Emergency medical material transportation system based on unmanned aerial vehicle

Abstract

The invention provides an emergency medical material transportation system based on an unmanned aerial vehicle. The emergency medical material transportation system comprises a resource layer, a communication layer, a data layer and an application layer which are sequentially arranged from bottom to top, the resource layer is used for providing hardware support for the transportation of emergency medical supplies; the communication layer is provided with an unmanned aerial vehicle base station of a TD-LTE 4G network type, and is used for providing a wireless private network with wide area coverage and high quality; the data layer is used for calculating and processing the data transmitted by the communication layer in real time; the application layer is used for providing application services for medical personnel; according to the invention, by setting the TD-LTE 4G network type unmanned aerial vehicle base station, large-range and long-distance wireless private network coverage is realized; secondly, a special propagation loss model is constructed and used for determining the optimal communication frequency, and the signal coverage range is expanded; besides, the influence of Doppler frequency shift on the communication quality is considered, and the communication quality of the rescue unmanned aerial vehicle is improved to the maximum extent by determining the optimal communication frequency band in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles (UAVs) and emergency medical rescue, and more specifically, to an emergency medical material transportation system based on unmanned aerial vehicles (UAVs). Background Art

[0002] In recent years, drones have played an increasingly important role in disaster detection, on-site assessment, monitoring and tracking, precise positioning, auxiliary rescue, auxiliary supervision, and delivery of relief supplies. After an extreme disaster, due to the destruction or closure of ground transportation, traditional human resources are difficult to quickly reach the disaster area for rescue and disaster assessment. Drones are an ideal choice due to their portability and autonomous flight capabilities.

[0003] In the field of emergency medical rescue, it is generally believed that treatment within 1 hour after injury is the key "golden time" to save lives and reduce disabilities. However, in many areas of my country, due to the lack of communication and transportation, coupled with traffic congestion caused by accidents, the time for medical personnel to arrive at the scene is greatly delayed. Especially in mountainous areas and urban areas with traffic congestion, the response time for on-site first aid often exceeds 2 hours. Even if an ambulance is dispatched, it may not arrive at the scene in time to provide necessary medical assistance due to factors such as traffic, thus missing the best time for treatment. Therefore, improving the first aid response speed is crucial to improving the success rate of rescue.

[0004] On the one hand, drones can overcome the limitations of terrain and environment and easily fly over damaged buildings, obstacles and complex terrain. On the other hand, drone swarms can cover large areas in a short time, greatly improving the efficiency of search and rescue. In addition, drones can also quickly identify closed roads and rescue difficulties, and provide real-time image and video transmission, so that the command center and rescue personnel can make quick decisions. Therefore, building a material transportation system based on drones can quickly deliver materials to the accident site within the golden time of rescue, provide emergency rescue, and thus improve the success rate of rescue.

[0005] However, when natural disasters occur, communication equipment will be damaged in the short term. Disaster areas often face large-scale communication interruptions, power outages, and road interruptions. Ordinary emergency communication methods cannot quickly restore communications. At the same time, the ground base stations for rescue drones are limited and the deployment cost is high. Considering that some rescue sites are far away, it is impossible to achieve ultra-long-distance communication coverage by setting up multiple ground base stations in a short period of time. Based on this, it is necessary to design a highly stable and reliable drone communication solution for post-disaster scenarios to improve the communication capabilities of rescue drones, thereby further improving the overall rescue efficiency. Summary of the invention

[0006] In order to overcome the defect that the rescue drones in the above-mentioned prior art lack stable and reliable communication means in post-disaster rescue scenarios, the present invention provides an emergency medical supplies transportation system based on drones, which can provide a reliable communication means for the rescue drones, thereby further improving the overall rescue efficiency.

