Emergency treatment data quality control analysis method and device based on big data

Abstract

The embodiment of the invention provides an emergency data quality control analysis method and device based on big data. Wherein a microcirculation dynamic mapping model with time-space correlation is constructed and integrated through a nanofiber sensing array of the flexible electronic skin device. And performing time reference synchronous compensation and space coordinate transformation mapping on the model and spatio-temporal data of emergency treatment monitoring equipment to form a fusion data field, thereby realizing joint feature distribution of microcirculation parameters and emergency treatment parameters. The fused data field is subjected to mode coupling verification with a microcirculation compensation track map in a shock compensation mode feature library through a dynamic quality control engine, and when it is detected that mode mismatching exists between a parameter evolution path and a historical track, space-time signal fragments are reconstructed based on phase features and space distortion features; and generating a quality control report associated with the abnormal skin perfusion gradient node. According to the technical scheme provided by the embodiment of the invention, the quality control effect of the emergency data of the shock patient is improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of data quality control analysis, and in particular, to a method and device for emergency data quality control analysis based on big data. Background Art

[0002] With the increasing urgent clinical monitoring requirements for the microcirculation dysfunction and organ perfusion imbalance of shock patients, the technical requirements of the application scenario need to achieve the synchronous acquisition of multimodal data of capillary oscillation frequency spectrum, skin perfusion gradient and three-dimensional mechanical distribution topology, establish a spatio-temporal dynamic coupling model between microcirculation parameters and emergency vital signs, and have the ability of real-time early warning of pattern mismatch based on historical compensation trajectories.

[0003] Existing solutions currently use flexible electronic skin devices to achieve the acquisition of microcirculation parameters, obtain the mechanical distribution on the skin surface through nanofiber sensor arrays, and combine laser speckle imaging technology to monitor the blood perfusion gradient. Some systems optimize the device scheduling efficiency through time series prediction models, but lack a joint analysis mechanism for mechanical distortion and phase fluctuation.

[0004] The defects of existing solutions are that traditional electronic skin has a delay in capturing the high-frequency oscillation phase characteristics during the shock compensation period, resulting in a lag in the recognition of abnormal microcirculation states; the dynamic correlation modeling between the three-dimensional mechanical distribution topology and the skin perfusion gradient is insufficient, affecting the analysis accuracy of spatial distortion characteristics; the existing quality control engine relies on single-modal data verification and lacks a collaborative analysis mechanism for multimodal parameters, resulting in insufficient accuracy in abnormal node positioning and inability to achieve accurate matching of cross-scale parameter evolution paths. Summary of the Invention

[0005] The embodiments of the present application provide a method and device for emergency data quality control analysis based on big data to solve the problem of insufficient accuracy in abnormal node positioning in the prior art.

[0006] In a first aspect, the embodiments of the present application provide a method for emergency data quality control analysis based on big data, including:

[0007] Synchronously collect the capillary oscillation frequency spectrum, skin perfusion gradient distribution map and three-dimensional mechanical distribution topology of shock patients through the nanofiber sensor array of the flexible electronic skin device, and generate a microcirculation dynamic mapping model integrating the time dimension and spatial correlation;

[0008] Dynamically register the microcirculation dynamic mapping model with the spatio-temporal data of the emergency monitoring device, and form a fusion data field through time reference synchronous compensation and spatial coordinate transformation mapping. The fusion data field includes the joint distribution characteristics of microcirculation parameters and emergency parameters;

[0009] Input the fused data field into a dynamic quality control engine and perform pattern coupling verification with the microcirculation compensation trajectory atlas in the shock compensation mode feature library. The microcirculation compensation trajectory atlas is constructed based on the historical correlation between the capillary oscillation frequency spectrum and the skin perfusion gradient distribution map of multi-center shock cases;

[0010] When the dynamic quality control engine detects a pattern mismatch between the parameter evolution path of the fused data field and the microcirculation compensation trajectory atlas, reconstruct the spatio-temporal signal segment based on the phase characteristics of the capillary oscillation frequency spectrum and the spatial distortion characteristics of the three-dimensional mechanical distribution topology structure, and generate a quality control report related to the abnormal nodes of the skin perfusion gradient distribution map.

[0011] Optionally, the dynamic registration of the microcirculation dynamic mapping model with the spatio-temporal data of the emergency monitoring device, and the formation of a fused data field through time reference synchronization compensation and spatial coordinate transformation mapping, includes:

[0012] Establish a pulse synchronization channel between the time axis of the microcirculation dynamic mapping model and the clock source of the emergency monitoring device, and eliminate the time reference offset of multi-source data acquisition through an adaptive interpolation algorithm to generate a sequence of microcirculation oscillation waveforms aligned in the time dimension;

[0013] Extract the three-dimensional spatial coordinate system of the emergency monitoring device, and use an affine transformation algorithm to map the spatial characteristics of the three-dimensional mechanical distribution topology structure to the three-dimensional spatial coordinate system of the emergency monitoring device to form a spatial transformation matrix containing the distribution of the microvascular stress field;

[0014] Perform tensor fusion on the sequence of microcirculation oscillation waveforms aligned in the time dimension and the spatial transformation matrix to generate a fused data field with spatio-temporal continuity. The fused data field contains the dynamic propagation trajectory of the capillary oscillation frequency spectrum in the three-dimensional spatial coordinate system of the emergency monitoring device;

[0015] Verify the topological structure integrity of the fused data field through an edge computing node. When the mapping residual between the three-dimensional mechanical distribution topology structure and the three-dimensional spatial coordinate system of the emergency monitoring device exceeds the preset tolerance, mark the abnormal spatial nodes in the microcirculation dynamic mapping model and feedback them to the nanofiber sensing array for signal compensation and calibration.

[0016] Optionally, the inputting the fused data field into a dynamic quality control engine and performing pattern coupling verification with the microcirculation compensation trajectory atlas in the shock compensation mode feature library includes:

[0017] Establish a dynamic alignment channel between the time axis of the fused data field and the time reference of the shock compensation mode feature library, eliminate the time-phase deviation between historical case data and real-time data acquisition through a multi-scale time interpolation algorithm, and generate a time-dimension synchronized microcirculation parameter evolution sequence;

[0018] Extract the spatial distribution characteristics of the capillary oscillation frequency spectrum in the fused data field, and use a deep convolutional network to construct the spatial topological mapping relationship of the microcirculation compensation trajectory map in the shock compensation mode feature library based on the spatial distribution characteristics, so as to generate a spatial correlation matrix including the blood flow propagation path;

[0019] Input the microcirculation parameter evolution sequence and the spatial correlation matrix into the dynamic attention coupling module, adjust the feature contribution degrees of different regions through a spatio-temporal attention weight dynamic allocation mechanism, and generate a compensation mode similarity distribution map;

[0020] Based on the deviation detection result of the compensation mode similarity distribution map, when the local region similarity value is lower than the preset threshold, mark the abnormal nodes in the skin perfusion gradient distribution map, trigger the frequency domain signal compensation mechanism of the nanofiber sensing array, and update the historical correlation parameters of the shock compensation mode feature library.

[0021] Optionally, the inputting the microcirculation parameter evolution sequence and the spatial correlation matrix into the dynamic attention coupling module, adjusting the feature contribution degrees of different regions through a spatio-temporal attention weight dynamic allocation mechanism, and generating a compensation mode similarity distribution map includes:

[0022] Establish a spatio-temporal feature cross-correlation channel, perform three-dimensional tensor fusion on the oscillation phase fluctuation feature in the microcirculation parameter evolution sequence and the blood flow propagation path feature in the spatial correlation matrix, and perform multi-scale decomposition on the three-dimensional tensor fusion result through a frequency domain enhanced attention mechanism to generate an initial attention distribution map including spatio-temporal coupling weights;

[0023] Use a multi-head dynamic attention mechanism to decouple the regional features of the initial attention distribution map. Each attention head extracts the microvascular mechanical distortion gradient feature, the tissue perfusion phase difference feature, and the blood flow direction topological constraint feature respectively, and combines the sliding window mechanism to retain the complete context correlation of the most recent time step to generate a multi-scale spatio-temporal attention weight set;

[0024] Construct a dynamic gated residual fusion network, perform a residual perception connection on the multi-scale spatio-temporal attention weight set and the initial attention distribution map, and dynamically adjust the contribution weights of each attention head through a differentiable gated unit to generate an optimized spatio-temporal attention weight tensor;

[0025] Based on the optimized spatio-temporal attention weight tensor, calculate the similarity of compensation patterns, use the graph convolution propagation algorithm for the diffusion weight values in the spatial correlation matrix, generate a compensation pattern similarity distribution map in combination with the topological constraint conditions of the blood flow propagation path, and calibrate the frequency domain signals of the deviated regions through a residual compensation mechanism.

[0026] Optionally, to construct the dynamic gated residual fusion network, perform a residual-aware connection between the multi-scale spatio-temporal attention weight set and the initial attention distribution map, and dynamically adjust the contribution weights of each attention head through a differentiable gated unit to generate an optimized spatio-temporal attention weight tensor, including:

[0027] Establish a cross-level residual connection structure based on the hierarchical distribution characteristics of the multi-scale spatio-temporal attention weight set and the spatial topology constraints of the initial attention distribution map, couple the hierarchical distribution pattern of the microvascular mechanical distortion gradient features with the blood flow direction topological constraint features in the initial attention distribution map to form a three-dimensional attention cube containing a mechanical distortion compensation path;

[0028] Perform multi-level feature interaction on the three-dimensional attention cube through the cross-level residual connection structure, jointly encode the tissue perfusion phase difference features in the initial attention distribution map with the mechanical distortion gradient levels of the three-dimensional attention cube, and generate an attention enhancement map with residual compensation characteristics, where the residual compensation characteristics are derived from the spatial correlation parameters of the skin perfusion gradient distribution map in the microcirculation dynamic mapping model;

[0029] Generate a dynamic gated mask based on the time evolution pattern of the microvascular mechanical distortion gradient features, and the dynamic gated mask dynamically allocates the contribution weights of different mechanical distortion gradient levels in the attention enhancement map through a non-linear constraint mechanism to form a gated attention weight distribution that fuses microvascular mechanical features and blood flow propagation paths;

[0030] Perform spatial topology calibration on the gated attention weight distribution and the initial attention distribution map, and perform distortion compensation on the gated attention weight distribution based on the mapping residuals between the three-dimensional mechanical distribution topology structure and the three-dimensional space coordinate system of the emergency monitoring device to generate an optimized spatio-temporal attention weight tensor.

[0031] Optionally, to perform multi-level feature interaction on the three-dimensional attention cube through the cross-level residual connection structure, jointly encode the tissue perfusion phase difference features in the initial attention distribution map with the mechanical distortion gradient levels of the three-dimensional attention cube, and generate an attention enhancement map with residual compensation characteristics, where the residual compensation characteristics are derived from the spatial correlation parameters of the skin perfusion gradient distribution map in the microcirculation dynamic mapping model, including:

[0032] Establish a multi-level interaction path based on the spatio-temporal correlation between the distribution pattern of the mechanical distortion gradient hierarchy and the tissue perfusion phase difference characteristics in the initial attention distribution map, and embed the spatial correlation parameters of the skin perfusion gradient distribution map as the residual compensation benchmark into the channel coupling process of the multi-level interaction path;

[0033] Introduce a phase synchronization mechanism in the multi-level interaction path, cross-level match the time-varying fluctuation pattern of the tissue perfusion phase difference characteristics with the spatial evolution trend of the mechanical distortion gradient hierarchy, and generate a compensation channel containing a phase synchronization feature vector, where the phase synchronization feature vector inherits the phase constraint conditions of the capillary oscillation frequency spectrum in the microcirculation dynamic mapping model;

[0034] Enhance the features of the compensation channel through a spatial weight compensation module, non-linearly superimpose the spatial distortion parameters of the abnormal nodes in the skin perfusion gradient distribution map with the residual compensation benchmark of the mechanical distortion gradient hierarchy, and generate an attention-enhanced mapping with spatial constraints;

[0035] Input the phase synchronization feature vector in the attention-enhanced mapping and the residual compensation benchmark of the microvascular mechanical distortion gradient feature into a dynamic gating mask generation module, adaptively adjust the compensation intensity of the multi-level interaction path based on the spatial correlation parameters of the skin perfusion gradient distribution map, and generate an attention-enhanced mapping with residual compensation characteristics, where the residual compensation characteristics come from the spatial correlation parameters of the skin perfusion gradient distribution map in the microcirculation dynamic mapping model.

