-series correlation attribution, accurately identify pathological changes and environmental interference; implement macro trend early warning and stage assessment, realizing the upgrade from instantaneous alarm to continuous perception; empower the whole process quality control and review through visualization playback tools, and feed the analysis results back to the front-end algorithm to optimize anti-interference and adjudication strategies.

[0106] Optionally, the quick-start monitoring module 10 is also used for: After the system is powered on, it automatically completes the edge terminal self-test and core module initialization; The system prioritizes driving fast-response sensors, which immediately initiate continuous data acquisition and real-time calculation of key parameters upon signal access; these fast-response sensors include ECG sensors, blood oxygen sensors, and respiration sensors. Based on the core vital sign data that is already in place, dynamic monitoring curves are rendered in real time and basic data recording services are started to quickly build a visualized basic monitoring view. During basic monitoring operations, the system supports on-demand dynamic access of the imaging diagnostic devices and automatically completes device identification and time-series association.

[0107] Optionally, the hardware coordination and time alignment module 20 is also used for: Configure the edge terminal as a multi-interface aggregation node, and connect to a variety of medical sensors and devices in parallel through wired and / or wireless links to establish a multi-channel data input environment; Define and parse predefined data frames containing device identifiers, data types, acquisition serial numbers, and raw payloads to achieve unified identification and standardized management of heterogeneous data from different sources; Based on the built-in hardware clock, the system performs a receiving stamp the instant the data physically arrives, and performs reverse compensation calculations in conjunction with a preset interface transmission delay model to generate an accurate collection timestamp. The device-to-device deviation is corrected by periodic clock synchronization handshake, the data is strictly time-sequentially sorted using a global continuous time axis queue, and the time alignment problem of multi-frequency data is solved by interpolation or resampling techniques. Real-time detection of the continuity of the collected sequence number, automatic marking of packet loss or abnormal events, and activation of the local caching mechanism when device communication is interrupted to ensure that data is not lost during the disconnection period; After the device is reconnected, the cached data is seamlessly merged into the global timeline in timestamp order, ensuring the continuity and temporal consistency of multimodal monitoring data throughout its lifecycle.

[0108] Optionally, the motion interference recognition module 30 is also used for: Raw data is collected using an inertial measurement unit, and multidimensional features including time domain, frequency domain, and attitude are extracted using a sliding window algorithm. Combined with pattern recognition technology, attitude changes and motion patterns are determined in real time. The identified motion states are converted into independent modal data, and synchronously fused with vital sign data based on a unified timestamp to generate a physiological motion coupling data stream with motion state labels and quality scores. Motion context information is then extracted from the physiological motion coupling data stream. The signal processing strategy is dynamically adjusted based on the motion context: under motion interference, adaptive filtering and baseline drift suppression algorithms are automatically enabled, or the weight of specific sensors is reduced and intermittent measurements are delayed, in order to eliminate motion artifacts and prevent false alarms. Establish a two-way correlation mechanism between movement patterns and clinical events, utilize movement characteristics to help identify the causes of physiological abnormalities, and execute alarm suppression, downgrading, or triggering specific emergency alarms accordingly.

[0109] Optionally, the multimodal cross-validation module 40 is also used for: Configure multi-source sensing channels based on different physical sensing principles to monitor the same physiological indicator in parallel. The multimodal signal processing algorithm is run in parallel to extract feature parameters from different sensing channels and all calculation results are mapped to a global continuous time axis to form a multidimensional parameter vector for comparison. Real-time calculation of the deviation of multi-source observations and application of adaptive dynamic threshold to determine consistency; For consistent data, improve its quality level and use a signal-to-noise ratio-based weighted average algorithm for fusion output to reduce random errors and improve measurement accuracy; When the difference between multi-source data exceeds the limit, the anomaly adjudication mechanism is triggered. By combining the motion context to trace the signal quality, low-confidence channels are automatically marked, alarm weights are dynamically adjusted, or false alarms are suppressed.

