A simulation data analysis method and system for virtual training of first responders

Abstract

The application relates to the technical field of virtual training, and provides a simulation data analysis method and system for virtual training of first aid personnel, which is applied to a simulation server and comprises the following steps: obtaining target information and first aid information of the first aid personnel on a target virtual patient; the first aid information comprises original time stamps of each first aid event in multiple first aid events and transmission delay time lengths of each first aid event; for a target first aid event in the multiple first aid events, original time stamps of the target first aid event and transmission delay time lengths of the target first aid event are determined according to the target information, the original time stamps of the target first aid event and the transmission delay time lengths of the target first aid event; the target first aid event is any one of the multiple first aid events; the adjusted time stamps of the multiple first aid events are arranged in chronological order to obtain training process data of virtual training of the first aid personnel. The simulation data analysis timing accuracy of virtual training of the first aid personnel can be improved.

Description

Technical Field

[0001] This application relates to the field of virtual training technology, and more specifically, to a simulation data analysis method and system for virtual training of emergency responders. Background Technology

[0002] Virtual simulation technology is playing an increasingly important role in the professional training system for emergency responders. Initially, to allow emergency responders to safely and repeatedly practice their skills, the training system was designed in a single-person mode. In this mode, all training-related data, including every hand movement of the emergency responder, every button press of the device, and real-time changes in the virtual patient's vital signs, are centrally processed and recorded by a central computer. Because all data sources are directly connected to this central computer and share its unique internal system clock, all recorded event timestamps are guaranteed to be perfectly synchronized. This centralized architecture ensures the accuracy and consistency of the training data, enabling the system to accurately assess the individual operational procedures and skill proficiency of emergency responders.

[0003] However, with the increasing demands on teamwork in emergency medical practice and the advancement of virtual simulation technology, the training center began seeking more advanced training models. To more realistically simulate the complexity of multi-person coordination in actual emergency scenarios and effectively improve the overall collaborative capabilities of emergency medical teams, the training system was gradually upgraded to support collaborative emergency medical training by multiple personnel. This system architecture upgrade transformed the original centralized processing model into a distributed architecture. Specifically, the VR headsets and hand tracking controllers worn by each participating emergency medical personnel are no longer directly connected to a shared central computer, but rather to their own independent local computers. Simultaneously, to simulate more complex physiological responses and disease progression, the virtual patient's vital signs model and its state evolution logic run on another independent server. All these independent computers and servers communicate and exchange information through an internal local area network.

[0004] In this new distributed network environment, the inherent characteristics of data transmission lead to unavoidable network latency. Although each local computer and server has its own precise timestamp generation mechanism, accurately recording the occurrence time of its local events, the arrival times of these data packets, transmitted through the local area network and ultimately converged to the central analysis system, will have slight and inconsistent differences. This results in multiple operations or events that occur almost simultaneously in the real world having random timestamp misalignments of tens to hundreds of milliseconds in the data records received by the central analysis system. This misalignment does not stem from timing errors in the operations themselves, but rather from an objective phenomenon during data transmission, leading to lower timing accuracy in the simulation data analysis of virtual training for emergency responders. Summary of the Invention

[0005] This application provides a simulation data analysis method and system for virtual training of emergency responders, which can improve the timing accuracy of simulation data analysis for virtual training of emergency responders.

[0006] To achieve the above objectives, this application adopts the following technical solution:

[0007] In a first aspect, this application discloses a simulation data analysis method for virtual training of emergency responders, applied to a simulation server. The method includes: acquiring target information and emergency responder information for a target virtual patient; the emergency responder information includes the original timestamp of each emergency event in multiple emergency events and the transmission delay duration of each emergency event, the multiple emergency events including causal emergency events and effect emergency events; the target information includes the causal emergency event in the multiple emergency events, the effect emergency event that has a causal relationship with the causal emergency event, and a preset interval range between the causal emergency event and the effect emergency event; the original timestamp is the timestamp generated by the device that generates the emergency event, and the transmission delay duration is the transmission delay duration between the device and the simulation server; for the target emergency event in the multiple emergency events, determining the adjusted timestamp of the target emergency event based on the target information, the original timestamp of the target emergency event, and the transmission delay duration of the target emergency event; the target emergency event is any one of the multiple effect emergency events; arranging the adjusted timestamps of the multiple emergency events in chronological order to obtain the training process data for the virtual training of the emergency responder.

[0008] Furthermore, according to the simulation data analysis method for virtual training of emergency responders, the adjusted timestamp of the target emergency event is determined based on the target information, the original timestamp of the target emergency event, and the transmission delay duration of the target emergency event. This includes: determining the corresponding emergency event of the target emergency event in the target information and the preset interval duration range between the target emergency event and the corresponding emergency event, wherein the corresponding emergency event is the cause emergency event of the target emergency event; determining the first interval duration between the target emergency event and the corresponding emergency event based on the original timestamp of the target emergency event, the transmission delay duration of the target emergency event, the original timestamp of the corresponding emergency event, and the transmission delay duration of the corresponding emergency event; and determining the adjusted timestamp of the target emergency event based on the first interval duration and the preset interval duration range.

[0009] Furthermore, according to the simulation data analysis method for virtual training of emergency responders, the first interval between the target emergency event and the corresponding emergency event is determined based on the original timestamp of the target emergency event, the transmission delay duration of the target emergency event, the original timestamp of the corresponding emergency event, and the transmission delay duration of the corresponding emergency event. This includes: determining the initial timestamp of the target emergency event; the duration between the initial timestamp of the target emergency event and the original timestamp of the target emergency event being the transmission delay duration of the target emergency event; the initial timestamp of the target emergency event being after the original timestamp of the target emergency event; determining the initial timestamp of the corresponding emergency event; the duration between the initial timestamp of the corresponding emergency event and the original timestamp of the corresponding emergency event being the transmission delay duration of the corresponding emergency event; the initial timestamp of the corresponding emergency event being after the original timestamp of the corresponding emergency event; and using the duration between the initial timestamp of the target emergency event and the initial timestamp of the corresponding emergency event as the first interval duration.

[0010] In some preferred embodiments, according to the simulation data analysis method for virtual training of emergency responders, determining the adjustment timestamp of the target emergency event based on the first interval duration and the preset interval duration range includes: determining whether the first interval duration is within the preset interval duration range, and obtaining a determination result; the determination result being yes or no; if the determination result is no, determining the adjustment timestamp of the target emergency event based on the original timestamp of the corresponding emergency event and the preset interval duration range; if the determination result is yes, using the original timestamp of the target emergency event as the adjustment timestamp of the target emergency event.

[0011] As an optional solution, according to the simulation data analysis method for virtual training of emergency personnel, the adjustment timestamp of the target emergency event is determined based on the first interval duration and the preset interval duration range, including: determining whether the first interval duration is within the preset interval duration range, and obtaining a determination result; the determination result being yes or no; if the determination result is yes, using the original timestamp of the target emergency event as the adjustment timestamp of the target emergency event; if the determination result is no, obtaining the health status information of the target virtual patient; the health status information indicating that the target virtual patient has an underlying disease or indicating that the target virtual patient does not have an underlying disease; and determining the adjustment timestamp of the target emergency event based on the health status information.