[0036] Optionally, when the dynamic quality control engine detects a pattern mismatch between the parameter evolution path of the fusion data field and the microcirculation compensation trajectory map, based on the phase characteristics of the capillary oscillation frequency spectrum and the spatial distortion characteristics of the three-dimensional mechanical distribution topology, reconstruct the spatio-temporal signal segment, and generate a quality control report related to the abnormal nodes of the skin perfusion gradient distribution map, including:

[0037] Based on the pattern mismatch event detected by the dynamic quality control engine, extract the discontinuous fluctuation segment of the phase characteristics in the capillary oscillation frequency spectrum and the set of mechanical distortion vectors in the spatial distortion characteristics of the three-dimensional mechanical distribution topology, and generate a phase unlocking feature set;

[0038] Input the phase unlocking feature set into the historical phase fluctuation pattern library of the microcirculation compensation trajectory map, match the historical phase compensation parameters consistent with the spatial distribution pattern of the set of mechanical distortion vectors within the current time window, and generate a spatio-temporal signal segment;

[0039] Based on the mapping residual calibration result of the three-dimensional mechanical distribution topological structure and the three-dimensional space coordinate system of the emergency monitoring device, perform mechanical distortion vector compensation on the spatio-temporal signal segment to generate a reconstructed mechanical distribution model that integrates phase correction features and mechanical distribution constraints;

[0040] Perform spatial topological mapping between the spatial nodes of the reconstructed mechanical distribution model and the abnormal nodes of the skin perfusion gradient distribution map, and generate a quality control report based on the discontinuous fluctuation segments of the phase-locked feature set and the spatial distortion features of the mechanical distortion vector set.

[0041] In a second aspect, an embodiment of the present application provides a big data-based emergency data quality control analysis device, including:

[0042] An acquisition module, configured to synchronously acquire the capillary oscillation frequency spectrum, the skin perfusion gradient distribution map, and the three-dimensional mechanical distribution topological structure of a shock patient through the nanofiber sensing array of the flexible electronic skin device, and generate a microcirculation dynamic mapping model that integrates the time dimension and spatial correlation;

[0043] A registration module, configured to perform dynamic registration on the microcirculation dynamic mapping model and the spatio-temporal data of the emergency monitoring device, and form a fusion data field through time reference synchronous compensation and spatial coordinate transformation mapping. The fusion data field includes the joint distribution characteristics of microcirculation parameters and emergency parameters;

[0044] A verification module, configured to input the fusion data field into a dynamic quality control engine and perform pattern coupling verification with the microcirculation compensation trajectory map in the shock compensation mode feature library. The microcirculation compensation trajectory map is constructed based on the historical correlation between the capillary oscillation frequency spectrum and the skin perfusion gradient distribution map of multi-center shock cases;

[0045] A reconstruction module, configured to, when the dynamic quality control engine detects a pattern mismatch between the parameter evolution path of the fusion data field and the microcirculation compensation trajectory map, reconstruct the spatio-temporal signal segment based on the phase characteristics of the capillary oscillation frequency spectrum and the spatial distortion characteristics of the three-dimensional mechanical distribution topological structure, and generate a quality control report associated with the abnormal nodes of the skin perfusion gradient distribution map.

[0046] In a third aspect, an embodiment of the present application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a big data-based emergency data quality control analysis method as described in the first aspect.

[0047] Fourthly, an embodiment of the present application provides a computer storage medium storing a computer program, which when executed by a computer, implements a method for quality control analysis of emergency data based on big data as described in the first aspect.

[0048] In the embodiment of the present application, the nano-fiber sensing array of the flexible electronic skin device is used to synchronously collect the capillary oscillation frequency spectrum, the skin perfusion gradient distribution map, and the three-dimensional mechanical distribution topology of a shock patient, and generate a microcirculation dynamic mapping model integrating the time dimension and spatial correlation; the microcirculation dynamic mapping model is dynamically registered with the spatio-temporal data of the emergency monitoring device, and a fusion data field is formed through time-base synchronous compensation and spatial coordinate transformation mapping, and the fusion data field includes the joint distribution characteristics of microcirculation parameters and emergency parameters; the fusion data field is input into a dynamic quality control engine and pattern coupling verification is performed with the microcirculation compensation trajectory map in the shock compensation mode feature library, and the microcirculation compensation trajectory map is constructed based on the historical correlation between the capillary oscillation frequency spectrum and the skin perfusion gradient distribution map of multi-center shock cases; when the dynamic quality control engine detects a pattern mismatch between the parameter evolution path of the fusion data field and the microcirculation compensation trajectory map, based on the phase characteristics of the capillary oscillation frequency spectrum and the spatial distortion characteristics of the three-dimensional mechanical distribution topology, a spatio-temporal signal segment is reconstructed, and a quality control report associated with the abnormal nodes of the skin perfusion gradient distribution map is generated.

[0049] The technical solution of the present application has the following beneficial effects:

[0050] By synchronously collecting the capillary oscillation frequency spectrum and the three-dimensional mechanical distribution topology through the nano-fiber sensing array, the synchronous acquisition efficiency of multi-modal physiological parameters is improved; the time drift and spatial misalignment of multi-source device data are eliminated by adopting a spatio-temporal reference synchronous compensation strategy, and the joint feature fusion of microcirculation parameters and emergency parameters is promoted; the reliability of shock compensation mechanism recognition is improved by pattern coupling verification based on the historical compensation trajectory map, and the real-time monitoring ability of the microcirculation parameter evolution path is enhanced; the precise positioning and topological correlation analysis of the abnormal nodes of the skin perfusion gradient distribution map are promoted by combining the phase fluctuation trajectory decomposition and the spatial distortion vector field reconstruction algorithm; the mismatch warning of the microcirculation compensation trajectory is realized through the dynamic quality control engine, and the formulation efficiency of the individualized treatment strategy for shock patients is improved.

[0051] Furthermore, by dynamically registering the spatio-temporal data of the microcirculation dynamic mapping model and the emergency monitoring device, eliminating the time drift between devices through time reference synchronization compensation, and combining spatial coordinate transformation mapping to achieve spatial alignment of multi-source data, a fusion data field containing the joint distribution characteristics of microcirculation parameters and emergency parameters is generated. This method improves the spatio-temporal consistency between microcirculation parameters and emergency vital signs, eliminates the time misalignment and spatial offset of multi-source device data, enhances the joint analysis ability of microcirculation perfusion gradient and hemodynamic parameters, and provides cross-scale data support for the dynamic verification of shock compensation mechanisms.

[0052] These aspects or other aspects of the present application will be more clearly understood in the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0054] Figure 1 The flowchart of an emergency data quality control analysis method based on big data provided by the present application is shown;

[0055] Figure 2 The structural schematic diagram of an emergency data quality control analysis device provided by the present application is shown;

[0056] Figure 3 The structural schematic diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0057] In order to enable those skilled in the art to better understand the solutions of the present application, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application.

[0058] In some processes described in the specification, claims, and the above drawings of the present application, there are multiple operations that appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear herein or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first" and "second" in this article are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do they limit that "first" and "second" are of different types.

[0059] The solution of this application aims to improve the monitoring accuracy and real-time performance of the microcirculation state of shock patients through advanced flexible electronic skin devices and data fusion technologies. Its core concept is to integrate multiple physiological parameters (such as capillary oscillation frequency spectrum, skin perfusion gradient distribution map, etc.) into a dynamic model, and through registration with the data of emergency monitoring devices, finally achieve effective prediction and abnormal detection of the shock compensation trajectory. This process not only enhances the capabilities of the clinical decision support system but also provides a scientific basis for personalized treatment.

[0060] Next, the technical solutions in the embodiments of this application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of this application.

[0061] Figure 1 The following is a flowchart of a method for quality control analysis of emergency data based on big data provided for the embodiments of this application, as Figure 1 shown, the method includes:

[0062] 101. Synchronously collect the capillary oscillation frequency spectrum, skin perfusion gradient distribution map, and three-dimensional mechanical distribution topology of a shock patient through the nanofiber sensing array of the flexible electronic skin device, and generate a microcirculation dynamic mapping model that integrates the time dimension and spatial correlation;

[0063] In this step, the flexible electronic skin device is an advanced sensor technology that realizes high-sensitivity acquisition of physiological signals through the nanofiber sensing array, fits the human body surface, and provides continuous and non-invasive monitoring functions.

[0064] The nanofiber sensing array consists of a large number of tiny fibrous sensors for synchronously collecting multiple physiological signals, including the capillary oscillation frequency spectrum, skin perfusion gradient distribution map, and three-dimensional mechanical distribution topology.

[0065] The capillary oscillation frequency spectrum is a biophysical signal reflecting the microcirculation state, and the local blood circulation is evaluated by analyzing the blood flow fluctuation frequency in the capillaries.

[0066] The skin perfusion gradient distribution map is a map for evaluating the local blood flow condition, showing the differences in skin perfusion in different regions, which helps to identify the specific location of microcirculation disorders.

[0067] The three-dimensional mechanical distribution topology describes the mechanical properties of the skin surface, and helps to understand the mechanical stress distribution under the skin by measuring the pressure distribution and deformation of the skin surface.

[0068] The microcirculation dynamic mapping model is a dynamic model that includes the time dimension and spatial correlation, showing the time evolution process of the patient's microcirculation state and the spatial correlation between different parts, providing a comprehensive data basis for subsequent analysis.

[0069] In the embodiments of the present application, first, a flexible electronic skin device is deployed on the patient's body surface to start collecting the physiological signals mentioned above. Then, these data are processed using a special algorithm to construct a dynamic model that can reflect the changes in the microcirculation state.

[0070] Suppose in an emergency room environment, a doctor wears this new sensor device for a suspected shock patient. Through real-time monitoring and analysis, it is found that the patient has significant signs of microcirculation disorders, which provides key information for subsequent emergency interventions. In particular, the capillary oscillation frequency spectrum shows abnormal fluctuations, suggesting possible serious blood circulation problems; the skin perfusion gradient distribution map further clarifies the specific affected areas; and the three-dimensional mechanical distribution topology helps to determine the mechanical stress distribution under the skin.

[0071] 102. Dynamically register the microcirculation dynamic mapping model with the spatio-temporal data of the emergency monitoring device, and form a fused data field through time reference synchronization compensation and spatial coordinate transformation mapping;

[0072] In this step, the fused data field contains the joint distribution characteristics of microcirculation parameters and emergency parameters. Dynamic registration is a technology that aligns data from different sources in time and space, ensuring that all data is based on the same time reference and spatial coordinate system, thereby forming a fused data field.

[0073] The spatio-temporal data includes time series data and spatial distribution data, such as the data provided by electrocardiogram monitors and other vital sign monitoring devices, which reflect the state changes of the patient at different time periods and body parts.

[0074] The fused data field contains the joint distribution characteristics of microcirculation parameters and emergency parameters (such as heart rate, blood pressure, etc.), providing a comprehensive view to observe the overall health status of the patient.

[0075] In the embodiments of the present application, first, time synchronization processing is performed on the data from different sources to ensure that all data is based on the same time reference. Then, spatial coordinate transformation technology is applied to enable the spatial data from different sources to be compared and analyzed under a unified coordinate system. Finally, a comprehensive data field integrating multiple parameters is generated.

[0076] For example, continuing with the previous example, assume that after obtaining the preliminary dynamic mapping model of microcirculation, the doctor decides to combine it with the data from an electrocardiogram monitor and other vital sign monitoring devices. Through dynamic registration technology, a fused data field is successfully created. This data field not only shows the changing trend of microcirculation but also incorporates key indicators such as heart rate and blood pressure, thus providing a more comprehensive view of the patient's condition.

[0077] 103. Input the fused data field into a dynamic quality control engine and perform pattern coupling verification with the microcirculation compensation trajectory atlas in the shock compensation mode feature library.

[0078] In this step, the microcirculation compensation trajectory atlas is constructed based on the historical correlation between the capillary oscillation frequency spectrum and the skin perfusion gradient distribution map of multi-center shock cases. The dynamic quality control engine is a tool for verifying and analyzing multi-source data. Through pattern coupling verification, the consistency and accuracy of the data are ensured.

[0079] The microcirculation compensation trajectory atlas is a model constructed based on the historical data analysis of multi-center shock cases, reflecting the typical change patterns of microcirculation parameters in different stages of shock.

[0080] Pattern coupling verification compares the fused data field with the known microcirculation compensation trajectory atlas to determine whether there is a pattern match or mismatch, thereby identifying potential abnormalities.

[0081] In the embodiments of the present application, first, the fused data field is input into the dynamic quality control engine for analysis. Then, it is compared with the pre-established microcirculation compensation trajectory atlas to determine whether there is a pattern match or mismatch. If a mismatch is detected, the reason needs to be further analyzed and corresponding measures need to be taken.

[0082] For example, continuing with the previous example, when the fused data field is input into the dynamic quality control engine, it is automatically compared with the known shock compensation trajectory atlas. The results show that although most parameters meet expectations, some specific microcirculation parameters show abnormal deviations, indicating that there may be unforeseen complications or special situations. The doctor adjusted the treatment plan based on this information and increased targeted intervention measures.

[0083] 104. When the dynamic quality control engine detects a pattern mismatch between the parameter evolution path of the fused data field and the microcirculation compensation trajectory atlas, based on the phase characteristics of the capillary oscillation frequency spectrum and the spatial distortion characteristics of the three-dimensional mechanical distribution topology, reconstruct the spatio-temporal signal segment and generate a quality control report associated with the abnormal nodes of the skin perfusion gradient distribution map.

[0084] In this step, pattern mismatch refers to the situation where the parameter evolution path in the fused data field is inconsistent with the predefined microcirculation compensation trajectory atlas, indicating possible abnormalities or unforeseen problems.

[0085] The phase feature is the phase information in the capillary oscillation frequency spectrum, which is used to describe the periodicity and stability of the signal and helps to identify abnormal fluctuations.

[0086] The spatial distortion feature is the abnormal morphology in the three-dimensional mechanical distribution topological structure, indicating abnormal mechanical stress distribution under the skin surface.