[0110] Optionally, the traceable event logging module 50 is also used for: Based on a unified hardware clock reference, all acquired multimodal data are assigned millisecond-level timestamps and appended to the storage medium in strict timing order to form a continuous data stream that retains the complete timing topology. Real-time scanning of key physiological parameters and system status; precise location and marking of event trigger points based on multiple trigger conditions; the multiple trigger conditions include at least one of threshold exceeding limits, deterioration of nonlinear trends, abnormal multimodal consistency, and specific clinical operation instructions; At the moment an event is triggered, dynamic time window data of the preceding and following time periods are adaptively extracted according to the event type and copied from the circular buffer to a dedicated read-only event protection area; Integrate raw waveform data, high-frequency parameter trends, image keyframes / files, environmental motion tags, and operation metadata, encapsulate them into atomic structured event packages containing unique IDs and index pointers according to a predefined data structure, and store them securely. A bidirectional indexing mechanism is built in the database to link event packages with the original continuous time series data stream. This mechanism supports quick location of the original data by event ID or highlights event markers when replaying the data stream, enabling fast retrieval of massive amounts of data. Based on a unified timeline, vital sign waveforms, video images, motion postures, and / or operation logs are synchronously reproduced.

[0111] Optionally, the clinical analysis and decision support module 60 is also used for: By integrating discrete physiological parameters, imaging evidence, and medical operation logs on a continuous time axis, the limitations of single-point numerical values ​​are broken, and the continuous events of the patient throughout the entire transfer process are reconstructed. Automatically analyze the time lag and correlation between specific medical and nursing procedures and subsequent physiological responses to assess the effectiveness of treatment, and couple and map environmental disturbance events with fluctuations in vital signs; Based on long-term data fitting, we can identify gradual trends that have not crossed the boundary but continue to deteriorate and provide early warnings. At the same time, we divide the entire process into different treatment stages, quantify the parameter stability and event frequency of each stage, and form a complete set of data.

[0112] Specific limitations regarding hardware-based collaborative multimodal monitoring systems can be found in the limitations of hardware-based collaborative multimodal monitoring methods described above, and will not be repeated here. Each module in the aforementioned hardware-based collaborative multimodal monitoring system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can invoke and execute the corresponding operations of each module.

[0113] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 4As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores data related to a hardware-coordinated multimodal monitoring method. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements a hardware-coordinated multimodal monitoring method.

[0114] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the hardware-coordinated multimodal monitoring method described in the above embodiment; to avoid repetition, this will not be repeated here. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the hardware-coordinated multimodal monitoring system embodiment; to avoid repetition, this will not be repeated here.

[0115] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When executed by a processor, the computer program implements the hardware-cooperative multimodal monitoring method described in the above embodiment. To avoid repetition, this will not be described again here. Alternatively, when executed by a processor, the computer program implements the functions of each module / unit in the hardware-cooperative multimodal monitoring system described in this embodiment. To avoid repetition, this will not be described again here.

