Emergency low-altitude response and in-hospital collaborative treatment platform based on 5G-A integrated sensing

Abstract

The application provides a 5G-A integrated emergency low-altitude response and in-hospital collaborative treatment platform, and relates to the technical field of resource scheduling management.The application calculates a relative communication compression coefficient by monitoring the state of a sudden obstacle, adaptively switches time / space division multiplexing or a cross-base station physical stripping mode, solves the defects of underlying communication sensing same-frequency resource occupation and self-interference, calculates effective throughput in combination with the coefficient, performs semantic grading and dimension reduction transmission on medical data, performs Doppler frequency shift pre-compensation by using radar sensing speed, generates a predicted picture by using an interframe algorithm, avoids expert misjudgment caused by communication lag, and relies on a multi-layer electronic geographic fence to automatically link in-hospital operating room preheating and transfer route locking.The application guarantees the continuity of emergency data in a complex low-altitude environment, realizes zero-time difference collaboration of data transmission and in-hospital physical reception, and improves the success rate of treatment.

Description

Technical Field

[0001] This invention relates to the field of resource scheduling and management technology, specifically to a 5G-A integrated emergency low-altitude response and in-hospital collaborative treatment platform. Background Technology

[0002] With the deep integration of low-altitude economy and smart healthcare technologies, emergency low-altitude response and in-hospital collaborative treatment platforms based on 5G-A (5G-Advanced) sensing and communication integration technology have become an important development direction in the field of critical care. In typical "mobile airborne ICU" scenarios (such as cross-hospital helicopter or large emergency drone transfers), this platform aims to transmit vital sign data collected by airborne multi-parameter monitors and high-bandwidth images such as portable ultrasound images to the in-hospital emergency room terminal in real time and without loss via the 5G-A network. This allows in-hospital experts to provide remote real-time guidance using AR / VR technologies and to initiate operating room preparations in advance, achieving "zero-time-difference" collaborative treatment between pre-hospital emergency care and in-hospital reception, greatly improving the success rate of treatment during the "golden rescue period."

[0003] However, in practical applications, existing low-altitude emergency response systems still have significant technical shortcomings when dealing with complex urban environments. The most obvious manifestation is that high-bandwidth medical emergency data is prone to sudden interruptions and high latency during dynamic flight. When emergency aircraft travel at high speeds through urban low-altitude areas, the inherent multipath effect, Doppler shift, and frequent cell handovers across base stations, coupled with physical obstructions from tall buildings, lead to highly unstable communication links. This complexity of the physical environment directly causes sudden pixelation or stuttering of high-definition wound videos or ultrasound images received at the hospital receiving end, and even delays of several seconds in critical vital signs data such as electrocardiograms. This prevents hospital experts from accurately assessing the patient's real-time condition, seriously threatening the timeliness of resuscitation for critically ill patients.

[0004] Further analysis reveals that the underlying cause of the aforementioned surface-level communication interruptions lies in the existing 5G-A Integrated Sensing and Communication (ISAC) architecture's flaws in the underlying physical and MAC layers, including issues with co-frequency resource contention and self-interference between "radar sensing" and "data communication." Under the integrated sensing and communication mechanism, base stations reuse the same set of radio frequency hardware and spectrum resources to simultaneously perform communication and sensing tasks. When emergency medical aircraft fly in open areas, base stations can maintain normal resource allocation; however, when the aircraft enters complex urban high-rise buildings or encounters unknown obstacles (such as birds or tower cranes), to prevent the aircraft from crashing, the base station must instantly increase the "sensing" accuracy, significantly allocating spectrum resources and computing power for high-frequency scanning. At this time, severe sensing resource contention occurs, not only causing a sharp compression of the communication channels allocated to medical data transmission and a drastic drop in the signal-to-noise ratio, but also resulting in severe self-interference from high-power sensing echoes to the received communication signals. The lack of an effective "dynamic decoupling and adaptive allocation mechanism for sensing resources" in existing technologies leads to a sudden surge in the underlying sensing resources when base stations are dealing with sudden and complex low-altitude environments. This directly causes a sudden interruption in the transmission of emergency medical data, failing to meet the stringent requirements for high reliability and low latency communication in emergency scenarios. Summary of the Invention

[0005] The purpose of this invention is to provide an emergency low-altitude response and in-hospital collaborative treatment platform based on 5G-A sensing integration, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: The 5G-A integrated sensing emergency low-altitude response and in-hospital collaborative treatment platform specifically includes: The flight environment situational awareness module is used to pre-acquire a three-dimensional electronic map of the city at low altitude and receive in real time the basic flight track data and environmental echo data generated by the 5G-A integrated sensing base station scanning the emergency rescue aircraft. The sensor resource conflict prediction module is used to monitor the status of sudden obstacles in front of the emergency rescue aircraft in real time, generate an obstacle avoidance emergency set, predict the current sensor resource consumption required by the 5G-A base station based on the obstacle avoidance emergency set, and preliminarily determine whether a sensor resource frequency competition conflict is about to occur. The sensing adaptive decoupling scheduling module constructs a set of base station resource states based on the conflict prediction results obtained by the sensing resource conflict prediction module, and generates the relative communication squeezing coefficient Kcx for the current emergency rescue service based on the set of base station resource states. Based on the result of the relative communication squeezing coefficient Kcx, the module substitutes the current 5G-A base station into the resource decoupling mode, and determines whether it is necessary to start the "sense task physical stripping" strategy across base stations by combining the idle channel data information of adjacent base stations. The medical data semantic classification transmission module simultaneously classifies emergency data generated by multi-parameter monitors and portable ultrasound in the emergency medical aircraft into semantic levels, and then determines whether various types of emergency data can be successfully matched with the current available communication bandwidth under different resource decoupling modes; when high-bandwidth medical data fails to be successfully matched with the current available bandwidth, an adaptive dimensionality reduction transmission strategy is obtained, and finally it is determined whether the emergency data needs to enter the frame-skipping or downsampling mode. The in-hospital collaborative receiving and compensation module is used to receive emergency data sent by the medical data semantic hierarchical transmission module and perform synchronous rendering on the terminal in the in-hospital emergency room. At the same time, it triggers zero-time-difference preparation instructions for the in-hospital operating room based on the real-time trajectory of the emergency drone.