[0087] The quality control report is the reconstruction result based on the spatio-temporal signal segments, providing a detailed analysis report on the abnormal nodes in the skin perfusion gradient distribution map to assist doctors in making diagnosis and treatment decisions.

[0088] In the embodiment of the present application, when the dynamic quality control engine detects a pattern mismatch, it reconstructs the spatio-temporal signal segments based on the phase feature of the capillary oscillation frequency spectrum and the spatial distortion feature of the three-dimensional mechanical distribution topological structure. A quality control report associated with the abnormal nodes in the skin perfusion gradient distribution map is generated to provide doctors with detailed abnormal analysis and suggestions.

[0089] For example, continuing with the above example, when the doctor found that some microcirculation parameters deviated abnormally, the dynamic quality control engine was further used for detailed analysis. The system reconstructed the relevant spatio-temporal signal segments and generated a detailed quality control report. The report showed that there were significant abnormalities in the skin perfusion gradient distribution map of a certain part of the patient, indicating possible local blood circulation disorders. Based on this report, the doctor adjusted the treatment plan in a timely manner to avoid the deterioration of the condition.

[0090] Through the implementation of steps 101 to 104, the accurate monitoring and analysis of the microcirculation state of shock patients are realized, forming a complete process from data acquisition, dynamic registration, pattern verification to abnormal detection. This series of steps not only improves the accuracy and efficiency of emergency data quality control analysis, but also provides strong support for clinical doctors, enabling them to identify potential problems earlier and take effective intervention measures.

[0091] To improve the monitoring accuracy and real-time response ability of the microcirculation state of shock patients, especially in solving the time and space synchronization problems of multi-source data, a new method is proposed. This method establishes a pulse synchronization channel between the time axis of the microcirculation dynamic mapping model and the clock source of the emergency monitoring device, extracts the three-dimensional space coordinate system of the emergency monitoring device, and uses the affine transformation algorithm to map the spatial characteristics of the three-dimensional mechanical distribution topology structure to the three-dimensional space coordinate system of the emergency monitoring device, generating a spatial transformation matrix containing the distribution of the microvascular stress field. Subsequently, a fused data field with spatio-temporal continuity is generated through tensor fusion, and the integrity of the topology structure is verified through edge computing nodes, marking abnormal spatial nodes and feeding them back to the nanofiber sensing array for signal compensation and calibration. In some embodiments, the verification by the intelligent quality control module in step 102 includes:

[0092] 1021. Establish a pulse synchronization channel between the time axis of the microcirculation dynamic mapping model and the clock source of the emergency monitoring device, eliminate the time reference offset of multi-source data acquisition through an adaptive interpolation algorithm, and generate a sequence of microcirculation oscillation waveforms aligned in the time dimension;

[0093] In step 1021, the time axis of the microcirculation dynamic mapping model is a time series describing the dynamic changes of the microcirculation, recording physiological signal data at different time points, such as blood pressure fluctuations and blood oxygen saturation, etc. These data are used for subsequent time alignment and analysis.

[0094] The pulse synchronization channel of the emergency monitoring device clock source ensures the consistency of the time reference for multi-source data acquisition, aligning the data of different sensors to the same time reference through a high-precision clock signal.

[0095] The adaptive interpolation algorithm is a technique for eliminating the time reference offset, automatically adjusting the interpolation interval based on the change characteristics of the data, thereby effectively eliminating the time phase deviation and generating a sequence of microcirculation oscillation waveforms aligned in the time dimension.

[0096] The sequence of microcirculation oscillation waveforms aligned in the time dimension is the sequence of microcirculation oscillation waveforms after time alignment, ensuring the consistency of all data in the time dimension and providing an accurate basis for subsequent analysis.

[0097] In the embodiments of the present application, first, the multi-source data collected is preprocessed, including filtering and smoothing processing, to remove noise and outliers. This step uses advanced digital signal processing techniques, such as Kalman filters and wavelet transforms, to ensure the quality of the data. Next, the data collected by different sensors is aligned with their corresponding emergency monitoring device clock sources. To achieve precise time reference alignment, an adaptive interpolation algorithm is used. This algorithm can automatically adjust the interpolation interval according to the change characteristics of the data, thereby effectively eliminating the time phase deviation.

[0098] 1022. Extract the three-dimensional spatial coordinate system of the emergency monitoring device, and use the affine transformation algorithm to map the spatial features of the three-dimensional mechanical distribution topological structure to the three-dimensional spatial coordinate system of the emergency monitoring device, forming a spatial transformation matrix containing the distribution of the microvascular stress field;

[0099] In step 1022, the three-dimensional spatial coordinate system of the emergency monitoring device describes the spatial positioning information of the emergency monitoring device, which is usually composed of multiple sensor nodes, and each node has its unique spatial position information.

[0100] The affine transformation algorithm is a technology that maps the spatial features of the three-dimensional mechanical distribution topological structure to the three-dimensional spatial coordinate system of the emergency monitoring device, keeping the properties of straight lines and parallel lines unchanged.

[0101] The spatial transformation matrix contains the spatial feature mapping of the microvascular stress field distribution and is used for subsequent analysis to help doctors better understand the local blood flow condition.

[0102] In the embodiment of the present application, the three-dimensional spatial coordinate system is extracted from the emergency monitoring device. These coordinate systems are usually composed of multiple sensor nodes, and each node has its unique spatial position information. High-precision geometric calibration technology is used to ensure the accuracy of these coordinate systems.

[0103] Use high-resolution medical imaging technology (such as laser speckle imaging or ultrasonic imaging) to obtain the spatial features of the three-dimensional mechanical distribution topological structure. These features include the stress distribution of microvessels in different regions and can reflect the local blood flow condition.

[0104] Use the affine transformation algorithm to map the spatial features of the above three-dimensional mechanical distribution topological structure to the three-dimensional spatial coordinate system of the emergency monitoring device. Affine transformation is a linear transformation method that can keep the properties of straight lines and parallel lines unchanged. Through this method, the complex mechanical distribution features are converted into a spatial transformation matrix that is easy to understand and analyze.

[0105] 1023. Perform tensor fusion on the time-dimension-aligned microcirculation oscillation waveform sequence and the spatial transformation matrix to generate a fusion data field with spatio-temporal continuity;

[0106] In step 1023, the time-dimension-aligned microcirculation oscillation waveform sequence describes the microcirculation oscillation waveform sequence after time alignment to ensure the consistency of all data in the time dimension.

[0107] Tensor fusion is a technology for generating a fusion data field with spatio-temporal continuity to ensure the consistency of data in the time and space dimensions.

[0108] The fused data field contains a data field with spatio-temporal continuity for subsequent analysis.

[0109] In the embodiments of the present application, first, a sequence of microcirculation oscillation waveforms aligned in the time dimension and a spatial transformation matrix are prepared.

[0110] The above two types of data are fused using tensor fusion technology. Tensor fusion is a multi-dimensional data analysis method that can integrate data of different dimensions to generate a unified data field. Specifically, first, the sequence of microcirculation oscillation waveforms aligned in the time dimension is preliminarily aligned with the spatial transformation matrix, and then a tensor decomposition algorithm is applied to fuse the data features of the two.

[0111] After completing the tensor fusion, a fused data field with spatio-temporal continuity is generated. This data field contains the time-varying information of the microcirculation oscillation waveforms and the spatial distribution characteristics of the microvascular stress field, and can provide comprehensive data support for subsequent analysis.

[0112] 1024. The topological structure integrity of the fused data field is verified by an edge computing node. When it is detected that the mapping residual between the three-dimensional mechanical distribution topological structure and the three-dimensional space coordinate system of the emergency monitoring device exceeds a preset tolerance, the abnormal spatial nodes in the microcirculation dynamic mapping model are marked and fed back to the nanofiber sensing array for signal compensation and calibration.

[0113] In step 1024, the three-dimensional mechanical distribution topological structure describes the spatial characteristics of the microvascular mechanical distribution.

[0114] The three-dimensional space coordinate system of the emergency monitoring device describes the spatial positioning information of the emergency monitoring device.

[0115] The mapping residual describes the difference between the three-dimensional mechanical distribution topological structure and the three-dimensional space coordinate system of the emergency monitoring device.

[0116] The abnormal spatial nodes are the abnormal nodes marked when the mapping residual exceeds the preset tolerance.

[0117] The signal compensation and calibration of the nanofiber sensing array is a technique for correcting the frequency-domain signals of abnormal nodes to ensure the accuracy of the data.

[0118] In the embodiments of the present application, the topological structure integrity of the fused data field is verified by an edge computing node. Specifically, it is detected whether the mapping residual between the three-dimensional mechanical distribution topological structure and the three-dimensional space coordinate system of the emergency monitoring device exceeds the preset tolerance. The edge computing node can perform efficient data processing locally, reduce data transmission delay, and improve the real-time response ability of the system.

[0119] When the mapping residual is detected to exceed the preset tolerance, the abnormal spatial nodes in the microcirculation dynamic mapping model are marked. For example, through the edge computing node, the local area where the mapping residual exceeds the preset tolerance is automatically identified and marked as an abnormal node. This step requires an accurate error detection algorithm, such as a statistics-based threshold method or a machine learning classifier, to ensure the accuracy of the marking.

[0120] Then, it is fed back to the nanofiber sensing array for signal compensation and calibration. Specifically, the signal compensation and calibration mechanism of the nanofiber sensing array is triggered to correct these abnormal nodes. For example, through the frequency-domain signal compensation mechanism, the frequency-domain signals of the abnormal nodes are adjusted to make them more consistent with the data within the normal range. This step requires advanced signal processing techniques, such as the fast Fourier transform (FFT) and adaptive filters, to ensure the accuracy and consistency of the signals.

[0121] Update the historical correlation parameters: Finally, update the historical correlation parameters of the shock compensation mode feature library to ensure high-precision prediction and diagnosis of future cases. For example, by analyzing the signal compensation results of the abnormal nodes, the relevant parameters in the feature library are updated to achieve a higher level of prediction and diagnosis capabilities in future cases.

[0122] The following is a specific example:

[0123] In an emergency room environment, a doctor attaches a new type of flexible electronic skin device to a patient suspected of being in shock and begins to collect physiological signals. In step 1021, first, a pulse synchronization channel between the timeline of the microcirculation dynamic mapping model and the clock source of the emergency monitoring device is established. Specifically, an adaptive interpolation algorithm is used to eliminate the time reference offset of multi-source data acquisition, generating a sequence of microcirculation oscillation waveforms aligned in the time dimension. For example, first, the real-time physiological signal data of the patient (such as blood pressure fluctuations, blood oxygen saturation, etc.) is obtained, and then it is compared with the clock source of the emergency monitoring device. By applying the adaptive interpolation algorithm, the time axes of these data can be precisely aligned, generating a sequence of microcirculation oscillation waveforms aligned in the time dimension. The key to this step is to ensure the consistency of all data in the time dimension, providing an accurate basis for subsequent analysis. In step 1022, first, the three-dimensional spatial coordinate system of the emergency monitoring device is extracted, and an affine transformation algorithm is used to map the spatial features of the three-dimensional mechanical distribution topology to the three-dimensional spatial coordinate system of the emergency monitoring device, forming a spatial transformation matrix containing the distribution of the microvascular stress field. Specifically, high-resolution medical imaging techniques (such as laser speckle imaging or ultrasound imaging) are used to obtain the spatial features of the three-dimensional mechanical distribution topology, and then it is converted into the three-dimensional spatial coordinate system of the emergency monitoring device through the affine transformation algorithm. In step 1023, the sequence of microcirculation oscillation waveforms aligned in the time dimension is tensor-fused with the spatial transformation matrix to generate a fused data field with spatio-temporal continuity. Specifically, first, the sequence of microcirculation oscillation waveforms aligned in the time dimension is combined with the spatial transformation matrix, and then a fused data field is generated through tensor fusion technology. For example, by fusing the sequence of microcirculation oscillation waveforms aligned in the time dimension and the spatial transformation matrix of the right forearm area, a fused data field with spatio-temporal continuity is generated, helping the doctor better understand the patient's microcirculation state. This process ensures that the features of different regions can be reasonably allocated in the final result, thus improving the accuracy of the analysis results. Finally, in step 1024, the topological structure integrity of the fused data field is verified through an edge computing node. Specifically, it is detected whether the mapping residual between the three-dimensional mechanical distribution topology and the three-dimensional spatial coordinate system of the emergency monitoring device exceeds a preset tolerance. When it is detected that the mapping residual exceeds the preset tolerance, the abnormal spatial nodes in the microcirculation dynamic mapping model are marked and feedback is sent to the nanofiber sensing array for signal compensation calibration. For example, through the edge computing node, the local areas where the mapping residual exceeds the preset tolerance are automatically identified and marked as abnormal nodes. Then, the signal compensation calibration mechanism of the nanofiber sensing array is triggered to correct these abnormal nodes. The key to this step is to timely detect and correct abnormal nodes, thus improving the quality and reliability of the overall data.