[0012] Based on this, according to the simulation data analysis method for virtual training of emergency personnel, the adjustment timestamp of the target emergency event is determined based on the health status information, including: when the health status information indicates that the target virtual patient does not have an underlying disease, determining the adjustment timestamp of the target emergency event based on the original timestamp of the corresponding emergency event and the preset interval range; when the health status information indicates that the target virtual patient has an underlying disease, sending a request message to the target virtual patient simulation server; the request message is used to request a determination of whether the first interval range outside the preset interval range meets physiological requirements when the target virtual patient has an underlying disease; receiving a response message from the target virtual patient simulation server indicating whether it meets or does not meet physiological requirements; when the response message indicates that it meets physiological requirements, using the original timestamp of the target emergency event as the adjustment timestamp of the target emergency event; when the response message indicates that it does not meet physiological requirements, determining the adjustment timestamp of the target emergency event based on the original timestamp of the corresponding emergency event and the preset interval range.

[0013] More specifically, in some implementation schemes, according to the simulation data analysis method for virtual training of emergency responders, the adjustment timestamp of the target emergency event is determined based on the original timestamp of the corresponding emergency event and the preset interval range, including: if the first interval is less than the target value, adding the minimum value in the preset interval range to the original timestamp of the corresponding emergency event to obtain the adjustment timestamp of the target emergency event; the target value is the median value in the preset interval range; or, if the first interval is greater than or equal to the target value, adding the maximum value in the preset interval range to the original timestamp of the corresponding emergency event to obtain the adjustment timestamp of the target emergency event.

[0014] In one embodiment, according to the simulation data analysis method for virtual training of emergency responders, when there are multiple virtual patients, the method obtains the original timestamp of each emergency event among multiple emergency events involving the emergency responder to the virtual patients. This includes: obtaining the emergency responder's eye-tracking data and the original timestamps of multiple original emergency events; when the eye-tracking data indicates that the emergency responder's gaze lingers on a target virtual patient among the multiple virtual patients for a duration exceeding a preset lingering time threshold, determining the gaze lingering time period corresponding to the target virtual patient; the start time of the gaze lingering time period is the moment when the emergency responder's gaze begins to focus on the target virtual patient, and the end time of the gaze lingering time period is the moment when the emergency responder's gaze ends to focus on the target virtual patient, where the target virtual patient is any one of the multiple virtual patients; and determining the original timestamp of each emergency event among the multiple emergency events involving the emergency responder to the virtual patients based on the gaze lingering time period and the original timestamps of the multiple original emergency events.

[0015] Preferably, according to the simulation data analysis method for virtual training of emergency personnel, the original timestamp of each emergency event among multiple emergency events involving the emergency personnel and the virtual patient is determined based on the gaze lingering time period and the original timestamps of multiple original emergency events. This includes: obtaining the distance between the emergency personnel's hand and the target virtual patient during the gaze lingering time period; determining the hand approaching time period during the gaze lingering time period; ensuring that the distance is less than a preset distance threshold during the hand approaching time period; and taking the emergency events whose original timestamps fall within the hand approaching time period as the emergency events corresponding to the target virtual patient, thereby obtaining the original timestamp of each emergency event among multiple emergency events involving the emergency personnel and the target virtual patient.

[0016] Secondly, this application also discloses a simulation data analysis system for virtual training of emergency responders, applied to a simulation server. The system includes: an acquisition device and a processing device; the acquisition device is used to acquire target information and emergency responder information for a target virtual patient; the emergency responder information includes the original timestamp of each emergency response event in multiple emergency events and the transmission delay duration of each emergency response event; the target information includes a causal emergency response event in the multiple emergency events, an effect emergency response event that has a causal relationship with the causal emergency response event, and a preset interval range between the causal emergency response event and the effect emergency response event; the original timestamp is the timestamp generated by the device that generates the emergency response event, and the transmission delay duration is the transmission delay duration between the device and the simulation server; the processing device is used to determine an adjusted timestamp for a target emergency response event in the multiple emergency events, based on the target information, the original timestamp of the target emergency response event, and the transmission delay duration of the target emergency response event; the target emergency response event is any one of the multiple effect emergency response events; the processing device is used to arrange the adjusted timestamps of the multiple emergency events in chronological order to obtain training data for the virtual training of the emergency responder.

[0017] Beneficial effects

[0018] This application discloses a simulation data analysis method for virtual training of emergency responders. By acquiring the original timestamps and transmission delay durations of emergency events and combining them with preset causal relationships and interval ranges, the timestamps of the emergency events are adjusted to eliminate the impact of network latency on event timing, ultimately generating training data that accurately reflects the virtual training process of emergency responders. This method effectively solves the problem in existing technologies where network latency causes random misalignment of event timestamps in distributed virtual training systems, thus affecting team collaboration efficiency and the accuracy of operational precision assessment. By accurately correcting the timestamps, this application can provide more realistic and reliable training process data, improving the timing accuracy of simulation data analysis for virtual training of emergency responders. Attached Figure Description

[0019] Figure 1 A flowchart illustrating a simulation data analysis method for virtual training of first aid personnel provided in this application;

[0020] Figure 2 A flowchart illustrating another simulation data analysis method for virtual training of emergency responders provided in this application;

[0021] Figure 3 A flowchart illustrating another simulation data analysis method for virtual training of emergency responders provided in this application;

[0022] Figure 4This application provides a schematic diagram of the architecture of a simulation data analysis system for virtual training of emergency responders. Detailed Implementation

[0023] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0024] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0025] Virtual simulation technology is playing an increasingly important role in the professional training system for emergency responders. Initially, to allow emergency responders to safely and repeatedly practice their skills, the training system was designed in a single-person mode. In this mode, all training-related data, including every hand movement of the emergency responder, every button press of the device, and real-time changes in the virtual patient's vital signs, are centrally processed and recorded by a central computer. Because all data sources are directly connected to this central computer and share its unique internal system clock, all recorded event timestamps are guaranteed to be perfectly synchronized. This centralized architecture ensures the accuracy and consistency of the training data, enabling the system to accurately assess the individual operational procedures and skill proficiency of emergency responders.

[0026] However, with the increasing demands on teamwork in emergency medical practice and the advancement of virtual simulation technology, the training center began seeking more advanced training models. To more realistically simulate the complexity of multi-person coordination in actual emergency scenarios and effectively improve the overall collaborative capabilities of emergency medical teams, the training system was gradually upgraded to support collaborative emergency medical training by multiple personnel. This system architecture upgrade transformed the original centralized processing model into a distributed architecture. Specifically, the VR headsets and hand tracking controllers worn by each participating emergency medical personnel are no longer directly connected to a shared central computer, but rather to their own independent local computers. Simultaneously, to simulate more complex physiological responses and disease progression, the virtual patient's vital signs model and its state evolution logic run on another independent server. All these independent computers and servers communicate and exchange information through an internal local area network.

[0027] In this new distributed network environment, the inherent characteristics of data transmission lead to unavoidable network latency. Although each local computer and server has its own precise timestamp generation mechanism, accurately recording the occurrence time of its local events, the arrival times of these data packets, transmitted through the local area network and ultimately converged to the central analysis system, will have slight and inconsistent differences. This results in multiple operations or events that occur almost simultaneously in the real world having random timestamp misalignments of tens to hundreds of milliseconds in the data records received by the central analysis system. This misalignment does not stem from timing errors in the operations themselves, but rather from an objective phenomenon during data transmission, leading to lower timing accuracy in the simulation data analysis of virtual training for emergency responders.