[0007] Furthermore, the sensory resource conflict prediction module monitors the status of sudden obstacles in real time and generates an emergency obstacle avoidance set. This emergency set includes the relative approach speed Vr between the sudden obstacle and the emergency rescue aircraft, the estimated collision time Tc, and the sensing computing power safety threshold G set for the current 5G-A base station. safe Simultaneously, the relationship between the expected collision time Tc and the preset obstacle avoidance extreme value Tmin is determined to ascertain whether the current 5G-A base station needs to interrupt normal resource allocation and enter a strong sensing state. The specific process includes: When the estimated collision time Tc ≤ the preset obstacle avoidance extreme value Tmin, the emergency rescue aircraft is determined to be in a high-risk blind flight environment. The current 5G-A base station needs to instantly increase its sensing transmission power and is marked as "a sensing resource conflict is about to occur". When the estimated collision time Tc is greater than the preset obstacle avoidance extreme value Tmin, the flight environment is determined to be open, and the current 5G-A base station maintains the normal time-division multiplexing state, which is marked as "no sensor resource conflict".

[0008] Furthermore, the sensory adaptive decoupling scheduling module constructs a base station resource status set based on the marker of "imminent sensory resource conflict," wherein the base station resource status set includes the remaining communication bandwidth Br of the current 5G-A base station and the minimum guaranteed bandwidth Breq required for emergency medical data; and generates a relative communication squeeze coefficient Kcx based on the base station resource status set, specifically: The difference between the minimum guaranteed bandwidth required to obtain emergency medical data and the remaining communication bandwidth of the current 5G-A base station is calculated, and the ratio of this difference to the minimum guaranteed bandwidth is used as the bandwidth loss rate. Then, the ratio of the radio frequency power allocated by the current base station to radar sensing to the total radio frequency power of the base station is obtained as the power preemption rate. The bandwidth loss rate is multiplied by a first preset weighting coefficient to obtain a first product, and the power preemption rate is multiplied by a second preset weighting coefficient to obtain a second product. The first product and the second product are added together, and then the quantified value of the self-interference intensity caused by the sensing echo to the communication reception is added to obtain the relative communication squeezing coefficient.

[0009] Furthermore, based on the results of the relative communication squeezing coefficient Kcx, the resource decoupling mode of the current 5G-A base station is determined, and the specific process is as follows: When the relative communication squeezing coefficient Kcx≤0, the time division multiplexing decoupling mode is enabled. At this time, the current 5G-A base station relies on its own computing power to alternately perform radar perception and data communication in millisecond-level time slots, and emergency data is continuously connected. When 0 < Kcx ≤ preset squeezing threshold Kth, the spatial multiplexing decoupling mode is activated. At this time, the current 5G-A base station starts beamforming, aligns the high-power sensing beam with the obstacle, and generates a spatial null in the direction of the communication beam to cancel self-interference. When the relative communication squeezing coefficient Kcx > the preset squeezing threshold Kth, it is determined that the current base station can no longer take into account both communication and sensing, and the cross-base station physical separation mode is activated. At this time, the current 5G-A base station only retains the "radar sensing" function to guide the aircraft to avoid obstacles. At the same time, it issues instructions through the core network to instantly and forcibly switch the "data communication" link of the emergency rescue aircraft to the adjacent idle cooperative base station.

[0010] Furthermore, the medical data semantic classification transmission module divides emergency data into semantic levels, including extremely high priority data and high priority data. Specifically, extremely high priority data includes low-bandwidth but extremely latency-sensitive vital sign data such as electrocardiograms and blood oxygen saturation, as well as alarm signals; high priority data includes high-bandwidth data such as high-definition wound videos and portable ultrasound images, which are easily affected by frequent cell handover. Simultaneously, based on the relative communication squeezing coefficient Kcx, an adaptive dimensionality reduction transmission strategy is obtained, and the effective throughput evaluation value THeff of the current channel is calculated. Specifically: Subtracting the value from the relative communication squeezing coefficient yields the basic allocation ratio, and multiplying the basic allocation ratio by the theoretical maximum throughput of the current channel yields the basic throughput assessment value; the product of the capacity loss constant caused by a single base station handover and the number of cell handovers expected to occur within a set future time period is obtained as the handover loss value; the basic throughput assessment value is subtracted from the handover loss value to obtain the final effective throughput assessment value.

[0011] Furthermore, based on the effective throughput assessment value TH eff Next, it is determined whether various emergency medical data can be successfully matched with the currently available communication bandwidth, specifically: If the effective throughput evaluation value TH eff >When the bandwidth required for high-priority data is low, it indicates that the communication link is stable and all medical emergency data is transmitted in lossless original quality. If the effective throughput evaluation value TH eff ≤ Bandwidth required for high-priority data, and TH eff When the bandwidth required for extremely high priority data is reduced, it indicates that the communication channel has become severely narrowed. At this point, frame dropping or downsampling mode is activated to dynamically reduce the ultrasound images and high-definition videos to standard definition or extract key frames for transmission. Meanwhile, extremely high priority vital sign data continues to be transmitted through the ultra-reliable low-latency dedicated 5G-A slice channel, ensuring that hospital experts can continuously assess the patient's vital signs.