[0007] In order to solve the above technical problems, the technical solution of the present invention is as follows: An emergency medical supplies transportation system based on drones, comprising: The resource layer includes drone resources and infrastructure resources. The drone resources include several rescue drones. The infrastructure resources include several types of equipment installed on the rescue drones. The resource layer is used to provide hardware support for the transportation of emergency medical supplies. The communication layer includes a UAV base station, wherein the UAV base station adopts the TD-LTE 4G network standard, improves the signal coverage by optimizing a preset propagation loss model, and improves the communication quality by optimizing the Doppler frequency shift; the communication layer is used to provide wide-area coverage and high-quality wireless private network; The data layer includes an edge server, a cloud platform, a priori knowledge database and a decision support database; the edge server is used to perform real-time calculation and processing on the data transmitted by the communication layer; the cloud platform is used to store the data processing results of the edge server; the priori knowledge database is used to pre-store the priori knowledge required for edge computing; the decision support database is used to provide real-time decision support during the transportation of emergency medical supplies by rescue drones; the data layer is used to perform real-time calculation and processing on the data transmitted by the communication layer; The application layer establishes a communication connection with the cloud platform and is used to provide application services for medical personnel; The resource layer, communication layer, data layer and application layer are arranged in sequence from the bottom layer to the top layer.

[0008] Preferably, the equipment in the infrastructure resources includes: Multispectral camera, used to obtain real-time image information in the rescue area and transmit live video in real time; Laser tracking imager, used to obtain hot spot information and radar point cloud information in the emergency medical supplies loading area in real time; the hot spot information is used to detect potential temperature anomalies in medical supplies; the radar point cloud information is used to detect height differences in the emergency medical supplies loading area to ensure uniform distribution of medical supplies during transportation and avoid unstable loading conditions; The autonomous positioning bionic robotic arm is used to obtain the image and position coordinates of the emergency medical supplies loaded by the rescue drone, so as to further monitor the status information of the loaded medical supplies; the autonomous positioning bionic robotic arm is also provided with an infrared sensing device, based on which the robotic arm can autonomously grasp the medical supplies; by carrying the bionic robotic arm with autonomous positioning function, the loading of materials is ensured to be accurate, and the automated loading equipment reduces manual operation and improves loading efficiency; The multi-sensor module includes a thermal imaging device and a GPS positioning device, which is used to sense and accurately locate the position of the trapped person in real time; the multi-sensor module also includes a temperature and humidity sensor, which is used to sense the environment of the rescue area and the status of medical supplies in real time; based on the multi-sensor module, the present invention can realize the accurate sensing and positioning of the trapped person, as well as the status monitoring of medical supplies and the environment, to ensure the safe delivery of the supplies; LTE terminal, used to achieve real-time wireless communication between the rescue drone and the drone base station.

[0009] Preferably, at least two drone base stations are provided in the communication layer, at least one drone base station is used as a primary base station, and the other drone base stations are used as secondary base stations, so as to build a TD-LTE 4G network as a rescue drone cluster private network in a one-primary-multiple-secondary manner; Divide the rescue area into several sub-areas and sort them according to the severity of the disaster. Deploy the primary base station in the most important sub-area and deploy the secondary base stations in other sub-areas. The spectrum efficiency of the TD-LTE private network can reach 5~10bps / Hz, which means that under the same spectrum resources, TD-LTE can provide a higher data transmission rate; at the same time, when cluster applications are performed in the private network, TD-LTE's call delay is shorter, which is crucial for emergency communication scenarios that require rapid response; therefore, the present invention sets up multiple UAV LTE base stations to form a wireless network, which greatly expands the overall coverage of the wireless network and can meet the communication needs of emergency medical supplies transportation.

[0010] Preferably, the drone base stations are all aerial LTE base stations based on drones; The drone base stations all include a baseband control unit eBBU, a radio frequency remote processing unit eRRU and a directional antenna, all of which adopt an S1 configuration, and at the same time, the drone hovering circle, the directional wide-beam antenna coverage circle and the coverage area circle are approximately concentric circles to achieve stable coverage of the drone base station signal; For the hardware configuration of a single UAV base station, the present invention adopts eBBU and eRRU, and the base station performance is better to meet the higher data rate and more complex network requirements in the rescue area; at the same time, a single-sub-area single-antenna configuration is adopted to avoid antenna switching in the base station and ensure communication quality.