[0028] To address this problem, this application provides a simulation data analysis method for virtual training of emergency responders, applied to a simulation server. The method includes: acquiring target information and emergency responder information for a target virtual patient; the emergency information includes the original timestamp of each emergency event in multiple emergency events and the transmission delay duration of each emergency event, the multiple emergency events include causal emergency events and effect emergency events; the target information includes causal emergency events in the multiple emergency events, effect emergency events that have a causal relationship with the causal emergency events, and a preset interval range between the causal emergency events and the effect emergency events; the original timestamp is the timestamp generated by the device that generates the emergency event, and the transmission delay duration is the transmission delay duration between the device and the simulation server; for the target emergency event in the multiple emergency events, determining the adjusted timestamp of the target emergency event based on the target information, the original timestamp of the target emergency event, and the transmission delay duration of the target emergency event; the target emergency event is any one of the multiple effect emergency events; arranging the adjusted timestamps of the multiple emergency events in chronological order to obtain the training process data of the virtual training of emergency responders.

[0029] This application provides a simulation data analysis method for virtual training of emergency responders, aiming to solve the problem of random misalignment of data timestamps caused by distributed systems and network latency in existing technologies, thereby improving the accuracy and objectivity of virtual training evaluation for emergency responders. This method acquires target information and emergency responder information regarding a target virtual patient. The emergency responder information includes the original timestamps and transmission delay durations of multiple emergency events, distinguishing between causal and consequential emergency events. The target information also includes causal emergency events, consequential emergency events with causal relationships, and preset interval ranges between them. For a specific target emergency event, this method can determine its adjusted timestamp based on the target information, the original timestamp of the target emergency event, and the transmission delay duration. Finally, the adjusted timestamps of all emergency events are arranged chronologically to obtain the training process data for virtual training of emergency responders. In this way, this application can effectively correct timestamp deviations caused by network latency, ensuring the temporal consistency of training data, improving the temporal accuracy of simulation data analysis for virtual training of emergency responders, and thus improving the accuracy of evaluating the team collaboration efficiency and operational precision of emergency responders.

[0030] This embodiment discloses a simulation data analysis method for virtual training of emergency responders, applied to a simulation server. For ease of understanding, the key terms in this embodiment will be explained below.

[0031] An "emergency event" refers to any recordable event triggered during virtual training of emergency personnel by the actions of an emergency responder or changes in the status of a virtual patient. Examples include emergency personnel performing chest compressions or defibrillation on a virtual patient, or changes in the virtual patient's heart rate or blood pressure. Each emergency event is accompanied by an "original timestamp," generated by the device that produced the event (such as a VR headset, hand tracking controller, or virtual patient simulation server) at the time the event occurs.

[0032] "Transmission delay duration" refers to the time interval between the moment the emergency response device generates the original timestamp and the moment the simulation server receives the emergency response data packet. Due to the complexity of the network environment, this duration is usually variable.

[0033] "Cause-related emergency events" and "effect-related emergency events" refer to a pair of emergency events that have a causal relationship in the emergency response process. For example, after emergency personnel perform a "defibrillation shock" (cause-related emergency event), the virtual patient's "heart rate returns to normal" (effect-related emergency event). "Target information" includes these causal pairs and the "preset interval range" between them that conforms to physiological or operational guidelines. This range defines the reasonable time interval between cause-related and effect-related emergency events.

[0034] "Target virtual patient" refers to a specific virtual patient in which emergency personnel are currently performing emergency procedures in a multi-virtual patient training scenario.

[0035] "Adjusted timestamp" refers to the timestamp obtained by correcting the original timestamp after taking into account transmission delay and causality.

[0036] The simulation data analysis method in this embodiment is applied to a simulation server, which is responsible for receiving, processing and analyzing data from different devices and generating training evaluation reports.

[0037] This embodiment provides a simulation data analysis method for virtual training of emergency responders. Its core lies in correcting timestamp discrepancies caused by network transmission delays to obtain more accurate training process data. For example... Figure 1 As shown, the method includes the following steps:

[0038] S101. Obtain target information and emergency medical information of the target virtual patient from emergency personnel.

[0039] Specifically, the target information can be obtained by the simulation server in the following ways:

[0040] One implementation involves the simulation server pre-loading a configuration database that stores all preset causal pairs of emergency rescue events and their corresponding preset interval ranges. For example, in a CPR training scenario, the database might contain "start chest compressions" as the causal emergency rescue event, "first effective compression" as the effect emergency rescue event, and set the preset interval range between them to 0-2 seconds. When training begins, the simulation server reads the relevant target information from this database.

[0041] Another approach is that, before training begins, the training administrator can manually input or select the cause-response emergency events, effect-response emergency events, and their preset interval ranges required for this training through a user interface. The simulation server then receives and stores these inputs as target information.

[0042] Another implementation is that the simulation server can communicate with an external training scenario management system, which automatically generates and sends the corresponding target information to the simulation server based on the currently loaded training scenario.

[0043] At the same time, the simulation server also needs to obtain emergency information from emergency personnel regarding the target virtual patient.

[0044] One implementation involves the simulation server receiving data streams in real-time from VR devices worn by emergency responders (such as VR headsets and hand trackers) and a virtual patient simulation server via a network interface. These data streams contain multiple emergency events, each accompanied by an original timestamp generated by the device and a transmission delay recorded by the network transport protocol. For example, when an emergency responder performs a chest compression, the hand tracker generates a data packet containing the compression event type and the original timestamp, and sends it to the simulation server. Upon receiving this data packet, the simulation server records the reception time and calculates the transmission delay by combining it with the original timestamp in the data packet.

[0045] Another approach is for the simulation server to read all first aid event data generated by first aiders during training from a local cache or log file. This data already includes the original timestamps and transmission delay information when it is written to the cache or log file.

[0046] S102. For a target emergency event among multiple emergency events, determine the adjusted timestamp of the target emergency event based on the target information, the original timestamp of the target emergency event, and the transmission delay duration of the target emergency event.

[0047] Specifically, the simulation server iterates through all received emergency medical events. For each target emergency medical event identified as a resultant event, the simulation server searches for causal emergency medical events that are causally related to the target event from the acquired target information, as well as the corresponding preset interval range between them. For example, if the target emergency medical event is "first effective compression," the simulation server will search for its corresponding causal emergency medical event "chest compressions started," as well as the preset interval range between them.

[0048] Subsequently, the simulation server uses the original timestamp and transmission delay of the target emergency rescue event to calculate its actual occurrence time in the virtual scene. For example, a simple calculation could be: Actual occurrence time of the target emergency rescue event = Original timestamp of the target emergency rescue event + Transmission delay of the target emergency rescue event. This method can initially correct for time deviations caused by transmission delays in individual events.

[0049] However, this preliminary correction may still be insufficient to completely resolve the timestamp misalignment problem between causal events. Therefore, this method further combines the original timestamp and transmission delay of the emergency event with a preset interval range to more accurately determine the adjusted timestamp of the target emergency event. For example, if the original timestamp of "chest compressions started" is T1, and the transmission delay is D1; ​​the original timestamp of "first effective compression" is T2, and the transmission delay is D2. The preset interval range is [Min_Interval, Max_Interval]. The simulation server calculates an initial actual interval (T2+D2)-(T1+D1). If this initial interval is not within the preset interval range, T2 needs to be adjusted so that the interval between its adjusted timestamp and T1+D1 falls within the preset range.

