Visual real-time data monitoring method and device based on MR technology

Abstract

The application discloses a visual real-time data monitoring method and device based on MR technology, and belongs to the technical field of MR data measurement, and comprises the following steps: after a central controller receives a CPR training request signal, the central controller synchronously maps a dummy entity model to a visual interface by using MR technology to display a real-time training state, and transmits a preliminary verification signal to each sensing terminal connected to the dummy entity model; CPR verification data corresponding to each sensing terminal connected to the dummy entity model is collected within a preset verification period, and each sensing terminal verification result is obtained by analysis; and corresponding terminal verification processing is performed, whereby CPR training real-time sensing data is collected and uploaded to the central controller; CPR specification indexes are obtained by analysis, and it is judged whether a voice prompt is sent or not. The application solves the problem of inaccurate training effect determination caused by inaccurate sensing device detection in the prior art.

Description

Technical Field

[0001] This invention relates to the field of MR data measurement technology, and in particular to a method and apparatus for visual real-time data monitoring based on MR technology. Background Technology

[0002] With the continuous advancement of information technology, the importance of data monitoring is becoming increasingly prominent. MR technology can integrate the virtual and the real to achieve three-dimensional visualization of data, thereby enabling more intuitive and efficient monitoring and analysis of data, improving decision-making efficiency and accuracy. Therefore, MR technology is particularly important.

[0003] However, in the process of implementing the inventive technical solution in the embodiments of this application, it was found that the above-mentioned technology has at least the following technical problems:

[0004] Existing MR real-time data monitoring systems collect data through sensors and perform simple, direct measurements. However, the performance of these measurements is affected by factors such as sensor parameters and response speed. Poor device performance can lead to inaccurate measurement of training effectiveness. Therefore, there is a problem of inaccurate training effectiveness measurement due to inaccurate sensor detection. Summary of the Invention

[0005] This application provides a visualization-based real-time data monitoring method and device based on MR technology, which solves the problem of inaccurate training effect measurement caused by inaccurate detection of the sensing device in the prior art, and enables trainees to master the CPR operation essentials and standard specifications in real scenarios.

[0006] This application provides a visualization-based real-time data monitoring method based on MR technology, comprising the following steps: After receiving a CPR training request signal, the central controller synchronously uses MR technology to map a dummy entity model onto a visualization interface for real-time training status display, and transmits preliminary verification signals to each sensor terminal connected to the dummy entity model; within a preset verification period, it collects CPR verification data corresponding to each sensor terminal connected to the dummy entity model, and analyzes the data to obtain the verification results of each sensor terminal; based on the verification results of each sensor terminal, it performs corresponding terminal verification processing, and after the terminal verification processing, it transmits an implementable CPR training signal through the central controller, thereby collecting real-time CPR training sensor data and uploading it to the central controller; based on the real-time CPR training sensor data, it analyzes the CPR standard indicators to obtain CPR indicators, and thereby determines whether to issue a voice reminder.

[0007] This application provides a visualization real-time data monitoring device based on MR technology, including: a signal receiving and mapping module, a data acquisition and analysis module, a terminal verification processing and signal transmission module, and an operation specification judgment and voice reminder module. The signal receiving and mapping module is used to, after the central controller receives a CPR training request signal, synchronously use MR technology to map the dummy entity model onto a visualization interface for real-time training status display, and transmit preliminary verification signals to each sensor terminal connected to the dummy entity model. The data acquisition and analysis module is used to collect CPR verification data corresponding to each sensor terminal connected to the dummy entity model within a preset verification period, and analyze it to obtain the verification results of each sensor terminal. The terminal verification processing and signal transmission module is used to perform corresponding terminal verification processing based on the verification results of each sensor terminal, and transmit an implementable CPR training signal through the central controller after terminal verification processing, thereby collecting real-time CPR training sensor data and uploading it to the central controller. The operation specification judgment and voice reminder module is used to analyze the real-time CPR training sensor data to obtain CPR specification indicators, and thereby determine whether to issue a voice reminder.

[0008] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0009] 1. The visualization real-time data monitoring method based on MR technology provided by the present invention uses MR technology to map the dummy entity model onto the visualization interface for real-time training status display, thereby realizing real-time monitoring and feedback of training personnel operation, effectively solving the problem of inaccurate training effect measurement caused by inaccurate detection of sensing devices in the prior art;

[0010] 2. This invention collects CPR verification data from various sensor terminals connected to a dummy model, including pressure sensor verification data and oxygen supply sensor verification data, thereby achieving multi-dimensional data fusion analysis. This helps to provide operational accuracy and real-time feedback data, avoiding the problems of single monitoring data and difficulty in assessing operational standardization.

[0011] 3. By analyzing the verification performance indicators of the pressure sensor terminal and the oxygen supply sensor terminal, the verification results of each sensor terminal are obtained. In case of abnormality, the backup sensor terminal is activated, thereby ensuring the continuity of data acquisition when the sensor fails. This achieves the stability and integrity of training data acquisition and avoids the problem of data acquisition interruption or instability due to sensor failure. Attached Figure Description

[0012] Figure 1 A flowchart of a real-time data monitoring method based on MR technology provided in this application embodiment;

[0013] Figure 2 This is a schematic diagram of the structure of a real-time data monitoring device based on MR technology provided in an embodiment of this application. Detailed Implementation

[0014] This application provides a visualization-based real-time data monitoring method and apparatus based on MR technology, solving the problem of inaccurate training effect measurement caused by inaccurate detection of sensing devices in the prior art. After receiving the CPR training request signal through the central controller, it synchronously uses MR technology to map the dummy entity model onto the visualization interface for real-time training status display, and transmits preliminary verification signals to each sensing terminal connected to the dummy entity model. During the preset verification period, it collects CPR verification data corresponding to each sensing terminal connected to the dummy entity model, and analyzes it to obtain the verification results of each sensing terminal. Based on the verification results of each sensing terminal, it performs corresponding terminal verification processing, and transmits the CPR training execution signal through the central controller after the terminal verification processing, thereby collecting real-time CPR training sensing data and uploading it to the central controller. Based on the analysis of the real-time CPR training sensing data, it obtains CPR standard indicators, and determines whether to issue a voice prompt, enabling trainees to master the CPR operation essentials and standard specifications in real-world scenarios.

[0015] The technical solution in this application aims to solve the problem of inaccurate training effect measurement caused by inaccurate detection by the sensing device. The overall approach is as follows: After receiving the CPR training request signal through the central controller, the MR technology is used to simultaneously map the dummy model onto the visualization interface for real-time training status display, and transmits preliminary verification signals to each sensing terminal connected to the dummy model; CPR verification data corresponding to each sensing terminal connected to the dummy model is collected within a preset verification period and analyzed to obtain the verification results of each sensing terminal; based on the verification results of each sensing terminal, corresponding terminal verification processing is performed, and after the terminal verification processing, the central controller transmits the CPR training signal that can be implemented, thereby collecting real-time CPR training sensing data and uploading it to the central controller; based on the real-time CPR training sensing data, CPR standard indicators are obtained, and it is determined whether to issue a voice reminder, thus enabling trainees to master the essentials and standards of CPR operation in real-world scenarios.

