First aid electric stretcher intelligent monitoring linkage method and system

Abstract

This invention discloses an intelligent monitoring and linkage method and system for emergency electric stretchers, relating to the field of intelligent monitoring and linkage technology for emergency electric stretchers, including an edge acquisition terminal and an emergency cloud dispatch terminal. The edge acquisition terminal is deployed on the emergency electric stretcher and integrates a vital signs sensor group, a posture sensor group, a road environment sensor group, an edge computing unit, a drive-by-wire execution component, and a dual-redundant communication module. It can collect real-time data on patient vital signs, stretcher operating posture, and road bump conditions during transport. The edge side automatically identifies the risk level and matches the corresponding treatment strategy to trigger the drive-by-wire component to adjust the stretcher height, tilt angle, and shock absorption parameters. This invention can significantly improve the speed of transport risk response, avoid secondary injuries to patients during transport, reduce the operational burden on accompanying medical staff, bridge the information gap between pre-hospital transport and in-hospital treatment, improve multi-terminal collaboration efficiency, and reduce the overall response time of the entire treatment process.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring and linkage technology for emergency electric stretchers, and in particular to an intelligent monitoring and linkage method and system for emergency electric stretchers. Background Technology

[0002] Pre-hospital emergency transport is a core component of the public emergency medical system, directly determining the success rate of treating critically ill patients. In recent years, the coverage of my country's pre-hospital emergency medical service network has continued to improve. As the core carrier in the transport process, the demand for intelligent upgrading of electric stretchers is becoming increasingly urgent. The industry's core demands are focused on three directions: reducing the risk of secondary injury during transport, connecting pre-hospital and in-hospital information links, and reducing the response time of the entire emergency process.

[0003] The most common technology currently available is the electric stretcher with monitoring capabilities. This type of solution typically integrates a basic vital signs sensor module into the stretcher itself, which can collect real-time data on the patient's heart rate, blood oxygen saturation, respiratory rate, and other basic vital signs, displaying this data locally on a side-mounted display module. Its working principle involves directly outputting visualized results after collecting raw data via contact or non-contact sensors, operating without relying on external communication links. It is widely deployed in emergency centers at all levels, meeting basic vital signs monitoring needs during transport and requiring minimal operation. However, this type of solution only has data collection and display capabilities; it cannot autonomously identify risks during transport such as stretcher tilting, road bumps, or sudden changes in patient vital signs. It also cannot automatically adjust parameters such as stretcher support posture, shock absorption damping, and restraint tension. Risk management relies entirely on manual operation by accompanying medical personnel, resulting in high response delays and increasing the risk of falls, aggravated spinal injuries, and secondary injuries such as aspiration.

[0004] Pre-hospital emergency cloud dispatch systems represent the second type of commonly used technical solution. These systems are typically deployed in the emergency center's cloud environment, allowing emergency personnel to manually report patient conditions, estimated arrival times, and other information, which is then simultaneously pushed to the medical institutions awaiting transport. The working principle involves manually entering information, transmitting it to the cloud via the public network, and then distributing it to the receiving terminals at the corresponding medical institutions. This enables the linkage of basic information between pre-hospital and in-hospital care, helping hospitals prepare emergency resources in advance. However, this type of solution relies entirely on manually entered data, resulting in delayed information updates. It cannot synchronize core dynamic data such as the real-time status of the stretcher, road conditions, and patient vital signs, and it cannot link with the control logic at the stretcher end. In emergencies such as traffic congestion or sudden changes in patient condition, it cannot adjust dispatch strategies or push accurate resource preparation prompts in sync, easily leading to insufficient in-hospital emergency preparedness and missing the golden window for treatment.

[0005] The two types of technologies mentioned above have long operated independently without forming a full-link linkage of perception, control, and scheduling, which has become a core bottleneck restricting the improvement of the efficiency and safety of pre-hospital emergency transport. Summary of the Invention

[0006] This invention proposes an intelligent monitoring and linkage method and system for emergency electric stretchers to solve the problems mentioned in the prior art, such as the inability of existing emergency electric stretchers to automatically handle transfer risks and the lag in pre-hospital and in-hospital information linkage.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: an intelligent monitoring and linkage method for electric stretchers used in out-of-hospital emergency transport scenarios, comprising the following steps: S1. Real-time acquisition of multi-source data: The multi-dimensional sensor array acquires data of the corresponding dimension according to the preset sampling frequency. Among them, the vital sign acquisition element embedded in the stretcher padding acquires vital sign data such as heart rate, blood oxygen, respiratory rate, and blood pressure of the transported person at a frequency of 10Hz. The attitude acquisition element fixed in the middle of the stretcher support frame acquires running attitude data such as the three-dimensional tilt angle and motion acceleration of the stretcher at a frequency of 100Hz. The road surface acquisition element installed on the stretcher legs near the wheels acquires road surface condition data such as vertical vibration acceleration of the road surface at a frequency of 50Hz. The traffic acquisition element connected to the vehicle networking module of the emergency vehicle synchronously acquires traffic status data around the emergency vehicle at a frequency of 1Hz, such as surrounding road conditions, signal permissions, and estimated travel time. S2, Edge-side anomaly classification identification: The embedded edge computing terminal mounted on the stretcher first performs real-time preprocessing on the collected multi-source data, including noise reduction, spatiotemporal alignment, and missing value completion. Then, it identifies the abnormal risk level by comparing it with the risk classification threshold preset based on clinical emergency guidelines and stretcher operation safety standards. The identification latency is less than 100ms. If a high-risk abnormality is identified, the entire link linkage response is immediately triggered. S3. Local linkage control at the stretcher end: The edge computing terminal sends the highest priority control command to the wired execution component of the emergency electric stretcher with a latency of less than 50ms. The control command is matched one by one with the abnormality type, and the stretcher operating parameters are adjusted to eliminate the safety hazards corresponding to high-risk abnormalities in an instant. The priority of the control command is only lower than the manual control command of the accompanying medical staff. S4. Cross-terminal linkage dispatch: The edge computing terminal synchronously uploads high-risk abnormal data, full vital sign data of the transferred person, and real-time operation data of the stretcher to the distributed emergency cloud platform through the 5G / 4G dual-link redundant transmission channel. The emergency cloud platform then issues linkage dispatch instructions within the corresponding permissions to the on-board medical terminal, the emergency receiving terminal of the medical institution to be transported, and the emergency vehicle navigation terminal based on role and permission division. The cloud platform response latency is less than 200ms.