[0050] S103. Arrange the adjustment timestamps of multiple emergency events in chronological order to obtain the training process data of the virtual training of emergency personnel.

[0051] Specifically, after the adjusted timestamps for all emergency events (including cause-and-effect events) are determined, the simulation server uses these adjusted timestamps as the precise timestamps when the events actually occur in the virtual training. Subsequently, the simulation server globally sorts all emergency events based on these adjusted timestamps, generating a temporally continuous and accurate training process data stream. This data stream clearly shows the true timing and interrelationships of the emergency responders' actions during training; for example, it can accurately determine when chest compressions were resumed after defibrillation and whether the duration of any interruption in compressions conformed to protocol.

[0052] The core innovation of this method lies in its consideration not only of the original timestamp and transmission delay of individual emergency events, but more importantly, its introduction of causal relationships between emergency events and preset interval ranges to fine-tune the timestamps. In this way, the method can effectively correct timestamp deviations caused by network latency, ensuring the temporal consistency of the training data.

[0053] Compared with existing technologies, the advantages of this application are:

[0054] First, this method can accurately identify and correct timestamp misalignment caused by distributed systems and network latency. By obtaining the transmission delay duration of each emergency rescue event and incorporating it into the timestamp adjustment calculation, this method can more realistically reflect the actual time of the event in the virtual training scenario, avoiding misjudgments caused by data transmission delays in traditional methods.

[0055] Secondly, this method incorporates the causal relationship of emergency events and a preset interval range, making the adjustment of timestamps more logical and reasonable. For example, in the coordinated training of CPR and defibrillation, this method can adjust the timestamp of the "resume chest compressions" event based on the preset interval range between "defibrillation shock" and "resumption of chest compressions," ensuring that the interval between it and the "defibrillation shock" event conforms to medical standards, thereby more accurately assessing the team's coordination.

[0056] Finally, by arranging the adjustment timestamps of all emergency events in chronological order, this method generates a temporally continuous and accurate training process data. This enables the training evaluation system to provide more objective and instructive feedback, helping emergency responders improve teamwork and operational accuracy, thereby enhancing their trust in the system's evaluation.

[0057] In summary, the simulation data analysis method proposed in this application effectively solves the problem of timestamp misalignment in the prior art through an innovative timestamp adjustment mechanism, providing a more accurate and reliable data analysis basis for virtual training of emergency personnel, and has significant technological progress and practical value.

[0058] In a design, such as Figure 2 As shown, this application further proposes a method for determining the adjusted timestamp of a target emergency medical event, which includes:

[0059] S201. Determine the corresponding emergency medical event of the target emergency medical event in the target information and the preset interval range between the target emergency medical event and the corresponding emergency medical event.

[0060] The corresponding emergency event is the cause of the target emergency event.

[0061] When determining the adjustment timestamp for a target emergency event, it is first necessary to identify the corresponding emergency event in the target information and the preset interval range between the target emergency event and the corresponding emergency event. The corresponding emergency event can be understood as the "cause" event that leads to the occurrence of the target emergency event, while the target emergency event is the "effect" event resulting from that "cause." For example, if "administering medication" is a cause emergency event, then "decreased heart rate" might be an effect emergency event. The preset interval range refers to the expected time window from the occurrence of the cause emergency event to the occurrence of the effect emergency event in a physiological or medical context. Its purpose is to provide a reference standard for judging whether the timing of emergency events is reasonable.

[0062] S202. Determine the first interval between the target emergency event and the corresponding emergency event based on the original timestamp of the target emergency event, the transmission delay of the target emergency event, the original timestamp of the corresponding emergency event, and the transmission delay of the corresponding emergency event.

[0063] After determining the causal relationship and the preset interval range, the first interval between the target emergency event and the corresponding emergency event needs to be determined based on the original timestamp of the target emergency event, the transmission delay of the target emergency event, and the original timestamp and transmission delay of the corresponding emergency event. The original timestamp refers to the time when the emergency event is generated at the generating device, and the transmission delay refers to the time required for the event data to be transmitted from the generating device to the simulation server. By correcting the original timestamp and the transmission delay, the actual time when the emergency event occurs in the simulation environment can be obtained. The first interval is the time difference between the corrected occurrence time of the target emergency event and the corrected occurrence time of the corresponding emergency event, reflecting the actual elapsed time between the causal emergency event and the effect emergency event in the simulation environment.

[0064] S203. Determine the adjustment timestamp of the target emergency event based on the first interval duration and the preset interval duration range.

[0065] This step aims to verify or correct the timestamp of the target emergency event by comparing the actual measured first interval duration with the preset physiological or logical interval duration range, so as to ensure that it conforms to the expected causal sequence relationship.

[0066] This application's solution introduces a preset interval range between causal and consequential emergency events and compares the calculated first interval duration with the preset range, thereby achieving a more accurate and physiologically reasonable determination of the adjusted timestamp for the target emergency event. Specifically, by identifying the causal relationship of emergency events and combining it with physiological or medical time interval standards, event time deviations caused by transmission delays or other factors can be effectively corrected. When the actually measured event interval does not conform to the preset range, corresponding adjustment strategies can be adopted to make the adjusted timestamp more consistent with the actual physiological response logic, avoiding potential irrationality caused by simple calculations.

[0067] In one design, the method for determining the duration of the first interval further proposed in this application includes:

[0068] S301. Determine the initial timestamp of the target emergency medical event.

[0069] The duration between the initial timestamp and the original timestamp of the target emergency medical event is the transmission delay duration of the target emergency medical event; the initial timestamp of the target emergency medical event is after the original timestamp of the target emergency medical event.

[0070] The initial timestamp of a target emergency response event refers to the actual time when the event occurred at the device that generated it. It is determined by adding the transmission delay to the original timestamp of the target emergency response event. The original timestamp is the time recorded when the device generates the event, while the transmission delay is the time required for the event information to travel from the device to the simulation server. Therefore, adding the transmission delay to the original timestamp yields the actual time when the event occurred at the device, after adjusting for transmission delay—the initial timestamp.

[0071] S302. Determine the initial timestamp of the corresponding emergency rescue event.

[0072] The duration between the initial timestamp and the original timestamp of the corresponding emergency event is the transmission delay duration of the corresponding emergency event; the initial timestamp of the corresponding emergency event is after the original timestamp of the corresponding emergency event.

[0073] Similarly, the initial timestamp of the corresponding emergency event is determined in the same way as in S301, that is, the original timestamp of the corresponding emergency event plus its transmission delay. The initial timestamp of the target emergency event is after the original timestamp of the target emergency event, and the initial timestamp of the corresponding emergency event is after the original timestamp of the corresponding emergency event, reflecting the objective existence of the transmission delay.

[0074] S303. The duration between the initial timestamp of the target emergency event and the initial timestamp of the corresponding emergency event shall be used as the first interval duration.

[0075] The first interval duration is defined as the time difference between the initial timestamp of the target emergency event and the initial timestamp of the corresponding emergency event. Its purpose is to accurately reflect the interval between the actual occurrence times of two causally related emergency events.

