A medical emergency early warning method based on digital twin technology

Abstract

The application relates to the technical field of intelligent medical equipment monitoring, and discloses a medical emergency early warning method based on digital twin technology, which comprises the following steps: step one, constructing a digital twin model of multiple intelligent medical equipment, wherein the digital twin model forms a virtual image according to hardware parameters and use environment data of the equipment; step two, collecting operation data of the multiple intelligent medical equipment in real time by using an edge computing device; and step three, pre-processing the collected operation data by the edge computing device. The technical scheme of constructing a multiple-equipment digital twin model, fusing and analyzing a cooperative working state, and judging a linkage fault expansion range by using virtual prediction achieves the technical effect of accurately identifying and early warning linkage faults among multiple equipment, and compared with a monitoring scheme in the prior art which depends on single-equipment state feedback, the technical scheme solves the problems of lacking multiple-equipment comprehensive monitoring capability and being unable to early warn linkage faults.

Description

Technical Field

[0001] This invention relates to the field of intelligent medical device monitoring technology, specifically a medical emergency early warning method based on digital twin technology. Background Technology

[0002] Due to advancements in medical technology, intelligent medical devices such as pacemakers and insulin pumps are playing an increasingly important role in the daily monitoring and treatment of patients. The safety and stability of these devices are directly related to the patient's life safety.

[0003] However, existing security monitoring systems have revealed the following shortcomings in application:

[0004] Existing monitoring systems mostly rely on the status feedback of single devices and lack the comprehensive monitoring capability for the coordinated operation of multiple devices, making it difficult to provide timely early warnings when linkage failures occur between devices.

[0005] Existing monitoring systems rely on central servers for data analysis. The transmission of massive amounts of data over long distances can cause network delays, resulting in slow early warning response times and making it difficult to meet the real-time requirements of medical emergencies.

[0006] Existing technologies aggregate all raw operational data to a central server for processing, which increases the network bandwidth burden on data transmission and puts enormous computational pressure on the central server, affecting the system's operating efficiency.

[0007] To address these issues, this invention proposes a medical emergency early warning method based on digital twin technology. Summary of the Invention

[0008] To address the shortcomings of existing technologies, this invention provides a medical emergency early warning method based on digital twin technology to solve the problems mentioned in the background section.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a medical emergency early warning method based on digital twin technology, comprising:

[0010] Step 1: Construct digital twin models of multiple smart medical devices. The digital twin models form virtual images based on the hardware parameters of the devices and the usage environment data.

[0011] Step 2: Use edge computing devices to collect real-time operational data from multiple smart medical devices;

[0012] Step 3: The edge computing device preprocesses the collected operational data and calculates the collaborative working status between devices by fusing and analyzing the operational data.

[0013] Step 4: The edge computing device monitors the risk of linkage failure based on the collaborative working status. When a linkage failure risk is detected, a digital twin model is used to make a virtual prediction to determine the scope of the extended impact of the linkage failure risk.

[0014] Step 5: Based on the expanded impact range, confirm the risk of linkage failure. The edge computing device sends an immediate warning signal and automatically adjusts the device parameters of the smart medical device through the emergency response mechanism.

[0015] Preferably, calculating the collaborative working status between devices includes: establishing a collaborative working status model between the multiple intelligent medical devices, and calculating quantitative indicators of the collaborative working status in real time based on the operating data;

[0016] The virtual prediction using the digital twin model includes: combining the preset interdependencies between the smart medical devices to virtually simulate the risk of the linked failure, so as to determine the scope of the extended impact.

[0017] Preferably, the virtual prediction using the digital twin model includes: combining the preset interdependencies between the intelligent medical devices to virtually simulate the risk of the linked failure, so as to determine the extended impact range.

[0018] Preferably, the monitoring of linkage failure risks includes: monitoring operational or status abnormalities in the collaborative working state, and calculating the possibility that the abnormality will affect other intelligent medical devices.

[0019] Preferably, the automatic adjustment of the device parameters of the intelligent medical device is used to reduce the risk of the linkage failure spreading among multiple intelligent medical devices;

[0020] Sending the real-time warning signal includes: sending the real-time warning signal to the terminal device of medical personnel;

[0021] The multiple intelligent medical devices include pacemakers and insulin pumps.

