Clinical first-aid equipment information acquisition management method and system

Abstract

The invention relates to the technical field of computers, and discloses a clinical first-aid equipment information acquisition management method and system. The method comprises the steps that a video frame sequence is collected through a visual perception unit, a three-dimensional space map is constructed, and a patient and equipment are positioned in real time; acquiring a device vital sign data stream; based on double criteria of spatial proximity and trajectory correlation, the device and patient identity are automatically bound, and a structured emergency record is generated. The system comprises a visual perception unit, a three-dimensional mapping and positioning unit, a data receiving unit, a space behavior analysis unit and an intelligent binding decision-making unit. Through non-sensitive and automatic association, the accuracy, timeliness and data integrity of first-aid information acquisition are remarkably improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of computers, and particularly relates to a clinical emergency equipment information collection management method and system. BACKGROUND

[0002] With the rapid development of smart medical systems, the real-time, accuracy and automation level of device information management in clinical emergency scenarios are put forward with higher requirements. In the emergency room, ambulance and other high-pressure, fast-paced emergency environments, medical staff need to quickly bind various life support devices (such as ventilators, defibrillators, infusion pumps) with specific patients to ensure the correct collection of vital sign data and the reliability of subsequent treatment decisions. The traditional method relies on manual operation, including manually scanning device barcodes, inputting device numbers or selecting associated patients through touch screens. This process not only occupies valuable rescue time, but also is prone to mismatching, missing or repeated binding under high workloads, resulting in chaotic data flow, distorted diagnosis and treatment records, and even medical errors.

[0003] Device automatic identification technology based on Internet of Things and intelligent sensing has gradually become a research hotspot. Existing solutions attempt to introduce radio frequency identification (RFID), Bluetooth beacons or Wi-Fi positioning to realize device identity recognition and rough location tracking, but due to signal interference, multipath effect and the influence of complex indoor environments, the positioning accuracy is difficult to support the demand for one-to-one precise binding of "device-patient". Especially in the emergency scene where multiple people coexist and devices are densely mobile, simply relying on signal strength matching mechanism cannot effectively distinguish between spatially adjacent but logically unrelated devices and patients, often resulting in misbinding or binding delay problems.

[0004] The existing technology generally has the following defects: first, it lacks the ability to dynamically model the three-dimensional spatial structure of the emergency scene, and cannot accurately capture the relative position relationship between the device and the patient; second, the device identification and data binding process is fragmented, and the spatial proximity, movement trajectory correlation and device state information are not fused for judgment; third, existing systems mostly use rigid threshold matching strategies, which are difficult to adapt to non-ideal working conditions such as frequent movement, obstruction and signal fluctuations of devices in the emergency process. The above problems result in insufficient robustness of existing solutions in real clinical environments, and they cannot achieve high-reliability device-patient intelligent binding without human intervention, which seriously hinders the efficiency improvement and safety closed-loop construction of emergency information systems. Therefore, there is an urgent need for a clinical emergency equipment information collection management method that integrates spatial computing and fuzzy matching algorithms to solve the technical problems of device automatic perception, precise positioning and intelligent data binding. SUMMARY

[0005] The application provides a clinical first-aid equipment information collection management method and system, aiming to solve the technical problems of low efficiency, high error rate and data stream fragmentation caused by manual binding of equipment and patients due to the variety of equipment and the tense operation environment in the first-aid field. By constructing an automatic identification and binding mechanism based on visual perception and spatial calculation, real-time, accurate and non-invasive association between first-aid equipment, patient identity and vital sign data stream is realized, thereby improving the informatization level and clinical response efficiency of the first-aid process.