[0076] This application's solution effectively eliminates time errors caused by different devices generating timestamps and data transmission delays by separately determining the initial timestamps of the target emergency event and its corresponding emergency event, and calculating the first interval duration based on these initial timestamps. The original timestamp only represents the recording time of the event when it is generated by the device, while the transmission delay duration reflects the time required for data to be transmitted from the device to the simulation server. Without correction, directly using the original timestamp or only considering the one-sided transmission delay to calculate the event interval may lead to misjudgment of the true time relationship between emergency events. By adding the original timestamp of each event to its corresponding transmission delay duration, the initial timestamp of the event that actually occurred at the device and has been corrected for transmission delay can be obtained. Therefore, the first interval duration calculated based on these corrected initial timestamps can more accurately reflect the true time interval between the cause and effect emergency events, thus providing a more reliable basis for subsequent timestamp adjustments.

[0077] In one design, this application further proposes a step for determining the adjusted timestamp of the target emergency rescue event based on the aforementioned first interval duration and the aforementioned preset interval duration range, including:

[0078] Determine whether the first interval duration is within the preset interval duration range and obtain the determination result; the determination result is yes or no; if the determination result is no, determine the adjustment timestamp of the target emergency event based on the original timestamp of the corresponding emergency event and the preset interval duration range; if the determination result is yes, use the original timestamp of the target emergency event as the adjustment timestamp of the target emergency event.

[0079] Specifically, determining whether the first interval duration falls within the preset interval duration range involves comparing the calculated first interval duration between the target emergency event and the corresponding emergency event with a pre-set preset interval duration range that represents a normal physiological or operational time window. This preset interval duration range is typically determined by medical experts or training designers based on actual emergency scenarios and physiological principles, and its purpose is to define a reasonable time interval between causal emergency events. A result of "yes" indicates that the first interval duration meets expectations, while "no" indicates an abnormality.

[0080] Specifically, if the judgment result is "yes," meaning the first interval duration falls within the preset interval duration range, it indicates that the timing of the target emergency medical event is reasonable and no additional adjustments are needed. In this case, the original timestamp of the target emergency medical event is directly used as the adjusted timestamp to maintain the originality and authenticity of the data.

[0081] In practical applications, a "no" result, meaning the first interval exceeds the preset interval range, may indicate data transmission anomalies, emergency personnel errors, or abnormal virtual patient conditions. To correct this anomaly, the adjusted timestamp of the target emergency event needs to be determined based on the original timestamp of the corresponding emergency event and the preset interval range. This adjustment aims to correct the timestamp of the target emergency event to a range that conforms to logical and physiological principles, thereby ensuring the accuracy of the simulation data.

[0082] This application's solution intelligently identifies anomalies in the timing of emergency events by introducing a judgment on whether the first interval duration falls within a preset interval duration range. When the first interval duration is within the normal range, the original timestamp is used directly, ensuring the authenticity of the data without unnecessary modification. However, when the first interval duration exceeds the preset range, the original timestamp is no longer simply relied upon; instead, the adjusted timestamp of the resulting emergency event is recalculated based on the original timestamp of the emergency event and a preset reasonable time window. This mechanism ensures that even in cases of data bias or operational anomalies, the final generated training process data reflects the event timing in accordance with logical and physiological laws, thus providing more accurate and valuable feedback for the virtual training of emergency personnel.

[0083] In one design, this application further proposes a method for determining the adjustment timestamp of a target emergency event based on a first interval duration and a preset interval duration range, comprising: determining whether the first interval duration is within the preset interval duration range, and obtaining a determination result; determining whether the determination result is yes or no; if the determination result is yes, using the original timestamp of the target emergency event as the adjustment timestamp of the target emergency event; if the determination result is no, obtaining the health status information of the target virtual patient; the health status information indicating that the target virtual patient has an underlying disease or indicating that the target virtual patient does not have an underlying disease; and determining the adjustment timestamp of the target emergency event based on the health status information.

[0084] Specifically, when determining the adjusted timestamp of the aforementioned target emergency medical event, it is first necessary to determine whether the first interval duration is within the preset interval duration range, thereby obtaining a judgment result, which can be "yes" or "no". The first interval duration is calculated based on the original timestamp of the target emergency medical event, the transmission delay duration of the target emergency medical event, the original timestamp of the corresponding emergency medical event, and the transmission delay duration of the corresponding emergency medical event. It reflects the actual occurrence interval of the two related emergency medical events on the simulation server side. The preset interval duration range represents the expected time interval between the cause emergency medical event and the effect emergency medical event under normal physiological conditions.

[0085] When the judgment result is "yes," it means that the first interval duration is indeed within the preset interval duration range. This indicates that the emergency responder's operation time interval conforms to the expected physiological or operational standards. In this case, there is no need to adjust the original timestamp of the target emergency event; it can be directly used as the adjusted timestamp of the target emergency event to maintain the originality and authenticity of the data.

[0086] However, when the judgment result is "no," meaning the first interval duration exceeds the preset interval duration range, this may indicate an abnormality in the emergency responder's operation or that the virtual patient's physiological condition caused this abnormality. For a more accurate assessment, this application will further obtain the target virtual patient's health status information in this situation. Health status information can be understood as data describing the virtual patient's current or historical physiological condition, such as whether they suffer from underlying diseases like diabetes, hypertension, or heart disease. Health status information can indicate whether the target virtual patient has an underlying disease or not. This information can be obtained by querying the virtual patient simulation server or a preset patient file database.

[0087] Furthermore, after acquiring health status information, the adjustment timestamp for the target emergency response event will be determined based on this information. This means that for events where the first interval duration exceeds the preset range, adjustments will no longer be made mechanically, but rather based on the specific health condition of the virtual patient. For example, if the virtual patient has an underlying medical condition that may prolong or shorten their reaction time to a specific emergency response, then even if the first interval duration exceeds the conventional preset range, it may still be considered to meet the specific physiological requirements of the virtual patient, and therefore may not require adjustment or specific adjustments.

[0088] This application's solution addresses the inaccuracy issues that traditional methods may encounter when the first interval exceeds a preset range by incorporating consideration of the target virtual patient's health status. Specifically, when the first interval between emergency events exceeds a preset physiological or operational standard, instead of making a uniform adjustment directly, the system first determines whether the virtual patient has an underlying medical condition. This mechanism allows the system to distinguish between two situations: one is an anomaly caused by improper operation by emergency personnel or simulation system errors, and the other is an "anomaly" caused by the virtual patient's own physiological characteristics (such as underlying diseases) but still conforming to their individual physiological patterns. Because of this distinction, this application avoids unnecessary or erroneous adjustments to emergency events that reasonably exceed the preset range due to the virtual patient's special physiological condition, thereby improving the accuracy and realism of simulation data analysis.

[0089] In one design, this application further proposes a step for determining the adjusted timestamp of a target emergency medical event based on health status information, including:

[0090] When the health status information indicates that the target virtual patient does not have an underlying disease, the adjustment timestamp of the target emergency event is determined based on the original timestamp of the corresponding emergency event and a preset interval range. This includes: when the health status information indicates that the target virtual patient has an underlying disease, sending a request message to the target virtual patient simulation server; the request message is used to request a determination of whether a first interval range outside the preset interval range meets physiological requirements when the target virtual patient has an underlying disease; receiving a response message from the target virtual patient simulation server indicating whether the physiological requirements are met or not; if the response message indicates that the physiological requirements are met, using the original timestamp of the target emergency event as the adjustment timestamp of the target emergency event; if the response message indicates that the physiological requirements are not met, determining the adjustment timestamp of the target emergency event based on the original timestamp of the corresponding emergency event and the preset interval range.