[0022] Preferably, step one further includes:

[0023] Sub-step A device mechanism model is constructed based on the hardware parameters. The device mechanism model This is used to describe the basic physical characteristics and operating logic of the intelligent medical device;

[0024] Sub-step Based on the aforementioned usage environment data Building a data-driven bias correction model The deviation correction model Used to compensate the device mechanism model Deviations in actual operation;

[0025] Sub-step The device mechanism model is integrated. With the aforementioned deviation correction model To construct the digital twin model, the digital twin model outputs a virtual state. That is, the virtual image, the virtual state The calculation formula is:

[0026] ,

[0027] in, For the current time, It is in a virtual state. For the device mechanism model The running function, For control input signals of intelligent medical devices. For hardware parameters, For the deviation correction model The correction function, This refers to the usage environment data at the current moment;

[0028] Sub-step Set the model fidelity threshold And using the actual operating data of the intelligent medical device Calculate the virtual state Real-time fidelity The real-time fidelity The calculation formula is:

[0029] ,

[0030] in, For intelligent medical devices Actual operational data at any given time For real-time fidelity;

[0031] Sub-step Perform model update determination; if the real-time fidelity... Less than the model fidelity threshold Trigger the deviation correction model Online updates, utilizing the latest usage environment data. Retrain the bias correction model This is to ensure the accuracy of the virtual image.

[0032] Preferably, step two further includes:

[0033] Sub-step Determine the device set of the multiple intelligent medical devices. The set of devices Include A set of devices, and the set of devices. The first in Taiwanese equipment Configure data sampling period ;

[0034] Sub-step The edge computing device according to the data sampling period minimum value Determine the system sampling time The edge computing device at the system sampling time Poll the set of devices , obtain the Taiwanese equipment The original dataset ;

[0035] Sub-step The original dataset Based on the device status The sensor feedback information With the environmental change information constitute;

[0036] Sub-step For the original dataset The sensor feedback information in Perform data validity determination The data validity determination The conditions are:

[0037] ≤ ≤ ,

[0038] , ,

[0039] in, For the first Taiwanese equipment The lower limit of the sensor's measurement range. For the first Taiwanese equipment The upper limit of the sensor's measurement range, For the data to be valid, The data is invalid.

[0040] Sub-step For the original dataset Perform data integrity determination The data integrity determination The conditions are:

[0041] ,

[0042] in, The data field is missing. For data integrity, Data is missing;

[0043] Sub-step When the data validity is determined The result is And the data integrity determination The result is At that time, the edge computing device is the original dataset. Additional synchronization timestamp And will be appended with the synchronization timestamp. The original dataset Store in edge local data cache ;

[0044] Sub-step The edge local data cache area The data set stored in the middle constitutes the running data, which is used for the fusion analysis in step three.

[0045] Preferably, step three further includes:

[0046] Sub-step The edge local data cache Extract the running data, the running data constitutes The intelligent medical device described above was at the system sampling time Synchronized dataset ;

[0047] Sub-step For the synchronized dataset The sensor feedback information in Perform data normalization preprocessing to generate normalized data. The calculation formula for the data normalization preprocessing is as follows:

[0048] ,

[0049] in, For the first The equipment is in Normalized data at any given time For the first The minimum preset range of the sensor on the device. For the first The preset maximum range of the sensor on the device;

[0050] Sub-step Construct a collaborative working state model among the multiple intelligent medical devices. The collaborative working state model At the system sampling time state vector The normalized data from all devices The state vector constitutes The expression is:

[0051] ,

[0052] in, For state vectors, This represents the total number of intelligent medical devices. Transpose of a vector;

[0053] Sub-step Define the baseline state vector of the collaborative working state model. The reference state vector This represents the ideal state when the aforementioned multiple intelligent medical devices work together;

[0054] Sub-step Calculate the state vector With the reference state vector Cooperative deviation between The cooperative deviation This refers to the quantitative indicator of the collaborative working state, namely the collaborative deviation. The calculation formula is:

[0055] ,

[0056] in, For coordination bias, For the first Preset collaboration weights for the devices Reference state vector The first in Quantity;

[0057] Sub-step Set a cooperative deviation threshold The edge computing device is based on the cooperative deviation. With the aforementioned collaborative deviation threshold The comparison determines the current collaborative working state. ;

[0058] Sub-step When the collaborative working state The determination criteria are:

[0059] ,

[0060] Among them, the collaborative working state This provides input for the monitoring and linkage failure risk in step four.