[0006] The application provides a clinical first-aid equipment information collection management method, which comprises: collecting a continuous video frame sequence containing a patient and first-aid equipment through at least one visual perception unit deployed in the first-aid field; constructing a three-dimensional spatial map of the first-aid scene based on the video frame sequence, and performing real-time semantic segmentation and spatial positioning of the patient and each first-aid equipment in the three-dimensional spatial map to obtain the spatial coordinates and posture information of the patient and the spatial coordinates, equipment type and running state of each first-aid equipment; acquiring a vital sign data stream output by each first-aid equipment, and establishing a mapping relationship between the vital sign data stream and the corresponding equipment physical identifier; monitoring the spatial motion trajectory of the patient and each first-aid equipment within a preset time window, calculating the spatial proximity index and trajectory correlation coefficient between any first-aid equipment and the patient; when the spatial proximity index is lower than a first preset threshold and the trajectory correlation coefficient is higher than a second preset threshold, it is determined that the first-aid equipment is in a use state for the patient, and the physical identifier, equipment type, running state of the first-aid equipment and the corresponding vital sign data stream are automatically bound with the identity information of the patient to generate a structured first-aid event record.

[0007] Preferably, the visual perception unit is a binocular camera built in the augmented reality glasses worn by medical staff, or a multi-view wide-angle camera array fixedly installed on the ceiling or wall of the first-aid room; the frame rate of the visual perception unit is not less than 30 frames per second, the resolution is not less than 1.92 million pixels, and the visual perception unit has infrared light supplement function to adapt to low-illumination environment.

[0008] Preferably, the construction of the three-dimensional spatial map of the first-aid scene specifically comprises: feature point extraction and matching of the continuous video frame sequence, generation of a sparse point cloud map by using a simultaneous localization and mapping algorithm; on this basis, pixel-level semantic segmentation of each frame of image is performed by using a deep neural network to identify the patient trunk region, head region and appearance contour of each first-aid equipment; combined with the sparse point cloud and the semantic segmentation result, a dense three-dimensional grid model containing semantic labels is generated by a dense reconstruction algorithm to serve as the three-dimensional spatial map.

[0009] Preferably, the real-time semantic segmentation and spatial localization of the patient and each emergency device specifically comprises: fitting the patient area in the semantic segmentation result as an ellipsoid geometric model, and taking the centroid coordinates as the spatial coordinates of the patient; fitting each identified emergency device area as a cuboid bounding box, and taking the geometric center coordinates as the spatial coordinates of the device; and simultaneously, extracting the device unique physical identifier and device type information from the device surface nameplate or display screen through device appearance template matching or optical character recognition technology.

[0010] Preferably, the emergency device comprises one or more of a ventilator, a defibrillator, an electrocardiograph monitor, an infusion pump, and a blood oxygen saturation monitor; and the operating state comprises a power-on state, a standby state, a working state, a fault state, and a low power state; and the operating state is determined by analyzing the device display screen content, the indicator light color and flicker frequency, or the device shell vibration characteristics.

[0011] Preferably, the acquisition of the vital sign data stream output by each emergency device specifically comprises: receiving physiological parameter data packets transmitted from the built-in sensors of the emergency device through a wireless communication module; the physiological parameter data packets contain a timestamp, a device physical identifier, a parameter type, and a parameter value; and the parameter type comprises one or more of a heart rate, a respiratory rate, a blood oxygen saturation, a non-invasive blood pressure, a body temperature, and a ventilation volume.

[0012] Preferably, the spatial proximity index between any emergency device and the patient is specifically calculated as follows: in a preset time window, a sequence of Euclidean distances between the device spatial coordinates and the patient spatial coordinates is taken, and a time-weighted average value thereof is calculated as the spatial proximity index; and the time weighting adopts an exponential decay function, and the distance closer to the current time has a higher weight.

[0013] Preferably, the calculation of the trajectory correlation coefficient specifically comprises: extracting three-dimensional motion trajectory vector sequences of the patient and the emergency device in a preset time window, respectively; performing dynamic time warping alignment on the two trajectory vector sequences; and calculating a Pearson correlation coefficient of the aligned sequences as the trajectory correlation coefficient.

[0014] Preferably, the duration of the preset time window is 10 seconds to 60 seconds; the first preset threshold is 1.5 meters; and the second preset threshold is 0.7 meters.

[0015] Preferably, after the binding of the device and the patient is completed, the method further comprises: pushing the binding result to a hospital information system in real time to update the device use record in the electronic medical record; and if it is detected that the same device simultaneously satisfies the binding condition with multiple patients within a short time, triggering a conflict checking mechanism, arbitrating according to whether the device type supports multiple patients sharing, a patient priority score, and a trajectory stability index, and retaining only one valid binding relationship.