[0116] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0117] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0118] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A hardware-coordinated multimodal monitoring method, characterized in that, include: After the system is powered on, it completes self-test initialization, prioritizes the collection of core vital signs and builds a basic monitoring view, and supports hot-swapping and plug-and-play access of imaging diagnostic equipment to achieve seamless data integration. Heterogeneous medical devices are accessed in parallel through multiple interface aggregation nodes, and standardized data frames are parsed for unified management; By utilizing a hardware clock to perform time base assignment and transmission delay compensation, a unified time axis is established; then, through clock skew correction and resampling technology, multi-source data is accurately mapped to the same continuous time axis, and data is automatically and seamlessly merged after the device disconnection is restored. Based on the unified time axis, multidimensional motion features are extracted and the transport environment status is identified to construct a physiological motion coupled data system; the generated motion tags are shared to the signal processing module in real time to drive the adaptive anti-interference algorithm to dynamically switch filtering strategies, and the motion features are used to identify the authenticity of physiological abnormalities; The observation values ​​of multiple physical quantities of the same physiological index are acquired in parallel through a heterogeneous redundant observation architecture, and spatiotemporal alignment is performed in combination with the motion tag. A dynamic consistency analysis strategy is used to perform real-time comparison and confidence determination of multi-source data to obtain comparison results and confidence determination results. Based on the comparison results and confidence determination results, the observation values ​​of the multiple physical quantities are weighted and fused to obtain physiological parameters. When an anomaly is detected in the comparison results, the anomaly intelligent adjudication mechanism is activated to trace the source of interference and dynamically block low-quality channels. Deploy a multi-dimensional event monitoring engine to receive abnormal physiological parameter signals and the confidence level determination results in real time; When an event trigger point is captured, the full waveform, image, and metadata within the dynamic time window are locked, encapsulated into an immutable multidimensional holographic structured event package, and a bidirectional index association is established. Based on holographic data containing the multidimensional holographic structured event package, a full-time clinical evolution view is constructed, multidimensional data time-series correlation attribution is performed, and pathological changes and environmental interference are accurately identified; macro-trend early warning and stage assessment are implemented to upgrade from instantaneous alarm to continuous perception; full-process quality control and review are empowered by visualization playback tools, and the analysis results are fed back to the front-end algorithm to optimize anti-interference and adjudication strategies.

2. The hardware-coordinated multimodal monitoring method according to claim 1, characterized in that, After power-on, the system completes self-test initialization, prioritizes the acquisition of core vital signs and constructs a basic monitoring view, and simultaneously supports hot-swapping and plug-and-play access of imaging diagnostic devices to achieve seamless data fusion, including: After the system is powered on, it automatically completes the edge terminal self-test and core module initialization; The fast-response sensor is prioritized and immediately initiates continuous data acquisition and real-time calculation of key parameters upon signal access; the fast-response sensor includes an electrocardiogram sensor, a blood oxygen sensor, and / or a respiration sensor. Based on the core vital sign data that is already in place, dynamic monitoring curves are rendered in real time and basic data recording services are started to quickly build a visualized basic monitoring view. During basic monitoring operations, the system supports on-demand dynamic access of the imaging diagnostic devices and automatically completes device identification and time-series association.

3. The hardware-coordinated multimodal monitoring method according to claim 1, characterized in that, The system connects heterogeneous medical devices in parallel through multiple interface aggregation nodes and parses standardized data frames for unified management. A unified timeline is established by utilizing a hardware clock to perform time base assignment and transmission delay compensation; then, through clock skew correction and resampling techniques, multi-source data is accurately mapped to the same continuous timeline, and seamless data merging is automatically performed after device disconnection and recovery, including: Configure the edge terminal as a multi-interface aggregation node, and connect to a variety of medical sensors and devices in parallel through wired and / or wireless links to establish a multi-channel data input environment; Define and parse predefined data frames containing device identifiers, data types, acquisition serial numbers, and raw payloads to achieve unified identification and standardized management of heterogeneous data from different sources; Based on the built-in hardware clock, the system performs a receiving stamp the instant the data physically arrives, and performs reverse compensation calculations in conjunction with a preset interface transmission delay model to generate an accurate collection timestamp. The device-to-device deviation is corrected by periodic clock synchronization handshake, the data is strictly time-sequentially sorted using a global continuous time axis queue, and the time alignment problem of multi-frequency data is solved by interpolation or resampling techniques. Real-time detection of the continuity of the collected sequence number, automatic marking of packet loss or abnormal events, and activation of the local caching mechanism when device communication is interrupted to ensure that data is not lost during the disconnection period; After the device is reconnected, the cached data is seamlessly merged into the global timeline in timestamp order, ensuring the continuity and temporal consistency of multimodal monitoring data throughout its lifecycle.