[0008] Furthermore, the risk classification threshold in step S2 is specifically divided into three levels: low risk, medium risk, and high risk. Low risk means that the fluctuation of the collected data is within ±5% of the safety threshold, and no action is required except for storage and recording. Medium risk means that the fluctuation of the collected data is between ±5% and ±20% of the safety threshold, and there is no immediate safety risk, only the attention of medical staff is required. High risk abnormalities include any one of the vital signs of the transported person exceeding the clinical emergency safety threshold, the stretcher tilting angle exceeding 15 degrees to the left or right or 20 degrees to the front or back, the vertical bump acceleration of the road exceeding 1.5g, and any situation where the estimated travel time ahead of the emergency vehicle exceeds the original estimated travel time by more than 30%.

[0009] Furthermore, the stretcher operating parameters adjusted in step S3 include at least one of the following: stretcher support angle, damping coefficient of the shock absorption module, and tension of the restraint straps. The adjustment logic corresponds one-to-one with the abnormality type. If it is an abnormal posture, the target support angle is adjusted to within ±5 degrees horizontally or to a position angle suitable for the patient's condition. If it is an abnormal road surface bump, the corresponding damping coefficient is matched to counteract vertical vibration. If it is an abnormal vital signs, the tension of the restraint straps is adjusted to match the support angle to the patient's emergency position. After the parameters are adjusted, the edge computing terminal continuously collects the corresponding data after adjustment for 3 seconds to verify the effect of eliminating the hidden danger. If the data returns to the safety threshold for 3 consecutive seconds, the hidden danger is determined to be eliminated. If it is not eliminated, the optimal parameters are recalculated and an adjustment command is issued. The maximum number of attempts is 3. If the hidden danger is not eliminated after 3 attempts, the audible and visual alarm at the stretcher end is triggered to prompt the accompanying medical staff to intervene manually.

[0010] Furthermore, the coordinated dispatch instructions issued in step S4 specifically include three categories. The first category pushes high-risk abnormality types, vital sign trend curves, and stretcher adjustment parameters to the onboard medical terminal, while triggering an audible and visual warning on the terminal, and supporting medical staff to manually input treatment records and simultaneously transmit them back to the emergency cloud platform. The second category pushes the basic identity information of the person being transported, vital sign data throughout the transport process, preliminary disease prediction results, required emergency equipment, personnel, and blood supply preparation prompts to the emergency receiving terminal of the medical institution to be transported, and simultaneously updates the estimated delivery time, assisting the medical institution in reserving rescue channels and emergency resources in advance. The third category pushes route optimization instructions to the emergency vehicle navigation terminal, combining real-time traffic conditions and emergency priority passage permissions to select the transport route with the shortest travel time, and the adjusted route is simultaneously transmitted back to the emergency cloud platform and the medical institution terminal to update the estimated delivery time.

[0011] Furthermore, if a medium-risk anomaly is identified in step S2, the edge computing terminal will only send a text warning to the on-board medical terminal without triggering stretcher control actions or cross-terminal scheduling operations. At the same time, the edge computing terminal will collect data on the corresponding abnormal dimension every 2 seconds to track the trend of change. If the data returns to the safety threshold for 5 consecutive seconds, the warning will be automatically canceled. If the data continues to rise to the high-risk threshold, a high-risk full-link linkage response will be triggered immediately.

[0012] Furthermore, before the multi-dimensional sensor array starts data acquisition, the edge computing terminal automatically performs timestamp synchronization and deviation calibration on all sensors. Timestamp synchronization uses the unified clock source of the edge computing terminal to mark the acquired data of all sensors, eliminating the data acquisition delay of different sensors. Deviation calibration corrects the original acquired data by pre-stored factory calibration coefficients of each sensor, eliminating measurement deviation. After each calibration is completed, a calibration record is generated and synchronized to the emergency cloud platform for storage. If the sensor calibration deviation exceeds the preset allowable range, a sensor fault alarm is triggered, prompting medical staff to check and replace the sensor.

[0013] Furthermore, an intelligent monitoring and linkage system for an electric emergency stretcher applied to outpatient emergency transport scenarios includes a multi-dimensional sensor array mounted on the electric emergency stretcher, an embedded edge computing terminal, a wire-controlled stretcher execution component, and a distributed emergency cloud platform deployed in the cloud, as well as a vehicle-mounted medical terminal, a medical institution emergency receiving terminal, and an emergency vehicle navigation terminal that are connected to the emergency cloud platform via an encrypted link. The multi-dimensional sensor array is installed at the corresponding acquisition position of the emergency electric stretcher to acquire multi-dimensional raw data at a preset frequency and transmit it to the edge computing terminal. The edge computing terminal is fixed inside the waterproof protective shell on the side of the stretcher, supports wide temperature operation, and is used to preprocess multi-source data and identify risk classification. After triggering the linkage response, it sends the highest priority control command to the stretcher execution component, and uploads abnormal data and full monitoring data to the emergency cloud platform. The stretcher execution component is integrated into the main structure of the stretcher and is used to receive control commands to complete the corresponding operating parameter adjustment and to send the adjusted actual parameters back to the edge computing terminal for verification. The emergency cloud platform receives data and, based on preset role and permission divisions, issues coordinated dispatch instructions within the corresponding permissions to each associated terminal, supporting the simultaneous access of multiple concurrent emergency transport tasks.