[0016] The application provides a clinical first-aid equipment information acquisition management system, which comprises: a visual perception unit for acquiring a sequence of continuous video frames containing a patient and first-aid equipment; a three-dimensional scene mapping and target positioning unit for constructing a three-dimensional space map of a first-aid scene based on the sequence of video frames, and performing real-time semantic segmentation and spatial positioning of the patient and each first-aid equipment in the three-dimensional space map to obtain spatial coordinates, posture information of the patient, and spatial coordinates, equipment type and running state of each first-aid equipment; a vital sign data receiving unit for acquiring a vital sign data stream output by each first-aid equipment, and establishing a mapping relationship between the vital sign data stream and the corresponding equipment physical identifier; a spatial behavior analysis unit for monitoring the spatial motion trajectory of the patient and each first-aid equipment within a preset time window, calculating the spatial proximity index and trajectory correlation coefficient between any first-aid equipment and the patient; an intelligent binding decision unit for determining that the first-aid equipment is in a use state for the patient when the spatial proximity index is lower than a first preset threshold and the trajectory correlation coefficient is higher than a second preset threshold, and automatically binding the physical identifier, equipment type, running state of the first-aid equipment and the corresponding vital sign data stream with the identity information of the patient to generate a structured first-aid event record.

[0017] Preferably, the three-dimensional scene mapping and target positioning unit comprises a feature extraction subunit, a simultaneous localization and mapping subunit, a semantic segmentation subunit and a dense reconstruction subunit; the feature extraction subunit extracts image key points by using a scale-invariant feature transform algorithm or an ORB feature algorithm; the simultaneous localization and mapping subunit uses a visual-inertial odometry framework based on graph optimization; the semantic segmentation subunit uses a convolutional neural network with an encoder-decoder structure, and the training data set of the convolutional neural network contains first-aid scene images labeled with the patient and the categories of various first-aid equipment; the dense reconstruction subunit uses a multi-view stereo vision algorithm or a neural radiance field reconstruction algorithm.

[0018] Preferably, the spatial behavior analysis unit comprises a trajectory extraction subunit, a dynamic time warping subunit and a correlation calculation subunit; the trajectory extraction subunit extracts a target centroid coordinate sequence in chronological order from the three-dimensional space map; the dynamic time warping subunit performs nonlinear alignment of two trajectories by using a standard dynamic time warping algorithm; and the correlation calculation subunit calculates a covariance matrix of the aligned trajectories in three spatial dimensions, and normalizes to obtain a trajectory correlation coefficient.

[0019] Preferably, the intelligent binding decision unit further includes a conflict arbitration subunit. When a binding conflict is detected, the conflict arbitration subunit queries the device metadata to determine whether it supports multi-patient mode. If it does not support it, it sorts patients according to their triage level scores and retains only the binding relationship of the patient with the highest score. If it supports it, it further calculates the trajectory stability index of each candidate binding relationship. The trajectory stability index is the reciprocal of the standard deviation of the trajectory speed, and the binding relationship with the highest stability index is retained.

[0020] Preferably, the system further includes a data synchronization interface unit, used to transmit the generated structured emergency event records to the electronic medical record system, equipment asset management platform and clinical data center through the hospital's internal network protocol, so as to achieve data consistency maintenance among multiple systems.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0022] This invention integrates visual perception, 3D spatial mapping, semantic understanding, and spatiotemporal behavior analysis technologies to construct an automatic patient binding mechanism for emergency medical devices without human intervention. Compared to traditional methods relying on barcode scanning or manual input, this invention completely eliminates binding omissions, mismatches, or delays caused by busy operations, significantly improving the accuracy and timeliness of information collection at emergency sites. The invention employs dual criteria of spatial proximity and trajectory correlation to effectively distinguish between the device's "physical proximity" and "actual usage" states, avoiding misbinding; a conflict arbitration mechanism ensures the rigor of binding logic in complex multi-patient scenarios. Furthermore, this invention achieves real-time structured association between device operating status, vital sign data streams, and patient identity, providing a high-quality and complete data foundation for subsequent clinical decision support, device usage efficiency analysis, and medical quality traceability, thus promoting the development of emergency medicine towards intelligence and automation. Attached Figure Description