[0016] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0017] like Figure 1The diagram shows a flowchart of a real-time data monitoring method based on MR technology provided in this application. The method includes the following steps: After receiving a CPR training request signal, the central controller synchronously uses MR technology to map a dummy entity model onto a visual interface for real-time training status display and transmits preliminary verification signals to each sensor terminal connected to the dummy entity model; within a preset verification period, CPR verification data corresponding to each sensor terminal connected to the dummy entity model is collected and analyzed to obtain the verification results of each sensor terminal; based on the verification results of each sensor terminal, corresponding terminal verification processing is performed, and after the terminal verification processing, a CPR training signal that can be implemented is transmitted through the central controller, thereby collecting real-time sensor data for CPR training and uploading it to the central controller; based on the real-time sensor data for CPR training, CPR standard indicators are obtained through analysis, and it is determined whether to issue a voice reminder.

[0018] In this embodiment, MR technology is used to map a dummy model onto a visual interface, allowing trainees to intuitively view the real-time status of CPR training on a virtual interface, enhancing the accuracy of the operation. Data from various sensor terminals connected to the dummy model, including compression and oxygen supply sensor verification data, is collected within a preset verification period. Real-time analysis of this data allows for the evaluation of the accuracy of the training operation and further derives the verification results of each sensor terminal. Based on the verification results of each sensor terminal, corresponding verification processing is performed. For example, based on the performance of the pressure and oxygen supply sensors, the working status of the sensor terminals is determined, and a "CPR training is feasible" signal is transmitted to the trainee, ensuring the reliability and accuracy of the sensors and guaranteeing the precision of subsequent data collection. Upon receiving the "CPR training is feasible" signal, real-time CPR training sensor data is collected. The central controller performs in-depth analysis of this real-time data and generates CPR standardization indicators to evaluate the standardization of the current operation. CPR standardization indicators are a quantitative reflection of the trainee's actual operational quality and have important evaluation and feedback functions. Based on the CPR standardization indicators, a voice prompt is triggered to provide real-time feedback on the standardization of the operation. If the standard indicators are not met, a voice prompt will be issued to guide the trainees to adjust their operations, thereby improving the standardization of operations and ensuring the quality of training.

[0019] It should be noted that a central controller is an electronic device that can store and retrieve data, and receive and send instructions.

[0020] It should also be noted that the database is used to store historical data for each operation, as well as equipment operation logs and other data. The database includes various mapping relationships (such as interference factors and their corresponding interference values ​​for pressure sensor terminal verification performance indicators and oxygen supply sensor terminal verification performance indicators), various thresholds (such as the comprehensive verification performance threshold for oxygen supply sensor terminals), and various benchmark sets (such as the CPR training target set and the network interference parameter reference set). The mapping relationships can be obtained by querying the corresponding historical data stored in the database. For example, querying the interference factor with the same historical value and the corresponding historical interference value for the same historical interference factor can be used as the current interference value for the pressure sensor terminal verification performance indicator. The thresholds can be obtained by using the corresponding historical data thresholds, and the benchmark sets are formed by combining the optimal values ​​from the corresponding historical data.

[0021] Furthermore, CPR verification data corresponding to each sensor terminal connected to the dummy model is collected within a preset verification period. This CPR verification data includes pressure sensor verification data and oxygen supply sensor verification data. The specific collection process is as follows: Within the preset verification period, the timing and pressure of each compression are collected from the pressure sensor terminal connected to the dummy model. The actual compression timing and depth of each compression by the trainee are also collected using a high-definition scanner. The timing and pressure of each compression from the pressure sensor terminal, along with the actual compression timing and depth of each compression by the trainee, are combined to form the pressure sensor verification data. Within the preset verification period, the timing and termination of each oxygen supply are collected from the oxygen supply sensor terminal connected to the dummy model. The duration of each oxygen supply and the number of oxygen supply interruptions are statistically analyzed and combined to form the oxygen supply sensor verification data.

[0022] In this embodiment, a high-definition scanner records the actual timing and depth of each press by the trainee. This allows for step-by-step comparison between the trainee's actions and the sensor's monitoring data. By summarizing the press timing, pressure, actual press time, and actual press depth, a complete pressure sensor verification dataset is formed, accurately reflecting the actual pressing conditions and compliance during training. Comparing the trainee's actual operation data with the various data recorded by the sensors generates verification results, providing detailed feedback to the trainee.

[0023] Further analysis yields the verification results for each sensor terminal. Specific steps include: based on the pressure sensor verification data, extracting the measurement time and actual press time for each press, and performing difference processing to obtain the timing delay of each press; based on the measurement time of each press, calculating the average time difference between two adjacent press times, denoted as the average time difference of sensor measurement; simultaneously, based on each actual press time, calculating the average time difference between two adjacent actual press times, denoted as the average time difference of actual press; and finally, based on the timing delay of each press, the average time difference of sensor measurement, and the average time difference of actual press... The time difference is processed to obtain the verification performance indicators of the pressure sensing terminal; based on the oxygen supply sensing verification data, the oxygen supply time of each sensing and the actual oxygen supply time of each instance are extracted, and the difference is processed to obtain the sensing time delay of each oxygen supply sensing; based on each sensing oxygen supply time, the average oxygen supply time difference between two adjacent oxygen supply times is calculated and recorded as the average oxygen supply interval time difference of the sensing measurement; simultaneously, based on each actual oxygen supply time, the average interval time difference between two adjacent actual oxygen supply times is calculated and recorded as the average interval time difference of the actual oxygen supply; based on the sensing time delay of each oxygen supply sensing, the average oxygen supply interval time difference of the sensing measurement, the average interval time difference of the actual oxygen supply, and the oxygen supply... The number of presses was processed to obtain the verification performance indicators of the oxygen supply sensor terminal; the verification performance indicators of the pressure sensor terminal were used to characterize the performance of the pressure sensor terminal; the verification performance indicators of the oxygen supply sensor terminal were used to characterize the performance of the oxygen supply sensor terminal; CPR verification data corresponding to each sensor terminal connected to the dummy model were collected within a preset verification period and analyzed to obtain the verification results of each sensor terminal, including the verification results of the pressure sensor terminal and the verification results of the oxygen supply sensor terminal; the verification performance indicators of the pressure sensor terminal were obtained by averaging the sensing delay of each press. The average press delay is obtained, and the average press delay is proportionally normalized to the allowable press measurement delay value to obtain the press measurement delay normalization result. The absolute difference between the average interval time difference of the sensor measurement and the actual average interval time difference is processed to obtain the absolute difference of the average interval time difference. Then, the absolute difference of the average interval time difference is processed to obtain the press interval time difference normalization result. Based on the press measurement delay normalization result and the press interval time difference normalization result, and after introducing the corresponding influence weights for coupling processing, they are added to obtain the verification performance index of the pressure sensing terminal.The verification performance indicators of the oxygen supply sensor terminal were obtained through the following method: The mean time delay of each oxygen supply sensor reading was averaged to obtain the average oxygen supply measurement. This average time delay was then normalized proportionally to the allowable error of the oxygen supply sensor reading, yielding the normalized oxygen supply time delay result. The absolute difference between the average oxygen supply interval time difference measured by the sensor and the actual average oxygen supply interval time difference was then processed to obtain the absolute difference in oxygen supply interval time difference. This absolute difference in oxygen supply interval time difference was then normalized proportionally to the actual average oxygen supply interval time difference, yielding the normalized oxygen supply interval time difference result. The number of oxygen supply interruptions is processed by parallel shifting (shifting the number of oxygen supply interruptions one unit to the right, i.e., incrementing the number of oxygen supply interruptions by 1 to ensure that the denominator is always non-zero during data processing and to avoid calculation errors), then multiplied with the corresponding performance evaluation interference factor, and the reciprocal is taken to obtain the oxygen supply interruption processing result. Based on the oxygen supply delay normalization result, the oxygen supply interval time difference normalization result, and the oxygen supply interruption processing result, and after coupling processing with corresponding influence weights, these results are added together to obtain the verification performance index of the oxygen supply sensing terminal.