[0012] Furthermore, the flight environment situational awareness module also includes a perception feedback communication unit, used to solve the inherent Doppler frequency shift problem under high-speed movement conditions; and to acquire the absolute moving speed Va and radial speed Vrad relative to the base station of the emergency rescue aircraft detected by the 5G-A base station through radar perception function. Using the radial velocity Vrad, the Doppler frequency shift compensation amount Fd is directly calculated at the bottom physical layer. Before the emergency rescue aircraft sends medical data, the Doppler frequency shift compensation amount Fd is sent to the aircraft's communication baseband chip for pre-compensation, thereby avoiding communication demodulation failure and screen stuttering caused by high-speed movement.

[0013] Furthermore, the in-hospital collaborative receiving and compensation module monitors and acquires the medical data receiving status of the in-hospital emergency room terminal; when it detects that medical video data experiences sudden high latency or pixelation due to extremely complex physical environment, including severe obstruction by urban high-rise buildings; Extract normal ultrasound image frames and vital sign trends from the previous successfully transmitted emergency medical aircraft; use frame interpolation algorithms and previous vital sign trends to generate a smooth-transitioning predictive compensation screen on the large screen of the terminal in the hospital's emergency room, and avoid misjudgment by hospital experts due to sudden screen freeze by synchronously displaying a warning sign that "communication is limited, the current screen is a predictive screen".

[0014] Furthermore, the in-hospital collaborative receiving and compensation module also includes a zero-time-difference triggering unit; Based on the real-time three-dimensional coordinates of the emergency medical aircraft obtained by the 5G-A integrated sensing base station, a multi-layered electronic geofence is constructed in the airspace surrounding the hospital. When the emergency medical aircraft breaks through the outer geofence and the patient's extremely high priority data triggers the critical value alarm, the system automatically generates an operating room preheating command, links the Internet of Things to turn on the laminar flow air conditioner and anesthesia equipment in the hospital's operating room, and sends a positioning command to the relevant on-duty experts. When the emergency medical aircraft breaks through the inner geofence, the system automatically plans the optimal internal hospital transfer route from the emergency medical aircraft landing pad to the specific operating room, and locks the elevator access along the way, realizing zero-time-difference coordination between low-altitude emergency medical care and in-hospital reception.

[0015] Furthermore, when the cross-base station physical stripping mode is activated, the current 5G-A base station and the adjacent idle cooperative base station establish a microsecond-level data synchronization tunnel through the fiber optic backhaul link; the current 5G-A base station shares the three-dimensional attitude data of the sensed aircraft with the cooperative base station in real time through this tunnel, enabling the cooperative base station to adjust the downtilt angle and azimuth angle of the communication beam in advance, and eliminate the defects of co-frequency resource contention and self-interference caused by radar sensing and data communication under a single base station by aligning with the high-speed shuttle emergency rescue aircraft.

[0016] Compared with the prior art, the beneficial effects of the present invention are: This invention uses a sensory resource conflict prediction module and a sensory adaptive decoupling scheduling module to monitor in real time the relative approach velocity Vr of sudden obstacles and the emergency rescue aircraft, the estimated collision time Tc, and the sensing computing power safety threshold G. safe Furthermore, when the expected collision time Tc ≤ the preset obstacle avoidance extreme value Tmin, a base station resource state set is constructed based on the remaining communication bandwidth Br of the current 5G-A base station and the minimum guaranteed bandwidth Breq required for emergency medical data. A relative communication squeezing coefficient Kcx, including bandwidth loss rate and power preemption rate, is calculated. Based on the result of this relative communication squeezing coefficient Kcx, the system can adaptively activate a millisecond-level alternating time-division multiplexing decoupling mode, a space-division multiplexing decoupling mode that generates spatial nulls to offset self-interference, or a cross-base station physical stripping mode. Especially in the cross-base station physical stripping mode, the current 5G-A base station only retains radar sensing... The system fully guides emergency medical aircraft to avoid obstacles and establishes a microsecond-level data synchronization tunnel through a fiber optic backhaul link. It shares the perceived three-dimensional attitude data of the aircraft with adjacent cooperative base stations in real time. The cooperative base stations adjust the downtilt angle and azimuth angle of the communication beam in advance to take over the data communication. This achieves dynamic decoupling and adaptive allocation of sensing resources at the underlying mechanism, completely eliminating the defects of large-scale squeezing of co-frequency resources and self-interference of high-power sensing echo caused by the instantaneous increase in sensing accuracy due to collision avoidance in complex urban environments. It fundamentally eliminates the sudden interruption of emergency medical data transmission channels caused by the passive surge of underlying sensing resources. This invention also calculates the effective throughput assessment value TH of the current channel by combining the medical data semantic hierarchical transmission module with the relative communication squeezing coefficient Kcx. eff When the channel narrows significantly, priority is given to transmitting extremely high-priority vital sign data such as electrocardiograms through a dedicated slice channel. High-priority ultrasound images and high-definition videos undergo adaptive frame extraction or downsampling and dimensionality reduction for transmission. Simultaneously, the absolute moving velocity Va and radial velocity Vrad detected by radar are obtained using the sensing feedback communication unit. The Doppler frequency shift compensation amount Fd is directly calculated at the lower level and sent to the baseband chip for pre-compensation, effectively overcoming communication demodulation failures during high-speed shuttle operations. Furthermore, when encountering extreme physical obstructions leading to sudden high latency or mosaic effects, the in-hospital collaborative receiving and compensation module utilizes frame interpolation algorithms combined with… The preceding data generates a smooth-transition predictive compensation screen to avoid misjudgment by in-hospital experts. Relying on a multi-layered electronic geofence built on real-time 3D coordinates, it automatically triggers operating room laminar flow air conditioning and anesthesia equipment preheating commands and optimal transfer route elevator access lock when the outer and inner layers of the geofence are breached. This comprehensively resolves the problem of highly unstable communication links caused by complex low-altitude physical environments. It not only ensures the continuity of critical vital signs data and video images on the large screen in the in-hospital emergency room, but also achieves zero-time-difference coordination between low-altitude emergency data transmission and in-hospital physical space reception, greatly improving the success rate of treatment during the golden rescue period for critically ill patients. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the overall system flow of the emergency low-altitude response and in-hospital collaborative treatment platform based on 5G-A sensing integration in this invention. Figure 2 This is a schematic diagram of the operation flow of the medical data semantic hierarchical transmission module in this invention. Detailed Implementation