[0023] Fig. 1 This is a schematic diagram of the overall technical solution architecture of the present invention;

[0024] Fig. 2 This is a schematic diagram of the core principle framework of the emergency medical device-patient automatic binding mechanism based on visual perception and spatial computing in this invention;

[0025] Fig. 3 This is a logical flow diagram of the three-dimensional scene mapping and target localization stages in this invention;

[0026] Fig. 4 This is a logical flowchart of the spatial behavior analysis and intelligent binding decision-making stage in this invention;

[0027] Fig. 5This is a schematic diagram of the multi-level interaction relationship and data flow between the visual perception unit, the 3D scene mapping and target localization unit, and the vital signs data receiving unit in this invention;

[0028] Fig. 6 This is a schematic diagram of the logical framework of the internal conflict arbitration mechanism of the intelligent binding decision unit in this invention; Detailed Implementation

[0029] This invention provides a method and system for collecting and managing information on clinical emergency medical equipment. Its core lies in automatically constructing a three-dimensional spatial map containing the patient and emergency medical equipment through visual perception units deployed at the emergency scene, and achieving seamless and precise binding between the equipment and the patient based on spatial proximity and motion trajectory correlation. The following will be illustrated in conjunction with the appendix... Figs. 1 to 6 The specific implementation methods of each component of the system are described in detail to ensure the full disclosure and feasibility of the technical solution.

[0030] The method begins with step S1: acquiring a continuous sequence of video frames containing the patient and emergency equipment using at least one visual sensing unit deployed at the emergency scene. The visual sensing unit is either a binocular camera built into augmented reality glasses worn by medical personnel, or a multi-view wide-angle camera array fixedly mounted on the ceiling or wall of the emergency room. The visual sensing unit has a frame rate of at least 30 frames per second, a resolution of at least 1.92 million pixels, and infrared illumination to adapt to low-light environments. In actual deployment, if augmented reality glasses are used, their binocular cameras simultaneously acquire images from both eyes for subsequent depth estimation; if a fixed camera array is used, the timestamps of each camera are strictly synchronized to ensure that the multi-view images are aligned in the time dimension. All acquired video frame sequences are accurately timestamped and transmitted in real-time to the backend processing server via wired or wireless means. The acquisition of video frame sequences continues, covering the entire emergency operation process, from the patient's entry into the emergency area until the completion of initial treatment.

[0031] The following step, S2, is executed: A 3D spatial map of the emergency scene is constructed based on the video frame sequence. Real-time semantic segmentation and spatial localization of the patient and various emergency equipment are performed within this 3D spatial map to obtain the patient's spatial coordinates and posture information, as well as the spatial coordinates, equipment type, and operating status of each emergency equipment. This step is further subdivided into several sub-processing flows. First, feature point extraction and matching are performed on the continuous video frame sequence. The ORB feature algorithm is used for feature extraction. This algorithm has high computational efficiency while ensuring rotation and scale invariance, making it suitable for real-time processing scenarios. At least 500 stable keypoints are extracted from each frame image, and fast matching is performed using Hamming distance to generate inter-frame feature correspondences. Second, a graph-optimized visual inertial odometry framework is used to execute simultaneous localization and mapping algorithms to generate a sparse point cloud map. This framework integrates visual feature matching results with acceleration and angular velocity data provided by the inertial measurement unit, and solves for camera pose and 3D point positions through nonlinear optimization to form an initial sparse map. Each point in the sparse point cloud map is accompanied by 3D position information in the world coordinate system and an observation frame index.

[0032] After obtaining the sparse point cloud map, dense semantic reconstruction is performed. A convolutional neural network with an encoder-decoder structure is used to perform pixel-level semantic segmentation on each frame of the image. During the training phase, the network is trained using a large dataset of emergency scene images labeled with patient torso regions, head regions, and emergency equipment categories such as ventilators, defibrillators, ECG monitors, infusion pumps, and pulse oximeters. The network output is a semantic label map of the same size as the input image, where each pixel is assigned a category label. The semantic segmentation results are spatiotemporally aligned with the sparse point cloud map: for each frame of segmentation, it is projected onto the local coordinate system constructed from the sparse point cloud, and a dense 3D mesh model containing semantic labels is generated through triangulation or multi-view stereo matching algorithms. This dense 3D mesh model is the 3D spatial map, where each vertex on its surface carries semantic category information.