[0024] In this embodiment, the verification performance indicators of the pressure sensing terminal are obtained by the following method:

[0025] ;

[0026] In the formula, This represents the verification performance index of the pressure sensing terminal, where i represents the press number. I represents the total number of presses. This indicates the timing delay of the i-th press sensor. This indicates the permissible delay value for pressure measurement. This represents the average time difference between sensor measurements. This indicates the average time difference between actual compressions. Indicates the time difference between permitted intervals. This indicates the weight of the effect of the pressure sensor timing delay. This indicates the weighting of the average interval.

[0027] It should be noted that the performance evaluation indicators for the pressure sensing terminal are derived by analyzing the timing delay of each press, the average time difference of the sensing measurement interval, and the average time difference of the actual press interval. This is because the interrelationships between these parameters are taken into account. For example, the timing delay of the sensing is used to measure the real-time performance of the sensor response; the smaller the delay, the more timely the terminal's response. The average time difference of the sensing measurement interval and the average time difference of the actual press interval reflect the consistency between the frequency of the trainee's operation and the sensor response; the closer the two are, the better the sensor's stability. The length of the timing delay of each press directly affects the real-time performance of data monitoring; the longer the delay, the worse the real-time performance. These three factors are interrelated and work together to evaluate the performance of the pressure sensing terminal.

[0028] The pressure sensor timing delay impact weight represents the numerical value of the influence of the pressure sensor timing delay on the verification performance indicators of the pressure sensor terminal. When using it, the impact weight corresponding to the pressure sensor timing delay can be directly obtained from the database. The correspondence can be a pre-defined mapping relationship; for example, the pressure sensor timing delay and its impact weight form a mapping set. Inputting the real-time pressure sensor timing delay into this mapping set yields the corresponding impact weight. This mapping relationship can be one-to-one or many-to-one. The average interval impact weight represents the numerical value of the influence of the average interval on the verification performance indicators of the pressure sensor terminal. When using it, the impact weight corresponding to the average interval can be directly obtained from the database. The correspondence can also be a pre-defined mapping relationship; for example, the average interval and its impact weight form a mapping set. Inputting the real-time average interval into this mapping set yields the corresponding impact weight. This mapping relationship can be one-to-one or many-to-one.

[0029] The specific method for obtaining the verification performance indicators of the oxygen supply sensor terminal is as follows:

[0030] ;

[0031] In the formula, This represents the verification performance indicators of the oxygen supply sensor terminal, where j represents the oxygen supply number. J represents the total number of oxygen supply sessions. This indicates the timing delay of the oxygen supply sensor for the j-th time. This indicates the allowable timing delay error of the oxygen supply sensor. This indicates the average oxygen supply interval time difference measured by the sensor. This indicates the average time difference between actual oxygen supply intervals. This represents the performance evaluation interference factor corresponding to a single oxygen supply interruption. This indicates the number of oxygen supply interruptions during the r-th oxygen supply period, where r represents the number of the oxygen supply period. R represents the total number of oxygen supply periods. This indicates the weighting of the effect of timing delay on oxygen supply sensing. This indicates the weighting of the effect of the average oxygen supply interval time difference measured by the sensor.

[0032] It should be noted that the influence weight of oxygen supply sensor timing delay represents the numerical value of the degree of influence of oxygen supply sensor timing delay on the verification performance index of oxygen supply sensor terminal. When using it, the influence weight corresponding to the oxygen supply sensor timing delay can be directly obtained from the database. The correspondence can be a pre-set mapping relationship. For example, the oxygen supply sensor timing delay and the influence weight of oxygen supply sensor timing delay form a mapping set. Inputting the real-time oxygen supply sensor timing delay into the mapping set will obtain the influence weight corresponding to the oxygen supply sensor timing delay. The mapping relationship can be one-to-one or many-to-one. The influence weight of the average oxygen supply interval time difference in sensor measurement represents the numerical value of the degree of influence of the average oxygen supply interval time difference in sensor measurement on the verification performance index of the oxygen supply sensor terminal. When using it, the influence weight corresponding to the average oxygen supply interval time difference in sensor measurement can be directly obtained from the database. The correspondence can be a pre-set mapping relationship. For example, the average oxygen supply interval time difference in sensor measurement and the influence weight of the average oxygen supply interval time difference in sensor measurement form a mapping set. The real-time average oxygen supply interval time difference in sensor measurement is input into the mapping set to obtain the influence weight corresponding to the influence weight of the average oxygen supply interval time difference in sensor measurement. The mapping relationship can be one-to-one or many-to-one.

[0033] The verification performance indicators of the oxygen supply sensor terminal are obtained by analyzing the timing delay of oxygen supply sensor, the allowable error of oxygen supply sensor timing delay, the average oxygen supply interval time difference of sensor measurement, the actual average oxygen supply interval time difference, and the performance evaluation interference factor corresponding to a single oxygen supply interruption. This is to take into account the mutual influence between these parameters. For example, the length of the oxygen supply sensor timing delay is directly related to the real-time feedback capability of oxygen supply data. The shorter the timing delay of oxygen supply sensor, the higher the real-time performance of oxygen supply data, and the faster it can reflect changes in oxygen supply status; conversely, an excessively long timing delay of oxygen supply sensor may lead to data lag, affecting the timely judgment of oxygen supply status. At the same time, the allowable error range of the delay determines the boundary of data accuracy. The smaller the error, the closer the data is to the true value, which helps to improve the accuracy of decision-making. The average oxygen supply interval time difference of sensor measurement is a key indicator for measuring the time stability of the oxygen supply sensor terminal. The smaller the average oxygen supply interval time difference of sensor measurement, the better the time consistency maintained by the sensor terminal in continuous monitoring, the stronger the data continuity, and the more beneficial it is to analyze the stability of the oxygen supply process. Large fluctuations in the average oxygen supply interval time difference measured by the sensor indicate a temporal inconsistency in the collection of oxygen supply data, which may affect the understanding of the oxygen supply pattern. The actual average oxygen supply interval time difference reflects the periodicity or rhythm of the real oxygen supply operation. When the actual average oxygen supply interval time difference matches the average oxygen supply interval time difference measured by the sensor, it indicates that the sensor terminal can accurately capture and reflect the actual rhythm of oxygen supply, enhancing the reliability and practicality of the data. The more oxygen supply interruptions, the more fluctuations or faults occur during the oxygen supply process.