[0124] Through the implementation of steps 1021 to 1024, an evolution sequence of microcirculation parameters synchronized in the time dimension is generated, ensuring the consistency of multi-source data in the time dimension and providing an accurate basis for subsequent analysis. A deep convolutional network is used to construct the spatial topological mapping relationship of the microcirculation compensation trajectory map, generating a spatial correlation matrix containing blood flow propagation paths, which not only improves the spatial consistency of the data but also provides detailed spatial feature information for subsequent analysis. By means of a spatio-temporal attention weight dynamic allocation mechanism, the contribution degree of features in different regions is adjusted to generate a similarity distribution map of compensation patterns. This method ensures that the features of different regions can be reasonably allocated in the final result, thereby improving the accuracy of the analysis results. When the similarity value of a local region is lower than a preset threshold, abnormal nodes in the skin perfusion gradient distribution map are marked, and then the frequency-domain signal compensation mechanism of the nanofiber sensing array is triggered to correct these abnormal nodes and update the historical correlation parameters of the shock compensation pattern feature library. This process ensures the accuracy and consistency of the data and helps doctors identify and handle microcirculation abnormalities earlier.

[0125] To further improve the monitoring accuracy and real-time performance of the microcirculation state of shock patients, especially in dealing with the time and space alignment problems of multi-source data, an alternative solution is proposed. This solution generates an evolution sequence of microcirculation parameters synchronized in the time dimension and a spatial correlation matrix by establishing a dynamic alignment channel between the fusion data field and the shock compensation pattern feature library and using a multi-scale time interpolation algorithm, a deep convolutional network, and a dynamic attention coupling module, and finally generates a similarity distribution map of compensation patterns. This process not only improves the consistency and accuracy of the data but also provides more accurate diagnostic basis for clinicians. In some embodiments, the dynamic quality control engine verification described in step 103 includes:

[0126] 1031. Establish a dynamic alignment channel between the time axis of the fusion data field and the time reference of the shock compensation pattern feature library, and eliminate the time-phase deviation between historical case data and real-time data acquisition through a multi-scale time interpolation algorithm to generate an evolution sequence of microcirculation parameters synchronized in the time dimension;

[0127] In step 1031, the dynamic alignment channel is a technique for aligning between different time references, ensuring that the time-phase deviation between historical case data and real-time data acquisition is eliminated.

[0128] The multi-scale time interpolation algorithm is an advanced time synchronization technique that adjusts the time phase of data by interpolating at multiple time scales to ensure the time consistency between historical data and real-time data.

[0129] The evolution sequence of microcirculation parameters synchronized in the time dimension is the microcirculation parameter sequence processed by the time interpolation algorithm, ensuring the consistency in the time dimension and facilitating subsequent analysis.

[0130] In the embodiments of the present application, first, a dynamic alignment channel is established between the time axis of the fused data field and the time reference of the shock compensation mode feature library. Specifically, by comparing the differences between the two time references, the time points that need to be adjusted are identified. Then, a multi-scale time interpolation algorithm is applied to interpolate the data according to different time scales (such as second level, minute level) to eliminate the time phase deviation between the historical case data and the real-time data acquisition, and generate a time dimension synchronized microcirculation parameter evolution sequence.

[0131] 1032. Extract the spatial distribution characteristics of the capillary oscillation frequency spectrum in the fused data field, and use a deep convolutional network to construct the spatial topological mapping relationship of the microcirculation compensation trajectory map in the shock compensation mode feature library to generate a spatial correlation matrix including the blood flow propagation path;

[0132] In step 1032, the spatial distribution characteristics of the capillary oscillation frequency spectrum describe the distribution of the capillary oscillation frequency in different regions of the skin surface and are used to identify the local blood flow condition.

[0133] The deep convolutional network is a deep learning model that can automatically extract complex spatial features and is used to construct the spatial topological mapping relationship of the microcirculation compensation trajectory map.

[0134] The spatial correlation matrix contains the spatial topological relationship of the blood flow propagation path and helps to understand the interaction between different regions.

[0135] In the embodiments of the present application, the spatial distribution characteristics of the capillary oscillation frequency spectrum in the fused data field are extracted, and a deep convolutional network is used to construct the spatial topological mapping relationship of the microcirculation compensation trajectory map. Specifically, first, the data of the capillary oscillation frequency spectrum is input into the deep convolutional network, and the network gradually extracts the key spatial features through the convolutional layer and the pooling layer. Then, based on these features, the spatial topological mapping relationship of the microcirculation compensation trajectory map is constructed to generate a spatial correlation matrix including the blood flow propagation path.

[0136] 1033. Input the microcirculation parameter evolution sequence and the spatial correlation matrix into the dynamic attention coupling module, and adjust the contribution degree of the features in different regions through the spatio-temporal attention weight dynamic allocation mechanism to generate a compensation mode similarity distribution map;

[0137] In step 1033, the dynamic attention coupling module is a module for spatio-temporal data analysis, which adjusts the contribution degree of the features in different regions by assigning different weights to generate a compensation mode similarity distribution map.

[0138] The spatio-temporal attention weight dynamic allocation mechanism dynamically adjusts the weights of different regions according to the importance of data, ensuring that key information receives more attention.

[0139] The compensation mode similarity distribution map reflects the similarity degree of compensation modes in different regions, helping to identify abnormal regions.

[0140] In the embodiments of the present application, the evolution sequence of microcirculation parameters synchronized in the time dimension and the spatial correlation matrix are input into the dynamic attention coupling module. Specifically, the dynamic attention coupling module first calculates the weights of the time and spatial features of each region, and then dynamically adjusts the contribution degrees of the features of different regions according to these weights. Through this mechanism, the system can more accurately identify which regions have higher importance and generate the compensation mode similarity distribution map.

[0141] 1034. Based on the deviation detection result of the compensation mode similarity distribution map, when the similarity value of a local region is lower than the preset threshold, mark the abnormal nodes in the skin perfusion gradient distribution map, trigger the frequency domain signal compensation mechanism of the nanofiber sensing array, and update the historical correlation parameters of the shock compensation mode feature library.

[0142] In step 1034, the deviation detection result is the analysis result based on the compensation mode similarity distribution map, used to identify the abnormalities in the local region.

[0143] The abnormal nodes in the skin perfusion gradient distribution map mark the regions with significant abnormalities, indicating possible blood circulation problems.

[0144] The frequency domain signal compensation mechanism is a technology for correcting sensor signals, which improves the accuracy of data by adjusting the frequency response.

[0145] The historical correlation parameters of the shock compensation mode feature library are the updated database, containing the latest historical correlation parameters, used for subsequent analysis and model training.

[0146] In the embodiments of the present application, based on the deviation detection result of the compensation mode similarity distribution map, when the similarity value of a local region is lower than the preset threshold, mark the abnormal nodes in the skin perfusion gradient distribution map. Specifically, the system compares the similarity value of each region with the preset threshold. If it is lower than the threshold, mark the region as an abnormal node. Next, trigger the frequency domain signal compensation mechanism of the nanofiber sensing array to improve the accuracy of data by adjusting the frequency response of the sensor. Finally, update the historical correlation parameters of the shock compensation mode feature library to provide more accurate prediction and diagnosis support in future cases.

[0147] The following is a specific example:

[0148] In an emergency room environment, a doctor equipped a suspected shock patient with a new type of flexible electronic skin device and began to collect physiological signals. Through steps 101 and 102, a dynamic mapping model of microcirculation was generated and preliminary dynamic registration was completed. Next, in step 1031, a dynamic alignment channel was established between the time axis of the fused data field and the time reference of the shock compensation mode feature library. By comparing the differences between the two time references, the time points that needed to be adjusted were identified, and a multi-scale time interpolation algorithm was applied to perform interpolation according to different time scales, eliminating the time phase deviation and generating an evolution sequence of microcirculation parameters synchronized in the time dimension. In step 1032, the spatial distribution characteristics of the capillary oscillation frequency spectrum in the fused data field were extracted, and a deep convolutional network was used to construct the spatial topological mapping relationship of the microcirculation compensation trajectory map. Specifically, first, the data of the capillary oscillation frequency spectrum were input into the deep convolutional network, and the network gradually extracted the key spatial features through convolutional layers and pooling layers. Then, based on these features, the spatial topological mapping relationship of the microcirculation compensation trajectory map was constructed, generating a spatial correlation matrix containing the blood flow propagation path. In step 1033, the evolution sequence of microcirculation parameters synchronized in the time dimension and the spatial correlation matrix were input into the dynamic attention coupling module. Through the dynamic attention weight dynamic allocation mechanism, the feature contribution degrees of different regions were adjusted, generating a similarity distribution map of the compensation mode. Finally, in step 1034, based on the deviation detection results of the similarity distribution map of the compensation mode, it was found that the similarity values in some local regions were lower than the preset threshold, and the abnormal nodes of the skin perfusion gradient distribution map were marked. The frequency-domain signal compensation mechanism of the nanofiber sensing array was triggered, and the historical correlation parameters of the shock compensation mode feature library were updated. Based on this detailed information, the doctor timely adjusted the treatment plan, avoiding the deterioration of the condition.

[0149] Through the implementation of steps 1031 to 1034, the accurate monitoring and analysis of the microcirculation status of shock patients were successfully achieved, especially significant progress was made in the time and space synchronization of multi-source data. This process not only improved the accuracy and efficiency of emergency data processing, but also provided a powerful decision support tool for clinicians. Specifically, the system can identify microcirculation abnormalities earlier and provide instant feedback to help doctors take intervention measures in a timely manner. In addition, through continuous learning and updating of historical case data, the system gradually improved the shock compensation mode feature library, improving the accuracy of future diagnoses. This method greatly improved the treatment effect of patients, reduced the risk of complications, and provided a solid foundation for the formulation of personalized medical plans.

[0150] To further improve the monitoring accuracy and real-time performance of the microcirculation state of shock patients, especially in dealing with the time and space alignment problems of multi-source data, an optional solution is proposed. This solution generates an initial attention distribution map containing spatio-temporal coupling weights by establishing a spatio-temporal feature cross-correlation channel and using a frequency-domain enhanced attention mechanism, a multi-head dynamic attention mechanism, and a dynamic gating residual fusion network, and finally generates a compensation pattern similarity distribution map. This process not only improves the consistency and accuracy of the data but also provides more accurate diagnostic basis for clinicians. In some embodiments, the dynamic quality control engine verification described in step 1033 includes:

[0151] 201. Establish a spatio-temporal feature cross-correlation channel, perform three-dimensional tensor fusion on the oscillation phase fluctuation features in the microcirculation parameter evolution sequence and the blood flow propagation path features in the spatial correlation matrix, and perform multi-scale decomposition on the three-dimensional tensor fusion result through a frequency-domain enhanced attention mechanism to generate an initial attention distribution map containing spatio-temporal coupling weights;

[0152] In 201, the spatio-temporal feature cross-correlation channel is a technique for fusing the microcirculation parameter evolution sequence with synchronized time dimension and the spatial correlation matrix to ensure the cross-correlation of time and space features.

[0153] The oscillation phase fluctuation features describe the variation characteristics of capillary oscillation frequency in the time dimension and are used to identify the fluctuations of local blood circulation.

[0154] The blood flow propagation path features describe the propagation paths of blood flow in different regions of the skin surface and are used to understand the local blood flow conditions.

[0155] The three-dimensional tensor fusion fuses the oscillation phase fluctuation features in the microcirculation parameter evolution sequence with synchronized time dimension and the blood flow propagation path features in the spatial correlation matrix to form a three-dimensional tensor.

[0156] The frequency-domain enhanced attention mechanism is an attention mechanism for enhancing frequency-domain features. By performing multi-scale decomposition on the three-dimensional tensor fusion result, it generates an initial attention distribution map containing spatio-temporal coupling weights.

[0157] In the embodiments of the present application, first, a spatio-temporal feature cross-correlation channel is established, and three-dimensional tensor fusion is performed on the oscillation phase fluctuation features in the microcirculation parameter evolution sequence with synchronized time dimension and the blood flow propagation path features in the spatial correlation matrix. Then, the frequency-domain enhanced attention mechanism is applied to perform multi-scale decomposition on the three-dimensional tensor fusion result to generate an initial attention distribution map containing spatio-temporal coupling weights. Specifically, the system analyzes the oscillation phase fluctuation features and blood flow propagation path features at different time points, extracts the key spatio-temporal coupling information, and forms the initial attention distribution map.

[0158] 202. Use the multi - head dynamic attention mechanism to decouple the regional features of the initial attention distribution map. Each attention head extracts the microvascular mechanical distortion gradient feature, the tissue perfusion phase difference feature, and the blood flow direction topological constraint feature respectively, and combines the sliding window mechanism to retain the complete context correlation of the most recent time step, generating a multi - scale spatio - temporal attention weight set;

[0159] In 202, the multi - head dynamic attention mechanism is a mechanism for extracting different regional features. Each attention head extracts specific types of features, such as the microvascular mechanical distortion gradient feature, the tissue perfusion phase difference feature, and the blood flow direction topological constraint feature.

[0160] The microvascular mechanical distortion gradient feature describes the changes in the mechanical properties of microvessels in different regions and is used to identify the local mechanical stress distribution.

[0161] The tissue perfusion phase difference feature describes the phase differences of tissue perfusion in different regions and is used to identify the local blood flow condition.

[0162] The blood flow direction topological constraint feature describes the direction and topological relationship of blood flow in different regions and is used to understand the local blood flow propagation path.