[0033] Based on a 3D spatial map, geometric fitting and spatial positioning are performed on the patient and emergency equipment. The patient area is fitted as an ellipsoidal geometric model, with its principal axis direction determined by the extension direction of the patient's torso in 3D space, and the centroid coordinates serving as the patient's spatial coordinates. This ellipsoidal model can be dynamically updated to adapt to changes in patient position. Each identified emergency equipment area is fitted as a cuboid bounding box, with its six faces aligned with the outer contour of the equipment body, and the geometric center coordinates serving as the equipment's spatial coordinates. Simultaneously, optical character recognition technology is used to extract the unique physical identifier and equipment type information from the equipment's nameplate or display screen. If standard icons or template patterns exist on the equipment surface, a template matching algorithm is used for cross-validation to ensure identification accuracy. The equipment's operating status is determined by analyzing its display screen content, indicator light colors and flashing frequencies, or the vibration characteristics of the equipment casing. For example, a solid green indicator light indicates standby, a flashing red light indicates a malfunction, and "VENT ON" on the display screen indicates that the ventilator is in operation. Vibration characteristics are obtained by analyzing minute displacement changes in the equipment area across consecutive frames; infusion pumps in operation typically exhibit periodic micro-vibrations.

[0034] Step S3: Acquire vital sign data streams output by each emergency medical device and establish a mapping relationship between the vital sign data streams and the corresponding device physical identifiers. Emergency medical devices have multiple built-in physiological sensors that periodically broadcast physiological parameter data packets via Bluetooth or Wi-Fi modules. Each data packet contains a timestamp, device physical identifier, parameter type, and parameter value. Parameter types include one or more of heart rate, respiratory rate, blood oxygen saturation, non-invasive blood pressure, body temperature, and ventilation volume. The vital sign data receiving unit continuously monitors the wireless channel, receives data packets from all present devices, and classifies and stores the data streams according to the device physical identifier. Each device corresponds to an independent data buffer queue, in which the received parameter records are arranged in chronological order. The mapping relationship is stored in a memory database in key-value pair form, where the key is the device physical identifier and the value is a reference to the corresponding data queue. This mapping relationship is established when a device is first identified and marked as invalid when its operating state changes to power off or offline.

[0035] Then, step S4 is executed: The spatial motion trajectories of the patient and each emergency medical device are monitored within a preset time window, and the spatial proximity index and trajectory correlation coefficient between any emergency medical device and the patient are calculated. The duration of the preset time window is 10 to 60 seconds, preferably 30 seconds. Within this window, the patient's ellipsoid centroid coordinate sequence is extracted from the 3D spatial map in chronological order to form the patient's 3D motion trajectory vector. ,in For the first The coordinates of each time point are calculated. Similarly, the coordinate sequence of the center of the bounding box of any emergency medical device is extracted to form the three-dimensional motion trajectory vector of the device. .

[0036] The spatial proximity index is calculated using a time-weighted Euclidean distance average. Specifically, for the trajectory... and Perform time alignment so that both are at the same point in time. Each point in time has coordinate values. Calculate the coordinates for each time point. Euclidean distance between the upper device and the patient's coordinates:

[0037] ;

[0038] for Time points Above, trajectory and The three-dimensional Euclidean distance between them; , , For time points Time, trajectory Coordinates in the X, Y, and Z dimensions; , , For time points Time, trajectory Coordinates in the X, Y, and Z dimensions;

[0039] Subsequently, the distance sequence is weighted using an exponential decay function:

[0040] ;

[0041] in For the first The time decay weight corresponding to each object, For the current moment, This is the attenuation coefficient, with a value of 0.2. Spatial proximity index. Defined as weighted average distance:

[0042] ;

[0043] Weighted by time decay As weight, for Spatial distance of objects The smaller the weighted average, the closer the device is to the patient in the near future.