[0061] Preferably, step four further includes:

[0062] Sub-step Receive the collaborative working status determined in step three. And set trigger conditions for linkage fault risk monitoring. ;

[0063] Sub-step The triggering conditions for the linkage fault risk monitoring For: when the collaborative working state When a collaborative anomaly occurs, the edge computing device initiates the linkage fault risk monitoring.

[0064] Sub-step When the linkage fault risk monitoring trigger condition When the condition is met, the edge computing device calculates the collaborative deviation. The device contribution of each of the aforementioned intelligent medical devices The contribution of the device The calculation formula is:

[0065] ,

[0066] in, For the first Preset collaboration weights for the devices For the first Normalized data from the equipment. Reference state vector The first in Quantity;

[0067] Sub-step Determine the contribution of the device. Largest equipment For the source of abnormal equipment The source of the abnormal device index The formula for determining it is:

[0068] ,

[0069] Among them, the source abnormal device This constitutes the aforementioned risk of linkage failure;

[0070] Sub-step The system loads and constructs digital twin models of the multiple smart medical devices, and obtains a preset interdependency matrix among the smart medical devices. The interdependence matrix elements Indicates device For equipment The influence coefficient;

[0071] Sub-step Define the source abnormal device abnormal state The abnormal state The source of the abnormal device The normalized data express;

[0072] Sub-step The edge computing device performs the virtual prediction within the digital twin model, utilizing the abnormal state. With the aforementioned interdependence matrix Calculate the risk of the linked failure to other devices. Fault propagation ;

[0073] Sub-step The probability of fault propagation The calculation formula is:

[0074] ,

[0075] in, For the preset fault propagation function, For the abnormal equipment at the source For equipment The influence coefficient mentioned above, To predict the time step;

[0076] Sub-step Set a fault propagation threshold The edge computing device will propagate all the fault probabilities. Greater than the fault propagation threshold equipment The equipment was identified as affected.

[0077] Sub-step The collection of all the affected devices That is, the extended impact range constituting the aforementioned linked failure risk, the extended impact range The determination criteria are:

[0078] ,

[0079] The extended scope of influence The confirmed linkage failure risk is used in step five.

[0080] Preferably, step five further includes:

[0081] Sub-step Receive the expanded influence range determined in step four. The extended range of influence Includes all the affected devices;

[0082] Sub-step The edge computing device is based on the extended influence range. Calculate the comprehensive risk level of the linked failure risk. The aforementioned comprehensive risk level The calculation formula is:

[0083] ,

[0084] in, To expand the scope of influence The Middle Preset criticality weights for affected equipment in Taiwan. For the calculation of the first The probability of fault propagation in the affected equipment described above;

[0085] Sub-step Set risk confirmation thresholds The edge computing device performs the determination of the risk of linkage failure. The determination The conditions are:

[0086] when The risk of linkage failure has been confirmed;

[0087] when ≤ The risk of linkage failure has not been confirmed.

[0088] Sub-step When the determination When the result is confirmed, the edge computing device generates the real-time warning signal. The real-time warning signal Including the aforementioned comprehensive risk level With the aforementioned extended range of influence The edge computing device will transmit the real-time warning signal. Sending data to the terminal devices of medical personnel;

[0089] Sub-step When the determination When the result is confirmed, the edge computing device activates the emergency response mechanism, which is based on the comprehensive risk level. With the aforementioned extended range of influence Load the corresponding emergency adjustment strategy ;

[0090] Sub-step The emergency adjustment strategy For the extended range of influence Each of the affected devices mentioned above Calculate new equipment parameters The new device parameters The calculation formula is: ,

[0091] in, For affected equipment Current device parameters The parameter adjustment function is a preset parameter adjustment function. Based on the aforementioned comprehensive risk level With the aforementioned key weights Calculate the parameter adjustment amount;

[0092] Sub-step The edge computing device extends its influence to the surrounding area. The affected devices in Issue parameter adjustment instructions to apply the new device parameters. This completes the automatic adjustment of the device parameters of the intelligent medical device.