[0034] Furthermore, the verification results of each sensor terminal include: verification results of pressure sensor terminals and verification results of oxygen supply sensor terminals. The specific acquisition steps include: acquiring network interference parameters and analyzing and processing these parameters to obtain network interference factors; acquiring preset interference factor intervals in the database, as well as the corresponding interference values ​​of pressure sensor terminal verification performance indicators and oxygen supply sensor terminal verification performance indicators in each interference factor interval; matching and extracting the interference values ​​of pressure sensor terminal verification performance indicators and oxygen supply sensor terminal verification performance indicators corresponding to the intervals where the network interference factors are located, and recording them sequentially as the pressure sensor terminal verification impact value and the oxygen supply terminal verification impact value; and then verifying the performance of the pressure sensor terminal... The performance indicators of the pressure sensor terminal are added together with the verification impact value of the pressure sensor terminal to obtain the comprehensive verification performance index of the pressure sensor terminal. Similarly, the verification performance index of the oxygen supply sensor terminal is added together with the verification impact value of the oxygen supply sensor terminal to obtain the comprehensive verification performance index of the oxygen supply sensor terminal. The verification results of both the pressure and oxygen supply sensor terminals are obtained based on the comprehensive verification performance index of the pressure sensor terminal. Finally, a preset comprehensive verification performance threshold for the pressure sensor terminal is obtained from the database and compared with the comprehensive verification performance index of the pressure sensor terminal. If the comprehensive verification performance index of the pressure sensor terminal falls below the threshold of the pressure sensor terminal... If the comprehensive verification performance threshold of the pressure sensor terminal is above the threshold, the verification result is considered normal. If the comprehensive verification performance index of the pressure sensor terminal is below the threshold, the verification result is considered abnormal. The system then retrieves the preset comprehensive verification performance threshold of the oxygen supply sensor terminal from the database and compares it with the comprehensive verification performance index of the oxygen supply sensor terminal. If the comprehensive verification performance index of the oxygen supply sensor terminal is above the threshold, the verification result is considered normal. If the comprehensive verification performance index of the oxygen supply sensor terminal is below the threshold, the verification result is considered abnormal. If the threshold is reached, the oxygen supply sensor terminal verification result is considered abnormal. The network interference factor is obtained by comparing and normalizing the network interference parameters with a preset network interference parameter reference set in the database to obtain a normalized value. Based on this normalized value, corresponding influence weights are introduced for coupling processing, and then the values ​​are added together to obtain the network interference factor. The network interference parameters include signal strength, bit error rate, network load ratio, and network latency. The network interference parameter reference set includes reference values ​​for signal strength, bit error rate, network load ratio, and network latency.

[0035] In this embodiment, network interference parameters in the current environment are acquired and analyzed to generate a network interference factor. This factor represents the impact of the network environment on the stability of data transmission from the sensing terminal. By matching the network interference factor with a preset interference factor range in the database, the corresponding performance index interference values ​​for pressure sensing and oxygen supply sensing are obtained. These performance index interference values ​​effectively offset the bias caused by network fluctuations in the measurement data.

[0036] It should be noted that network interference parameters include signal strength, bit error rate, network load ratio, and network latency. These can be measured using network testing devices (such as Ping and Traceroute). The network interference factor is obtained by comparing these parameters with a reference set of network interference parameters (signal strength reference value, bit error rate reference value, network load ratio reference value, and network latency reference value) in a database.

[0037] ;

[0038] In the formula, Indicates network interference factor. Indicates signal strength. Indicates the signal strength reference value. Indicates bit error rate, This represents the bit error rate reference value. Indicates the network load ratio. This represents a reference value for the network load ratio. Indicates network latency. This represents a reference value for network latency. This represents the influence weight of the o-th network interference parameter, o=1,2,3,4. This indicates that signal strength affects the weights. This indicates that the bit error rate affects the weight. This indicates that the network load ratio affects the weight. This indicates that network latency affects the weights.

[0039] It's important to note that the network interference factor, derived by analyzing signal strength, bit error rate (BER), network load ratio, and network latency, takes into account their interrelationships. For example, weakened signal strength directly leads to decreased data transmission stability and an increased BER, as signal attenuation makes transmitted bits more susceptible to errors. Simultaneously, a higher BER prompts more frequent packet retransmissions, increasing network load and thus pushing up the network load ratio. Under high load, packet queuing intensifies, further increasing network latency. Increased latency, in turn, prevents timely processing of data with high real-time requirements, resulting in more bit errors or signal attenuation, further raising the BER. Furthermore, when both the BER and network load ratio increase, increased network latency may force computers to use higher power signal transmission to compensate for packet loss, thereby affecting signal strength and overall network power consumption.

[0040] Signal strength influence weight represents the numerical value of the impact of signal strength on network interference factors. When using it, the influence weight corresponding to signal strength can be directly obtained from a database. The correspondence can be a pre-defined mapping relationship; for example, signal strength and signal strength influence weight form a mapping set. Inputting the real-time signal strength into the mapping set yields the influence weight corresponding to the signal strength. This mapping relationship can be one-to-one or many-to-one. Bit error rate influence weight represents the numerical value of the impact of bit error rate on network interference factors. When using it, the influence weight corresponding to bit error rate can be directly obtained from a database. The correspondence can be a pre-defined mapping relationship; for example, bit error rate and bit error rate influence weight form a mapping set. Inputting the real-time bit error rate into the mapping set yields the influence weight corresponding to the bit error rate. This mapping relationship can be one-to-one or many-to-one. The network load ratio impact weight represents the numerical value of the influence of the network load ratio on network interference factors. When using it, the impact weight corresponding to the network load ratio can be directly retrieved from the database. The correspondence can be a pre-defined mapping relationship; for example, a mapping set is formed between the network load ratio and its impact weight. Inputting the real-time network load ratio into this mapping set yields the impact weight corresponding to the network load ratio. This mapping relationship can be one-to-one or many-to-one. Similarly, the network latency impact weight represents the numerical value of the influence of network latency on network interference factors. When using it, the impact weight corresponding to the network latency can be directly retrieved from the database. The correspondence can also be a pre-defined mapping relationship; for example, a mapping set is formed between the network latency and its impact weight. Inputting the real-time network latency into this mapping set yields the impact weight corresponding to the network latency. This mapping relationship can be one-to-one or many-to-one.

[0041] Furthermore, based on the verification results of each sensor terminal, corresponding terminal verification processing is performed. The specific terminal verification processing steps include: judging based on the verification results of the pressure sensor terminal and the oxygen supply sensor terminal; if the pressure sensor terminal verification result is normal, the backup pressure sensor terminal is not activated; if the pressure sensor terminal verification result is abnormal, the backup pressure sensor terminal is verified; if the backup pressure sensor terminal verification result is normal, the backup pressure sensor terminal is activated. If the backup pressure sensor terminal verification result is abnormal, an early warning will be issued. If the oxygen supply sensor terminal verification result is normal, the backup oxygen supply sensor terminal will not be activated. If the oxygen supply sensor terminal verification result is abnormal, the backup oxygen supply sensor terminal will be verified. If the backup oxygen supply sensor terminal verification result is normal, it will be activated. If the backup oxygen supply sensor terminal verification result is abnormal, an early warning will be issued.