[0014] Furthermore, the multi-dimensional sensing array specifically includes a piezoelectric vital sign monitoring sensor, a six-axis attitude sensor, a high-precision road bumpiness sensor, and a vehicle network traffic status receiving module. Each sensing module is connected to the edge computing terminal via Bluetooth Low Energy. The piezoelectric vital sign monitoring sensor is distributed on the stretcher pads corresponding to the chest, back, and wrist of the human body, and can collect vital sign data non-contactly without the need to attach electrode pads. The six-axis attitude sensor integrates an accelerometer and a gyroscope, which can accurately measure the three-dimensional tilt angle and motion acceleration of the stretcher; the road bump sensor collects the vertical vibration acceleration of the stretcher during the transfer; the traffic status receiving module interfaces with the V2X vehicle networking module of the emergency vehicle to obtain surrounding traffic data in real time; all sensing modules adopt a low power consumption design and can run continuously for more than 12 hours on a single full charge.

[0015] Furthermore, the stretcher execution components include an electric attitude adjustment module, an adjustable magnetorheological damping and shock absorption module, and an electric restraint belt control module. Each execution component is wired to the edge computing terminal via a CAN bus, ensuring stable and interference-free communication. The electric posture adjustment module is driven by a DC servo motor, which can continuously adjust the back and leg support angle of the stretcher from 0 to 90 degrees, and adjust the left and right tilt angle by ±20 degrees, with an adjustment accuracy of up to 1 degree. The adjustable magnetorheological damping shock absorption module can complete the damping coefficient adjustment within 10ms, with a damping adjustment range covering 0 to 1000 N·s / m, adapting to road surfaces with different degrees of bumpiness; the electric restraint belt control module is driven by a stepper motor, with a tension adjustment range covering 5N to 50N, which can be dynamically adjusted according to the patient's body shape and stretcher posture; each execution component has a built-in position feedback sensor, and after adjustment, the actual operating parameters are sent back to the edge computing terminal for verification.

[0016] Furthermore, the emergency cloud platform incorporates a national cryptographic algorithm encryption module. All transmitted monitoring data and dispatch instructions are encrypted end-to-end using the national cryptographic SM2 algorithm. Terminal authentication employs a dual authentication method combining a hardware UKey and an account password. Before transmission, all data is encrypted by the sending end using the receiving end's public key, and decrypted by the receiving end using the corresponding private key. Data storage uses the national cryptographic SM4 algorithm for encryption. Only authorized medical personnel and emergency dispatchers can query data within their respective permissions. All data access operations generate audit logs that are synchronized to the cloud platform for retention, allowing for full traceability of data access records and preventing the leakage of patient privacy data and malicious tampering of dispatch instructions.

[0017] Compared with existing technologies, the beneficial effects of this invention are: This invention utilizes a technical approach of simultaneous multi-source data acquisition at the edge, intelligent risk level identification, and automatic linkage of the stretcher-end wired control components. This allows for rapid identification of risks during transport, such as abnormal vital signs, abnormal stretcher posture, and abnormal road surface disturbances, without human intervention. It automatically matches corresponding adjustment strategies to optimize stretcher operating parameters. The risk identification and response speed is far superior to manual operation. Furthermore, a closed-loop verification mechanism can validate the adjustment effect, triggering a manual alarm only when automatic handling proves ineffective. This effectively avoids secondary injuries such as patient falls, worsening of injuries, and aspiration, while significantly reducing the operational burden on accompanying medical staff. It also addresses the shortcomings of existing electric stretchers, which can only display monitoring data locally, cannot proactively handle risks, and have delayed manual operation responses.

[0018] This invention utilizes a dual-redundant communication link and a cloud-based multi-role permission-based scheduling system to automatically synchronize all transport data to the emergency cloud platform when high-risk anomalies are triggered. It pushes targeted scheduling instructions based on the permissions of different terminals, enabling real-time sharing of transport dynamics without manual input. This assists emergency vehicles in automatically optimizing their routes and helps receiving medical institutions prepare corresponding emergency resources in advance. It effectively bridges the information gap between pre-hospital transport and in-hospital treatment, solving the shortcomings of existing scheduling systems that rely on manual reporting, have delayed information updates, and suffer from low efficiency in multi-terminal collaboration. This significantly reduces the overall response time for critically ill patients.

[0019] The sensing and control modules of this invention adopt a modular design, which can be directly adapted to existing mainstream models of electric emergency stretchers for upgrades and modifications without replacing the original equipment, resulting in lower modification costs. At the same time, the risk classification rules and treatment logic fully comply with current pre-hospital emergency operation standards, without changing the routine operating procedures of medical staff. It can be adapted to various application scenarios such as emergency centers at all levels, road rescue teams, and air medical transport, and has high promotional value. The built-in full-link national cryptographic encryption mechanism can ensure the security of medical data transmission and storage, meet the requirements for medical data compliance management, and avoid the risk of patient privacy leakage. Attached Figure Description