4. The hardware-coordinated multimodal monitoring method according to claim 1, characterized in that, The process involves extracting multidimensional motion features based on the unified timeline and identifying the transport environment state to construct a physiological-motor coupled data system. The generated motion tags are shared in real-time to the signal processing module, driving an adaptive anti-interference algorithm to dynamically switch filtering strategies. Motion features are then used to identify the authenticity of physiological abnormalities. This includes: Raw data is collected using an inertial measurement unit, and multidimensional features including time domain, frequency domain, and attitude are extracted using a sliding window algorithm. Combined with pattern recognition technology, attitude changes and motion patterns are determined in real time. The identified motion states are converted into independent modal data, and synchronously fused with vital sign data based on a unified timestamp to generate a physiological motion coupling data stream with motion state labels and quality scores. Motion context information is then extracted from the physiological motion coupling data stream. The signal processing strategy is dynamically adjusted based on the motion context: under motion interference, adaptive filtering and baseline drift suppression algorithms are automatically enabled, or the weight of specific sensors is reduced and intermittent measurements are delayed, in order to eliminate motion artifacts and prevent false alarms. Establish a two-way correlation mechanism between movement patterns and clinical events, utilize movement characteristics to help identify the causes of physiological abnormalities, and execute alarm suppression, downgrading, or triggering specific emergency alarms accordingly.

5. The hardware-coordinated multimodal monitoring method according to claim 1, characterized in that, The observations of multiple physical quantities of the same physiological index are acquired in parallel through a heterogeneous redundant observation architecture, and spatiotemporal alignment is performed in combination with the motion tags. A dynamic consistency analysis strategy is used to perform real-time comparison and confidence determination of multi-source data to obtain comparison results and confidence determination results. Based on the comparison results and confidence determination results, the observation values ​​of the multiple physical quantities are weighted and fused to obtain physiological parameters. When an anomaly is detected in the comparison results, an intelligent anomaly adjudication mechanism is activated to trace the source of interference and dynamically block low-quality channels, including: Configure multi-source sensing channels based on different physical sensing principles to monitor the same physiological indicator in parallel. The multimodal signal processing algorithm is run in parallel to extract feature parameters from different sensing channels and all calculation results are mapped to a global continuous time axis to form a multidimensional parameter vector for comparison. Real-time calculation of the deviation of multi-source observations and application of adaptive dynamic threshold to determine consistency; For consistent data, improve its quality level and use a signal-to-noise ratio-based weighted average algorithm for fusion output to reduce random errors and improve measurement accuracy; When the difference between multi-source data exceeds the limit, the anomaly adjudication mechanism is triggered. By combining the motion context to trace the signal quality, low-confidence channels are automatically marked, alarm weights are dynamically adjusted, or false alarms are suppressed.

6. The hardware-coordinated multimodal monitoring method according to claim 1, characterized in that, The deployed multi-dimensional event monitoring engine receives abnormal physiological parameter signals and confidence level determination results in real time. Upon capturing an event trigger point, it locks the full waveform, image, and metadata within a dynamic time window, encapsulates them into an immutable multi-dimensional holographic structured event package, and establishes a bidirectional index association, including: Based on a unified hardware clock reference, all acquired multimodal data are assigned millisecond-level timestamps and appended to the storage medium in strict timing order to form a continuous data stream that retains the complete timing topology. Real-time scanning of key physiological parameters and system status; precise location and marking of event trigger points based on multiple trigger conditions; the multiple trigger conditions include at least one of threshold exceeding limits, deterioration of nonlinear trends, abnormal multimodal consistency, and specific clinical operation instructions; At the moment an event is triggered, dynamic time window data of the preceding and following time periods are adaptively extracted according to the event type and copied from the circular buffer to a dedicated read-only event protection area; Integrate raw waveform data, high-frequency parameter trends, image keyframes / files, environmental motion tags, and operation metadata, encapsulate them into atomic structured event packages containing unique IDs and index pointers according to a predefined data structure, and store them securely. A bidirectional indexing mechanism is built in the database to link event packages with the original continuous time series data stream. This mechanism supports quick location of the original data by event ID or highlights event markers when replaying the data stream, enabling fast retrieval of massive amounts of data. Based on a unified timeline, vital sign waveforms, video images, motion postures, and / or operation logs are synchronously reproduced.