[0042] In this embodiment, by judging the verification results of the pressure sensor terminal and the oxygen supply sensor terminal, a backup terminal will be automatically activated when an "abnormal" situation occurs, ensuring the integrity and continuity of the data. Automated monitoring and judgment based on real-time verification results ensure stable and uninterrupted data acquisition during training, supporting training effectiveness. After activating the backup sensor terminal, switching to the backup channel maintains the continuity of data acquisition and prevents sensor failure from affecting the overall monitoring accuracy. In CPR training, ensuring the stability of data acquisition is crucial, especially for pressure sensors and oxygen supply sensors, as they reflect the trainee's compression and oxygen supply operations and directly affect the evaluation of the entire training process. When a sensor malfunctions or becomes abnormal, the backup sensor can be activated promptly, preventing data acquisition interruption, effectively reducing data loss due to sensor failure, ensuring the integrity of training data, and providing accurate basis for evaluation and feedback. Automated activation of the backup sensor avoids human intervention, reduces the burden on operators, and improves the level of intelligence. Furthermore, the use of backup sensors enables continuous and uninterrupted data acquisition, thus ensuring the smoothness of the training process. In addition, timely switching of backup sensors helps optimize data integrity, making subsequent data analysis more accurate.

[0043] Furthermore, real-time sensing data of CPR training is collected, specifically including: using MR technology to construct a virtual physiological recovery model based on a dummy physical model to simulate the CPR training process, and dividing the simulated CPR training process into several simulated monitoring periods with a preset duration; collecting real-time sensing data of CPR training, including the average training heart rate and average training body surface temperature of the physiological recovery model at each sensing moment during the simulated monitoring period, as well as the average training compression depth, average training compression force, and average training oxygen supply rate of the trainee.

[0044] In this embodiment, it should be noted that the dummy entity model refers to a real dummy model, while the virtual physiological recovery model can be simulated using 3D modeling software (such as Maya, 3ds Max, etc.).

[0045] By dividing the simulated training process into several monitoring periods, data collection becomes more granular, facilitating subsequent analysis and effectively presenting the trainees' actual operational status while providing comprehensive monitoring information. Real-time collection of data such as heart rate and body surface temperature allows for a direct reflection of the dummy model's physiological response. Simultaneously, data collection on compression depth and force intensity assesses the trainees' proficiency in CPR movements, ensuring the scientific rigor and standardization of the training. Acquiring data on oxygen delivery duration provides a reference indicator for trainees, ensuring the consistency and effectiveness of their oxygen delivery procedures. This contributes to an objective evaluation of the training process and facilitates subsequent results analysis.

[0046] This function can also be applied to scenarios with high requirements for training effectiveness, such as medical training and first aid training. For medical first aid training, comprehensive and real-time data collection enables panoramic monitoring of training operations, making trainees' actions more targeted and the training effect more obvious. In emergency rescue scenarios, this function can also significantly improve trainees' proficiency in key rescue techniques, thereby enhancing the effectiveness and timeliness of actual operations.

[0047] Furthermore, CPR standardization indicators are obtained by analyzing real-time sensor data from CPR training. The specific steps are as follows: Obtain a preset CPR training target set from the database, which includes: target training heart rate, target training body surface temperature, target average training compression depth, target average training compression force, and target average training oxygen delivery rate; analyze the real-time sensor data from CPR training with the CPR training target set to obtain CPR standardization indicators; these indicators characterize the degree of standardization in the CPR operations performed by the trainees.

[0048] In this embodiment, the introduction of CPR standard indicators allows trainers' performance evaluation to move beyond subjective judgment and instead rely on a comprehensive analysis of a series of objective data. This includes key information such as real-time heart rate, body surface temperature, and compression depth, as well as process indicators like oxygen delivery during training, effectively reflecting the precision of the trainer's actions. This data-driven evaluation method provides trainers with more specific and accurate feedback, helping them identify problems in their training procedures.

[0049] The quantification of CPR standard indicators enables a unified evaluation of trainers' operational effectiveness, thus providing a basis for subsequent teaching and training feedback. By comparing with the target training set, trainers can clearly understand the gap between their actual operation and the standard, thereby paying more attention to details in future training and continuously improving the accuracy of their operations, helping trainers to master standardized operating procedures more quickly.

[0050] Furthermore, the CPR standard indicators are obtained, specifically through the following method:

[0051] The absolute difference between the real-time sensor data of CPR training and the CPR training target set is processed to obtain the absolute difference of each target parameter. The absolute difference of each target parameter is then divided by each target parameter to obtain the processing result of each sensor data. Based on the processing result of each sensor data, the corresponding influence weight is introduced and coupled, and then the results are added to obtain the CPR specification index.

[0052] ;

[0053] In the formula, Indicators of CPR standards This represents the average training heart rate of the physiological recovery model during the simulated monitoring period. This represents the average body surface temperature of the physiological recovery model during the simulated monitoring period. This indicates the average compression depth of trainees during the simulated monitoring period. This represents the target average training compression depth. This indicates the average training pressure level of trainees during the simulated monitoring period. This represents the average training pressure applied to the target. This represents the average oxygen supply rate of trainees during the simulated monitoring period. This represents the target average training oxygen delivery rate. This represents the influence weight of the real-time sensor data during the k-th CPR training, where k represents the index of the influence weight, k=1,2,3,4,5. This indicates the weighting of the influence of average training heart rate. This indicates the weighting of the average training body surface temperature. This indicates the weighting of the average training compression depth by the trainers. This indicates that the average training pressure affects the weights. This indicates the weight of the average training oxygen supply rate.

[0054] In this embodiment, the evaluation mechanism for CPR training operations is refined. The weights and parameters in the formula work together to provide trainers with a quantifiable operational standard, enabling an intuitive evaluation of operational compliance.

[0055] The CPR standard indicators were derived by analyzing the average training heart rate, average training body surface temperature, and the average training compression depth, average training compression force, and average training oxygen delivery rate of the trainees. This analysis takes into account the interrelationships between these parameters. For example, the average training heart rate is affected by compression depth and force; appropriate depth and force can increase heart rate. Body surface temperature is affected by exercise intensity (indirectly reflected by heart rate) and oxygen delivery duration; high-intensity training and adequate oxygen delivery help maintain a suitable body temperature. Compression depth and force need to work synergistically to achieve the best cardiopulmonary resuscitation effect. Oxygen delivery duration directly affects oxygen supply, which in turn affects heart rate recovery and body temperature maintenance.