[0163] The sliding window mechanism retains the complete context correlation of the most recent time step, ensuring that important information is not lost during the feature extraction process.

[0164] In the embodiments of this application, the multi - head dynamic attention mechanism is used to decouple the regional features of the initial attention distribution map. Each attention head extracts the microvascular mechanical distortion gradient feature, the tissue perfusion phase difference feature, and the blood flow direction topological constraint feature respectively. Combining the sliding window mechanism, the complete context correlation of the most recent time step is retained, generating a multi - scale spatio - temporal attention weight set. Specifically, the system extracts the above three features from the initial attention distribution map respectively through the multi - head dynamic attention mechanism, and ensures the continuity and integrity of these features through the sliding window mechanism.

[0165] 203. Construct a dynamic gating residual fusion network, perform residual - aware connection between the multi - scale spatio - temporal attention weight set and the initial attention distribution map, and dynamically adjust the contribution weights of each attention head through a differentiable gating unit to generate an optimized spatio - temporal attention weight tensor;

[0166] In 203, the dynamic gating residual fusion network is a network for optimizing spatio - temporal attention weights. It dynamically adjusts the contribution weights of each attention head through a differentiable gating unit to generate an optimized spatio - temporal attention weight tensor.

[0167] The residual - aware connection is a technique for connecting the initial attention distribution map and the multi - scale spatio - temporal attention weight set, ensuring that the original information is not lost during the optimization process.

[0168] The differentiable gating unit is a unit used to dynamically adjust the contribution weights of attention heads. By learning the weight distribution during the optimization process, it generates an optimized spatio-temporal attention weight tensor.

[0169] In the embodiments of the present application, a dynamic gating residual fusion network is constructed to perform residual-aware connection between the multi-scale spatio-temporal attention weight set and the initial attention distribution map. The contribution weights of each attention head are dynamically adjusted by the differentiable gating unit to generate an optimized spatio-temporal attention weight tensor. Specifically, the system optimizes the multi-scale spatio-temporal attention weight set through the dynamic gating residual fusion network, combining residual-aware connection and the differentiable gating unit, to generate a more accurate spatio-temporal attention weight tensor.

[0170] 204. Calculate the compensation mode similarity based on the optimized spatio-temporal attention weight tensor. Use the graph convolutional propagation algorithm to spread the weight values in the spatial correlation matrix, combine the topological constraint conditions of the blood flow propagation path to generate a compensation mode similarity distribution map, and calibrate the frequency-domain signals of the deviated regions through a residual compensation mechanism.

[0171] In 204, the compensation mode similarity calculation is to calculate the compensation mode similarity based on the optimized spatio-temporal attention weight tensor, which is used to identify abnormal regions.

[0172] The graph convolutional propagation algorithm is an algorithm used to spread the weight values in the spatial correlation matrix, which combines the topological constraint conditions of the blood flow propagation path to generate a compensation mode similarity distribution map.

[0173] The residual compensation mechanism is a technique used to calibrate the frequency-domain signals of the deviated regions, which improves the accuracy of the data by adjusting the frequency response of the sensors.

[0174] In the embodiments of the present application, calculate the compensation mode similarity based on the optimized spatio-temporal attention weight tensor. Use the graph convolutional propagation algorithm to spread the weight values in the spatial correlation matrix, combine the topological constraint conditions of the blood flow propagation path to generate a compensation mode similarity distribution map. Calibrate the frequency-domain signals of the deviated regions through the residual compensation mechanism. Specifically, the system uses the graph convolutional propagation algorithm, combines the optimized spatio-temporal attention weight tensor, to generate a compensation mode similarity distribution map, and calibrates the frequency-domain signals of the detected deviated regions through the residual compensation mechanism to ensure the accuracy of the data.

[0175] The following is a specific example:

[0176] In an emergency room environment, a patient suspected of shock was connected to a new flexible electronic skin monitoring system. To more accurately evaluate the patient's microcirculation status and provide effective treatment recommendations for clinicians, the method in step 1033 was used for data processing and analysis. In 201, the system first fused the time-dimension synchronized microcirculation parameter evolution sequence collected from the patient with the spatial correlation matrix. For example, assuming the data of the patient's left forearm area is of concern, the oscillatory phase fluctuation feature (describing the change of capillary oscillation frequency over time) and the blood flow propagation path feature (depicting how blood flows through this area) were extracted. These features were transformed into a three-dimensional tensor and underwent multi-scale decomposition through a frequency-domain enhanced attention mechanism, finally generating an initial attention distribution map containing spatio-temporal coupling weights. The key to this step is to ensure that data at different time points can accurately correspond to specific spatial positions for subsequent analysis. Next, in 202, the system adopted a multi-head dynamic attention mechanism to further analyze the initial attention distribution map obtained in the previous step. Each attention head is specifically responsible for extracting a specific type of feature. For example, one attention head focuses on identifying the microvascular mechanical distortion gradient feature (i.e., the change of microvascular mechanical properties in different regions), and another focuses on the tissue perfusion phase difference feature (i.e., the phase difference of tissue perfusion in different regions). In addition, there is an attention head for capturing the blood flow direction topological constraint feature (i.e., the direction and path of blood flow). By combining the sliding window mechanism, the system retains the complete context information of the most recent time step, thus generating a multi-scale spatio-temporal attention weight set. This process greatly improves the ability to identify microcirculation details, enabling doctors to more carefully understand the patient's local blood circulation status. In 203, the system constructed a dynamic gating residual fusion network to optimize the multi-scale spatio-temporal attention weight set generated by 202. This network dynamically adjusts the contribution weights of each attention head through differentiable gating units, thus generating a more optimized spatio-temporal attention weight tensor. Specifically, the system automatically adjusts the weight distribution between different attention heads according to the current physiological signal situation to ensure that the most relevant features can dominate in the final result. The advantage of this is not only to improve the accuracy of the analysis results but also to enhance the adaptability and flexibility of the system. Finally, in 204, the calculation of the compensation mode similarity was performed based on the optimized spatio-temporal attention weight tensor. Here, the graph convolutional propagation algorithm was used to diffuse the weight values in the spatial correlation matrix, and at the same time, the topological constraint conditions of the blood flow propagation path were combined to generate a compensation mode similarity distribution map. For those regions that deviate from the normal range, the system also applied a residual compensation mechanism to calibrate the frequency-domain signals to improve the reliability of the data. For example, when abnormal blood flow is detected in a certain region, the system can adjust the frequency response of the sensor to more accurately reflect the actual situation.Such a process enables doctors to quickly locate the problem and take corresponding treatment measures based on the detailed similarity distribution map of the compensation pattern. By implementing the above four steps, not only can the microcirculation status of shock patients be accurately monitored, but also strong support can be provided for clinicians to help them make more accurate diagnostic and treatment decisions. This method significantly improves the efficiency and accuracy of emergency data quality control analysis, contributing to the improvement of the patient's prognosis.

[0177] With the unique design of 201 to 204, a major breakthrough has been achieved in the monitoring and analysis of the microcirculation status of shock patients. This method solves the common problem of spatio-temporal data alignment in traditional technologies and greatly improves the speed and accuracy of emergency data analysis. By dynamically adjusting the attention mechanism and optimizing the fusion network, the system can more sensitively capture the subtle changes in the microcirculation, enabling clinicians to detect potential problems at an early stage and respond quickly. At the same time, the adaptive learning function of the system enables the continuous update and improvement of the shock compensation pattern feature library, thus achieving a higher level of prediction and diagnostic ability in future cases. These improvements not only enhance the performance of the clinical decision support system but also open up new ways for the personalized treatment of shock patients, ultimately promoting a significant improvement in the patient's prognosis.

[0178] To further improve the monitoring accuracy and real-time performance of the microcirculation status of shock patients, especially in dealing with the spatio-temporal alignment problems of multi-source data, an optional solution is proposed. This solution constructs a dynamic gated residual fusion network, performs residual-aware connection between the multi-scale spatio-temporal attention weight set and the initial attention distribution map, and dynamically adjusts the contribution weight of each attention head through a differentiable gated unit to generate an optimized spatio-temporal attention weight tensor. This process not only improves the consistency and accuracy of the data but also provides more accurate diagnostic basis for clinicians. In some embodiments, the dynamic quality control engine verification described in 203 includes:

[0179] 301. Establish a cross-level residual connection structure based on the hierarchical distribution characteristics of the multi-scale spatio-temporal attention weight set and the spatial topological constraints of the initial attention distribution map, couple the hierarchical distribution pattern of the microvascular mechanical distortion gradient feature with the blood flow direction topological constraint feature in the initial attention distribution map across channels, and form a three-dimensional attention cube containing a mechanical distortion compensation path;

[0180] In step 301, the hierarchical distribution characteristics describe the distribution of the multi-scale spatio-temporal attention weight set at different levels and are used to identify changes in local features.

[0181] The cross-level residual connection structure is a technology for connecting features at different levels, ensuring that information is not lost when transmitted between different levels.

[0182] The mechanical distortion compensation path is a path based on the mechanical distortion gradient characteristics of microvessels, used to correct the mechanical distortion in the three-dimensional attention cube.

[0183] The three-dimensional attention cube combines the mechanical distortion gradient characteristics of microvessels and the topological constraints of blood flow direction characteristics to form a three-dimensional structure containing the mechanical distortion compensation path.

[0184] In the embodiments of the present application, first, a cross-level residual connection structure is established based on the hierarchical distribution characteristics of the multi-scale spatio-temporal attention weight set and the spatial topological constraints of the initial attention distribution map. Specifically, the system performs cross-channel coupling on the hierarchical distribution pattern of the mechanical distortion gradient characteristics of microvessels and the topological constraints of blood flow direction characteristics in the initial attention distribution map to form a three-dimensional attention cube containing the mechanical distortion compensation path. The key to this step is to ensure that information at different levels can be effectively transmitted and complement each other, thereby improving the accuracy of the analysis results.

[0185] 302. Perform multi-level feature interaction on the three-dimensional attention cube through the cross-level residual connection structure, jointly encode the tissue perfusion phase difference characteristics in the initial attention distribution map and the mechanical distortion gradient levels of the three-dimensional attention cube, and generate an attention enhancement map with residual compensation characteristics, where the residual compensation characteristics come from the spatial correlation parameters of the skin perfusion gradient distribution map in the microcirculation dynamic mapping model;

[0186] In step 302, multi-level feature interaction is a technique for feature exchange between different levels, ensuring the integrity and consistency of features between different levels.

[0187] Joint encoding combines different features (such as tissue perfusion phase difference characteristics and mechanical distortion gradient levels) to generate an attention enhancement map with residual compensation characteristics.

[0188] The residual compensation characteristics come from the spatial correlation parameters of the skin perfusion gradient distribution map in the microcirculation dynamic mapping model and are used to correct the errors in the three-dimensional attention cube.

[0189] In the embodiments of the present application, multi-level feature interaction is performed on the three-dimensional attention cube through the cross-level residual connection structure, and the tissue perfusion phase difference characteristics in the initial attention distribution map and the mechanical distortion gradient levels of the three-dimensional attention cube are jointly encoded to generate an attention enhancement map with residual compensation characteristics. Specifically, the system uses the multi-level feature interaction technique to ensure that features at different levels can be effectively integrated in the three-dimensional attention cube to generate a more accurate attention enhancement map.

[0190] 303. Generate a dynamic gating mask based on the time-evolution pattern of the microvascular mechanical distortion gradient features. The dynamic gating mask dynamically allocates the contribution weights of different mechanical distortion gradient levels in the attention enhancement map through a non-linear constraint mechanism, forming a gating attention weight distribution that integrates microvascular mechanical features and blood flow propagation paths.

[0191] In step 303, the dynamic gating mask is a mechanism for dynamically allocating the contribution weights of different mechanical distortion gradient levels, which is achieved through a non-linear constraint mechanism.

[0192] The non-linear constraint mechanism is a technique for adjusting the contribution weights of different levels in the dynamic gating mask to ensure reasonable weight allocation for each level.

[0193] The gating attention weight distribution combines microvascular mechanical features and blood flow propagation paths to form an optimized attention weight distribution.

[0194] In the embodiments of this application, a dynamic gating mask is generated based on the time-evolution pattern of microvascular mechanical distortion gradient features. Specifically, the dynamic gating mask dynamically allocates the contribution weights of different mechanical distortion gradient levels in the attention enhancement map through a non-linear constraint mechanism, forming a gating attention weight distribution that integrates microvascular mechanical features and blood flow propagation paths. The key to this step is to ensure that features at different levels can be reasonably allocated in the final attention weight distribution through the dynamic gating mask, improving the accuracy of the analysis results.

[0195] 305. Perform spatial topological calibration on the gating attention weight distribution and perform distortion compensation on the gating attention weight distribution based on the mapping residuals between the three-dimensional mechanical distribution topological structure and the three-dimensional spatial coordinate system of the emergency monitoring device to generate an optimized spatio-temporal attention weight tensor.

[0196] In step 305, spatial topological calibration is a technique for correcting the gating attention weight distribution to ensure its consistency with the spatial topological structure of the initial attention distribution map.

[0197] The three-dimensional mechanical distribution topological structure describes the mechanical properties of the skin surface and is used to correct the errors in the gating attention weight distribution.