[0093] This invention provides a medical emergency early warning method based on digital twin technology. It has the following beneficial effects:

[0094] 1. This invention adopts a technical solution of constructing a digital twin model of multiple devices and integrating and analyzing their collaborative working status, and using virtual prediction to determine the expansion range of linkage faults. This achieves the technical effect of accurately identifying and warning of linkage faults between multiple devices. Compared with the existing monitoring schemes that rely on the status feedback of a single device, this invention solves the shortcomings of lacking comprehensive monitoring capabilities for multiple devices and being unable to warn of linkage faults.

[0095] 2. This invention adopts a technical solution that deploys data analysis, linkage fault identification, and early warning decision-making on edge computing devices, achieving the technical effect of rapid local response and instant early warning signal transmission on the device. Compared with the existing technology that relies on a central server for data analysis, this invention solves the shortcomings of slow early warning response speed and inability to meet the real-time requirements of medical emergencies caused by network delays in data transmission.

[0096] 3. This invention adopts a technical solution of performing local preprocessing and fusion analysis of raw operating data at the edge computing device, achieving the technical effect of completing the calculation at the data source and reducing the amount of network data transmission. Compared with the existing technology that gathers all raw operating data to the central server for processing, this invention solves the shortcomings of heavy network bandwidth burden and huge computing pressure on the central server, and improves the operating efficiency of the system. Attached Figure Description

[0097] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0098] To enable those skilled in the art to understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort should fall within the scope of protection of the present invention.

[0099] The present invention will now be described in detail with reference to the accompanying drawings:

[0100] Example:

[0101] Please see the appendix Figure 1 This invention provides a medical emergency early warning method based on digital twin technology, comprising:

[0102] Step 1: Construct digital twin models of multiple smart medical devices. The digital twin models form virtual images based on the hardware parameters of the devices and the usage environment data.

[0103] Step one further includes:

[0104] Sub-step Build a device mechanism model based on hardware parameters Equipment mechanism model Used to describe the basic physical characteristics and operating logic of intelligent medical devices;

[0105] Sub-step Based on usage environment data Building a data-driven bias correction model Deviation correction model Model for compensating equipment mechanism Deviations in actual operation;

[0106] Sub-step Fusion equipment mechanism model With deviation correction model To construct a digital twin model, the virtual state output by the digital twin model. This refers to a virtual image or virtual state. The calculation formula is:

[0107] ,

[0108] in, For the current time, It is in a virtual state. For the device mechanism model The running function, For control input signals of intelligent medical devices. For hardware parameters, For the deviation correction model The correction function, This refers to the usage environment data at the current moment;

[0109] Sub-step Set the model fidelity threshold Furthermore, utilizing the actual operational data of intelligent medical devices Calculate virtual state Real-time fidelity Real-time fidelity The calculation formula is:

[0110] ,

[0111] in, For intelligent medical devices Actual operational data at any given time For real-time fidelity;

[0112] Sub-step Perform model update determination; if real-time fidelity... Less than the model fidelity threshold Triggering the bias correction model Online updates, utilizing the latest usage environment data Retrain the bias correction model To ensure the accuracy of the virtual image;

[0113] Step 2: Use edge computing devices to collect real-time operational data from multiple smart medical devices. The operational data includes device status, sensor feedback information, and environmental change information of the smart medical devices' environment. The edge computing devices are smart gateways or local processing devices.

[0114] Step two further includes:

[0115] Sub-step Determine the equipment set of multiple intelligent medical devices. Equipment collection Include A set of devices, and a collection of devices. The first in Taiwanese equipment Configure data sampling period ;

[0116] Sub-step Edge computing devices are based on data sampling periods minimum value Determine the system sampling time Edge computing devices at system sampling time Polling device set , obtain the Taiwanese equipment The original dataset ;

[0117] Sub-step Original dataset Based on device status Sensor feedback information Information on environmental change constitute;

[0118] Sub-step For the original dataset Sensor feedback information Perform data validity determination Data validity determination The conditions are:

[0119] ≤ ≤ ,

[0120] , ,

[0121] in, For the first Taiwanese equipment The lower limit of the sensor's measurement range. For the first Taiwanese equipment The upper limit of the sensor's measurement range, For the data to be valid, The data is invalid.