[0091] Specifically, when the health status information indicates that the target virtual patient has no underlying disease, this means that the target virtual patient's physiological responses should follow normal physiological patterns. In this case, if the first interval exceeds the preset interval range, it is considered an abnormal situation. The adjusted timestamp of the target emergency event needs to be corrected based on the original timestamp of the corresponding emergency event and the preset interval range to align with normal physiological logic. For example, if the first interval is less than the target value, the minimum value within the preset interval range can be added to the original timestamp of the corresponding emergency event; conversely, if the first interval is greater than or equal to the target value, the maximum value within the preset interval range can be added to the original timestamp of the corresponding emergency event.

[0092] Furthermore, the situation becomes more complex when health status information indicates that the target virtual patient has an underlying disease. In this case, the first interval duration falling outside the preset interval duration range may not be entirely abnormal, but rather due to the influence of the underlying disease. To accurately determine whether this deviation meets physiological requirements, this application proposes sending a request message to the target virtual patient simulation server. This request message carries relevant information about the current emergency event and requests the simulation server to determine, based on its built-in pathological model and physiological parameters, whether the currently observed first interval duration meets physiological requirements under the specific condition that the target virtual patient has an underlying disease. The target virtual patient simulation server is a system specifically designed to simulate the complex physiological reactions and pathological changes of virtual patients. It integrates multiple disease models, drug response models, and dynamic physiological parameter adjustment mechanisms. Based on the received request message, it can comprehensively analyze factors such as the target virtual patient's health status information, the type of underlying disease, the nature of the current emergency event, and the first interval duration, thereby outputting a judgment result regarding whether the interval duration meets physiological requirements.

[0093] Upon receiving a response message from the target virtual patient simulation server, if the response message indicates that it meets physiological requirements, it means that although the first interval duration exceeds the conventional preset range, this deviation is reasonable considering the underlying disease of the target virtual patient. In this case, the original timestamp of the target emergency event is directly used as its adjustment timestamp to retain the true time point of the emergency responder's actual operation and avoid unnecessary corrections. Conversely, if the response message indicates that it does not meet physiological requirements, it means that even with the presence of an underlying disease, the current first interval duration still exceeds the physiologically acceptable range, which may indicate a problem with the emergency responder's operation or an anomaly in the simulation data. In this case, it is also necessary to correct the adjustment timestamp of the target emergency event based on the original timestamp of the corresponding emergency event and the preset interval duration range to ensure the physiological rationality of the training data.

[0094] This application's solution addresses the issue of data distortion that can result from simply adjusting timestamps based on preset intervals when the target virtual patient has underlying health conditions. Specifically, when health status information indicates the target virtual patient has an underlying condition and the first interval exceeds the preset range, the system no longer blindly adjusts the timestamps. Instead, it sends a request to the simulation server, utilizing the server's professional pathophysiological model for judgment. This mechanism distinguishes between physiological deviations caused by underlying conditions and genuine operational anomalies or data errors, thus avoiding erroneous corrections of physiological deviations. Consequently, it ensures more accurate and realistic adjustments to emergency event timestamps under complex pathological conditions, improving the authenticity and effectiveness of simulation training data.

[0095] In one design, this application further proposes a more refined method for determining the "adjustment timestamp of the target emergency rescue event", which aims to make a more reasonable adjustment based on the relative relationship between the "first interval duration" and the "preset interval duration range".

[0096] The method described above for determining the adjusted timestamp of the target emergency event based on the original timestamp of the corresponding emergency event and the preset interval range specifically includes:

[0097] If the first interval duration is less than the target value, the minimum value in the preset interval duration range is added to the original timestamp of the corresponding emergency event to obtain the adjusted timestamp of the target emergency event; the target value is the median value in the preset interval duration range; or, if the first interval duration is greater than or equal to the target value, the maximum value in the preset interval duration range is added to the original timestamp of the corresponding emergency event to obtain the adjusted timestamp of the target emergency event.

[0098] Specifically, the 'target value' can be understood as the midpoint of the 'preset interval duration range'. For example, if the 'preset interval duration range' is [Min, Max], then the 'target value' can be calculated as (Min + Max) / 2. Its purpose is to provide a benchmark for determining whether the 'first interval duration' is too short or too long. When the 'first interval duration' is less than the 'target value', it indicates that the actual interval time is relatively short. In this case, by adding the minimum value within the 'preset interval duration range' to the 'original timestamp of the corresponding emergency event', the time point of the 'target emergency event' can be shifted forward, ensuring that the interval with the 'corresponding emergency event' at least reaches the physiologically permissible minimum. Conversely, when the 'first interval duration' is greater than or equal to the 'target value', it indicates that the actual interval time is relatively long. In this case, by adding the maximum value within the 'preset interval duration range' to the 'original timestamp of the corresponding emergency event', the time point of the 'target emergency event' can be shifted backward, ensuring that the interval with the 'corresponding emergency event' does not exceed the physiologically permissible maximum.

[0099] This application's solution introduces a 'target value' as a judgment benchmark and, based on the comparison between the 'first interval duration' and the 'target value,' selectively adds the minimum or maximum value from the 'preset interval duration range' to the 'original timestamp of the corresponding emergency event,' thereby achieving refined determination of the 'adjustment timestamp of the target emergency event.' This mechanism can make targeted adjustments based on the specific direction (too short or too long) of the 'first interval duration' deviating from the 'preset interval duration range.' When the 'first interval duration' is too short, increasing the minimum value of the 'preset interval duration range' can adjust the occurrence time of the 'target emergency event' to a reasonable and physiologically acceptable minimum interval time point after the 'corresponding emergency event.' When the 'first interval duration' is too long, increasing the maximum value of the 'preset interval duration range' can adjust the occurrence time of the 'target emergency event' to a reasonable and physiologically acceptable maximum interval time point after the 'corresponding emergency event.' This two-way adjustment strategy ensures that the 'adjusted timestamp' is optimized in terms of physiological rationality, avoiding the inaccuracies that may result from simply adjusting the timestamp to a fixed point within a 'preset interval range'.

[0100] In one design, this application further proposes a method for obtaining the original timestamp of each emergency event among multiple emergency events involving virtual patients, in the case of multiple virtual patients, to ensure that emergency events can be accurately attributed to the corresponding virtual patients.

[0101] The method described above for obtaining the original timestamp of each emergency response event among multiple emergency responses to virtual patients by emergency responders includes:

[0102] Acquire eye-tracking data of emergency responders and original timestamps of multiple original emergency events; when the eye-tracking data indicates that the emergency responder's gaze lingers on the target virtual patient among multiple virtual patients for a duration exceeding a preset lingering time threshold, determine the gaze lingering time period corresponding to the target virtual patient; the start time of the gaze lingering time period is the moment when the emergency responder's gaze begins to focus on the target virtual patient, and the end time of the gaze lingering time period is the moment when the emergency responder's gaze ends to focus on the target virtual patient, and the target virtual patient is any one of multiple virtual patients; based on the gaze lingering time period and the original timestamps of multiple original emergency events, determine the original timestamp of each emergency event among the multiple emergency events involving the virtual patient.