[0011] Preferably, the propagation loss model between the rescue drone and the drone base station is:

[0012] in, It represents the propagation loss between the rescue drone and the drone base station, in dB; Indicates the operating frequency of the drone base station, in MHz; Indicates the distance between the rescue drone and the drone base station, in km; Indicates the flight altitude difference between the rescue drone and the drone base station; represents the base value of the propagation loss index; Represents the influence factor of flight altitude difference on propagation loss index; represents a Gaussian random variable with a mean of zero, which is used to represent the effect of shadow fading in the propagation loss model; According to the propagation loss model and the preset maximum flight height difference between the rescue drone and the drone base station, the optimal operating frequency of the drone base station is determined with the goal of minimizing the propagation loss between the rescue drone and the drone base station, and the signal coverage range of the drone base station is expanded.

[0013] Preferably, during the movement of the rescue drone, the Doppler frequency shift of the communication signal between the rescue drone and the drone base station is :

[0014] Where c is the speed of light; The relative moving speed between the rescue drone and the drone base station; is the angle between the UAV base station and the moving direction of the rescue UAV; The Doppler frequency shift is calculated in real time according to the optimal working frequency of the UAV base station, and the optimal communication frequency band of the rescue UAV is determined according to the calculation result of the Doppler frequency shift to improve the communication quality.

[0015] Preferably, the data transmitted by the communication layer includes: management data, control data and perception data; The management data includes: distribution and transportation strategies for emergency medical supplies, and rescue drone route planning data; The control data includes flight control instructions of the rescue drone; The perception data includes disaster-affected personnel perception data, environmental perception data, drone status perception data, drone scheduling execution perception data, drone flight route perception data and medical supplies status perception data collected by equipment on the rescue drone.

[0016] Preferably, the prior knowledge database and decision support database in the data layer are both deployed on the edge server; the edge server and the cloud platform both establish wireless connections with the drone base station; Medical personnel upload the management data to the cloud platform through the application layer and generate control data, which is then sent to the rescue drone through the drone base station for flight control; During the transportation of emergency medical supplies, the rescue drone uploads the perception data to the edge server in real time through the drone base station. The edge server calculates and processes the perception data based on the prior knowledge database and decision support database, and sends the processing results to the cloud platform for storage; The cloud platform sends the stored data to the application layer in real time for medical personnel to monitor and analyze in real time, and adjust the management data in real time to achieve closed-loop control of data; The present invention realizes a closed-loop process of "monitoring-analysis-rectification-issuance" through closed-loop data control, thereby improving the accuracy and reliability of data and ensuring the stability and effectiveness of medical material transportation.

[0017] Preferably, the application layer establishes a communication connection with an external geographic information system, and obtains map data and meteorological data of the rescue area before the transportation of emergency medical supplies, and constructs an initial data set in combination with the emergency resource data, personnel organization data and emergency plan data of the rescue area, and performs emergency event situation assessment, intelligent matching of emergency plans and material team deployment management based on the initial data set, thereby obtaining initial management data.

[0018] Preferably, the application layer includes a classification module, a data visualization module and an interactive analysis module; The classification module is used to classify the data on the cloud platform; the data visualization module is used to visualize the classified data; and the interactive analysis module is used to provide data interaction and data analysis services for medical personnel.