[0198] The three-dimensional spatial coordinate system of the emergency monitoring device is used for mapping with the three-dimensional mechanical distribution topological structure to ensure the consistency and accuracy of the data.

[0199] The optimized spatio-temporal attention weight tensor is the gating attention weight distribution after spatial topological calibration, ensuring the accuracy and consistency of the data.

[0200] In the embodiments of the present application, the spatial topology calibration is performed on the gated attention weight distribution and the initial attention distribution map, and the distortion compensation is performed on the gated attention weight distribution based on the mapping residual between the three-dimensional mechanical distribution topology structure and the three-dimensional space coordinate system of the emergency monitoring device, so as to generate an optimized spatio-temporal attention weight tensor. Specifically, the system ensures the consistency of the spatial topology structure between the gated attention weight distribution and the initial attention distribution map through the spatial topology calibration technology, and generates a more accurate spatio-temporal attention weight tensor.

[0201] The following is a specific example:

[0202] In a simulated emergency environment, a new type of flexible electronic skin monitoring system was used to evaluate the microcirculation status of a suspected shock patient. To ensure the accuracy and real-time nature of data processing, especially for the dynamic calibration and optimization of multi-source spatio-temporal data, the method in 203 was adopted for analysis. Assume that the data of the patient's left forearm area is of concern. First, the system established a cross-level residual connection structure based on the hierarchical distribution characteristics of the multi-scale spatio-temporal attention weight set and the spatial topological constraints of the initial attention distribution map. Specifically, the system performed cross-channel coupling on the hierarchical distribution pattern of the microvascular mechanical distortion gradient features and the blood flow direction topological constraint features in the initial attention distribution map to form a three-dimensional attention cube containing a mechanical distortion compensation path. For example, in this case, the system generated a three-dimensional model containing multiple mechanical distortion compensation paths by analyzing the microvascular mechanical changes at different levels, and these paths helped the system capture the changes in local microcirculation more accurately. In step 302, the system performed multi-level feature interaction on the three-dimensional attention cube through the cross-level residual connection structure, jointly encoded the tissue perfusion phase difference features in the initial attention distribution map with the mechanical distortion gradient levels of the three-dimensional attention cube, and generated an attention enhancement map with residual compensation characteristics. For example, the system used multi-level feature interaction technology to combine the tissue perfusion phase difference features at different levels with the mechanical distortion gradient levels in the three-dimensional attention cube to generate a more accurate attention enhancement map. This process not only improved the consistency and accuracy of the data but also provided a solid foundation for subsequent analysis. Next, in step 303, the system generated a dynamic gating mask based on the time evolution pattern of the microvascular mechanical distortion gradient features. Specifically, the dynamic gating mask dynamically allocated the contribution weights of different mechanical distortion gradient levels in the attention enhancement map through a non-linear constraint mechanism to form a gating attention weight distribution that fuses microvascular mechanical features and blood flow propagation paths. In this specific case, the system automatically generated a dynamic gating mask according to the time evolution pattern of the microvascular mechanical features, adjusted the contribution weights of each level through the non-linear constraint mechanism, and finally generated an optimized gating attention weight distribution. This step ensured that the features of each level could be reasonably allocated in the final attention weight distribution, thereby improving the accuracy of the analysis results.

[0203] Through the implementation of steps 301 to 305, the precise monitoring and analysis of the microcirculation status of shock patients have been achieved, and remarkable progress has been made especially in the temporal and spatial synchronization of multi-source data. This process not only improves the accuracy and efficiency of emergency data processing but also provides a powerful decision-making support tool for clinicians. Specifically, the system can identify microcirculation abnormalities earlier and provide immediate feedback to help doctors take intervention measures in a timely manner. In addition, through continuous learning and updating of historical case data, the system gradually improves the shock compensation mode feature library, enhancing the accuracy of future diagnoses. This method greatly improves the treatment effect of patients, reduces the risk of complications, and provides a solid foundation for the formulation of personalized medical plans. At the same time, the adaptive learning function of the system enables the continuous update and improvement of the shock compensation mode feature library, thereby achieving a higher level of prediction and diagnostic capabilities in future cases. These improvements not only enhance the performance of the clinical decision-making support system but also open up new avenues for the personalized treatment of shock patients, ultimately promoting a significant improvement in patient prognosis.

[0204] To further improve the monitoring accuracy and real-time performance of the microcirculation status of shock patients, especially in dealing with the temporal and spatial alignment problems of multi-source data, an alternative solution is proposed. This solution generates an attention-enhanced map with residual compensation characteristics by establishing a multi-level interaction path and using a phase synchronization mechanism and a spatial weight compensation module. This process not only improves the consistency and accuracy of the data but also provides more accurate diagnostic basis for clinicians. In some embodiments, the dynamic quality control engine verification described in step 302 includes:

[0205] 401. Establish a multi-level interaction path based on the spatio-temporal correlation between the distribution pattern of the mechanical distortion gradient hierarchy and the tissue perfusion phase difference characteristics in the initial attention distribution map, and embed the spatial correlation parameters of the skin perfusion gradient distribution map as a residual compensation benchmark into the channel coupling process of the multi-level interaction path;

[0206] In step 401, the distribution pattern of the mechanical distortion gradient hierarchy describes the changes in the mechanical properties of microvessels in different regions and is used to identify the local mechanical stress distribution.

[0207] The tissue perfusion phase difference characteristics in the initial attention distribution map describe the phase differences of tissue perfusion in different regions and are used to identify the local blood flow conditions.

[0208] The multi-level interaction path is a technology for feature exchange between different levels, ensuring the integrity and consistency of features between different levels.

[0209] The spatial correlation parameters of the skin perfusion gradient distribution map are embedded as a residual compensation benchmark into the channel coupling process of the multi-level interaction path to correct the errors in the three-dimensional attention cube.

[0210] In the embodiments of the present application, first, based on the spatio-temporal correlation between the distribution pattern of the mechanical distortion gradient hierarchy and the tissue perfusion phase difference features in the initial attention distribution map, a multi-level interaction path is established. Specifically, the system embeds the spatial correlation parameters of the skin perfusion gradient distribution map as the residual compensation benchmark into the channel coupling process of the multi-level interaction path. The key to this step is to ensure that information at different levels can be effectively transmitted and complement each other, thereby improving the accuracy of the analysis results.

[0211] 402. Introduce a phase synchronization mechanism into the multi-level interaction path, perform cross-level matching between the time-varying fluctuation pattern of the tissue perfusion phase difference features and the spatial evolution trend of the mechanical distortion gradient hierarchy, and generate a compensation channel containing a phase synchronization feature vector, where the phase synchronization feature vector inherits the phase constraint conditions of the capillary oscillation frequency spectrum in the microcirculation dynamic mapping model;

[0212] In step 402, the phase synchronization mechanism is a technique for adjusting the temporal and spatial matching between the tissue perfusion phase difference features and the mechanical distortion gradient hierarchy, ensuring the synchronization of features between different levels.

[0213] The phase synchronization feature vector inherits the phase constraint conditions of the capillary oscillation frequency spectrum in the microcirculation dynamic mapping model and is used to generate a compensation channel containing the phase synchronization feature vector.

[0214] The cross-level matching matches the time-varying fluctuation pattern of the tissue perfusion phase difference features with the spatial evolution trend of the mechanical distortion gradient hierarchy, ensuring the synchronization of features between different levels.

[0215] In the embodiments of the present application, a phase synchronization mechanism is introduced into the multi-level interaction path, perform cross-level matching between the time-varying fluctuation pattern of the tissue perfusion phase difference features and the spatial evolution trend of the mechanical distortion gradient hierarchy, and generate a compensation channel containing a phase synchronization feature vector. Specifically, the system ensures that features at different levels can be effectively integrated in the three-dimensional attention cube through the phase synchronization mechanism, generating a more accurate compensation channel.

[0216] 403. Feature enhancement is performed on the compensation channel through a spatial weight compensation module, and the spatial distortion parameters of the abnormal nodes in the skin perfusion gradient distribution map are non-linearly superimposed with the residual compensation benchmark of the mechanical distortion gradient hierarchy to generate an attention enhancement mapping with spatial constraints;

[0217] In step 403, the spatial weight compensation module is a technique for enhancing the features of the compensation channel, ensuring that the features in the compensation channel are fully enhanced.

[0218] The spatial distortion parameters of the abnormal nodes in the skin perfusion gradient distribution map are used for non-linear superposition with the residual compensation benchmark of the mechanical distortion gradient level to generate an attention enhancement map with spatial constraints.

[0219] Non-linear superposition is a technique for enhancing the features of the compensation channel to ensure that the features in the compensation channel are fully enhanced.

[0220] In the embodiments of the present application, the feature enhancement of the compensation channel is performed through the spatial weight compensation module. The spatial distortion parameters of the abnormal nodes in the skin perfusion gradient distribution map are non-linearly superimposed with the residual compensation benchmark of the mechanical distortion gradient level to generate an attention enhancement map with spatial constraints. Specifically, the system passes through the spatial weight compensation module to ensure that the features in the compensation channel are fully enhanced and generates a more accurate attention enhancement map.

[0221] 404. Input the phase synchronization feature vector in the attention enhancement map and the residual compensation benchmark of the microvascular mechanical distortion gradient feature into the dynamic gating mask generation module, and adaptively adjust the compensation intensity of the multi-level interaction path based on the spatial correlation parameters of the skin perfusion gradient distribution map to generate an attention enhancement map with residual compensation characteristics, where the residual compensation characteristics come from the spatial correlation parameters of the skin perfusion gradient distribution map in the microcirculation dynamic mapping model.

[0222] In step 404, the dynamic gating mask generation module is a technique for generating a dynamic gating mask to ensure that the contribution weight of each level is reasonably allocated.

[0223] Adaptive adjustment adaptively adjusts the compensation intensity of the multi-level interaction path according to the spatial correlation parameters of the skin perfusion gradient distribution map to ensure that the contribution weight of each level is reasonably allocated.

[0224] The residual compensation characteristics come from the spatial correlation parameters of the skin perfusion gradient distribution map in the microcirculation dynamic mapping model and are used to correct the errors in the attention enhancement map.

[0225] In the embodiments of the present application, the phase synchronization feature vector in the attention enhancement map and the residual compensation benchmark of the microvascular mechanical distortion gradient feature are input into the dynamic gating mask generation module, and the compensation intensity of the multi-level interaction path is adaptively adjusted based on the spatial correlation parameters of the skin perfusion gradient distribution map to generate an attention enhancement map with residual compensation characteristics. Specifically, the system passes through the dynamic gating mask generation module to ensure that the contribution weight of each level is reasonably allocated and generates a more accurate attention enhancement map.

[0226] The following is a specific example:

[0227] In a virtual clinical research environment, researchers used a set of advanced new flexible electronic skin monitoring systems to evaluate the microcirculation status of a shock patient. To ensure the accuracy and real-time nature of data processing, especially for the dynamic calibration and optimization of multi-source spatio-temporal data, the method in step 302 was used for analysis. Suppose the data of the patient's left ankle area is of concern. First, the system established multi-level interaction paths based on the spatio-temporal correlation between the distribution pattern of the mechanical distortion gradient hierarchy and the tissue perfusion phase difference characteristics in the initial attention distribution map. Specifically, the system embedded the spatial correlation parameters of the skin perfusion gradient distribution map as the residual compensation benchmark into the channel coupling process of the multi-level interaction path. For example, in this case, the system generated a three-dimensional model containing multiple mechanical distortion compensation paths by analyzing the mechanical changes of microvessels at different levels. These paths helped the system capture local microcirculation changes more accurately, especially in areas with complex blood flow such as the left ankle. In step 402, the system introduced a phase synchronization mechanism into the multi-level interaction path to perform cross-level matching between the time-varying fluctuation pattern of the tissue perfusion phase difference characteristics and the spatial evolution trend of the mechanical distortion gradient hierarchy, generating a compensation channel containing phase synchronization feature vectors. For example, the system used the phase synchronization mechanism to combine the tissue perfusion phase difference characteristics at different levels with the mechanical distortion gradient hierarchy in the three-dimensional attention cube to generate a more accurate compensation channel. In this specific case, the system ensured that features at different levels could be effectively integrated in the three-dimensional attention cube through the phase synchronization mechanism, generating a more accurate compensation channel, thus better reflecting the microcirculation status of the patient's left ankle area. Next, in step 403, the system enhanced the features of the compensation channel through the spatial weight compensation module, non-linearly superimposing the spatial distortion parameters of the abnormal nodes in the skin perfusion gradient distribution map with the residual compensation benchmark of the mechanical distortion gradient hierarchy to generate an attention enhancement map with spatial constraints. For example, the system ensured that the features in the compensation channel were fully enhanced through the spatial weight compensation module, generating a more accurate attention enhancement map. In this case, the system further improved the consistency and accuracy of the data by correcting the abnormal nodes in the skin perfusion gradient distribution map. Especially for the complex blood flow situation in the left ankle area, the system could more accurately identify potential abnormal points and perform compensation. Finally, in step 404, the system input the phase synchronization feature vectors in the attention enhancement map and the residual compensation benchmark of the microvascular mechanical distortion gradient features into the dynamic gating mask generation module, adaptively adjusting the compensation intensity of the multi-level interaction path based on the spatial correlation parameters of the skin perfusion gradient distribution map to generate an attention enhancement map with residual compensation characteristics. For example, the system ensured that the contribution weight of each level was reasonably allocated through the dynamic gating mask generation module, generating a more accurate attention enhancement map.In this case, the system ensures that the finally generated attention-enhanced map can more accurately reflect the patient's microcirculation state by adaptively adjusting the compensation intensity. Especially for the special blood flow pattern in the left ankle area, the system can provide more refined analysis results to help doctors make more accurate diagnoses.