[0044] Calculating the trajectory correlation coefficient requires dynamic time warping and alignment of the two trajectories. Since there may be slight differences in the motion sampling frequencies of the patient and the device, directly calculating the correlation can lead to bias. A standard dynamic time warping algorithm is used to construct a cumulative distance matrix and find the alignment path that minimizes the total warping distance. After alignment, trajectory sequences of equal length are obtained. and The Pearson correlation coefficient was then calculated. :

[0045]

[0046] in For the trajectory of the patients after the completion of the procedure The A coordinate vector, For the equipment trajectory after completion The A coordinate vector, and These are the mean vectors of the aligned trajectories. This is the length of the aligned trajectory sequence. This coefficient measures the consistency of the motion trend between the two trajectories, ranging from -1 to 1. A higher value indicates stronger motion synchronization.

[0047] Step S5: When the spatial proximity index is lower than a first preset threshold and the trajectory correlation coefficient is higher than a second preset threshold, it is determined that the emergency medical device is in use with the patient. The device's physical identifier, device type, operating status, and corresponding vital sign data stream are automatically bound to the patient's identity information, generating a structured emergency event record. The first preset threshold is set to 1.5 meters, and the second preset threshold is set to 0.7 meters. Patient identity information is obtained in the following ways: when the patient enters the emergency area, the QR code on their wristband is captured by the visual perception unit and decoded to obtain the patient's unique identifier; or identity information is obtained by comparing the facial recognition module with the hospital's registered photo database. The binding operation includes creating an associated record in the central database. Fields include the patient's identity identifier, device physical identifier, device type, operating status, binding start time, spatial proximity index value, trajectory correlation coefficient value, and a pointer to the corresponding vital sign data stream.

[0048] After binding is completed, the system executes a conflict resolution mechanism. If the same device is detected to meet the binding conditions with multiple patients simultaneously within a short period, an arbitration process is triggered. First, the device metadata is queried to determine if it supports multi-patient sharing mode. Dedicated devices such as ventilators and defibrillators typically do not support this, while some models of ECG monitors support multi-channel monitoring. If the device does not support multi-patient mode, patients are sorted according to their triage level score, and only the binding relationship of the patient with the highest score is retained. The triage level score is provided by the emergency triage system and is divided into Level 1 (critical), Level 2 (urgent), Level 3 (sub-urgent), and Level 4 (non-urgent). If the device supports multi-patient mode, the trajectory stability index of each candidate binding relationship is further calculated. The trajectory stability index is defined as the reciprocal of the standard deviation of the trajectory velocity. The velocity sequence is obtained by differentiating the trajectory coordinates; the smaller the standard deviation, the smoother the motion, and the higher the stability index. The system retains the binding relationship with the highest stability index, and the other candidate relationships are rejected.

[0049] The generated structured emergency event records are pushed in real time to the electronic medical record system, equipment asset management platform, and clinical data center via the hospital's internal network protocol. The pushed content includes complete binding information and a summary of the associated vital signs data stream. The electronic medical record system automatically updates equipment usage records, the equipment asset management platform records equipment usage duration and status logs, and the clinical data center is used for subsequent quality analysis and research.

[0050] At the system level, the clinical emergency equipment information acquisition and management system includes a visual perception unit, a 3D scene mapping and target localization unit, a vital signs data receiving unit, a spatial behavior analysis unit, and an intelligent binding decision-making unit. The visual perception unit is responsible for acquiring raw video. The 3D scene mapping and target localization unit includes feature extraction subunits, synchronous localization and mapping subunits, semantic segmentation subunits, and dense reconstruction subunits, collaboratively completing the conversion from images to a semantic 3D map. The vital signs data receiving unit maintains the mapping relationship between the device's physical identifier and the data stream. The spatial behavior analysis unit includes trajectory extraction subunits, dynamic time warping subunits, and correlation calculation subunits, responsible for trajectory processing and indicator calculation. The intelligent binding decision-making unit integrates threshold judgment logic and conflict arbitration subunits to ensure the uniqueness and rationality of the binding results. All units are interconnected through a high-speed internal bus, sharing a unified time reference and coordinate system to ensure data consistency.