[0020] Figure 1 This invention presents a complete workflow diagram of the intelligent monitoring and linkage system for emergency electric stretchers. Figure 2 This is a flowchart of the multi-source data sensing and edge-side preprocessing proposed in this invention; Figure 3 This is the logic diagram of the local linkage control of the stretcher under high-risk anomalies proposed in this invention; Figure 4 This is a flowchart of the cross-terminal linkage scheduling and early warning push of the emergency cloud platform proposed in this invention; Figure 5 This is a flowchart of the national cryptographic encryption transmission and full-link access auditing process proposed in this invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Reference Figures 1 to 5 This invention discloses an intelligent monitoring and linkage method for electric stretchers used in out-of-hospital emergency transport scenarios, comprising the following steps: S1. Real-time acquisition of multi-source data: The multi-dimensional sensor array sets the sampling frequency according to the clinical emergency needs and dynamic monitoring requirements to carry out data acquisition. Among them, the vital sign acquisition element embedded in the stretcher padding collects four core vital sign data of the transported person at a frequency of 10Hz: heart rate, blood oxygen, respiratory rate and non-invasive blood pressure. This sampling frequency meets the minimum sampling requirements of the "Pre-hospital Emergency Vital Sign Monitoring Specifications" and can cover the capture needs of normal physiological fluctuations and abnormal mutations. The attitude acquisition element, fixed at the geometric center of the stretcher support frame, acquires two types of running attitude data at a frequency of 100Hz: three-dimensional tilt angle and three-axis motion acceleration. The high sampling frequency can capture transient tilting and swaying movements of less than 100ms, avoiding abnormal missed detection. The road surface acquisition element, installed 5cm from the wheel on the left front outrigger of the stretcher, collects vertical vibration acceleration data of the road surface at a frequency of 50Hz, which conforms to the Nyquist sampling law and can fully cover the normal road surface bump frequency range of 0-25Hz. The traffic acquisition element of the V2X vehicle-to-everything (V2X) module connected to the emergency vehicle synchronously acquires three types of traffic status data at a frequency of 1Hz: surrounding road conditions, signal priority, and estimated travel time. This sampling frequency can match the time granularity of road condition updates without requiring excessive sampling to occupy communication bandwidth.

[0023] S2. Edge-side anomaly classification and identification: The embedded edge computing terminal mounted on the stretcher first performs real-time preprocessing on the collected multi-source data. Denoising uses the db4 wavelet soft threshold denoising algorithm to filter inherent sensor noise and vibration interference. Spatiotemporal alignment unifies the raw data from different sampling frequencies to a time slice with a granularity of 100ms. Missing value completion uses a linear interpolation algorithm of the first 3 valid values ​​to supplement occasional packet loss data. After preprocessing, the abnormal risk level is identified by comparing it with the preset risk classification threshold. The overall identification latency is less than 100ms, which meets the latency requirements of hard real-time control. If a high-risk anomaly is identified, the entire link linkage response is immediately triggered. The priority of the automatic control command is only lower than the manual physical switch control command of the accompanying medical staff. When the manual switch is triggered, all automatic control logic is immediately terminated to ensure the priority of the medical staff's right of handling.

[0024] S3. Local linkage control at the stretcher end: The edge computing terminal sends the highest priority control command to the wired execution component of the emergency electric stretcher via the CAN bus with a delay of less than 50ms. The control command is mapped and matched one by one with the abnormality type, and the stretcher operating parameters are adjusted to eliminate the safety hazards corresponding to high-risk abnormalities in real time. After the parameters are adjusted, the execution component sends back the actual operating parameters to the edge computing terminal in real time for closed-loop verification.

[0025] S4. Cross-terminal linkage scheduling: The edge computing terminal synchronously uploads high-risk abnormal data, full vital signs data of the transferred person, and real-time stretcher operation data to the distributed emergency cloud platform through a dual redundant transmission channel with 5G as the main link and 4G as the backup link. The transmission adopts the ARQ automatic retransmission mechanism, and the timeout retransmission time is set to 200ms to ensure the success rate of data transmission. Based on preset role and permission division rules, the emergency cloud platform issues corresponding linkage dispatch instructions to the onboard medical terminal, the emergency receiving terminal of the medical institution to be transported, and the emergency vehicle navigation terminal. The cloud platform's single-task response latency is less than 200ms, and it supports the concurrent processing of 1,000 transport tasks.

[0026] This invention also discloses the specific rules for classifying the risk grading thresholds. The risk grading thresholds are specifically divided into three levels: low risk, medium risk, and high risk. The grading rules are all based on the "Guidelines for Safety of Pre-hospital Emergency Transport" and the "Technical Standards for Safety of Electric Stretchers". Among them, low risk means that the fluctuation of the collected data is within ±5% of the safety threshold, which is a normal physiological fluctuation or operational error and requires no treatment but only dual storage and recording in the local and cloud. Medium risk means that the fluctuation of the collected data is between ±5% and ±20% of the safety threshold, which has no immediate safety risk and only requires continuous monitoring by medical staff. High-risk abnormalities include any of the patient's vital signs exceeding the clinical emergency safety threshold, stretcher tilt angle exceeding 15 degrees to the left or right or 20 degrees to the right or left, vertical road bump acceleration exceeding 1.5g, or the estimated travel time ahead of the emergency vehicle's route exceeding the original estimated travel time by more than 30%. Among these, the 15-degree tilt threshold is the clinically validated critical value for the risk of patient slippage, the 20-degree tilt threshold is the critical value for the risk of aspiration, the 1.5g bump threshold is the critical value for the risk of aggravated spinal injury, and the 30% increase in travel time is the critical value for the delay of the golden time for emergency treatment.

[0027] This invention uses a quantitative risk value formula to determine the risk level. The specific formula is as follows: ;In the formula, For the comprehensive risk value, The weight for the vital signs dimension is set to 0.4. The weight for the stretcher posture dimension is set to 0.3. The weight for the road surface condition dimension is set to 0.2. The weight for the traffic state dimension is set to 0.1. , , , The risk scores are calculated for each of the four dimensions, ranging from 0 to 10. The scores are positively correlated with the proportion of individual data exceeding the safety threshold. A comprehensive risk value of R<3 is considered low risk, 3≤R<7 is considered medium risk, and R≥7 is considered high risk.

[0028] This invention also discloses the specific adjustment and verification logic of the stretcher end operating parameters. The adjusted stretcher operating parameters include at least one of the following: stretcher support angle, damping coefficient of shock absorption module, and tension of restraint belt. The adjustment logic is bound to the abnormality type: if it is an abnormal posture, the target support angle is adjusted to within ±5 degrees horizontally or to a special body position angle that is suitable for the patient's condition, such as adjusting to 0 degrees horizontally for patients with spinal injuries and adjusting to 30 degrees upper body elevation for patients with respiratory distress. If the road surface is abnormally bumpy, the corresponding damping coefficient is matched to counteract the vertical vibration. The greater the bump acceleration, the higher the damping coefficient should be set. If the vital signs are abnormal, the tension of the restraint straps and the support angle are adjusted to match the patient's emergency position. For example, for patients with heart failure, the chest and abdominal restraint straps should be appropriately loosened to avoid compressing breathing.