[0228] Through the implementation of steps 401 to 404, the accurate monitoring and analysis of the microcirculation state of shock patients have been achieved, and remarkable progress has been made especially in dealing with the time and space synchronization of multi-source spatio-temporal data. This process not only improves the accuracy and efficiency of emergency data processing, but also provides a powerful decision-making support tool for clinicians. Specifically, it can identify microcirculation abnormalities earlier and provide immediate feedback to help doctors take intervention measures in a timely manner. In addition, by continuously learning and updating historical case data, the shock compensation mode feature library has been gradually improved, improving the accuracy of future diagnoses. This method greatly improves the treatment effect of patients, reduces the risk of complications, and provides a solid foundation for the formulation of personalized medical plans. At the same time, the adaptive learning function enables the continuous update and improvement of the shock compensation mode feature library, so as to achieve a higher level of prediction and diagnostic ability in future cases. These improvements not only enhance the performance of the clinical decision-making support system, but also open up new ways for the personalized treatment of shock patients, ultimately promoting a significant improvement in the prognosis of patients.

[0229] To further improve the monitoring accuracy and real-time performance of the microcirculation state of shock patients, especially in dealing with the time and space alignment problems of multi-source data, an optional solution is proposed. This solution generates a quality control report for abnormal nodes in the associated skin perfusion gradient distribution map by extracting the phase features in the capillary oscillation frequency spectrum and the spatial distortion features of the three-dimensional mechanical distribution topology. This process not only improves the consistency and accuracy of the data, but also provides more accurate diagnostic basis for clinicians. In some embodiments, the generation of the quality control report described in step 104 includes:

[0230] 1041. Based on the pattern mismatch events detected by the dynamic quality control engine, extract the discontinuous fluctuation segments of the phase features in the capillary oscillation frequency spectrum and the set of mechanical distortion vectors in the spatial distortion features of the three-dimensional mechanical distribution topology to generate a phase unlocking feature set;

[0231] In step 1041, the pattern mismatch event is an event triggered when the parameter evolution path of the fused data field does not match the microcirculation compensation trajectory map.

[0232] The phase features in the capillary oscillation frequency spectrum describe the variation characteristics of the capillary oscillation frequency in the time dimension and are used to identify the fluctuations of local blood circulation.

[0233] The spatial distortion characteristics of the three-dimensional mechanical distribution topological structure describe the propagation paths of blood flow in different regions of the skin surface and the changes in its mechanical properties, and are used to understand the local blood flow conditions.

[0234] The phase-locked feature set contains discontinuous fluctuation segments and a set of mechanical distortion vectors for subsequent analysis.

[0235] In the embodiments of the present application, based on the pattern mismatch events detected by the dynamic quality control engine, the system extracts the discontinuous fluctuation segments of the phase features in the capillary oscillation frequency spectrum and the set of mechanical distortion vectors in the spatial distortion features of the three-dimensional mechanical distribution topological structure to generate a phase-locked feature set. Specifically, the system first identifies the pattern mismatches existing in the current data, and then extracts the key phase features and mechanical distortion features from these mismatched data to form a feature set containing all relevant information.

[0236] 1042. Input the phase-locked feature set into the historical phase fluctuation pattern library of the microcirculation compensation trajectory map, match the historical phase compensation parameters consistent with the spatial distribution pattern of the set of mechanical distortion vectors within the current time window, and generate a spatio-temporal signal segment;

[0237] In step 1042, the historical phase fluctuation pattern library contains the phase fluctuation patterns recorded in past cases and is used to match the patterns in the current data.

[0238] The historical phase compensation parameters are the parameters matched from the historical phase fluctuation pattern library and are used to correct the phase fluctuations in the current data.

[0239] The spatio-temporal signal segment is a phase fluctuation sequence adjusted by the compensation parameters to ensure consistency with the historical pattern.

[0240] In the embodiments of the present application, input the phase-locked feature set into the historical phase fluctuation pattern library of the microcirculation compensation trajectory map, match the historical phase compensation parameters consistent with the spatial distribution pattern of the set of mechanical distortion vectors within the current time window, and generate a spatio-temporal signal segment. Specifically, the system finds the closest historical phase compensation parameters by comparing the phase-locked feature set in the current data with the records in the historical phase fluctuation pattern library, and applies these parameters to reconstruct the phase fluctuation sequence in the current data.

[0241] 1043. Combine the mapping residual calibration result of the three-dimensional mechanical distribution topological structure and the three-dimensional space coordinate system of the emergency monitoring device, perform mechanical distortion vector compensation on the spatio-temporal signal segment, and generate a reconstructed mechanical distribution model that combines phase correction features and mechanical distribution constraints;

[0242] In step 1043, the three-dimensional space coordinate system of the emergency monitoring device is used to map with the three-dimensional mechanical distribution topological structure to ensure the consistency and accuracy of the data.

[0243] The mechanical distortion vector compensation is a technique for correcting spatio-temporal signal segments to ensure its consistency with the actual mechanical distribution.

[0244] The reconstructed mechanical distribution model combines the phase correction feature and the mechanical distribution constraint to ensure the accuracy and consistency of the data.

[0245] In the embodiment of the present application, in combination with the mapping residual calibration result of the three-dimensional mechanical distribution topological structure and the three-dimensional space coordinate system of the emergency monitoring device, the mechanical distortion vector compensation is performed on the spatio-temporal signal segment to generate a reconstructed mechanical distribution model that combines the phase correction feature and the mechanical distribution constraint. Specifically, the system calibrates the mapping residual between the three-dimensional mechanical distribution topological structure and the three-dimensional space coordinate system of the emergency monitoring device, and performs further mechanical distortion vector compensation on the spatio-temporal signal segment to generate a more accurate reconstructed mechanical distribution model.

[0246] 1044. Perform a spatial topological mapping between the spatial nodes of the reconstructed mechanical distribution model and the abnormal nodes of the skin perfusion gradient distribution map, and generate a quality control report based on the discontinuous fluctuation segments of the phase-locked feature set and the spatial distortion features of the mechanical distortion vector set.

[0247] In step 1044, the spatial topological mapping is a technique for corresponding the spatial nodes of the reconstructed mechanical distribution model with the abnormal nodes of the skin perfusion gradient distribution map to ensure the consistency and accuracy of the data.

[0248] The quality control report is a detailed report generated based on the discontinuous fluctuation segments of the phase-locked feature set and the spatial distortion features of the mechanical distortion vector set, which helps doctors make diagnoses.

[0249] In the embodiment of the present application, a spatial topological mapping is performed between the spatial nodes of the reconstructed mechanical distribution model and the abnormal nodes of the skin perfusion gradient distribution map, and a quality control report is generated based on the discontinuous fluctuation segments of the phase-locked feature set and the spatial distortion features of the mechanical distortion vector set. Specifically, the system uses the spatial topological mapping technology to correspond the key nodes in the reconstructed mechanical distribution model with the abnormal nodes in the skin perfusion gradient distribution map, and generates a detailed quality control report to help doctors quickly locate and handle problems.

[0250] The following is a specific example:

[0251] Suppose in a large medical institution, a patient suspected of shock is connected to a new type of flexible electronic skin monitoring system. To more precisely evaluate the microcirculation status of this patient and provide effective treatment suggestions for clinicians, the method in step 104 is used for data processing and analysis. Suppose the data of the patient's right shoulder area is of concern. First, based on the pattern mismatch events detected by the dynamic quality control engine, the system extracts the discontinuous fluctuation segments of the phase features in the capillary oscillation frequency spectrum and the set of mechanical distortion vectors in the spatial distortion features of the three-dimensional mechanical distribution topology to generate a set of phase lock loss features. For example, in this case, by analyzing the data of the right shoulder area, the system identifies the pattern mismatch in the current data and extracts the key phase features and mechanical distortion features from it to form a feature set containing all relevant information. In step 1042, the system inputs the set of phase lock loss features into the historical phase fluctuation pattern library of the microcirculation compensation trajectory map, matches the historical phase compensation parameters consistent with the spatial distribution pattern of the set of mechanical distortion vectors within the current time window, and generates a spatio-temporal signal segment. For example, by comparing the set of phase lock loss features in the current data with the records in the historical phase fluctuation pattern library, the system finds the closest historical phase compensation parameters and applies these parameters to reconstruct the phase fluctuation sequence in the current data. In this specific case, through this method, the system successfully corrects the phase fluctuation sequence in the right shoulder area to make it consistent with the historical pattern. Next, in step 1043, the system combines the calibration result of the mapping residual between the three-dimensional mechanical distribution topology and the three-dimensional space coordinate system of the emergency monitoring device to perform mechanical distortion vector compensation on the spatio-temporal signal segment, and generates a reconstructed mechanical distribution model that fuses phase correction features and mechanical distribution constraints. For example, by calibrating the mapping residual between the three-dimensional mechanical distribution topology and the three-dimensional space coordinate system of the emergency monitoring device, the system performs further mechanical distortion vector compensation on the spatio-temporal signal segment to generate a more accurate reconstructed mechanical distribution model. In this case, through this compensation mechanism, the system ensures the accuracy of the data in the right shoulder area. Finally, in step 1044, the system performs a spatial topological mapping between the spatial nodes of the reconstructed mechanical distribution model and the abnormal nodes of the skin perfusion gradient distribution map, and generates a quality control report based on the discontinuous fluctuation segments of the set of phase lock loss features and the spatial distortion features of the set of mechanical distortion vectors. For example, through spatial topological mapping technology, the system corresponds the key nodes in the reconstructed mechanical distribution model with the abnormal nodes in the skin perfusion gradient distribution map to generate a detailed quality control report to help doctors quickly locate and handle problems. In this case, through the generated quality control report, the system helps doctors quickly identify the abnormal points in the right shoulder area and take corresponding treatment measures.

[0252] By implementing steps 1041 to 1044, the monitoring accuracy of the microcirculation state of shock patients has been significantly improved, especially in the synchronization of multi-source spatio-temporal data in complex regions (such as the right thigh); it can identify microcirculation abnormalities earlier and generate detailed quality control reports to help doctors quickly locate and handle problems; the quality control report is based on the spatial distortion characteristics of the phase-locked feature set and the mechanical distortion vector set, providing detailed information about abnormal nodes, making clinical intervention more timely and accurate.

[0253] Figure 2 The following is a schematic structural diagram of an emergency data quality control analysis device based on big data provided by an embodiment of the present application, as Figure 2 shown. The device includes:

[0254] An acquisition module 21, configured to synchronously acquire the capillary oscillation frequency spectrum, skin perfusion gradient distribution map, and three-dimensional mechanical distribution topology of shock patients through the nanofiber sensing array of the flexible electronic skin device, and generate a microcirculation dynamic mapping model integrating the time dimension and spatial correlation;

[0255] A registration module 22, configured to dynamically register the microcirculation dynamic mapping model with the spatio-temporal data of the emergency monitoring device, and form a fusion data field through time reference synchronous compensation and spatial coordinate transformation mapping. The fusion data field includes the joint distribution characteristics of microcirculation parameters and emergency parameters;

[0256] A verification module 23, configured to input the fusion data field into a dynamic quality control engine and perform pattern coupling verification with the microcirculation compensation trajectory map in the shock compensation mode feature library. The microcirculation compensation trajectory map is constructed based on the historical correlation between the capillary oscillation frequency spectrum and the skin perfusion gradient distribution map of multi-center shock cases;

[0257] A reconstruction module 24, configured to, when the dynamic quality control engine detects a pattern mismatch between the parameter evolution path of the fusion data field and the microcirculation compensation trajectory map, reconstruct spatio-temporal signal segments based on the phase characteristics of the capillary oscillation frequency spectrum and the spatial distortion characteristics of the three-dimensional mechanical distribution topology, and generate a quality control report associated with abnormal nodes of the skin perfusion gradient distribution map.

[0258] Figure 2 The described emergency data quality control analysis device based on big data can execute Figure 1 the emergency data quality control analysis method described in the embodiment shown. Its implementation principle and technical effects will not be elaborated. For the emergency data quality control analysis device based on big data in the above embodiment, the specific ways for each module and unit to perform operations have been described in detail in the embodiment related to the method, and will not be elaborated here.

[0259] In a possible design, Figure 2 An emergency data quality control analysis device based on big data in the illustrated embodiment can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0260] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32.

[0261] The processing component 32 is used for Figure 1 An emergency data quality control analysis method based on big data in the above

[0262] Among them, the processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components for executing the above method.

[0263] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0264] Of course, the computing device may also necessarily include other components, such as input / output interfaces, display components, communication components, etc.

[0265] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above peripheral interface module may be an output device, an input device, etc.

[0266] The communication component is configured to facilitate wired or wireless communication between the computing device and other devices, etc.