[0056] The average training heart rate influence weight represents the numerical value of the impact of average training heart rate on CPR performance indicators. When using it, the influence weight corresponding to the average training heart rate can be directly obtained from the database. The correspondence can be a pre-defined mapping relationship; for example, the average training heart rate and its influence weight form a mapping set. Inputting the real-time average training heart rate into this mapping set yields the corresponding influence weight. This mapping relationship can be one-to-one or many-to-one. Similarly, the average training body surface temperature influence weight represents the numerical value of the impact of average training body surface temperature on CPR performance indicators. When using it, the influence weight corresponding to average training body surface temperature can be directly obtained from the database. The correspondence can also be a pre-defined mapping relationship; for example, the average training body surface temperature and its influence weight form a mapping set. Inputting the real-time average training body surface temperature into this mapping set yields the corresponding influence weight. This mapping relationship can be one-to-one or many-to-one. The "Average Training Compression Depth Temperature Influence Weight" represents the numerical value of the influence of the trainee's average training compression depth temperature on CPR performance indicators. When using it, the influence weight corresponding to the trainee's average training compression depth temperature can be directly obtained from the database. The correspondence can be a pre-defined mapping relationship; for example, the trainee's average training compression depth temperature and its influence weight form a mapping set. Inputting the real-time average training compression depth temperature into this mapping set yields the corresponding influence weight. This mapping relationship can be one-to-one or many-to-one. The "Target Average Training Compression Pressure Influence Weight" represents the numerical value of the influence of the target average training compression pressure on CPR performance indicators. When using it, the influence weight corresponding to the target average training compression pressure can be directly obtained from the database. The correspondence can also be a pre-defined mapping relationship; for example, the target average training compression pressure and its influence weight form a mapping set. Inputting the real-time target average training compression pressure into this mapping set yields the corresponding influence weight. This mapping relationship can be one-to-one or many-to-one. The average training oxygen delivery rate (APRR) influence weight represents the numerical value of the degree of influence of the average training OCR on CPR performance indicators. When using it, the influence weight corresponding to the average training OCR can be directly obtained from the database. The correspondence can be a pre-defined mapping relationship. For example, the average training OCR and the average training OCR influence weight form a mapping set. Inputting the real-time average training OCR into the mapping set yields the influence weight corresponding to the average training OCR. The mapping relationship can be one-to-one or many-to-one.

[0057] Furthermore, the method for determining whether to issue a voice prompt is as follows: obtain the preset operation standard threshold in the database and compare it with the CPR standard indicator. If the CPR standard indicator is above the operation standard threshold, no voice prompt will be issued; if the CPR standard indicator is below the operation standard threshold, a voice prompt will be issued. This application provides a visualization real-time data monitoring device based on MR technology, comprising: a signal receiving and mapping module, used by the central controller to receive a CPR training request signal, synchronously using MR technology to map a dummy model onto a visualization interface for real-time training status display, and transmitting preliminary verification signals to each sensor terminal connected to the dummy model; a data acquisition and analysis module, used to collect CPR verification data corresponding to each sensor terminal connected to the dummy model within a preset verification period, and analyze the data to obtain the verification results of each sensor terminal; a terminal verification processing and signal transmission module, used to perform corresponding terminal verification processing based on the verification results of each sensor terminal, and transmit an implementable CPR training signal through the central controller after terminal verification processing, thereby collecting real-time CPR training sensor data and uploading it to the central controller; and an operation specification judgment and voice reminder module, used to analyze the real-time CPR training sensor data to obtain CPR specification indicators, and thereby determine whether to issue a voice reminder.

[0058] In this embodiment, the introduction of real-time voice prompts allows trainees to receive immediate feedback during operation, helping them quickly correct non-standard operations. By using preset standard thresholds in the database, the system can accurately determine whether an operation meets the standard and issue reminders when necessary, helping trainees master operational details during training. This feedback mechanism can significantly reduce the occurrence of repetitive errors, improve operational accuracy, and enhance training effectiveness.

[0059] like Figure 2The diagram shows the structure of a real-time visual data monitoring device based on MR technology provided in this application embodiment. The device includes: a signal receiving and mapping module, a data acquisition and analysis module, a terminal verification processing and signal transmission module, and an operation specification judgment and voice reminder module. The signal receiving and mapping module is used by the central controller to receive a CPR training request signal, synchronously map the dummy entity model onto a visual interface using MR technology for real-time training status display, and transmit preliminary verification signals to each sensor terminal connected to the dummy entity model. The data acquisition and analysis module is used to collect CPR verification data corresponding to each sensor terminal connected to the dummy entity model within a preset verification period and analyze it to obtain the verification results of each sensor terminal. The terminal verification processing and signal transmission module is used to perform corresponding terminal verification processing based on the verification results of each sensor terminal, and transmit an implementable CPR training signal through the central controller after terminal verification processing, thereby collecting real-time CPR training sensor data and uploading it to the central controller. The operation specification judgment and voice reminder module is used to analyze the real-time CPR training sensor data to obtain CPR specification indicators and determine whether to issue a voice reminder.

[0060] In summary, this embodiment utilizes MR technology to map a dummy model onto a visual interface for real-time training status display, thereby enabling real-time monitoring and feedback from trainees. This allows trainees to master CPR techniques and standards in real-world scenarios, effectively solving the problem of inaccurate training effect measurement caused by inaccurate sensing devices in existing technologies.