[0051] The entire system runs on a dedicated server cluster, equipped with high-performance graphics processors to accelerate neural network inference and 3D reconstruction calculations. After startup, the system continuously monitors the emergency area, immediately adding any new patient or activated device to the monitoring scope. All processing latency is controlled within 500 milliseconds to ensure real-time binding decisions. The system also features anomaly handling mechanisms: if the visual sensing unit signal is interrupted, it automatically switches to a backup camera; if vital sign data is lost, the device status is marked as a communication anomaly and a reconnection attempt is made; if 3D map reconstruction fails, it reverts to 2D planar positioning mode, using single-view images to estimate relative position and maintain basic binding functionality.

[0052] Through the above methods and systems, this invention achieves fully automatic and high-precision binding between emergency medical equipment and patients, completely eliminating manual operation and significantly improving the reliability and efficiency of emergency scene information management.

Claims

1. A method for collecting and managing information on clinical emergency medical equipment, characterized in that, include: A continuous sequence of video frames containing the patient and emergency equipment is acquired by at least one visual sensing unit deployed at the emergency scene. A three-dimensional spatial map of the emergency scene is constructed based on the video frame sequence, and real-time semantic segmentation and spatial positioning of the patient and each emergency device are performed in the three-dimensional spatial map to obtain the patient's spatial coordinates and posture information, as well as the spatial coordinates, device type and operating status of each emergency device. Acquire vital sign data streams output by each emergency medical device and establish a mapping relationship between the vital sign data streams and the corresponding device physical identifiers; Monitor the spatial movement trajectory of the patient and each emergency medical device within a preset time window, and calculate the spatial proximity index and trajectory correlation coefficient between any emergency medical device and the patient. When the spatial proximity index is lower than the first preset threshold and the trajectory correlation coefficient is higher than the second preset threshold, it is determined that the emergency equipment is in use for the patient, and the physical identifier, equipment type, operating status and corresponding vital sign data stream of the emergency equipment are automatically bound to the patient's identity information to generate a structured emergency event record.

2. The clinical emergency equipment information collection and management method according to claim 1, characterized in that, A three-dimensional spatial map of the emergency rescue scene is constructed based on the video frame sequence. Real-time semantic segmentation and spatial positioning of the patient and various emergency rescue devices are performed within this three-dimensional spatial map to obtain the patient's spatial coordinates and posture information, as well as the spatial coordinates, device type, and operating status of each emergency rescue device, including: Feature points are extracted and matched from a continuous video frame sequence, and a sparse point cloud map is generated using a simultaneous localization and mapping algorithm. Pixel-level semantic segmentation of each frame of image is performed using a deep neural network to identify the patient's torso region, head region, and the outline of various emergency equipment. By combining sparse point cloud and semantic segmentation results, a dense 3D mesh model containing semantic labels is generated through a dense reconstruction algorithm, which serves as the 3D spatial map. The patient region in the semantic segmentation results is fitted to an ellipsoidal geometric model, and its centroid coordinates are used as the patient's spatial coordinates. Each identified emergency medical device area is fitted as a cuboid bounding box, and its geometric center coordinates are used as the spatial coordinates of the device. By using equipment appearance template matching or optical character recognition technology, the unique physical identifier and equipment type information of the equipment can be extracted from the nameplate or display screen on the equipment surface.

3. The clinical emergency equipment information collection and management method according to claim 2, characterized in that, Acquire vital sign data streams output by each emergency medical device, and establish a mapping relationship between the vital sign data streams and the corresponding device physical identifiers, including: The system receives physiological parameter data packets transmitted from the built-in sensors of the emergency medical equipment via a wireless communication module. The physiological parameter data package includes a timestamp, device physical identifier, parameter type, and parameter value; The parameter types include one or more of the following: heart rate, respiratory rate, blood oxygen saturation, non-invasive blood pressure, body temperature, and ventilation. The physiological parameter data packets are classified and stored according to the device physical identifier, and a mapping relationship between the device physical identifier and the corresponding vital sign data stream is established.