[0267] Among them, the computing device may be a physical device or an elastic computing host provided by a cloud computing platform, etc. At this time, the computing device may refer to a cloud server, and the above processing component, storage component, etc. may be basic server resources leased or purchased from the cloud computing platform.

[0268] The embodiments of the present application also provide a computer storage medium storing a computer program, which when executed by a computer can implement the above-mentioned Figure 1 method for quality control analysis of emergency data based on big data in the illustrated embodiments.

[0269] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0270] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative effort.

[0271] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0272] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present application.

Claims

1. A method for quality control analysis of emergency data based on big data, characterized in that: include: The nanofiber sensor array of the flexible electronic skin device synchronously collects the capillary oscillation frequency spectrum, skin perfusion gradient distribution map and three-dimensional mechanical distribution topology of shock patients, and generates a microcirculatory dynamic mapping model that integrates the time dimension and spatial correlation. Dynamically aligning the microcirculation dynamic mapping model with the spatiotemporal data of the emergency monitoring device, forming a fused data field through time reference synchronization compensation and spatial coordinate transformation mapping, wherein the fused data field includes joint distribution characteristics of microcirculation parameters and emergency parameters; Inputting the fused data field into a dynamic quality control engine and performing pattern coupling verification with a microcirculation compensation trajectory map in a shock compensation pattern feature library, wherein the microcirculation compensation trajectory map is constructed based on the historical correlation between the capillary oscillation frequency spectrum and the skin perfusion gradient distribution map of multi-center shock cases; When the dynamic quality control engine detects that there is a pattern mismatch between the parameter evolution path of the fused data field and the microcirculation compensation trajectory map, the spatiotemporal signal segments are reconstructed based on the phase characteristics of the capillary oscillation frequency spectrum and the spatial distortion characteristics of the three-dimensional mechanical distribution topological structure to generate a quality control report of the abnormal nodes of the associated skin perfusion gradient distribution map.

2. The method according to claim 1, characterized in that The method of dynamically registering the microcirculation dynamic mapping model with the spatiotemporal data of the emergency monitoring device, and forming a fused data field through time reference synchronization compensation and spatial coordinate transformation mapping, includes: Establishing a pulse synchronization channel between the time axis of the microcirculation dynamic mapping model and the clock source of the emergency monitoring equipment, eliminating the time base offset of multi-source data acquisition through an adaptive interpolation algorithm, and generating a microcirculation oscillation waveform sequence aligned in the time dimension; Extracting the three-dimensional spatial coordinate system of the emergency monitoring device, and using an affine transformation algorithm to map the spatial features of the three-dimensional mechanical distribution topological structure to the three-dimensional spatial coordinate system of the emergency monitoring device, to form a spatial transformation matrix containing the microvascular stress field distribution; Performing tensor fusion of the microcirculation oscillation waveform sequence aligned in the time dimension with the spatial transformation matrix to generate a fused data field with spatiotemporal continuity, wherein the fused data field includes a dynamic propagation trajectory of the capillary oscillation frequency spectrum in the three-dimensional spatial coordinate system of the emergency monitoring device; The topological integrity of the fused data field is verified through edge computing nodes. When it is detected that the mapping residual between the three-dimensional mechanical distribution topological structure and the three-dimensional spatial coordinate system of the emergency monitoring equipment exceeds the preset tolerance, the abnormal spatial nodes in the microcirculation dynamic mapping model are marked and fed back to the nanofiber sensor array for signal compensation calibration.

3. The method according to claim 1, characterized in that The step of inputting the fusion data field into a dynamic quality control engine and performing pattern coupling verification with a microcirculation compensation trajectory map in a shock compensation pattern feature library includes: Establishing a dynamic alignment channel between the time axis of the fused data field and the time reference of the shock compensation pattern feature library, eliminating the time phase deviation between historical case data and real-time data acquisition through a multi-scale time interpolation algorithm, and generating a microcirculation parameter evolution sequence synchronized in the time dimension; Extracting the spatial distribution characteristics of the capillary oscillation frequency spectrum in the fused data field, and using a deep convolutional network to construct a spatial topological mapping relationship of a microcirculation compensation trajectory map in a shock compensation pattern feature library based on the spatial distribution characteristics, so as to generate a spatial correlation matrix containing blood flow propagation paths; Input the microcirculation parameter evolution sequence and the spatial correlation matrix into the dynamic attention coupling module, adjust the contribution of different regional features through the dynamic allocation mechanism of spatiotemporal attention weights, and generate a compensation pattern similarity distribution map; Based on the deviation detection result of the compensation pattern similarity distribution map, when the local area similarity value is lower than a preset threshold, the abnormal node of the skin perfusion gradient distribution map is marked, the frequency domain signal compensation mechanism of the nanofiber sensor array is triggered and the historical correlation parameters of the shock compensation pattern feature library are updated.

4. The method according to claim 3, characterized in that The microcirculation parameter evolution sequence and the spatial correlation matrix are input into the dynamic attention coupling module, and the contribution of different regional features is adjusted through the spatiotemporal attention weight dynamic allocation mechanism to generate a compensation pattern similarity distribution map, including: Establishing a spatiotemporal feature cross-correlation channel, performing three-dimensional tensor fusion on the oscillation phase fluctuation features in the microcirculation parameter evolution sequence and the blood flow propagation path features in the spatial correlation matrix, performing multi-scale decomposition on the three-dimensional tensor fusion results through the frequency domain enhanced attention mechanism, and generating an initial attention distribution map containing spatiotemporal coupling weights; A multi-head dynamic attention mechanism is used to decouple the regional features of the initial attention distribution map. Each attention head extracts the microvascular mechanical distortion gradient features, tissue perfusion phase difference features and blood flow direction topological constraint features respectively. The sliding window mechanism is combined to retain the complete context association of the most recent time step to generate a multi-scale spatiotemporal attention weight set. Constructing a dynamic gated residual fusion network, performing residual-aware connection between the multi-scale spatiotemporal attention weight set and the initial attention distribution map, dynamically adjusting the contribution weight of each attention head through a differentiable gating unit, and generating an optimized spatiotemporal attention weight tensor; The compensation pattern similarity is calculated based on the optimized spatiotemporal attention weight tensor, and the diffusion weight value in the spatial association matrix is ​​calculated using the graph convolution propagation algorithm. Combined with the topological constraints of the blood flow propagation path, a compensation pattern similarity distribution map is generated, and the frequency domain signal of the deviated area is calibrated through the residual compensation mechanism.

5. The method according to claim 4, characterized in that The dynamic gated residual fusion network is constructed, the multi-scale spatiotemporal attention weight set is connected to the initial attention distribution map through residual perception, the contribution weight of each attention head is dynamically adjusted through a differentiable gating unit, and an optimized spatiotemporal attention weight tensor is generated, including: A cross-level residual connection structure is established based on the hierarchical distribution characteristics of the multi-scale spatiotemporal attention weight set and the spatial topological constraints of the initial attention distribution map, and the hierarchical distribution pattern of the microvascular mechanical distortion gradient characteristics is coupled with the blood flow direction topological constraint characteristics in the initial attention distribution map across channels to form a three-dimensional attention cube containing a mechanical distortion compensation path; The three-dimensional attention cube is subjected to multi-level feature interaction through the cross-level residual connection structure, and the tissue perfusion phase difference feature in the initial attention distribution map is jointly encoded with the mechanical distortion gradient level of the three-dimensional attention cube to generate an attention enhancement map with residual compensation characteristics, wherein the residual compensation characteristics are derived from the spatial correlation parameters of the skin perfusion gradient distribution map in the microcirculation dynamic mapping model; A dynamic gating mask is generated based on the time evolution pattern of the microvascular mechanical distortion gradient characteristics, and the dynamic gating mask dynamically allocates the contribution weights of different mechanical distortion gradient levels in the attention enhancement map through a nonlinear constraint mechanism to form a gated attention weight distribution that integrates microvascular mechanical characteristics and blood flow propagation paths; The gated attention weight distribution is spatially topologically calibrated with the initial attention distribution map, and the gated attention weight distribution is distortion compensated based on the mapping residual between the three-dimensional mechanical distribution topological structure and the three-dimensional spatial coordinate system of the emergency monitoring equipment to generate an optimized spatiotemporal attention weight tensor.

6. The method according to claim 5, characterized in that The three-dimensional attention cube is subjected to multi-level feature interaction through the cross-level residual connection structure, and the tissue perfusion phase difference feature in the initial attention distribution map is jointly encoded with the mechanical distortion gradient level of the three-dimensional attention cube to generate an attention enhancement map with residual compensation characteristics, wherein the residual compensation characteristics are derived from the spatial correlation parameters of the skin perfusion gradient distribution map in the microcirculation dynamic mapping model, including: A multi-level interaction path is established based on the temporal and spatial correlation between the distribution pattern of the mechanical distortion gradient level and the tissue perfusion phase difference characteristics in the initial attention distribution map, and the spatial correlation parameters of the skin perfusion gradient distribution map are embedded in the channel coupling process of the multi-level interaction path as a residual compensation benchmark; Introducing a phase synchronization mechanism into the multi-level interactive path, cross-level matching of the time-varying fluctuation pattern of the tissue perfusion phase difference characteristic and the spatial evolution trend of the mechanical distortion gradient level is performed to generate a compensation channel including a phase synchronization feature vector, wherein the phase synchronization feature vector inherits the phase constraint condition of the capillary oscillation frequency spectrum in the microcirculation dynamic mapping model; The compensation channel is feature enhanced by a spatial weight compensation module, and the spatial distortion parameters of the abnormal nodes of the skin perfusion gradient distribution map are nonlinearly superimposed with the residual compensation benchmark of the mechanical distortion gradient level to generate an attention enhancement map with spatial constraints; The phase synchronization feature vector in the attention enhancement map and the residual compensation reference of the microvascular mechanical distortion gradient feature are input into a dynamic gated mask generation module, and the compensation intensity of the multi-level interaction path is adaptively adjusted based on the spatial correlation parameters of the skin perfusion gradient distribution map to generate an attention enhancement map with residual compensation characteristics. The residual compensation characteristics are derived from the spatial correlation parameters of the skin perfusion gradient distribution map in the microcirculation dynamic mapping model.

7. The method according to claim 1, characterized in that When the dynamic quality control engine detects that there is a mode mismatch between the parameter evolution path of the fusion data field and the microcirculation compensation trajectory map, based on the phase characteristics of the capillary oscillation frequency spectrum and the spatial distortion characteristics of the three-dimensional mechanical distribution topological structure, the spatiotemporal signal fragment is reconstructed to generate a quality control report of the abnormal node of the associated skin perfusion gradient distribution map, including: Based on the mode mismatch events detected by the dynamic quality control engine, a set of mechanical distortion vectors of discontinuous fluctuation segments of phase characteristics in the capillary oscillation frequency spectrum and spatial distortion characteristics of the three-dimensional mechanical distribution topological structure are extracted to generate a set of phase loss features; Inputting the phase-unlocked feature set into the historical phase fluctuation pattern library of the microcirculation compensation trajectory map, matching the historical phase compensation parameters consistent with the spatial distribution pattern of the mechanical distortion vector set in the current time window, and generating a spatiotemporal signal segment; Combining the mapping residual calibration result of the three-dimensional mechanical distribution topological structure with the three-dimensional space coordinate system of the emergency monitoring equipment, the mechanical distortion vector compensation is performed on the spatiotemporal signal segment to generate a reconstructed mechanical distribution model integrating phase correction features and mechanical distribution constraints; The spatial nodes of the reconstructed mechanical distribution model are spatially topologically mapped with the abnormal nodes of the skin perfusion gradient distribution map, and a quality control report is generated based on the discontinuous fluctuation fragments of the phase-loss lock feature set and the spatial distortion features of the mechanical distortion vector set.

8. An emergency data quality control analysis device based on big data, characterized in that: include: An acquisition module is used to synchronously acquire the capillary oscillation frequency spectrum, skin perfusion gradient distribution map and three-dimensional mechanical distribution topology of shock patients through the nanofiber sensor array of the flexible electronic skin device, and generate a microcirculation dynamic mapping model that integrates the time dimension and spatial correlation; A registration module, used for dynamically registering the microcirculation dynamic mapping model with the spatiotemporal data of the emergency monitoring device, forming a fused data field through time reference synchronization compensation and spatial coordinate transformation mapping, wherein the fused data field contains the joint distribution characteristics of microcirculation parameters and emergency parameters; A verification module, used for inputting the fused data field into a dynamic quality control engine and performing pattern coupling verification with a microcirculation compensation trajectory map in a shock compensation pattern feature library, wherein the microcirculation compensation trajectory map is constructed based on a historical correlation between a capillary oscillation frequency spectrum and a skin perfusion gradient distribution map of multi-center shock cases; A reconstruction module is used to reconstruct the space-time signal segments and generate a quality control report of abnormal nodes of the associated skin perfusion gradient distribution map based on the phase characteristics of the capillary oscillation frequency spectrum and the spatial distortion characteristics of the three-dimensional mechanical distribution topological structure when the dynamic quality control engine detects that there is a pattern mismatch between the parameter evolution path of the fusion data field and the microcirculation compensation trajectory map.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement the emergency data quality control analysis method based on big data as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, the emergency data quality control analysis method based on big data as described in any one of claims 1 to 7 is implemented.

Images