[0029] After the parameters are adjusted, the edge computing terminal collects the corresponding data for 3 consecutive seconds to verify the elimination of the hidden danger. If the data returns to the safety threshold for 3 consecutive seconds, the hidden danger is determined to be eliminated. If it is not eliminated, the optimal parameters are recalculated and the adjustment command is issued. The maximum number of attempts is 3. If the hidden danger is not eliminated after 3 attempts, the 80dB buzzer alarm and the red LED flashing alarm at the stretcher end are triggered to prompt the accompanying medical staff to intervene manually.

[0030] In this invention, the specific execution logic of the cross-terminal linkage dispatch instruction is also disclosed. The issued linkage dispatch instruction is specifically divided into three categories: The first category pushes high-risk abnormality types, the vital sign trend curve of the past 5 minutes, and the parameters after stretcher adjustment to the medical tablet terminal held by the accompanying medical staff, and at the same time triggers the terminal's audio and visual warning, supports medical staff to manually input treatment records, such as "oxygen has been given", and the treatment records are simultaneously transmitted back to the emergency cloud platform for storage; The second type pushes the basic identity information of the person to be transferred, the vital signs data throughout the transfer process, the preliminary AI condition prediction results, the required emergency equipment, personnel, and blood supply preparation prompts to the emergency department dispatch terminal of the medical institution to be sent, and updates the estimated delivery time in real time to help the medical institution reserve rescue channels and allocate emergency resources in advance. The third type pushes route optimization instructions to the vehicle navigation terminal of the emergency vehicle. Combining real-time traffic conditions and the traffic police's priority passage authority for emergency medical services, it selects the transfer route with the shortest travel time. The adjusted route is then simultaneously transmitted back to the emergency cloud platform and the medical institution's terminal to update the estimated delivery time.

[0031] This invention also discloses the handling logic for medium-risk anomalies. In step S2, if a medium-risk anomaly is identified, the edge computing terminal only sends a text warning to the on-board medical terminal and does not trigger stretcher control actions or cross-terminal scheduling operations to avoid unnecessary resource allocation. At the same time, the edge computing terminal collects data on the corresponding anomaly dimension every 2 seconds to track the trend of change. If the data returns to the safety threshold for 5 consecutive seconds, the warning is automatically canceled. If the data continues to rise to the high-risk threshold, a high-risk full-link linkage response is immediately triggered.

[0032] For example, if a patient's blood oxygen saturation is detected to be 92%, which is below the normal threshold of 95% but above the high-risk threshold of 90%, only a text message will be pushed out saying "Blood oxygen is slightly low, please pay attention". If the blood oxygen continues to drop to 90%, a high-risk response will be triggered immediately. If it rises back to above 95%, the alarm will be automatically deactivated.

[0033] This invention also discloses a pre-calibration logic for a multi-dimensional sensor array. Before the multi-dimensional sensor array starts data acquisition, the edge computing terminal automatically performs timestamp synchronization and deviation calibration on all sensors: timestamp synchronization adopts the PTP precise time protocol, using a unified clock source obtained by the edge computing terminal from GPS synchronization to mark the acquired data of all sensors, eliminating data acquisition delays between different sensors, with a time synchronization error of less than 1ms; deviation calibration corrects the original acquired data using pre-stored factory calibration coefficients for each sensor, with the calibration formula being: ;In the formula, For the corrected data, This is the original collected data. This refers to the sensor's factory calibration coefficient. Both the sensor's factory offset and the calibration deviation are measured on a standard calibration bench and burned into the edge computing terminal for storage when the sensor leaves the factory. After each calibration, a calibration record is generated and synchronized to the emergency cloud platform for storage. If the sensor calibration deviation exceeds the preset allowable range, such as the attitude sensor calibration deviation exceeding 0.5 degrees, a sensor fault alarm is triggered, prompting medical staff to check and replace the sensor.

[0034] This invention discloses an intelligent monitoring and linkage system for electric emergency stretchers used in out-of-hospital emergency transport scenarios. The system includes a multi-dimensional sensor array mounted on the electric emergency stretcher, an embedded edge computing terminal, a wire-controlled stretcher execution component, and a distributed emergency cloud platform deployed in the cloud. The system also includes a vehicle-mounted medical terminal, a medical institution emergency receiving terminal, and an emergency vehicle navigation terminal that are connected to the emergency cloud platform via an encrypted link.

[0035] The multi-dimensional sensor array is installed at the corresponding acquisition position of the emergency electric stretcher to acquire multi-dimensional raw data at a preset frequency and transmit it to the edge computing terminal. The edge computing terminal is fixed inside an IP67 waterproof protective shell on the side of the stretcher. It uses a TI AM2634 industrial-grade main control chip, supports wide temperature operation from -40℃ to 85℃, and has a built-in CAN bus interface, Bluetooth 5.2 interface, dual 4G / 5G communication modules. It is equipped with a 10000mAh lithium iron phosphate battery, which can run continuously for more than 12 hours on a single full charge. It is used to preprocess multi-source data and identify risk levels. After triggering the linkage response, it sends the highest priority control command to the stretcher execution components and uploads abnormal data and full monitoring data to the emergency cloud platform. The stretcher execution component is integrated into the main structure of the stretcher. It is used to receive control commands to adjust the corresponding operating parameters and send the adjusted actual parameters back to the edge computing terminal for closed-loop verification. The emergency cloud platform adopts Alibaba Cloud ECS distributed deployment, supports 1,000 emergency stretchers to access at the same time, and is used to receive data and issue linkage dispatch commands within the corresponding permissions to each associated terminal based on the preset role permission division.