[0061] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0062] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0063] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0064] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0065] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0066] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for real-time data monitoring based on MR technology, characterized in that, Includes the following steps: After receiving the CPR training request signal, the central controller synchronously uses MR technology to map the dummy model onto the visualization interface for real-time training status display, and transmits the preliminary verification signal to each sensor terminal connected to the dummy model. During the preset verification period, CPR verification data corresponding to each sensor terminal connected to the dummy model is collected and analyzed to obtain the verification results of each sensor terminal. The specific steps for analyzing and obtaining the verification results of each sensing terminal include: Based on the pressure sensor verification data, the measurement time of each press and the actual press time are extracted, and the difference is processed to obtain the timing delay of each press. Based on the timing of each press sensor measurement, the average time difference between two adjacent press times is calculated and recorded as the average time difference of sensor measurement. Simultaneously, based on the actual press time, the average time difference between two adjacent actual press times is calculated and recorded as the average time difference of actual press. Based on the timing delay of each press sensing, the average time difference of sensing measurement interval, and the average time difference of actual press interval, the verification performance indicators of the pressure sensing terminal are obtained. Based on the oxygen supply sensor verification data, the oxygen supply time of each sensor and the actual oxygen supply time of each sensor are extracted and the difference is processed to obtain the sensing time delay of each oxygen supply sensor. Based on each oxygen supply time of the sensor, the average oxygen supply time difference between two adjacent oxygen supply times is calculated and recorded as the average oxygen supply interval time difference of the sensor measurement. Simultaneously, based on each actual oxygen supply time, the average interval time difference between two adjacent actual oxygen supply times is calculated and recorded as the actual oxygen supply average interval time difference. Based on the time delay of each oxygen supply sensing, the average oxygen supply interval time difference of the sensing measurement, the average oxygen supply interval time difference of the actual oxygen supply, and the number of oxygen supply interruptions, the verification performance indicators of the oxygen supply sensing terminal are obtained. The verification performance indicators of the pressure sensing terminal are used to characterize the performance of the pressure sensing terminal. The verification performance indicators of the oxygen supply sensing terminal are used to characterize the performance of the oxygen supply sensing terminal. During the preset verification period, CPR verification data corresponding to each sensor terminal connected to the dummy model is collected and analyzed to obtain the verification results of each sensor terminal. The verification results of each sensor terminal include: pressure sensor terminal verification results and oxygen supply sensor terminal verification results. The specific method for obtaining the verification performance indicators of the pressure sensing terminal is as follows: The average pressure delay of each press sensing time is processed to obtain the average pressure delay. The average pressure delay is then normalized by proportionally normalizing the average pressure measurement delay to obtain the normalized pressure measurement delay result. The absolute difference between the average interval time difference of the sensing measurement and the actual average interval time difference of the press is processed to obtain the absolute difference of the average interval time difference. The absolute difference of the average interval time difference is then normalized by proportionally normalizing the absolute difference of the interval time difference and the allowable interval time difference to obtain the normalized pressure interval time difference result. Based on the normalized pressure measurement delay result and the normalized pressure interval time difference result, and after introducing the corresponding influence weights, they are coupled and then added to obtain the verification performance index of the pressure sensing terminal. The specific method for obtaining the verification performance indicators of the oxygen supply sensing terminal is as follows: The average oxygen supply delay of each oxygen supply sensor measurement is processed to obtain the average oxygen supply measurement. The average oxygen supply delay is then proportionally normalized to the allowable error of the oxygen supply sensor delay to obtain the oxygen supply delay normalization result. The absolute difference between the average oxygen supply interval time difference of the sensor measurement and the actual average oxygen supply interval time difference is processed to obtain the absolute difference of the oxygen supply interval time difference. The absolute difference of the oxygen supply interval time difference is then proportionally normalized to the actual average oxygen supply interval time difference to obtain the oxygen supply interval time difference normalization result. The number of oxygen supply interruptions is then multiplied by the corresponding performance evaluation interference factor after parallel shifting, and the reciprocal is taken to obtain the oxygen supply interruption processing result. Based on the oxygen supply delay normalization result, the oxygen supply interval time difference normalization result, and the oxygen supply interruption processing result, and after coupling processing with corresponding influence weights, they are added together to obtain the verification performance index of the oxygen supply sensor terminal. Based on the verification results of each sensor terminal, corresponding terminal verification processing is performed, and after the terminal verification processing, the CPR training signal is transmitted through the central controller, thereby collecting real-time sensor data for CPR training and uploading it to the central controller. Based on the analysis of real-time sensor data from CPR training, CPR standard indicators are obtained, and the appropriate voice prompt is determined accordingly.

2. The visualization real-time data monitoring method based on MR technology as described in claim 1, characterized in that: The process involves collecting CPR verification data from each sensor terminal connected to the dummy model within a preset verification period. This CPR verification data includes pressure sensor verification data and oxygen supply sensor verification data. The specific data collection procedure is as follows: During the preset verification period, the pressure sensing time and pressure of each press of the pressure sensing terminal connected to the dummy model were collected, and the actual pressing time and actual pressing depth of each press of the trainee were collected by a high-definition scanner. The pressure sensing verification data is obtained by combining the pressure sensing measurement time of each press of the pressure sensing terminal, the pressure of each press, the actual press time of each press by the trainee, and the actual press depth of each press. During the preset verification period, the oxygen supply time and the oxygen supply termination time of each oxygen supply sensing terminal connected to the dummy model were collected. The oxygen supply time period and the number of oxygen supply interruptions were statistically obtained and used as the oxygen supply sensing verification data.

3. The visualization real-time data monitoring method based on MR technology as described in claim 1, characterized in that: The verification results of each sensor terminal include: verification results of pressure sensor terminals and verification results of oxygen supply sensor terminals. The specific steps for obtaining these results include: Obtain network interference parameters, analyze and process them to obtain network interference factors; Obtain the preset interference factor intervals in the database and the interference values ​​of the pressure sensor terminal verification performance index and the oxygen supply sensor terminal verification performance index corresponding to each interference factor interval. Match and extract the interference values ​​of the pressure sensor terminal verification performance index and the oxygen supply sensor terminal verification performance index corresponding to the interval where the network interference factor is located, and record them as the pressure sensor terminal verification impact value and the oxygen supply sensor terminal verification impact value in turn. The verification performance index of the pressure sensing terminal is added to the verification impact value of the pressure sensing terminal to obtain the comprehensive verification performance index of the pressure sensing terminal. The verification performance index of the oxygen supply sensing terminal is added to the verification impact value of the oxygen supply sensing terminal to obtain the comprehensive verification performance index of the oxygen supply sensing terminal. The verification results of the pressure sensing terminal and the oxygen supply sensing terminal were obtained by analyzing the comprehensive verification performance indicators of the pressure sensing terminal and the oxygen supply sensing terminal, respectively. Obtain the preset comprehensive verification performance threshold of the pressure sensor terminal in the database and compare it with the comprehensive verification performance index of the pressure sensor terminal. If the comprehensive verification performance index of the pressure sensor terminal is above the comprehensive verification performance threshold of the pressure sensor terminal, the verification result of the pressure sensor terminal is normal. If the comprehensive verification performance index of the pressure sensor terminal is less than the comprehensive verification performance threshold of the pressure sensor terminal, the verification result of the pressure sensor terminal is abnormal. Obtain the preset comprehensive verification performance threshold of the oxygen supply sensor terminal in the database and compare it with the comprehensive verification performance index of the oxygen supply sensor terminal. If the comprehensive verification performance index of the oxygen supply sensor terminal is above the comprehensive verification performance threshold, the verification result of the oxygen supply sensor terminal is normal. If the comprehensive verification performance index of the oxygen supply sensor terminal is below the comprehensive verification performance threshold, the verification result of the oxygen supply sensor terminal is abnormal. The specific method for obtaining the network interference factor is as follows: The network interference parameters are compared and normalized with the preset network interference parameter reference set in the database to obtain the comparison and normalization value of the network interference parameters. Based on the comparison and normalization value of the network interference parameters, the corresponding influence weights are introduced and coupled, and then the values ​​are added to obtain the network interference factor. The network interference parameters include signal strength, bit error rate, network load ratio, and network latency; The network interference parameter reference set includes signal strength reference values, bit error rate reference values, network load ratio reference values, and network latency reference values.

4. The visualization real-time data monitoring method based on MR technology as described in claim 1, characterized in that: The verification process is based on the verification results of each sensor terminal and performs corresponding terminal verification processing. The specific terminal verification processing steps include: The judgment is based on the verification results of the pressure sensor terminal and the oxygen supply sensor terminal. If the pressure sensor terminal verification result is normal, the backup pressure sensor terminal will not be activated. If the pressure sensor terminal verification result is abnormal, the backup pressure sensor terminal will be verified. If the backup pressure sensor terminal verification result is normal, the backup pressure sensor terminal will be activated. If the backup pressure sensor terminal verification result is abnormal, an early warning will be issued. If the oxygen supply sensor terminal verification result is normal, the backup oxygen supply sensor terminal will not be activated. If the oxygen supply sensor terminal verification result is abnormal, the backup oxygen supply sensor terminal will be verified. If the backup oxygen supply sensor terminal verification result is normal, the backup oxygen supply sensor terminal will be activated. If the backup oxygen supply sensor terminal verification result is abnormal, an early warning will be issued.