[0019] Compared with the prior art, the technical solution of the present invention has the following beneficial effects: The present invention provides an emergency medical material transportation system based on drones, comprising a resource layer, a communication layer, a data layer and an application layer arranged in sequence from the bottom layer to the top layer; the resource layer is used to provide hardware support for the transportation of emergency medical materials; the communication layer is provided with a drone base station of the TD-LTE 4G network standard, which is used to realize data transmission of rescue drones; the data layer is used to perform real-time calculation and processing on the data transmitted by the communication layer; the application layer is used to provide application services for medical personnel; As for the improvement of the communication layer, the present invention realizes large-scale and long-distance wireless private network coverage by setting up a drone base station of the TD-LTE 4G network standard, which can meet the communication needs of emergency medical material transportation; secondly, the present invention constructs a dedicated propagation loss model to determine the optimal communication frequency to maximize the coverage range; in addition, the present invention further considers the impact of Doppler frequency shift on communication quality when the drone moves at a high speed, determines the optimal communication frequency band in real time, and improves the communication quality of the rescue drone to the greatest extent; Overall, the system of the present invention can be applied to the transportation of medical supplies in major disasters, quickly deliver urgently needed supplies to disaster areas or hard-to-reach places, provide timely medical support to injured or sick people, and improve the effectiveness and speed of emergency rescue; secondly, the drone is equipped with sensors and identification technology, which can track and locate the supplies, help the command department to grasp the use of the materials in a timely manner, and thus scientifically dispatch, distribute and replenish the materials; in addition, the drone is equipped with various sensors, which can provide real-time aerial perspectives, data collection and transmission, help the command department make wise decisions, and provide effective support for rescue personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a structural diagram of an emergency medical supplies transportation system based on drones provided in Example 1.

[0021] Figure 2 This is a structural diagram of an emergency medical supplies transportation system based on drones provided in Example 2.

[0022] Figure 3 This is the data closed-loop control flow chart provided in Example 2.

[0023] Figure 4 This is a network architecture diagram suitable for an emergency medical supplies transportation system provided in Example 3. DETAILED DESCRIPTION

[0024] The drawings are for illustrative purposes only and should not be construed as limiting the present patent; In order to better illustrate the present embodiment, some parts in the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product; It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0025] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0026] Example 1 like Figure 1As shown, this embodiment provides an emergency medical supplies transportation system based on a drone, including: The resource layer includes drone resources and infrastructure resources. The drone resources include several rescue drones. The infrastructure resources include several types of equipment installed on the rescue drones. The resource layer is used to provide hardware support for the transportation of emergency medical supplies. The communication layer includes a UAV base station, wherein the UAV base station adopts the TD-LTE 4G network standard, improves the signal coverage by optimizing a preset propagation loss model, and improves the communication quality by optimizing the Doppler frequency shift; the communication layer is used to provide wide-area coverage and high-quality wireless private network; The data layer includes an edge server, a cloud platform, a priori knowledge database and a decision support database; the edge server is used to perform real-time calculation and processing on the data transmitted by the communication layer; the cloud platform is used to store the data processing results of the edge server; the priori knowledge database is used to pre-store the priori knowledge required for edge computing; the decision support database is used to provide real-time decision support during the transportation of emergency medical supplies by rescue drones; the data layer is used to perform real-time calculation and processing on the data transmitted by the communication layer; The application layer establishes a communication connection with the cloud platform and is used to provide application services for medical personnel; The resource layer, communication layer, data layer and application layer are arranged in sequence from the bottom layer to the top layer.