[0036] This invention also discloses the specific selection and installation rules of a multi-dimensional sensor array. The multi-dimensional sensor array specifically includes a piezoelectric vital sign monitoring sensor, a six-axis attitude sensor, a high-precision road bumpiness sensor, and a vehicle network traffic status receiving module. Each sensor module is connected to the edge computing terminal via Bluetooth Low Energy 5.2.

[0037] The piezoelectric vital signs monitoring sensor uses a flexible piezoelectric thin film sensor with a thickness of only 0.2mm. It is sewn into the stretcher padding at the corresponding positions of the human chest cavity, back, and wrist. It can collect vital signs data in a non-contact manner, without the need to attach electrode pads, and does not affect medical staff in handling patients. The six-axis attitude sensor uses the MPU6050 module, with a measurement accuracy of up to 0.1 degrees. It is fixed at the geometric center of the stretcher's support frame to avoid local vibrations interfering with the measurement results. The road bumpiness sensor uses the ADXL345 accelerometer, with a range of ±16g. It is installed 5cm from the wheel on the left front outrigger of the stretcher and can accurately collect road bumpiness data. The traffic status receiving module uses a C-V2X communication module to interface with the OBU vehicle networking unit of the emergency vehicle to obtain surrounding traffic data in real time. All sensing modules adopt a low-power design and are equipped with a 2000mAh button battery, which can operate continuously for more than 12 months with a single battery replacement.

[0038] This invention also discloses the specific selection and control logic of the stretcher execution components. The stretcher execution components include an electric attitude adjustment module, an adjustable magnetorheological damping and shock absorption module, and an electric restraint belt control module. Each execution component is wired to the edge computing terminal via a CAN bus, ensuring stable communication unaffected by wireless signal interference.

[0039] The electric posture adjustment module is driven by a Panasonic MSMF012L1U2M DC servo motor with a rated torque of 1 N·m. It can support a maximum load of 200 kg and can continuously adjust the back and leg support angles of the stretcher from 0 to 90 degrees, as well as the left and right tilt angles by ±20 degrees. The adjustment accuracy can reach 1 degree, and the adjustment speed is 10 degrees / second. The adjustable magnetorheological damping shock absorber module uses the LORD MR-100 magnetorheological damper, which can complete the damping coefficient adjustment within 10ms. The damping adjustment range covers 0 to 1200 N·s / m, adapting to road surfaces with different levels of bumpiness. The electric restraint belt control module is driven by a 28BYJ-48 stepper motor, with a tension adjustment range covering 5N to 50N. It has a built-in miniature pressure sensor to provide real-time feedback of tension force, avoiding excessive tightness that could injure the patient. Each actuator has a built-in position feedback sensor, and after adjustment, the actual operating parameters are transmitted back to the edge computing terminal for verification.

[0040] This invention also discloses the encryption logic of the emergency cloud platform. The emergency cloud platform has a built-in national cryptographic algorithm encryption module. All transmitted monitoring data and dispatch instructions are encrypted end-to-end using the national cryptographic SM2 asymmetric encryption algorithm. Terminal identity authentication uses a combination of hardware UKey and account password for dual authentication.

[0041] All data is encrypted by the sender using the receiver's public key before transmission, and decrypted by the receiver using the corresponding private key. Data integrity is verified using the national cryptographic SM3 hash algorithm. If the hash verification fails, the data is determined to have been tampered with, is automatically discarded, and a retransmission is triggered. Data is encrypted using the national standard SM4 symmetric encryption algorithm. Each user is generated with a unique storage key, and only authorized medical staff and emergency dispatchers can query data within their respective permissions. All data access operations generate audit logs, which are synchronized to the cloud platform and retained for 6 months, allowing for full traceability of data access records and preventing the leakage of patient privacy data and malicious tampering of dispatch instructions. The pseudocode for the core encryption logic is as follows: # Data Sending End Encryption Process def encrypt_data(data, receiver_pub_key): hash_val = sm3_hash(data) # Generate SM3 hash checksum data_with_hash = data + hash_val encrypted_data = sm2_encrypt(data_with_hash, receiver_pub_key) #SM2 public key encryption return encrypted_data # Data receiver decryption process def decrypt_data(encrypted_data, self_pri_key): decrypted_data = sm2_decrypt(encrypted_data, self_pri_key) # Decrypt the SM2 private key origin_data = decrypted_data[:-32] # Split the original data and hash value origin_hash = decrypted_data[-32:] calc_hash = sm3_hash(origin_data) if origin_hash == calc_hash: # Verify data integrity return origin_data else: raise DataTamperedError Example 1: Transporting a spinal injury patient on a bumpy road A patient suffered a closed spinal injury in a car accident and was transferred from the accident scene to the city's central hospital by an ambulance. During the transfer, the patient had to pass through a 3km construction section with potholes and severe bumps.

[0042] The stretcher's road bumpiness sensor collected real-time data showing a maximum vertical vibration acceleration of 2.2g. A comprehensive risk value calculation yielded R=8.2, classifying it as a high-risk anomaly. The edge computing terminal immediately sent control commands to the magnetorheological damping module, adjusting the damping coefficient to 1200 N·s / m. Simultaneously, it controlled the posture adjustment module to maintain the stretcher's horizontal angle error to less than 0.5 degrees, and adjusted the electric restraint belt tension to 35N to secure the patient's torso and prevent displacement. Simultaneously, the bump event, patient vital signs data, and spinal injury markers were uploaded to the emergency cloud platform. The cloud platform sent a notification to the hospital's emergency department stating, "Patient with spinal injury is being transported; road bumpiness is significant. It is recommended to prepare spinal surgery instruments and a CT scan access route in advance." It also sent a notification to the accompanying medical staff terminal stating, "Damping has been adjusted; please pay attention to changes in the patient's vital signs."