[0103] Specifically, eye-tracking data refers to information collected in real time by eye-tracking devices (e.g., eye sensors integrated into virtual reality headsets) regarding the direction of a first responder's gaze, fixation point, and fixation duration. This data reflects the focus of the first responder in the virtual environment. The preset fixation duration threshold can be set according to actual training needs and experience; for example, it can be set to 0.5 seconds, 1 second, or longer. Its purpose is to filter out brief glances and ensure that the first responder is truly focused on the virtual patient. The fixation time precisely records the range of time the first responder's gaze is continuously focused on a specific virtual patient, with its start and end times corresponding to the instant the gaze begins and ends on that virtual patient, respectively. By combining this information, it is possible to preliminarily determine which virtual patient the first responder is currently operating on or focusing on.

[0104] This application's solution addresses the issue of unclear attribution of emergency events in multi-virtual-patient scenarios by introducing eye-tracking data. Specifically, when emergency responders operate among multiple virtual patients, their gaze is typically focused on the virtual patient currently being treated. Due to this natural behavioral pattern, by acquiring the emergency responder's eye-tracking data and analyzing the duration of their gaze on different virtual patients, the "target virtual patient" currently being addressed can be accurately identified. When the emergency responder's gaze lingers on a target virtual patient for a duration exceeding a preset threshold, it is considered that the emergency responder is operating on or evaluating that target virtual patient, thus determining the corresponding gaze duration. Emergency events occurring within this gaze duration can then be reasonably attributed to that target virtual patient. This mechanism effectively utilizes the emergency responder's natural behavioral pattern, providing a reliable basis for accurate attribution of emergency events and avoiding inaccurate training data caused by event confusion in multi-patient scenarios.

[0105] In one design, this application further proposes a step for determining the original timestamp of each of the multiple emergency events involving the emergency responder to the virtual patient, based on the aforementioned gaze duration and the original timestamps of multiple original emergency events, including:

[0106] The system obtains the distance between the emergency responder's hand and the target virtual patient during the gaze stagnation period; determines the hand proximity time period during the gaze stagnation period; identifies the distance within the hand proximity time period as less than a preset distance threshold; and uses the emergency events whose original timestamps fall within the hand proximity time period as the corresponding emergency events for the target virtual patient, thus obtaining the original timestamp of each emergency event among the multiple emergency events for the target virtual patient.

[0107] Specifically, during the aforementioned gaze fixation period, it is necessary to obtain the distance between the emergency responder's hand and the target virtual patient. This distance can be measured in various ways. For example, an inertial measurement unit (IMU) or optical tracking tag can be worn on the emergency responder's hand, and corresponding sensors or tags can be placed on the target virtual patient. The spatial distance between the two can then be calculated. Here, the hand can be understood as the primary limb part used by the emergency responder for manipulation, such as the palm and fingers.

[0108] Furthermore, after obtaining the distance between the hand and the target virtual patient, it is necessary to determine the hand proximity time period within the gaze lingering time period. This hand proximity time period refers to the time period during which the distance between the emergency responder's hand and the target virtual patient is less than a preset distance threshold. The preset distance threshold can be set according to actual emergency response habits and scenarios. For example, it can be set as the typical distance between the emergency responder's hand and the patient's body when performing palpation, chest compressions, bandaging, etc., usually several tens of centimeters. The purpose is to identify moments when there is actual physical interaction or close-range operation between the emergency responder and the target virtual patient.

[0109] Therefore, the emergency events whose original timestamps fall within the aforementioned hand proximity time period are considered the emergency events corresponding to the target virtual patient. This means that only emergency events that occur when the emergency responder's gaze is fixed on the target virtual patient and their hand is actually close to or in contact with the target virtual patient will be accurately attributed to that target virtual patient.

[0110] This application's solution, by incorporating distance information between the emergency responder's hand and the target virtual patient, combined with the duration of eye contact, enables more accurate identification of the actual actions performed by the emergency responder on a specific virtual patient. In emergency training scenarios involving multiple virtual patients, the emergency responder's gaze may quickly switch between different patients or linger on a single patient for extended periods, but not all observations are accompanied by actual intervention. The introduction of the hand proximity duration allows the system to filter out emergency events that only involve eye contact without actual intervention, thus ensuring a high degree of consistency between the acquired emergency event data and the emergency responder's actual actions. This approach, combining eye tracking and hand interaction information, effectively solves the problem of ambiguous event attribution that may arise from relying solely on eye tracking.

[0111] This application also discloses a simulation data analysis system for virtual training of emergency responders, applied to a simulation server, comprising: an acquisition device and a processing device; the acquisition device is used to acquire target information and emergency responder information for a target virtual patient; the emergency information includes the original timestamp of each emergency event in multiple emergency events and the transmission delay duration of each emergency event; the target information includes causal emergency events in multiple emergency events, causal emergency events related to the causal emergency events, and a preset interval range between the causal emergency events and the causal emergency events, the original timestamp being the timestamp generated by the device that generates the emergency event, and the transmission delay duration being the transmission delay duration between the device and the simulation server; the processing device is used to determine the adjustment timestamp of the target emergency event based on the target information, the original timestamp of the target emergency event, and the transmission delay duration of the target emergency event; the target emergency event is any one of the multiple causal emergency events; the processing device is used to arrange the adjustment timestamps of the multiple emergency events in chronological order to obtain training data for the virtual training of emergency responders.

[0112] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A simulation data analysis method for virtual training of first aid personnel, applied to a simulation server, characterized in that, The method includes: The system acquires target information and emergency medical information for a virtual patient. The emergency medical information includes the original timestamp of each emergency event and the transmission delay of each emergency event. The multiple emergency events include causal emergency events and effect emergency events. The target information includes the causal emergency events, the effect emergency events that have a causal relationship with the causal emergency events, and the preset interval range between the causal emergency events and the effect emergency events. The original timestamp is the timestamp generated by the device that generates the emergency event, and the transmission delay is the transmission delay between the device and the simulation server. For a target emergency event among the multiple emergency events, an adjusted timestamp for the target emergency event is determined based on the target information, the original timestamp of the target emergency event, and the transmission delay duration of the target emergency event; the target emergency event is any one of the multiple emergency events. Arrange the adjustment timestamps of the multiple emergency events in chronological order to obtain the training process data of the virtual training of the emergency personnel; The step of determining the adjusted timestamp of the target emergency event based on the target information, the original timestamp of the target emergency event, and the transmission delay duration of the target emergency event includes: Determine the corresponding emergency event of the target emergency event in the target information and the preset interval between the target emergency event and the corresponding emergency event, wherein the corresponding emergency event is the cause emergency event of the target emergency event; The first interval between the target emergency event and the corresponding emergency event is determined based on the original timestamp of the target emergency event, the transmission delay duration of the target emergency event, the original timestamp of the corresponding emergency event, and the transmission delay duration of the corresponding emergency event. The adjustment timestamp of the target emergency medical event is determined based on the first interval duration and the preset interval duration range; The step of determining the adjusted timestamp of the target emergency medical event based on the first interval duration and the preset interval duration range includes: Determine whether the first interval duration is within the preset interval duration range to obtain a determination result; the determination result is either yes or no. If the judgment result is negative, the adjustment timestamp of the target emergency event is determined based on the original timestamp of the corresponding emergency event and the preset interval duration range; If the determination result is yes, the original timestamp of the target emergency rescue event is used as the adjusted timestamp of the target emergency rescue event.