[0027] In the specific implementation process, post-disaster rescue is urgent and time-sensitive. In view of the severe environment such as large-scale communication interruption, power outage, road interruption, etc. faced by the transportation of emergency medical supplies, as well as the high cost of adding base stations of traditional operators, this embodiment provides a solution for using drones as air base stations to realize relay communication, so as to ensure that rescue drones can carry emergency medical supplies to the victims in time and realize emergency rescue. Regarding the selection of base station networks, since traditional operator base stations are difficult to cover remote areas and post-disaster areas, and the cost of adding them is high, this embodiment chooses to independently build a low-cost, easy-to-deploy TD-LTE wireless cluster private network; The spectrum efficiency of TD-LTE private networks can reach 5~10bps / Hz, which means that with the same spectrum resources, TD-LTE can provide higher data transmission rates. At the same time, when cluster applications are used in private networks, TD-LTE has shorter call delays, which is crucial for emergency communication scenarios that require rapid response. Based on the characteristics of TD-LTE private network that can support short-delay drone clusters, it can greatly improve command efficiency and rescue level; more importantly, TD-LTE private network has the function of direct mode operation (DMO), which can ensure intercom communication between the command center and the rescue drone terminal in the case of damage to the core network and base station, ensuring that medical rescue work continues uninterrupted, effectively ensuring the stable and reliable transportation of medical supplies; in addition, based on the broadband multimedia video scheduling function of TD-LTE, the application layer can further realize functions such as video uploading, monitoring, distribution and point calling, realizing visual command and expert consultation anytime and anywhere, and further improving the rescue level; During the rescue process, the system provided by this embodiment is first built, multiple drone base stations are deployed in the rescue area to form a network, and the rescue drones, edge servers, and cloud platforms are wirelessly connected to the drone base stations respectively, and the cloud platform is connected to the application layer; The application layer can provide medical personnel with a human-computer interaction interface and various services (such as data monitoring, analysis, and decision support). Before the transportation of emergency medical supplies, medical personnel upload the initial medical supplies transportation and distribution strategy and the planned rescue drone routes to the cloud platform through the application layer. The cloud platform automatically generates control data after analysis, and then sends the control data to the rescue drone through the drone base station for flight control; During the transportation of emergency medical supplies, the rescue drone uploads the perception data to the edge server in real time through the drone base station. The edge server calculates and processes the perception data based on the prior knowledge database and decision support database, and sends the processing results to the cloud platform for storage; The cloud platform then sends the stored data to the application layer in real time for medical staff to monitor and analyze in real time, as well as adjust and manage data in real time, thus achieving closed-loop control of data. Based on the system provided in this embodiment, a closed-loop process of "monitoring-analysis-rectification-issuance" can be realized, thereby improving the accuracy and reliability of data and ensuring the stability and effectiveness of medical supplies transportation; This system achieves large-scale, long-distance wireless private network coverage by setting up drone base stations with TD-LTE 4G network standards, which can meet the communication needs of emergency medical supplies transportation, thereby further improving the overall rescue efficiency.