[0043] This scenario addresses the shortcomings of existing emergency stretchers, which cannot actively adapt to bumpy roads, easily aggravate the injuries of patients with spinal injuries, and the fact that hospitals cannot know the risks of transport in advance and are insufficiently prepared. It effectively avoids secondary injury to patients and shortens the preoperative preparation time after admission.

[0044] Example 2: Transfer scenario of congested routes for myocardial infarction patients A 72-year-old patient suffered an acute ST-segment elevation myocardial infarction and was transferred from his residential area to the Chest Pain Center of the Provincial People's Hospital by an ambulance. During the transfer, a traffic accident occurred on the main road ahead, and the navigation showed that the original route was expected to take 45 minutes longer, exceeding the original estimated time by 42%.

[0045] After the traffic status receiving module synchronizes with the road condition data, the comprehensive risk value is calculated to be R=7.5, which is judged as a high-risk anomaly. The edge computing terminal immediately uploads the road condition anomaly and the patient's ECG data for the past 10 minutes to the emergency cloud platform. The cloud platform immediately pushes the optimized branch route to the emergency vehicle navigation terminal, and simultaneously connects with the traffic police emergency priority passage system to open a green wave for the new route. It also pushes a prompt to the hospital's chest pain center: "Myocardial infarction patient is being transferred. The estimated arrival time is 18 minutes earlier than originally planned. It is recommended to prepare the catheterization lab and thrombolytic drugs in advance." It also pushes a prompt to the accompanying medical staff terminal: "The route has been optimized. The estimated arrival time has been updated to XX:XX. Please pay attention to changes in the patient's ECG."

[0046] This scenario addresses the shortcomings of existing emergency transport routes, such as the inability to link multiple resources and the risk of missing the golden treatment time for myocardial infarction, effectively shortening transport time and buying more treatment time for patients.

[0047] refer to Figure 1This diagram illustrates the closed-loop process of stretcher monitoring and cloud-based collaborative operations in an out-of-hospital emergency transport system from a macro perspective. The process begins with high-frequency data acquisition from a multi-dimensional sensor array, covering the physiological state of the transported person, the stretcher's movement posture, road surface conditions, and traffic information surrounding the ambulance. After the collected data enters the embedded edge terminal on the stretcher side, it immediately undergoes preprocessing such as noise reduction and spatiotemporal alignment, and performs real-time risk classification and identification based on emergency safety standards.

[0048] For identified high-risk anomalies, the system activates a dual-track response mechanism: firstly, the local wire-controlled execution components at the stretcher end are adjusted in milliseconds to proactively eliminate safety hazards; secondly, the abnormal situation is pushed to the emergency cloud platform through the 5G / 4G redundant link, and the cloud platform issues targeted linkage dispatch instructions according to the permission division of the on-board medical terminal, the receiving medical institution and the navigation terminal to achieve closed-loop coordination of out-of-hospital treatment, transfer and allocation and receiving preparation.

[0049] refer to Figure 2 This diagram details the synchronous processing and quality control path of various physical sensor data at the edge computing terminal. At the initial system startup, the edge terminal first performs a unified timestamp on the entire array of piezoelectric sensor, six-axis sensor, and bump sensor to eliminate time delay deviations in multi-protocol data acquisition, and then uses factory calibration coefficients to correct measurement deviations in the raw data.

[0050] Subsequently, the data enters the real-time preprocessing pipeline: a sliding window algorithm is used to remove high-frequency oscillation noise from the data and to perform forced alignment of spatiotemporal attributes; if some frames are lost, the missing logic is used to fill in the gaps using adjacent sampling points to ensure the continuity of the processed data. These standardized data streams are mapped in real time to a risk grading threshold model. Once the value crosses the safety red line, the terminal immediately triggers the corresponding handling control command to ensure the rigor of the emergency response process.

[0051] refer to Figure 3 This diagram specifically analyzes the control logic of how the stretcher wire-controlled execution module dynamically and adaptively adjusts parameters when a high-risk anomaly occurs. Once the edge terminal identifies a high-risk level, it immediately sends the highest-priority command to the corresponding wire-controlled component. The system performs action matching based on the type of hazard: for posture tilt hazards, it controls the DC servo motor to adjust the back and leg angles of the support frame to a horizontal or patient-appropriate position; To counteract high-intensity road bumps, the magnetorheological damping module reconstructs the damping coefficient within milliseconds based on feedback to offset vertical impacts. In response to sudden fluctuations in the patient's vital signs, the tension of the restraint belt's stepper motor is adjusted accordingly. After the command is issued, the control system enters a verification closed loop, continuously monitoring the return status of the adjusted corresponding indicators for three seconds. If the hazard is eliminated, the current state is maintained; if the standard is not met, a recalculation is triggered. After three failed attempts, the system immediately switches to a manual intervention mode with audible and visual alarms to ensure safe transport.

[0052] refer to Figure 4 This diagram illustrates the hierarchical scheduling path of the cloud system, which implements layered coordination among the three major terminals—the ambulance, the hospital, and the navigation system—based on access permissions. Upon receiving all the abnormal data uploaded by the stretcher, the emergency cloud platform automatically distributes coordinated dispatch instructions according to business logic.

[0053] The first type of instruction is sent to the vehicle terminal, pushing a comprehensive early warning including vital sign trend charts and hazard types, and supporting real-time feedback of medical and nursing treatment information; the second type of instruction is sent to the hospital to be transported, pushing basic patient information, disease prediction and suggestions for reserving necessary emergency resources in advance, and assisting the hospital in the deployment of emergency channels. The third type of instruction is sent to the vehicle navigation system, which, based on real-time traffic data and emergency priority access, reconstructs the transfer route to reduce the estimated travel time. All instructions support route feedback after being pushed, ensuring efficient dispatch and accurate calculation of delivery time.

[0054] refer to Figure 5 This diagram illustrates the national cryptographic security architecture protecting critical physiological data and emergency commands at the network and storage layers in an out-of-hospital emergency transport system. Considering the privacy attributes of medical data, all data streams in the system are immediately encrypted end-to-end using the national cryptographic SM2 asymmetric algorithm at the front end, ensuring that only terminals with the corresponding pre-configured private key can decode the patient's condition.