7. The hardware-coordinated multimodal monitoring method according to claim 1, characterized in that, The system constructs a full-time clinical evolution view based on holographic data containing the multidimensional holographic structured event package, performs multidimensional data temporal correlation attribution, and accurately identifies pathological changes and environmental interference; implements macro-trend early warning and stage assessment, achieving an upgrade from instantaneous alarm to continuous perception; empowers full-process quality control and review through visualization playback tools, and feeds the analysis results back to the front-end algorithm to optimize anti-interference and adjudication strategies, including: By integrating discrete physiological parameters, imaging evidence, and medical operation logs on a continuous time axis, the limitations of single-point numerical values ​​are broken, and the continuous events of the patient throughout the entire transfer process are reconstructed. Automatically analyze the time lag and correlation between specific medical and nursing procedures and subsequent physiological responses to assess the effectiveness of treatment, and couple and map environmental disturbance events with fluctuations in vital signs; Based on long-term data fitting, we can identify gradual trends that have not crossed the boundary but continue to deteriorate and provide early warnings. At the same time, we divide the entire process into different treatment stages, quantify the parameter stability and event frequency of each stage, and form a complete set of data.

8. A hardware-coordinated multimodal monitoring system, characterized in that, include: The quick-start monitoring module is used to complete self-test initialization after the system is powered on, prioritize the collection of core vital signs and build a basic monitoring view, and support hot-swappable and plug-and-play access of imaging diagnostic equipment to achieve seamless data integration. The hardware collaboration and time alignment module is used to connect heterogeneous medical devices in parallel through multiple interface aggregation nodes and parse standardized data frames for unified management. By utilizing a hardware clock to perform time base assignment and transmission delay compensation, a unified time axis is established; then, through clock skew correction and resampling technology, multi-source data is accurately mapped to the same continuous time axis, and data is automatically and seamlessly merged after the device disconnection is restored. The motion interference identification module is used to extract multi-dimensional motion features based on the unified time axis and identify the transport environment status, and construct a physiological motion coupling data system; the generated motion tags are shared to the signal processing module in real time to drive the adaptive anti-interference algorithm to dynamically switch the filtering strategy, and the motion features are used to identify the authenticity of physiological abnormalities; The multimodal cross-validation module is used to acquire multiple physical quantity observations of the same physiological indicator in parallel through a heterogeneous redundant observation architecture, and perform spatiotemporal alignment in combination with the motion tag. A dynamic consistency analysis strategy is used to perform real-time comparison and confidence determination of multi-source data to obtain comparison results and confidence determination results. Based on the comparison results and confidence determination results, the observation values ​​of the multiple physical quantities are weighted and fused to obtain physiological parameters. When an anomaly is detected in the comparison results, the anomaly intelligent adjudication mechanism is activated to trace the source of interference and dynamically block low-quality channels. The traceable event recording module is used to receive abnormal physiological parameter signals and the confidence level determination results in real time by deploying a multi-dimensional event monitoring engine; When an event trigger point is captured, the full waveform, image, and metadata within the dynamic time window are locked, encapsulated into an immutable multidimensional holographic structured event package, and a bidirectional index association is established. The clinical analysis and decision support module is used to construct a full-time clinical evolution view based on holographic data containing the multidimensional holographic structured event package, perform multidimensional data time-series correlation attribution, accurately identify pathological changes and environmental interference; implement macro trend early warning and stage assessment, realizing the upgrade from instantaneous alarm to continuous perception; empower the whole process quality control and review through visualization playback tools, and feed the analysis results back to the front-end algorithm to optimize anti-interference and adjudication strategies.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the hardware-coordinated multimodal monitoring method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the hardware-coordinated multimodal monitoring method according to any one of claims 1 to 7.

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