[0028] Example 2 like Figure 2 As shown, this embodiment provides an emergency medical supplies transportation system based on a drone, including: The resource layer includes drone resources and infrastructure resources. The drone resources include several rescue drones. The infrastructure resources include several types of equipment installed on the rescue drones. The resource layer is used to provide hardware support for the transportation of emergency medical supplies. The communication layer includes a drone base station, which adopts the TD-LTE 4G network standard, improves the signal coverage by optimizing a preset propagation loss model, and improves the communication quality by optimizing the Doppler frequency shift; the communication layer is used to provide wide-area coverage and high-quality wireless private network; in this embodiment, when the coverage range is not large, the drone base station can also use a 5G network with faster speed, shorter delay and larger bandwidth; The data layer includes an edge server, a cloud platform, a priori knowledge database and a decision support database; the edge server is used to perform real-time calculation and processing on the data transmitted by the communication layer; the cloud platform is used to store the data processing results of the edge server; the priori knowledge database is used to pre-store the priori knowledge required for edge computing; the decision support database is used to provide real-time decision support during the transportation of emergency medical supplies by rescue drones; the data layer is used to perform real-time calculation and processing on the data transmitted by the communication layer; The application layer establishes a communication connection with the cloud platform and is used to provide application services for medical personnel; The resource layer, communication layer, data layer and application layer are arranged in order from the bottom layer to the top layer; The equipment in the infrastructure resources include: Multispectral camera, used to obtain real-time image information in the rescue area and transmit live video in real time; Laser tracking imager, used to obtain hot spot information and radar point cloud information in the emergency medical supplies loading area in real time; the hot spot information is used to detect potential temperature anomalies in medical supplies; the radar point cloud information is used to detect height differences in the emergency medical supplies loading area to ensure uniform distribution of medical supplies during transportation and avoid unstable loading conditions; The autonomous positioning bionic robotic arm is used to obtain the image and position coordinates of the emergency medical supplies loaded by the rescue drone, so as to further monitor the status information of the loaded medical supplies; the autonomous positioning bionic robotic arm is also provided with an infrared sensing device, based on which the robotic arm can autonomously grasp the medical supplies; by carrying the bionic robotic arm with autonomous positioning function, the loading of materials is ensured to be accurate, and the automated loading equipment reduces manual operation and improves loading efficiency; The multi-sensor module includes a thermal imaging device and a GPS positioning device, which is used to sense and accurately locate the position of the trapped person in real time; the multi-sensor module also includes a temperature and humidity sensor, which is used to sense the environment of the rescue area and the status of medical supplies in real time; based on the multi-sensor module, this embodiment can realize the accurate sensing and positioning of the trapped person, as well as the status monitoring of medical supplies and the environment, to ensure the safe delivery of the supplies; LTE terminal, used to achieve real-time wireless communication between the rescue drone and the drone base station; At least two drone base stations are set in the communication layer, at least one drone base station is used as the main base station, and the other drone base stations are used as auxiliary base stations. In a one-main-multiple-auxiliary mode, a TD-LTE 4G network is built as a rescue drone cluster dedicated network; Divide the rescue area into several sub-areas and sort them according to the severity of the disaster. Deploy the primary base station in the most important sub-area and deploy the secondary base stations in other sub-areas. This embodiment sets up multiple drone LTE base stations to form a wireless network, which greatly expands the overall coverage of the wireless network and can meet the communication needs of emergency medical supplies transportation; The drone base stations are all aerial LTE base stations based on drones; The drone base stations all include a baseband control unit eBBU, a radio frequency remote processing unit eRRU and a directional antenna, all of which adopt an S1 configuration, and at the same time, the drone hovering circle, the directional wide-beam antenna coverage circle and the coverage area circle are approximately concentric circles to achieve stable coverage of the drone base station signal; For the hardware configuration of a single drone base station, this embodiment uses eBBU and eRRU, and the base station performance is better to meet the higher data rate and more complex network requirements in the rescue area; at the same time, a single antenna configuration of a single sub-area is adopted to avoid antenna switching of the base station and ensure communication quality; In addition, in this embodiment, the drone base station can also add the drone self-organizing network LR-WIFI (long-distance WIFI) protocol function on the basis of TD-LTE wireless communication, so as to facilitate WIFI communication between the drone base stations on site and realize real-time synchronization and sharing of the data of each base station; The propagation loss model between the rescue drone and the drone base station is:

[0029] in, It represents the propagation loss between the rescue drone and the drone base station, in dB; Indicates the operating frequency of the drone base station, in MHz; Indicates the distance between the rescue drone and the drone base station, in km; Indicates the flight altitude difference between the rescue drone and the drone base station; represents the base value of the propagation loss index; Represents the influence factor of flight altitude difference on propagation loss index; represents a Gaussian random variable with a mean of zero, which is used to represent the effect of shadow fading in the propagation loss model; According to the propagation loss model and the preset maximum flight height difference between the rescue drone and the drone base station, the optimal operating frequency of the drone base station is determined with the goal of minimizing the propagation loss between the rescue drone and the drone base station, so as to expand the signal coverage range of the drone base station; When the rescue drone is moving, the Doppler frequency shift of the communication signal between it and the drone base station is :