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0019] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0020] Please see Figures 1 to 2 This invention provides a technical solution: a 5G-A integrated sensing emergency low-altitude response and in-hospital collaborative treatment platform, specifically comprising: The flight environment situational awareness module is used to pre-acquire a three-dimensional electronic map of the city at low altitude and receive in real time the basic flight track data and environmental echo data generated by the 5G-A integrated sensing base station scanning the emergency rescue aircraft. The sensor resource conflict prediction module is used to monitor the status of sudden obstacles in front of the emergency rescue aircraft in real time, generate an obstacle avoidance emergency set, predict the current sensor resource consumption required by the 5G-A base station based on the obstacle avoidance emergency set, and preliminarily determine whether a sensor resource frequency competition conflict is about to occur. The sensing adaptive decoupling scheduling module constructs a set of base station resource states based on the conflict prediction results obtained by the sensing resource conflict prediction module, and generates the relative communication squeezing coefficient Kcx for the current emergency rescue service based on the set of base station resource states. Based on the result of the relative communication squeezing coefficient Kcx, the module substitutes the current 5G-A base station into the resource decoupling mode, and determines whether it is necessary to start the "sense task physical stripping" strategy across base stations by combining the idle channel data information of adjacent base stations. The medical data semantic classification transmission module simultaneously classifies emergency data generated by multi-parameter monitors and portable ultrasound in the emergency medical aircraft into semantic levels, and then determines whether various types of emergency data can be successfully matched with the current available communication bandwidth under different resource decoupling modes; when high-bandwidth medical data fails to be successfully matched with the current available bandwidth, an adaptive dimensionality reduction transmission strategy is obtained, and finally it is determined whether the emergency data needs to enter the frame-skipping or downsampling mode. The in-hospital collaborative receiving and compensation module is used to receive emergency data sent by the medical data semantic hierarchical transmission module and perform synchronous rendering on the terminal in the in-hospital emergency room. At the same time, it triggers zero-time-difference preparation instructions for the in-hospital operating room based on the real-time trajectory of the emergency drone.

[0021] The sensory resource conflict prediction module monitors the status of sudden obstacles in real time and generates an emergency obstacle avoidance set. This emergency set includes the relative approach velocity Vr between the sudden obstacle and the emergency rescue aircraft, the estimated collision time Tc, and the sensing computing power safety threshold G set for the current 5G-A base station. safe Simultaneously, the relationship between the expected collision time Tc and the preset obstacle avoidance extreme value Tmin is determined to ascertain whether the current 5G-A base station needs to interrupt normal resource allocation and enter a strong sensing state. The specific process includes: When the estimated collision time Tc ≤ the preset obstacle avoidance extreme value Tmin, the emergency rescue aircraft is determined to be in a high-risk blind flight environment. The current 5G-A base station needs to instantly increase its sensing transmission power and is marked as "a sensing resource conflict is about to occur". When the estimated collision time Tc is greater than the preset obstacle avoidance extreme value Tmin, the flight environment is determined to be open, and the current 5G-A base station maintains the normal time-division multiplexing state, which is marked as "no sensor resource conflict".

[0022] The sensory adaptive decoupling scheduling module constructs a base station resource state set based on the marker of "imminent sensory resource conflict". This set includes the remaining communication bandwidth Br of the current 5G-A base station and the minimum guaranteed bandwidth Breq required for emergency medical data. A relative communication squeeze coefficient Kcx is generated based on the base station resource state set. Specifically: The difference between the minimum guaranteed bandwidth required to obtain emergency medical data and the remaining communication bandwidth of the current 5G-A base station is calculated, and the ratio of this difference to the minimum guaranteed bandwidth is used as the bandwidth loss rate. Then, the ratio of the radio frequency power allocated by the current base station to radar sensing to the total radio frequency power of the base station is obtained as the power preemption rate. The bandwidth loss rate is multiplied by a first preset weighting coefficient to obtain a first product, and the power preemption rate is multiplied by a second preset weighting coefficient to obtain a second product. The first product and the second product are added together, and then the quantified value of the self-interference intensity caused by the sensing echo to the communication reception is added to obtain the relative communication squeezing coefficient.

[0023] Furthermore, the relative communication compression coefficient Kcx is obtained using the following formula: ;

[0024] The significance of this formula lies in assessing the degree of compression and interference to the medical data communication channel when the current base station excessively consumes sensing resources in order to "see" obstacles. Here, Psens represents the radio frequency power allocated by the current base station to radar sensing, Ptotal represents the total radio frequency power of the base station, ω1 and ω2 represent the weighting coefficients of bandwidth loss and power preemption, respectively, and ΔI represents the quantified value of the self-interference intensity caused by sensing echo to communication reception.