2. The simulation data analysis method for virtual training of emergency responders according to claim 1, characterized in that, The first interval between the target emergency event and the corresponding emergency event is determined based on the original timestamp of the target emergency event, the transmission delay duration of the target emergency event, the original timestamp of the corresponding emergency event, and the transmission delay duration of the corresponding emergency event, including: Determine the initial timestamp of the target emergency medical event; the duration between the initial timestamp of the target emergency medical event and the original timestamp of the target emergency medical event is the transmission delay duration of the target emergency medical event; the initial timestamp of the target emergency medical event is after the original timestamp of the target emergency medical event. Determine the initial timestamp of the corresponding emergency rescue event; the duration between the initial timestamp of the corresponding emergency rescue event and the original timestamp of the corresponding emergency rescue event is the transmission delay duration of the corresponding emergency rescue event; the initial timestamp of the corresponding emergency rescue event is after the original timestamp of the corresponding emergency rescue event. The duration between the initial timestamp of the target emergency rescue event and the initial timestamp of the corresponding emergency rescue event is taken as the first interval duration.

3. The simulation data analysis method for virtual training of emergency responders according to claim 1, characterized in that, Determining the adjusted timestamp of the target emergency medical event based on the first interval duration and the preset interval duration range includes: Determine whether the first interval duration is within the preset interval duration range to obtain a determination result; the determination result is either yes or no. If the determination result is yes, the original timestamp of the target emergency rescue event is used as the adjusted timestamp of the target emergency rescue event; If the determination result is negative, the health status information of the target virtual patient is obtained; the health status information indicates that the target virtual patient has an underlying disease or indicates that the target virtual patient does not have an underlying disease. The adjustment timestamp for the target emergency medical event is determined based on the health status information.

4. The simulation data analysis method for virtual training of emergency responders according to claim 3, characterized in that, Determining the adjustment timestamp of the target emergency medical event based on the health status information includes: If the health status information indicates that the target virtual patient does not have an underlying disease, the adjusted timestamp of the target emergency event is determined based on the original timestamp of the corresponding emergency event and the preset interval range. If the health status information indicates that the target virtual patient has an underlying disease, a request message is sent to the target virtual patient simulation server; the request message is used to request a determination of whether the first interval duration being outside the preset interval duration range meets physiological requirements when the target virtual patient has an underlying disease. Receive a response message from the target virtual patient simulation server indicating whether the physiological requirements are met or not; If the response message indicates that the physiological requirements are met, the original timestamp of the target emergency event is used as the adjusted timestamp of the target emergency event. If the response message indication does not meet physiological requirements, the adjusted timestamp of the target emergency event is determined based on the original timestamp of the corresponding emergency event and the preset interval range.

5. The simulation data analysis method for virtual training of emergency responders according to claim 4, characterized in that, Determining the adjusted timestamp of the target emergency medical event based on the original timestamp of the corresponding emergency medical event and the preset interval range includes: If the first interval duration is less than the target value, the minimum value in the preset interval duration range is added to the original timestamp of the corresponding emergency event to obtain the adjusted timestamp of the target emergency event; the target value is the median value in the preset interval duration range. Alternatively, if the first interval duration is greater than or equal to the target value, the maximum value in the preset interval duration range is added to the original timestamp of the corresponding emergency event to obtain the adjusted timestamp of the target emergency event.

6. The simulation data analysis method for virtual training of emergency responders according to claim 1, characterized in that, When there are multiple virtual patients, obtain the original timestamp of each emergency response event among the multiple emergency events for the virtual patients, including: Obtain the eye-tracking data of the emergency responders and the original timestamps of multiple original emergency events; If the eye-tracking data indicates that the emergency responder's gaze lingers on a target virtual patient among multiple virtual patients for a duration exceeding a preset lingering time threshold, the gaze lingering time period corresponding to the target virtual patient is determined; the start time of the gaze lingering time period is the moment when the emergency responder's gaze begins to focus on the target virtual patient, and the end time of the gaze lingering time period is the moment when the emergency responder's gaze ends to focus on the target virtual patient, and the target virtual patient is any one of the multiple virtual patients; The original timestamp of each emergency event in the multiple emergency events for the virtual patient is determined based on the time period of gaze lingering and the original timestamps of multiple original emergency events.

7. The simulation data analysis method for virtual training of emergency responders according to claim 6, characterized in that, The original timestamp of each of the multiple emergency medical events performed by the emergency personnel on the virtual patient is determined based on the gaze duration and the original timestamps of multiple original emergency medical events, including: Obtain the distance between the emergency responder's hand and the target virtual patient during the time period of gaze fixation; Determine the time period during which the hand approaches the eye within the specified gaze duration; during the specified hand approach time period, the distance is less than a preset distance threshold; The emergency events whose original timestamps are located in the time period closest to the hand among the multiple original emergency events are taken as the emergency events corresponding to the target virtual patient, thus obtaining the original timestamp of each emergency event among the multiple emergency events of the emergency personnel for the target virtual patient.

8. A simulation data analysis system for virtual training of emergency responders, applied to a simulation server, characterized in that, include: Acquisition device and processing device; The acquisition device is used to acquire target information and emergency medical information of emergency personnel for the target virtual patient; the emergency medical information includes the original timestamp of each emergency medical event in multiple emergency medical events and the transmission delay duration of each emergency medical event; the target information includes the causal emergency medical event in the multiple emergency medical events, the causal emergency medical event that has a causal relationship with the causal emergency medical event, and the preset interval duration range between the causal emergency medical event and the causal emergency medical event, the original timestamp is the timestamp generated by the device that generates the emergency medical event, and the transmission delay duration is the transmission delay duration between the device and the simulation server; The processing device is used to determine the adjusted timestamp of a target emergency event among the multiple emergency events, based on the target information, the original timestamp of the target emergency event, and the transmission delay duration of the target emergency event. The target emergency event is any one of multiple emergency events; The processing device is used to arrange the adjustment timestamps of the multiple emergency events in chronological order to obtain training data for the virtual training of the emergency personnel. The step of determining the adjusted timestamp of the target emergency event based on the target information, the original timestamp of the target emergency event, and the transmission delay duration of the target emergency event includes: Determine the corresponding emergency event of the target emergency event in the target information and the preset interval between the target emergency event and the corresponding emergency event, wherein the corresponding emergency event is the cause emergency event of the target emergency event; The first interval between the target emergency event and the corresponding emergency event is determined based on the original timestamp of the target emergency event, the transmission delay duration of the target emergency event, the original timestamp of the corresponding emergency event, and the transmission delay duration of the corresponding emergency event. The adjustment timestamp of the target emergency medical event is determined based on the first interval duration and the preset interval duration range; The step of determining the adjusted timestamp of the target emergency medical event based on the first interval duration and the preset interval duration range includes: Determine whether the first interval duration is within the preset interval duration range to obtain a determination result; the determination result is either yes or no. If the judgment result is negative, the adjustment timestamp of the target emergency event is determined based on the original timestamp of the corresponding emergency event and the preset interval duration range; If the determination result is yes, the original timestamp of the target emergency rescue event is used as the adjusted timestamp of the target emergency rescue event.

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