4. The clinical emergency equipment information collection and management method according to claim 3, characterized in that, Monitor the spatial movement trajectories of the patient and various emergency medical devices within a preset time window, and calculate the spatial proximity index and trajectory correlation coefficient between any emergency medical device and the patient, including: Within a preset time window, the Euclidean distance sequence between the spatial coordinates of the device and the spatial coordinates of the patient is taken, and its time-weighted average value is calculated as the spatial proximity index. The time-weighted method uses an exponential decay function, where the distance weight increases as it approaches the current time. Extract the three-dimensional motion trajectory vector sequences of the patient and emergency equipment within a preset time window; Dynamic time warping and alignment of two trajectory vector sequences; The Pearson correlation coefficient of the aligned sequence is calculated and used as the trajectory correlation coefficient.

5. The clinical emergency equipment information collection and management method according to claim 4, characterized in that, The duration of the preset time window is 10 to 60 seconds; the first preset threshold is 1.5 meters; and the second preset threshold is 0.7 meters.

6. The clinical emergency equipment information collection and management method according to claim 5, characterized in that, After the device is successfully linked to the patient, the method further includes: The binding results are pushed to the hospital information system in real time to update the device usage records in the electronic medical records; If the same device is detected to meet the binding conditions with multiple patients simultaneously within a short period of time, a conflict verification mechanism is triggered. Arbitration is conducted based on whether the device type supports multiple patients sharing, patient priority scores, and trajectory stability indicators to retain a unique and valid binding relationship.

7. The clinical emergency equipment information collection and management method according to claim 6, characterized in that, The conflict verification mechanism includes: Query the device metadata to determine if it supports multi-patient mode; If not supported, patients will be sorted according to their triage level scores, and only the binding relationships of patients with the highest scores will be retained; If supported, the trajectory stability index of each candidate binding relationship is further calculated. The trajectory stability index is the reciprocal of the standard deviation of the trajectory velocity, and the binding relationship with the highest stability index is retained.

8. The clinical emergency equipment information collection and management method according to claim 7, characterized in that, The patient's identity information was obtained through the following methods: When a patient enters the emergency area, the QR code on their wristband is captured by the visual perception unit and decoded to obtain the patient's unique identification. Alternatively, identity information can be obtained by comparing photos in the hospital's registration database using a facial recognition module.

9. A clinical emergency equipment information collection and management system, characterized in that, include: A visual perception unit is used to acquire a continuous sequence of video frames containing the patient and emergency equipment. The three-dimensional scene mapping and target localization unit is used to construct a three-dimensional spatial map of the emergency scene based on the video frame sequence, and to perform real-time semantic segmentation and spatial localization of the patient and each emergency equipment in the three-dimensional spatial map to obtain the patient's spatial coordinates, posture information, and the spatial coordinates, equipment type and operating status of each emergency equipment. The vital signs data receiving unit is used to acquire the vital signs data streams output by each emergency medical device and establish a mapping relationship between the vital signs data streams and the corresponding device physical identifiers. The spatial behavior analysis unit is used to monitor the spatial movement trajectory of the patient and each emergency equipment within a preset time window, and to calculate the spatial proximity index and trajectory correlation coefficient between any emergency equipment and the patient. The intelligent binding decision unit is used to determine that the emergency medical device is in use with the patient when the spatial proximity index is lower than a first preset threshold and the trajectory correlation coefficient is higher than a second preset threshold. It automatically binds the physical identifier, device type, operating status and corresponding vital sign data stream of the emergency medical device with the patient's identity information to generate a structured emergency medical event record.

10. The clinical emergency equipment information collection and management system according to claim 9, characterized in that, The 3D scene mapping and target localization unit includes a feature extraction subunit, a simultaneous localization and mapping subunit, a semantic segmentation subunit, and a dense reconstruction subunit. The feature extraction subunit uses the scale-invariant feature transformation algorithm or the ORB feature algorithm to extract key points of the image. The synchronous positioning and mapping subunit adopts a graph-optimized visual inertial odometry framework; The semantic segmentation subunit adopts a convolutional neural network with an encoder-decoder structure, and its training dataset contains emergency scene images labeled with patients and various types of emergency equipment. The dense reconstruction subunit employs a multi-view stereo vision algorithm or a neural radiation field reconstruction algorithm.

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