[0025] Based on the results of the relative communication squeezing coefficient Kcx, the resource decoupling mode of the current 5G-A base station is determined. The specific process is as follows: When the relative communication squeezing coefficient Kcx≤0, the time division multiplexing decoupling mode is enabled. At this time, the current 5G-A base station relies on its own computing power to alternately perform radar perception and data communication in millisecond-level time slots, and emergency data is continuously connected. When 0 < Kcx ≤ preset squeezing threshold Kth, the spatial multiplexing decoupling mode is activated. At this time, the current 5G-A base station starts beamforming, aligns the high-power sensing beam with the obstacle, and generates a spatial null in the direction of the communication beam to cancel self-interference. When the relative communication squeezing coefficient Kcx > the preset squeezing threshold Kth, it is determined that the current base station can no longer take into account both communication and sensing, and the cross-base station physical separation mode is activated. At this time, the current 5G-A base station only retains the "radar sensing" function to guide the aircraft to avoid obstacles. At the same time, it issues instructions through the core network to instantly and forcibly switch the "data communication" link of the emergency rescue aircraft to the adjacent idle cooperative base station.

[0026] The medical data semantic classification transmission module divides emergency data into semantic levels, including extremely high priority data and high priority data. Specifically, extremely high priority data includes low-bandwidth but extremely latency-sensitive vital sign data such as electrocardiograms and blood oxygen saturation, as well as alarm signals; high priority data includes high-bandwidth data such as high-definition wound videos and portable ultrasound images, which are easily affected by frequent cell handover. Simultaneously, based on the relative communication squeezing coefficient Kcx, an adaptive dimensionality reduction transmission strategy is obtained, and the effective throughput evaluation value THeff of the current channel is calculated. Specifically: Subtracting the value from the relative communication squeezing coefficient yields the basic allocation ratio, and multiplying the basic allocation ratio by the theoretical maximum throughput of the current channel yields the basic throughput assessment value; the product of the capacity loss constant caused by a single base station handover and the number of cell handovers expected to occur within a set future time period is obtained as the handover loss value; the basic throughput assessment value is subtracted from the handover loss value to obtain the final effective throughput assessment value.

[0027] Furthermore, the effective throughput evaluation value THeff is obtained using the following formula: ;

[0028] The significance of this formula lies in predicting the remaining channel capacity actually available for transmitting medical data under conditions of sensor resource contention and frequent high-speed mobile handover; where TH max denoted as the theoretical maximum throughput, μ represents the capacity loss constant caused by a single base station handover, and Nho represents the number of cell handovers expected to occur in the next ten seconds.

[0029] Based on the effective throughput assessment value TH eff Next, it is determined whether various emergency medical data can be successfully matched with the currently available communication bandwidth, specifically: If the effective throughput evaluation value TH eff >When the bandwidth required for high-priority data is low, it indicates that the communication link is stable and all medical emergency data is transmitted in lossless original quality. If the effective throughput evaluation value TH eff ≤ Bandwidth required for high-priority data, and TH eff When the bandwidth required for extremely high priority data is reduced, it indicates that the communication channel has become severely narrowed. At this point, frame dropping or downsampling mode is activated to dynamically reduce the ultrasound images and high-definition videos to standard definition or extract key frames for transmission. Meanwhile, extremely high priority vital sign data continues to be transmitted through the ultra-reliable low-latency dedicated 5G-A slice channel, ensuring that hospital experts can continuously assess the patient's vital signs.

[0030] The flight environment situational awareness module also includes a perception feedback communication unit, which is used to solve the inherent Doppler frequency shift problem under high-speed movement conditions; and to obtain the absolute moving speed Va and radial speed Vrad relative to the base station of the emergency rescue aircraft detected by the 5G-A base station through the radar perception function. Using the radial velocity Vrad, the Doppler frequency shift compensation amount Fd is directly calculated at the bottom physical layer. Before the emergency rescue aircraft sends medical data, the Doppler frequency shift compensation amount Fd is sent to the aircraft's communication baseband chip for pre-compensation, thereby avoiding communication demodulation failure and screen stuttering caused by high-speed movement.

[0031] The in-hospital collaborative receiving and compensation module monitors and acquires the medical data receiving status of the terminal in the in-hospital emergency room; when it detects that medical video data has sudden high latency or mosaic due to extremely complex physical environment, including severe obstruction by urban high-rise buildings; Extract normal ultrasound image frames and vital sign trends from the previous successfully transmitted emergency medical aircraft; use frame interpolation algorithms and previous vital sign trends to generate a smooth-transitioning predictive compensation screen on the large screen of the terminal in the hospital's emergency room, and avoid misjudgment by hospital experts due to sudden screen freeze by synchronously displaying a warning sign that "communication is limited, the current screen is a predictive screen".

[0032] The in-hospital collaborative receiving and compensation module also includes a zero-time-difference triggering unit; Based on the real-time three-dimensional coordinates of the emergency medical aircraft obtained by the 5G-A integrated sensing base station, a multi-layered electronic geofence is constructed in the airspace surrounding the hospital. When the emergency medical aircraft breaks through the outer geofence and the patient's extremely high priority data triggers the critical value alarm, the system automatically generates an operating room preheating command, links the Internet of Things to turn on the laminar flow air conditioner and anesthesia equipment in the hospital's operating room, and sends a positioning command to the relevant on-duty experts. When the emergency medical aircraft breaks through the inner geofence, the system automatically plans the optimal internal hospital transfer route from the emergency medical aircraft landing pad to the specific operating room, and locks the elevator access along the way, realizing zero-time-difference coordination between low-altitude emergency medical care and in-hospital reception.

[0033] When the cross-base station physical stripping mode is activated, the current 5G-A base station and the adjacent idle cooperative base station establish a microsecond-level data synchronization tunnel through the fiber optic backhaul link. The current 5G-A base station shares the three-dimensional attitude data of the aircraft it senses with the cooperative base station in real time through this tunnel, enabling the cooperative base station to adjust the downtilt angle and azimuth angle of the communication beam in advance. By aligning with the high-speed emergency rescue aircraft, the defects of co-frequency resource contention and self-interference caused by radar sensing and data communication under a single base station are eliminated.