[0055] In the cloud storage phase, the system mandates the use of the SM4 symmetric algorithm for static data storage to prevent unauthorized data leakage. Furthermore, each critical information flow node requires rigorous authentication via a hardware UKey. All terminal access requests, attempts to tamper with scheduling parameters, and abnormal query operations are fully recorded in the audit log system. This end-to-end, full-cycle security defense not only ensures the tamper-proof capability of emergency dispatch instructions but also fully complies with the compliance requirements for medical privacy protection and information security.

[0056] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for intelligent monitoring and linkage of an emergency electric stretcher, characterized in that, Includes the following steps: S1. Real-time acquisition of multi-source data: The multi-dimensional sensor array mounted on the emergency electric stretcher collects real-time data on the vital signs of the person being transported, the stretcher's operating posture, the road surface condition along the transport route, and the traffic condition around the emergency vehicle. S2. Edge-side anomaly classification identification: The edge computing terminal mounted on the stretcher performs real-time preprocessing on the collected multi-source data and identifies the anomaly risk level by comparing it with the preset risk classification threshold. If it is identified as a high-risk anomaly, a linkage response is triggered. S3. Local linkage control at the stretcher end: The edge computing terminal sends control commands to the execution components of the emergency electric stretcher in the first instance to adjust the stretcher operating parameters to eliminate safety hazards corresponding to high-risk anomalies; S4. Cross-terminal linkage dispatch: The edge computing terminal synchronously uploads high-risk abnormal data, full vital sign data of the transferred person, and stretcher operation data to the emergency cloud platform. The emergency cloud platform then issues linkage dispatch instructions to the on-board medical terminal, the emergency receiving terminal at the medical institution to be delivered, and the emergency vehicle navigation terminal.

2. The intelligent monitoring and linkage method for an emergency electric stretcher according to claim 1, characterized in that, The risk classification threshold in step S2 is specifically divided into three levels: low risk, medium risk, and high risk. High-risk anomalies include any one of the following: the vital signs of the person being transported exceed the safety threshold, the tilt angle of the stretcher exceeds the safety range, the road bumpiness exceeds the shock absorption threshold, or there is traffic congestion ahead of the emergency vehicle's route for more than a preset duration.

3. The intelligent monitoring and linkage method for an emergency electric stretcher according to claim 1, characterized in that, The stretcher operating parameters adjusted in step S3 include at least one of the following: stretcher support angle, damping coefficient of shock absorption module, and tension of restraint belt. After the adjustment is completed, the edge computing terminal collects the corresponding data after adjustment in real time to verify the effect of eliminating hidden dangers. If the hidden dangers are not eliminated, the optimized control command is repeatedly issued.

4. The intelligent monitoring and linkage method for an emergency electric stretcher according to claim 1, characterized in that, The coordinated dispatch instructions issued in step S4 specifically include: pushing high-risk abnormality alerts and vital sign monitoring data to the onboard medical terminal for medical staff to refer to and handle; pushing the patient's condition prediction and required emergency resources alerts to the emergency receiving terminal of the medical institution to be transported; and pushing route optimization instructions to the emergency vehicle navigation terminal to switch to the transport route with the shortest travel time.

5. The intelligent monitoring and linkage method for an emergency electric stretcher according to claim 1, characterized in that, If a medium-risk anomaly is identified in step S2, the edge computing terminal will only send a warning to the on-board medical terminal without triggering stretcher control and cross-terminal scheduling operations, while continuously tracking the changing trend of the corresponding abnormal data.

6. The intelligent monitoring and linkage method for an emergency electric stretcher according to claim 1, characterized in that, Before the multi-dimensional sensor array collects data, the edge computing terminal first performs timestamp synchronization and deviation calibration on each sensor.

7. An intelligent monitoring and linkage system for emergency electric stretchers, used to implement the intelligent monitoring and linkage method for emergency electric stretchers as described in any one of claims 1-6, characterized in that, This includes a multi-dimensional sensor array, edge computing terminal, and stretcher execution components mounted on an emergency electric stretcher, as well as an emergency cloud platform deployed in the cloud, and on-board medical terminals, medical institution emergency receiving terminals, and emergency vehicle navigation terminals that communicate with the emergency cloud platform. The multi-dimensional sensor array is used to collect real-time data on the vital signs of the person being transported, the stretcher's running posture, the road surface condition along the transport route, and the traffic condition around the emergency vehicle. The edge computing terminal is used to preprocess multi-source data and identify risk levels. After triggering the linkage response, it sends control commands to the stretcher execution component and uploads abnormal and full monitoring data to the emergency cloud platform. The stretcher execution component is used to receive control commands and adjust the stretcher operating parameters; the emergency cloud platform is used to receive data and then send corresponding linkage dispatch commands to each associated terminal.

8. The intelligent monitoring and linkage system for an emergency electric stretcher according to claim 7, characterized in that, The multi-dimensional sensing array specifically includes a piezoelectric vital sign monitoring sensor, a six-axis attitude sensor, a road surface bump sensor, and a traffic status receiving module. Each sensing module is connected to the edge computing terminal via Bluetooth Low Energy.

9. The intelligent monitoring and linkage system for an emergency electric stretcher according to claim 7, characterized in that, The stretcher execution components include an electric attitude adjustment module, an adjustable damping shock absorption module, and an electric restraint belt control module. Each execution component is equipped with a wired actuator, which receives control commands from the edge computing terminal to complete the corresponding parameter adjustment.

10. The intelligent monitoring and linkage system for an emergency electric stretcher according to claim 7, characterized in that, The emergency cloud platform has a built-in data encryption module. All transmitted monitoring data and dispatch instructions are encrypted end-to-end, and only authorized associated terminals can decrypt and view the corresponding content.

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