[0122] Sub-step For the original dataset Perform data integrity determination Data integrity determination The conditions are:

[0123] ,

[0124] in, The data field is missing. For data integrity, Data is missing;

[0125] Sub-step When data validity is determined The result is And data integrity determination The result is At that time, the edge computing device is the raw dataset Additional synchronization timestamp And will be accompanied by a synchronization timestamp. The original dataset Store in edge local data cache ;

[0126] Sub-step Edge local data cache The data set stored in the middle constitutes the running data, which is used for the fusion analysis in step three;

[0127] Step 3: The edge computing device preprocesses the collected operational data and calculates the collaborative working status between devices by fusing and analyzing the operational data.

[0128] Step three further includes:

[0129] Sub-step From edge local data cache Extract runtime data from the runtime data; runtime data composition Taiwan intelligent medical device at system sampling time Synchronized dataset ;

[0130] Sub-step For synchronized datasets Sensor feedback information Perform data normalization preprocessing to generate normalized data. The calculation formula for data normalization preprocessing is:

[0131] ,

[0132] in, For the first The equipment is in Normalized data at any given time For the first The minimum preset range of the sensor on the device. For the first The preset maximum range of the sensor on the device;

[0133] Sub-step Construct a collaborative working state model among multiple intelligent medical devices. Collaborative work status model At the system sampling time state vector Normalized data from all devices Composition, state vector The expression is:

[0134] ,

[0135] in, For state vectors, This represents the total number of intelligent medical devices. Transpose of a vector;

[0136] Sub-step Define the baseline state vector of the collaborative working state model. Reference state vector This represents the ideal state when multiple intelligent medical devices work together;

[0137] Sub-step Calculate the state vector With reference state vector Cooperative deviation between Cooperative deviation This refers to the quantitative indicator of collaborative work status, specifically the collaborative deviation. The calculation formula is:

[0138] ,

[0139] in, For coordination bias, For the first Preset collaboration weights for the devices Reference state vector The first in Quantity;

[0140] Sub-step Set a cooperative deviation threshold Edge computing devices based on collaborative deviation With the cooperative deviation threshold The comparison determines the current collaborative work status. ;

[0141] Sub-step When in collaborative working state The determination criteria are:

[0142] ,

[0143] Among them, collaborative work status This provides input for monitoring and linkage failure risks in step four;

[0144] Step 4: The edge computing device monitors the risk of linkage failure based on the collaborative working status. When a linkage failure risk is detected, a digital twin model is used to make a virtual prediction to determine the scope of the extended impact of the linkage failure risk.

[0145] Step four further includes:

[0146] Sub-step Receive the collaborative work status determined in step three. And set trigger conditions for linkage fault risk monitoring. ;

[0147] Sub-step Triggering conditions for linkage fault risk monitoring For: When in collaborative working state In case of collaborative anomalies, edge computing devices initiate linkage fault risk monitoring;

[0148] Sub-step When the linkage fault risk monitoring trigger condition When the conditions are met, the edge computing device calculates the collaborative deviation. The contribution of each intelligent medical device in China Equipment contribution The calculation formula is:

[0149] ,

[0150] in, For the first Preset collaboration weights for the devices For the first Normalized data from the equipment. Reference state vector The first in Quantity;

[0151] Sub-step Determine the contribution of equipment Largest equipment For the source of abnormal equipment Abnormal equipment at the source index The formula for determining it is:

[0152] ,

[0153] Among them, abnormal equipment at the source This constitutes a risk of interconnected failures;

[0154] Sub-step Load the constructed digital twin models of multiple smart medical devices and obtain the preset interdependency matrix between the smart medical devices. Interdependence matrix elements Indicates device For equipment The influence coefficient;

[0155] Sub-step Define the source of the abnormal device abnormal state Abnormal state From the source of abnormal equipment Normalized data express;

[0156] Sub-step Edge computing devices perform virtual predictions within digital twin models, utilizing abnormal states. With the interdependency matrix Calculate the risk of linked failures to other equipment Fault propagation ;

[0157] Sub-step Fault propagation probability The calculation formula is:

[0158] ,

[0159] in, For the preset fault propagation function, For the abnormal equipment at the source For equipment Influence coefficient, To predict the time step;

[0160] Sub-step Set a fault propagation threshold Edge computing devices propagate all fault probabilities Greater than the fault propagation threshold equipment The equipment was identified as affected.