[0034] In this embodiment, the relative communication compression coefficient Kcx is a dimensionless pure numerical value. Based on the physical constraints of each component in the formula, the theoretical range of Kcx is from negative infinity to positive infinity. The lower limit is negative infinity, and the upper limit is 1; The value range is 0 to 1; ΔI is a positive constant. The value of Kcx directly quantifies the intensity of competition between the communication and sensing functions of the 5G-A base station for limited resources, as well as the crowding effect of the sensing function on the communication function.

[0035] When Kcx approaches a negative maximum, it indicates that the base station is in a state of extremely abundant communication resources. At this time, the remaining communication bandwidth Br is much greater than the minimum guaranteed bandwidth Breq required for emergency medical data. The bandwidth shortage rate is a large negative number, dominating Kcx towards negative infinity. Simultaneously, the power preemption rate is extremely low, and the impact of the self-interference intensity quantization value ΔI is negligible. This state corresponds to the aircraft being in an open, unobstructed airspace, where the risk of sensing resource conflict approaches zero, and the base station does not need to sacrifice any communication resources for sensing tasks. When Kcx approaches a positive maximum, it indicates that the base station has entered an extreme state of intense competition between communication and sensing resources. At this time, Br approaches 0, causing the bandwidth shortage rate to approach 1. The radio frequency power Psens allocated to radar sensing approaches the total radio frequency power Ptotal of the base station, causing the power preemption rate to approach 1. ΔI also reaches its peak. The superposition of these three positive components drives Kcx towards positive infinity. This state corresponds to the aircraft being in an extremely high-risk environment, where the base station is forced to use all resources for sensing to ensure flight safety, resulting in severe compression or even blockage of medical data transmission channels. Therefore, the monotonically increasing Kcx from negative infinity to positive infinity accurately describes the complete continuous spectrum of the system state from a completely conflict-free and smooth transition to an extremely conflict-ridden state.

[0036] The bandwidth missing rate is positively correlated with Kcx. The larger the difference, the higher the bandwidth deficit rate, and the larger the Kcx value. This positive correlation design replicates the congestion control axiom in the field of communications. Breq represents the rigid demand for ensuring emergency medical data transmission, and Br represents the elastic resources currently available from the base station. When Br is less than Breq, the communication link can no longer meet the transmission needs of basic medical data, forming a bandwidth gap; the larger this gap, the more severe the damage to communication quality, and the greater the positive contribution to Kcx. Conversely, when Br is greater than Breq, bandwidth resources are abundant, generating a negative contribution to reduce Kcx. This positive correlation design ensures that the sensing adaptive decoupling scheduling module can prioritize sensing the bottleneck of the communication link itself. If Br is already severely insufficient before the sensing task has started, Kcx will still increase, thereby triggering the resource decoupling strategy in advance, preventing the intervention of the sensing task from overwhelming the communication channel, and thus ensuring the continuous connection of emergency data.

[0037] Power preemption rate is positively correlated with Kcx. The higher the ratio, the larger the Kcx value. This positive correlation design is based on the power conservation and contention principles of radio resource management. Within a 5G-A base station, the total radio frequency power Ptotal is a shared resource with a fixed upper limit. The more power Psens allocated to radar sensing, the less power is available for downlink data communication. The power preemption rate directly quantifies the degree to which sensing functions encroach on physical layer energy resources for communication functions. An increase in this ratio leads to a decrease in the signal-to-noise ratio of the communication signal, a reduction in the modulation and coding order, an increase in the bit error rate, and a decrease in effective throughput. This positive correlation design makes Kcx extremely sensitive to sudden, strong sensing demands. When the expected collision time Tc is less than or equal to the preset obstacle avoidance extreme value Tmin, the base station instantaneously increases Psens. The ratio increases sharply and directly pushes up Kcx, enabling Kcx to quickly exceed the preset squeezing threshold Kth and trigger the cross-base station physical stripping mode in a timely manner. In emergency obstacle avoidance, the communication function is stripped to ensure the full operation of the sensing function.

[0038] The quantized value of self-interference intensity ΔI has a linear relationship with Kcx with a fixed positive offset; ΔI is a positive constant and is directly accumulated to Kcx. This fixed offset design stems from the inherent physical defects in full-duplex or simultaneous sensing and communication integrated systems. In 5G-A sensing and communication integrated base stations, when the base station simultaneously transmits high-power sensing pulses and receives weak communication uplink signals, its own transmitted signal leaks to the receiving link through the hardware coupling path, forming self-interference. This interference is a system-level, persistent background noise that does not dynamically change with bandwidth loss rate or power preemption rate, but permanently reduces the signal-to-noise ratio floor of the communication receiver. ΔI is the quantified expression of the systemic interference floor. This design ensures that Kcx never falls below ΔI. When both the bandwidth loss rate and power preemption rate are 0, Kcx is approximately equal to ΔI and greater than 0, eliminating the system's misjudgment of zero-collision states and forcing the system to confront the inherent self-interference problem at the physical layer. This design provides a theoretical basis for the spatial multiplexing decoupling mode: when 0 < Kcx ≤ Kth, beamforming and spatial nulling techniques are used to specifically combat and cancel the inherent self-interference represented by ΔI, restoring a clear channel in the communication beam direction, thereby refining the countermeasures against self-interference and improving communication quality in coexistence states.