5. The visualization real-time data monitoring method based on MR technology as described in claim 1, characterized in that: The acquisition of real-time sensing data for CPR training specifically includes: Using MR technology, a virtual physiological recovery model was constructed based on a dummy physical model to simulate the CPR training process, and the simulated CPR training process was divided into several simulated monitoring periods with a preset duration. Real-time sensor data of CPR training was collected, including the average training heart rate and average training body surface temperature of the physiological recovery model at each sensing moment during the simulated monitoring period, as well as the average training compression depth, average training compression force, and average training oxygen delivery rate of the trainees during the simulated monitoring period.

6. The visualization real-time data monitoring method based on MR technology as described in claim 1, characterized in that: The specific steps for obtaining CPR specification indicators by analyzing real-time sensor data from CPR training are as follows: Obtain a preset CPR training target set from the database. The CPR training target set includes: target training heart rate, target training body surface temperature, target average training compression depth, target average training compression force, and target average training oxygen delivery rate. By analyzing real-time sensor data from CPR training and the CPR training target set, CPR standard metrics are obtained. The CPR standardization index is used to characterize the degree of standardization in which trainers perform CPR operations.

7. The visualization real-time data monitoring method based on MR technology as described in claim 6, characterized in that: The specific method for obtaining the CPR standard indicators is as follows: The absolute difference between the real-time sensor data used in CPR training and the CPR training target set is processed to obtain the absolute difference of each target parameter. The absolute difference of each target parameter is then divided by the target parameter to obtain the processing result of each sensor data. Based on the processing result of each sensor data, the corresponding influence weights are introduced and coupled, and then the results are added to obtain the CPR specification index.

8. The visualization real-time data monitoring method based on MR technology as described in claim 1, characterized in that: The specific method for determining whether to issue a voice prompt is as follows: The system retrieves preset operational standard thresholds from the database and compares them with CPR standard indicators. If the CPR standard indicator is above the operational standard threshold, no voice prompt is given; if the CPR standard indicator is below the operational standard threshold, a voice prompt is given.

9. A visualization-based real-time data monitoring device based on MR technology, characterized in that, include: The module includes a signal receiving and mapping module, a data acquisition and analysis module, a terminal verification and signal transmission module, and an operation procedure judgment and voice prompt module. The signal receiving and mapping module is used to synchronously map the dummy model onto the visualization interface for real-time training status display after the central controller receives the CPR training request signal, and transmit the preliminary verification signal to each sensor terminal connected to the dummy model. The data acquisition and analysis module is used to collect CPR verification data corresponding to each sensor terminal connected to the dummy entity model within a preset verification period, and to analyze the data to obtain the verification results of each sensor terminal. The specific steps for analyzing and obtaining the verification results of each sensing terminal include: Based on the pressure sensor verification data, the measurement time of each press and the actual press time are extracted, and the difference is processed to obtain the timing delay of each press. Based on the timing of each press sensor measurement, the average time difference between two adjacent press times is calculated and recorded as the average time difference of sensor measurement. Simultaneously, based on the actual press time, the average time difference between two adjacent actual press times is calculated and recorded as the average time difference of actual press. Based on the timing delay of each press sensing, the average time difference of sensing measurement interval, and the average time difference of actual press interval, the verification performance indicators of the pressure sensing terminal are obtained. Based on the oxygen supply sensor verification data, the oxygen supply time of each sensor and the actual oxygen supply time of each sensor are extracted and the difference is processed to obtain the sensing time delay of each oxygen supply sensor. Based on each oxygen supply time of the sensor, the average oxygen supply time difference between two adjacent oxygen supply times is calculated and recorded as the average oxygen supply interval time difference of the sensor measurement. Simultaneously, based on each actual oxygen supply time, the average interval time difference between two adjacent actual oxygen supply times is calculated and recorded as the actual oxygen supply average interval time difference. Based on the time delay of each oxygen supply sensing, the average oxygen supply interval time difference of the sensing measurement, the average oxygen supply interval time difference of the actual oxygen supply, and the number of oxygen supply interruptions, the verification performance indicators of the oxygen supply sensing terminal are obtained. The verification performance indicators of the pressure sensing terminal are used to characterize the performance of the pressure sensing terminal. The verification performance indicators of the oxygen supply sensing terminal are used to characterize the performance of the oxygen supply sensing terminal. During the preset verification period, CPR verification data corresponding to each sensor terminal connected to the dummy model is collected and analyzed to obtain the verification results of each sensor terminal. The verification results of each sensor terminal include: pressure sensor terminal verification results and oxygen supply sensor terminal verification results. The specific method for obtaining the verification performance indicators of the pressure sensing terminal is as follows: The average pressure delay of each press sensing time is processed to obtain the average pressure delay. The average pressure delay is then normalized by proportionally normalizing the average pressure measurement delay to obtain the normalized pressure measurement delay result. The absolute difference between the average interval time difference of the sensing measurement and the actual average interval time difference of the press is processed to obtain the absolute difference of the average interval time difference. The absolute difference of the average interval time difference is then normalized by proportionally normalizing the absolute difference of the interval time difference and the allowable interval time difference to obtain the normalized pressure interval time difference result. Based on the normalized pressure measurement delay result and the normalized pressure interval time difference result, and after introducing the corresponding influence weights, they are coupled and then added to obtain the verification performance index of the pressure sensing terminal. The specific method for obtaining the verification performance indicators of the oxygen supply sensing terminal is as follows: The average oxygen supply delay of each oxygen supply sensor measurement is processed to obtain the average oxygen supply measurement. The average oxygen supply delay is then proportionally normalized to the allowable error of the oxygen supply sensor delay to obtain the oxygen supply delay normalization result. The absolute difference between the average oxygen supply interval time difference of the sensor measurement and the actual average oxygen supply interval time difference is processed to obtain the absolute difference of the oxygen supply interval time difference. The absolute difference of the oxygen supply interval time difference is then proportionally normalized to the actual average oxygen supply interval time difference to obtain the oxygen supply interval time difference normalization result. The number of oxygen supply interruptions is then multiplied by the corresponding performance evaluation interference factor after parallel shifting, and the reciprocal is taken to obtain the oxygen supply interruption processing result. Based on the oxygen supply delay normalization result, the oxygen supply interval time difference normalization result, and the oxygen supply interruption processing result, and after coupling processing with corresponding influence weights, they are added together to obtain the verification performance index of the oxygen supply sensor terminal. The terminal verification processing and signal transmission module is used to perform corresponding terminal verification processing based on the verification results of each sensor terminal, and transmit the CPR training signal that can be implemented through the central controller after the terminal verification processing, thereby collecting real-time sensor data for CPR training and uploading it to the central controller. The operation standard judgment and voice reminder module is used to analyze real-time sensor data from CPR training to obtain CPR standard indicators, and thereby determine whether to issue a voice reminder.

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