[0161] Sub-step The collection of all affected devices This constitutes the extended impact range of the risk of linked failures. The determination criteria are:

[0162] ,

[0163] Expanding the scope of influence Used to confirm the risk of linkage failure in step five;

[0164] Step 5: Based on the expanded impact range, confirm the risk of linkage failure, and the edge computing device sends an immediate warning signal. The edge computing device automatically adjusts the device parameters of the smart medical device through the emergency response mechanism.

[0165] Step five further includes:

[0166] Sub-step The extended scope of influence determined in step four. Expand the scope of influence Includes all affected devices;

[0167] Sub-step Edge computing devices expand their influence range. Calculate the comprehensive risk level of the risk of linkage failure. Overall risk level The calculation formula is:

[0168] ,

[0169] in, To expand the scope of influence The Middle Preset criticality weights for affected equipment in Taiwan. For the calculation of the first The probability of fault propagation in affected equipment;

[0170] Sub-step Set risk confirmation thresholds Determining the risk of failure in the execution of confirmation linkage by edge computing devices ,determination The conditions are:

[0171] when The risk of linkage failure has been confirmed;

[0172] when ≤ The risk of linkage failure has not been confirmed.

[0173] Sub-step When judged When the result is confirmed, the edge computing device generates an immediate warning signal. Real-time early warning signal Includes comprehensive risk level and expanding the scope of influence Edge computing devices will provide real-time warning signals. Sending data to the terminal devices of medical personnel;

[0174] Sub-step When judged When the result is confirmed, the edge computing device activates the emergency response mechanism, which is based on the comprehensive risk level. and expanding the scope of influence Load the corresponding emergency adjustment strategy ;

[0175] Sub-step Emergency adjustment strategy To expand the scope of influence Each of the affected devices Calculate new equipment parameters New equipment parameters The calculation formula is: ,

[0176] in, For affected equipment Current device parameters This is a preset parameter adjustment function. Based on comprehensive risk level With key weight Calculate the parameter adjustment amount;

[0177] Sub-step Edge computing devices are expanding their influence. Affected devices Issue parameter adjustment commands to apply the new device parameters. It automatically adjusts the device parameters of intelligent medical equipment.

[0178] By constructing a digital twin model that integrates equipment mechanisms and data-driven corrections, and introducing real-time fidelity verification and online update mechanisms, a virtual image that is highly synchronized with the physical equipment and can dynamically adapt can be created. This provides a simulation and deduction platform for subsequent fault prediction, ensuring the accuracy of virtual prediction results, which is the fundamental basis for achieving accurate early warning.

[0179] By performing unified data collection, validity and integrity checks on edge computing devices, and adding synchronized timestamps, it is ensured that all data entering the analysis phase is of high quality. The reliability of the input is guaranteed at the data source, avoiding misjudgments due to data quality issues, providing a solid data foundation for subsequent multi-device collaborative status analysis, and local caching creates conditions for low-latency processing.

[0180] By normalizing and fusing data from multiple devices on edge computing devices, quantitative indicators of collaborative working status are calculated. This transforms the monitoring of individual device status into a holistic assessment of the collaborative relationship between device systems. Furthermore, by performing calculations at the edge, the data transmission bandwidth and computational pressure on the central server are greatly reduced, thus solving the efficiency bottleneck of traditional architectures.

[0181] When a collaborative anomaly is detected, the source of the anomaly is located by calculating the contribution of each device. A digital twin model and a pre-defined interdependency matrix are used for virtual prediction to estimate the extent of the fault's spread. This achieves an intelligent upgrade from anomaly detection to consequence prediction, enabling the identification of the root cause of the problem and the proactive prediction of the fault's propagation path and impact range. It elevates early warning from simple status alerts to precise risk assessment.

[0182] By comprehensively quantifying the risk level of the predicted expanded impact range, a tiered early warning and automated emergency response are triggered based on this assessment, constructing a complete process from risk confirmation to closed-loop management. This enables rapid and intelligent emergency intervention. On the one hand, it provides medical personnel with key decision-making information including risk level and impact range; on the other hand, by automatically adjusting relevant equipment parameters, it can proactively suppress the occurrence or spread of malfunctions in their early stages, truly achieving the ultimate goal of ensuring patient safety. This is accomplished at the edge, ensuring the immediacy of the emergency response.