[0039] It should be noted that all calculation formulas in this application employ regression analysis, including but not limited to machine learning algorithms, to deeply analyze the collected parameters and identify their natural trends and interrelationships. Specialized software, such as Python's Scikit-learn library or the R language, is used to automatically generate mathematical models that match the data. Then, cross-validation and other methods are used to objectively evaluate the model performance, and continuous feedback and optimization are combined to ensure that the created formulas truly reflect the inherent laws of the data, thereby guaranteeing their effectiveness and accuracy. In all calculation formulas in this application, the parameters in each formula undergo dimensionless processing within a consistent range to ensure that different physical quantities are compared on the same scale; dimensionless processing techniques include, but are not limited to, min-max-normalization and Z-score standardization. The technical solution of this invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random-access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of this invention.

[0040] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0041] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A 5G-A integrated sensing and communication emergency low-altitude response and in-hospital collaborative treatment platform, characterized in that: Specifically, it includes: The flight environment situational awareness module is used to pre-acquire a three-dimensional electronic map of the city at low altitude and receive in real time the basic flight track data and environmental echo data generated by the 5G-A integrated sensing base station scanning the emergency rescue aircraft. The sensor resource conflict prediction module is used to monitor the status of sudden obstacles in front of the emergency rescue aircraft in real time, generate an obstacle avoidance emergency set, predict the current sensor resource consumption required by the 5G-A base station based on the obstacle avoidance emergency set, and preliminarily determine whether a sensor resource frequency competition conflict is about to occur. The sensing adaptive decoupling scheduling module constructs a set of base station resource states based on the conflict prediction results obtained by the sensing resource conflict prediction module, and generates the relative communication squeezing coefficient Kcx for the current emergency rescue service based on the set of base station resource states. Based on the result of the relative communication squeezing coefficient Kcx, the module substitutes the current 5G-A base station into the resource decoupling mode, and determines whether it is necessary to start the "sense task physical stripping" strategy across base stations by combining the idle channel data information of adjacent base stations. The medical data semantic classification transmission module simultaneously classifies the emergency data generated by the multi-parameter monitor and portable ultrasound in the emergency rescue aircraft into semantic levels, and then determines whether various types of emergency data can be successfully matched with the currently available communication bandwidth under different resource decoupling modes. When high-bandwidth medical data fails to be matched with the currently available bandwidth, an adaptive dimensionality reduction transmission strategy is obtained to ultimately determine whether the emergency data needs to enter frame-dropping or downsampling mode. The in-hospital collaborative receiving and compensation module is used to receive emergency data sent by the medical data semantic hierarchical transmission module and perform synchronous rendering on the terminal in the in-hospital emergency room. At the same time, it triggers zero-time-difference preparation instructions for the in-hospital operating room based on the real-time trajectory of the emergency drone.

2. The emergency low-altitude response and in-hospital collaborative treatment platform based on 5G-A sensing integration as described in claim 1, characterized in that: The sensory resource conflict prediction module monitors the status of sudden obstacles in real time and generates an emergency obstacle avoidance set. This emergency set includes the relative approach velocity Vr between the sudden obstacle and the emergency rescue aircraft, the estimated collision time Tc, and the sensing computing power safety threshold G set for the current 5G-A base station. safe Simultaneously, the relationship between the expected collision time Tc and the preset obstacle avoidance extreme value Tmin is determined to ascertain whether the current 5G-A base station needs to interrupt normal resource allocation and enter a strong sensing state. The specific process includes: When the estimated collision time Tc ≤ the preset obstacle avoidance extreme value Tmin, the emergency rescue aircraft is determined to be in a high-risk blind flight environment. The current 5G-A base station needs to instantly increase its sensing transmission power and is marked as "a sensing resource conflict is about to occur". When the estimated collision time Tc is greater than the preset obstacle avoidance extreme value Tmin, the flight environment is determined to be open, and the current 5G-A base station maintains the normal time-division multiplexing state, which is marked as "no sensor resource conflict".

3. The emergency low-altitude response and in-hospital collaborative treatment platform based on 5G-A sensing integration as described in claim 2, characterized in that: The sensory adaptive decoupling scheduling module constructs a base station resource state set based on the marker of "imminent sensory resource conflict". This set includes the remaining communication bandwidth Br of the current 5G-A base station and the minimum guaranteed bandwidth Breq required for emergency medical data. A relative communication squeeze coefficient Kcx is generated based on the base station resource state set. Specifically: The difference between the minimum guaranteed bandwidth required to obtain emergency medical data and the remaining communication bandwidth of the current 5G-A base station is calculated, and the ratio of this difference to the minimum guaranteed bandwidth is used as the bandwidth loss rate. Then, the ratio of the radio frequency power allocated by the current base station to radar sensing to the total radio frequency power of the base station is obtained as the power preemption rate. The bandwidth loss rate is multiplied by a first preset weighting coefficient to obtain a first product, and the power preemption rate is multiplied by a second preset weighting coefficient to obtain a second product. The first product and the second product are added together, and then the quantified value of the self-interference intensity caused by the sensing echo to the communication reception is added to obtain the relative communication squeezing coefficient.

4. The emergency low-altitude response and in-hospital collaborative treatment platform based on 5G-A sensing integration as described in claim 3, characterized in that: Based on the results of the relative communication squeezing coefficient Kcx, the resource decoupling mode of the current 5G-A base station is determined. The specific process is as follows: When the relative communication squeezing coefficient Kcx≤0, the time division multiplexing decoupling mode is enabled. At this time, the current 5G-A base station relies on its own computing power to alternately perform radar perception and data communication in millisecond-level time slots, and emergency data is continuously connected. When 0 < Kcx ≤ preset squeezing threshold Kth, the spatial multiplexing decoupling mode is activated. At this time, the current 5G-A base station starts beamforming, aligns the high-power sensing beam with the obstacle, and generates a spatial null in the direction of the communication beam to cancel self-interference. When the relative communication squeezing coefficient Kcx > the preset squeezing threshold Kth, it is determined that the current base station can no longer take into account both communication and sensing, and the cross-base station physical separation mode is activated. At this time, the current 5G-A base station only retains the "radar sensing" function to guide the aircraft to avoid obstacles. At the same time, it issues instructions through the core network to instantly and forcibly switch the "data communication" link of the emergency rescue aircraft to the adjacent idle cooperative base station.