[0183] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A medical emergency early warning method based on digital twin technology, characterized in that, include: Step 1: Construct digital twin models of multiple smart medical devices, including: A device mechanism model is constructed based on the hardware parameters of the intelligent medical device; A data-driven deviation correction model is constructed based on the usage environment data of the aforementioned intelligent medical device; The device mechanism model and the deviation correction model are fused to construct the digital twin model, and the virtual state is output. Further includes: Set the model fidelity threshold; The real-time fidelity of the virtual state is calculated based on the actual operating data of the intelligent medical device. When the real-time fidelity is less than the model fidelity threshold, the online update of the deviation correction model is triggered. Step two involves using edge computing devices to collect operational data from multiple smart medical devices, including: Determine the set of devices and configure the data sampling period for each smart medical device; The sampling time of the unified system is determined based on the minimum value in each data sampling period; At the system sampling time, the operating data of each intelligent medical device in the device set is collected in a polling manner to form the original dataset corresponding to each intelligent medical device; The validity of the sensor feedback information in the original dataset is determined. Perform a data integrity check on the original dataset; When the data validity and data integrity both meet the preset conditions, a unified synchronization timestamp is added to the original dataset. Write the original dataset with the unified synchronization timestamp appended to it into the edge local data cache; Step 3: Calculate the collaborative working status among multiple smart medical devices based on the synchronized dataset, including: Data with the same synchronization timestamp are extracted from the edge local data cache to form a synchronization dataset; The sensor feedback information in the synchronized dataset is normalized to obtain normalized data; Construct a multi-device state vector based on the normalized data; Define a reference state vector corresponding to the multi-device state vector; Calculate the coordinated deviation between the multi-device state vector and the reference state vector; The current collaborative working state is determined based on the comparison result between the collaborative deviation and the preset collaborative deviation threshold. Step four: When the collaborative working state is in a collaborative anomaly state, perform a linkage failure risk analysis, including: The device contribution of each intelligent medical device is calculated based on the aforementioned collaborative deviation. The source of the abnormal device is determined based on the contribution of the aforementioned device. Obtain the interdependency matrix among multiple intelligent medical devices; Based on the normalized data corresponding to the source malfunction device and the interdependence matrix, the digital twin model is used to perform virtual simulation to calculate the failure propagation probability of the source malfunction device spreading the fault to other intelligent medical devices. The set of affected devices is determined based on the fault propagation probability, and the set of affected devices is used as the extended scope of influence. Step 5, based on the expanded scope of impact, conduct risk assessment and emergency response, including: The comprehensive risk level is calculated based on the preset criticality weights of each affected device in the extended impact range and the corresponding failure propagation probability. The overall risk level is compared with a preset risk confirmation threshold; When the overall risk level exceeds the preset risk confirmation threshold, an immediate warning signal containing the overall risk level and the extended impact range is generated and sent to the medical personnel's terminal. Activate the emergency response mechanism; The emergency response mechanism includes: Based on the comprehensive risk level and the expanded scope of impact, an emergency adjustment strategy is loaded; Calculate the new equipment parameters for the affected equipment based on the emergency adjustment strategy; A parameter adjustment command is sent to the affected equipment to achieve automatic adjustment of the equipment parameters.

2. The medical emergency early warning method based on digital twin technology according to claim 1, characterized in that, The step of determining the current collaborative working state based on the comparison result between the collaborative deviation and the preset collaborative deviation threshold includes: when the collaborative deviation is less than or equal to the preset collaborative deviation threshold, the current collaborative working state is determined to be normal; when the collaborative deviation is greater than the preset collaborative deviation threshold, the current collaborative working state is determined to be abnormal.

3. The medical emergency early warning method based on digital twin technology according to claim 1, characterized in that, The step of determining the source abnormal device based on the device contribution includes: comparing the device contribution of each intelligent medical device, and determining the intelligent medical device with the largest device contribution as the source abnormal device.

4. A medical emergency early warning method based on digital twin technology according to claim 1, characterized in that, Based on the overall risk level and the expanded impact range, a corresponding emergency adjustment strategy is determined; based on the emergency adjustment strategy, new equipment parameters corresponding to the affected equipment are determined; based on the new equipment parameters, a parameter adjustment instruction is generated and sent to the corresponding affected equipment.

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