5. The emergency low-altitude response and in-hospital collaborative treatment platform based on 5G-A sensing integration as described in claim 4, characterized in that: The medical data semantic classification transmission module divides emergency data into semantic levels, including extremely high priority data and high priority data. Specifically, extremely high priority data includes low-bandwidth but extremely latency-sensitive vital sign data such as electrocardiograms and blood oxygen saturation, as well as alarm signals; high priority data includes high-bandwidth data such as high-definition wound videos and portable ultrasound images, which are easily affected by frequent cell handover. Simultaneously, based on the relative communication squeezing coefficient Kcx, an adaptive dimensionality reduction transmission strategy is obtained, and the effective throughput evaluation value THeff of the current channel is calculated. Specifically: Subtracting the value from the relative communication squeezing coefficient yields the basic allocation ratio, and multiplying the basic allocation ratio by the theoretical maximum throughput of the current channel yields the basic throughput assessment value; the product of the capacity loss constant caused by a single base station handover and the number of cell handovers expected to occur within a set future time period is obtained as the handover loss value; the basic throughput assessment value is subtracted from the handover loss value to obtain the final effective throughput assessment value.

6. The emergency low-altitude response and in-hospital collaborative treatment platform based on 5G-A sensing integration as described in claim 5, characterized in that: Based on the effective throughput assessment value TH eff Next, it is determined whether various emergency medical data can be successfully matched with the currently available communication bandwidth, specifically: If the effective throughput evaluation value TH eff >When the bandwidth required for high-priority data is low, it indicates that the communication link is stable and all medical emergency data is transmitted in lossless original quality. If the effective throughput evaluation value TH eff ≤ Bandwidth required for high-priority data, and TH eff When the bandwidth required for extremely high priority data is reduced, it indicates that the communication channel has become severely narrowed. At this point, frame dropping or downsampling mode is activated to dynamically reduce the ultrasound images and high-definition videos to standard definition or extract key frames for transmission. Meanwhile, extremely high priority vital sign data continues to be transmitted through the ultra-reliable low-latency dedicated 5G-A slice channel, ensuring that hospital experts can continuously assess the patient's vital signs.

7. The emergency low-altitude response and in-hospital collaborative treatment platform based on 5G-A sensing integration as described in claim 6, characterized in that: The flight environment situational awareness module also includes a perception feedback communication unit, which is used to solve the inherent Doppler frequency shift problem under high-speed movement conditions; and to obtain the absolute moving speed Va and radial speed Vrad relative to the base station of the emergency rescue aircraft detected by the 5G-A base station through the radar perception function. Using the radial velocity Vrad, the Doppler frequency shift compensation amount Fd is directly calculated at the bottom physical layer. Before the emergency rescue aircraft sends medical data, the Doppler frequency shift compensation amount Fd is sent to the aircraft's communication baseband chip for pre-compensation, thereby avoiding communication demodulation failure and screen stuttering caused by high-speed movement.

8. The emergency low-altitude response and in-hospital collaborative treatment platform based on 5G-A sensing integration as described in claim 7, characterized in that: The in-hospital collaborative receiving and compensation module monitors and acquires the medical data receiving status of the terminal in the in-hospital emergency room; when it detects that medical video data has sudden high latency or mosaic due to extremely complex physical environment, including severe obstruction by urban high-rise buildings; Extract normal ultrasound image frames and vital sign trends from the previous successfully transmitted emergency medical aircraft; use frame interpolation algorithms and previous vital sign trends to generate a smooth-transitioning predictive compensation screen on the large screen of the terminal in the hospital's emergency room, and avoid misjudgment by hospital experts due to sudden screen freeze by synchronously displaying a warning sign that "communication is limited, the current screen is a predictive screen".

9. The emergency low-altitude response and in-hospital collaborative treatment platform based on 5G-A sensing integration as described in claim 1, characterized in that: The in-hospital collaborative receiving and compensation module also includes a zero-time-difference triggering unit; Based on the real-time three-dimensional coordinates of the emergency medical aircraft obtained by the 5G-A integrated sensing base station, a multi-layered electronic geofence is constructed in the airspace surrounding the hospital. When the emergency medical aircraft breaks through the outer geofence and the patient's extremely high priority data triggers the critical value alarm, the system automatically generates an operating room preheating command, links the Internet of Things to turn on the laminar flow air conditioner and anesthesia equipment in the hospital's operating room, and sends a positioning command to the relevant on-duty experts. When the emergency medical aircraft breaks through the inner geofence, the system automatically plans the optimal internal hospital transfer route from the emergency medical aircraft landing pad to the specific operating room, and locks the elevator access along the way, realizing zero-time-difference coordination between low-altitude emergency medical care and in-hospital reception.

10. The emergency low-altitude response and in-hospital collaborative treatment platform based on 5G-A sensing integration as described in claim 4, characterized in that: When the cross-base station physical stripping mode is enabled, the current 5G-A base station and the adjacent idle cooperative base station establish a microsecond-level data synchronization tunnel through the fiber optic backhaul link. The current 5G-A base station will share the three-dimensional attitude data of the aircraft it senses with the cooperating base station in real time through this tunnel. This allows the cooperating base station to adjust the downtilt angle and azimuth angle of the communication beam in advance, and eliminate the defects of co-frequency resource contention and self-interference caused by radar sensing and data communication under a single base station by aligning it with the high-speed emergency rescue aircraft.

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