[0030] Where c is the speed of light; The relative moving speed between the rescue drone and the drone base station; is the angle between the UAV base station and the moving direction of the rescue UAV; Calculate the Doppler frequency shift in real time according to the optimal operating frequency of the drone base station, and determine the optimal communication frequency band of the rescue drone according to the calculation result of the Doppler frequency shift to improve the communication quality; The data transmitted by the communication layer includes: management data, control data and perception data; The management data includes: distribution and transportation strategies for emergency medical supplies, and rescue drone route planning data; The control data includes flight control instructions of the rescue drone; The perception data includes the perception data of disaster victims, environmental perception data, drone status perception data, drone dispatch execution perception data, drone flight route perception data and medical supplies status perception data collected by the equipment on the rescue drone; The prior knowledge database and decision support database in the data layer are both deployed on the edge server; the edge server and the cloud platform both establish wireless connections with the drone base station; and the edge server is deployed near the main base station in the drone base station to improve the computational processing efficiency of local data; The application layer establishes a communication connection with an external geographic information system, and obtains map data and meteorological data of the rescue area before the transportation of emergency medical supplies. At the same time, it combines the emergency resource data, personnel organization data and emergency plan data of the rescue area to build an initial data set, and performs emergency event situation assessment, emergency plan intelligent matching and material team deployment management based on the initial data set, thereby obtaining initial management data; Medical personnel upload the management data to the cloud platform through the application layer and generate control data, which is then sent to the rescue drone through the drone base station for flight control; During the transportation of emergency medical supplies, the rescue drone uploads the perception data to the edge server in real time through the drone base station. The edge server calculates and processes the perception data based on the prior knowledge database and decision support database, and sends the processing results to the cloud platform for storage; The cloud platform sends the stored data to the application layer in real time for medical personnel to monitor and analyze in real time, and adjust the management data in real time to achieve closed-loop control of data; This embodiment realizes the closed-loop process of "monitoring-analysis-rectification-issuance" through closed-loop data control, thereby improving the accuracy and reliability of data and ensuring the stability and effectiveness of medical supplies transportation; The application layer includes a classification module, a data visualization module and an interactive analysis module; The classification module is used to classify the data on the cloud platform; the data visualization module is used to visualize the classified data; and the interactive analysis module is used to provide data interaction and data analysis services for medical personnel.

[0031] In the specific implementation process, the emergency medical supplies transportation system in this embodiment includes a resource layer, a communication layer, a data layer and an application layer arranged in order from the bottom layer to the top layer; The resource layer is used to provide hardware support for the transportation of emergency medical supplies. It is equipped with multiple rescue drones, each of which is equipped with the following equipment: multi-spectral camera, laser tracking imager, autonomous positioning bionic robotic arm, multi-sensor, and LTE terminal; The resource layer can obtain relevant data on the loading status of the drone; 1) Through the multispectral camera, various types of image information in the area can be quickly obtained, and real-time live video broadcast can be transmitted back; based on the accumulation of big data, support vector machines can be further used to realize the rapid extraction and monitoring of aerial survey and remote sensing information, radar optoelectronic linkage control and rescue drone target classification and identification; 2) Through the laser tracking imager, hot spots in the medical supplies loading area can be quickly identified to help detect potential abnormalities in medical supplies, such as abnormal heating, uneven temperature, etc., and the height difference in the medical supplies loading area can be detected based on the collected radar point cloud information to ensure the uniform distribution of medical supplies during transportation and avoid unstable loading conditions; 3) The autonomous positioning bionic robotic arm is used to obtain images and position coordinates of medical supplies loaded by rescue drones, helping Medical workers monitor the status information of loaded medical supplies, and use infrared sensing devices and bionic robotic arms to autonomously grasp objects, and combine with rescue drones to provide systematic delivery services to achieve intelligent, autonomous, three-dimensional, rapid and seamless instant delivery logistics; 4) Through multiple sensors, the position of trapped people can be sensed and accurately located in real time; at the same time, the environment of the rescue area and the temperature and humidity of medical supplies can be sensed and monitored in real time, and a safety response mechanism can be further set up. For example, when a harsh environment or abnormal temperature and humidity of medical supplies is detected, the rescue drone automatically returns and reloads medical supplies to ensure safe and accurate delivery of supplies; 5) Wireless communication between the rescue drone and the drone base station is realized through the LTE terminal, and the TD-LTE wireless network of the LTE terminal and the drone base station is set up in conjunction; The communication layer is equipped with a drone base station of the TD-LTE 4G network standard, which is used to realize data transmission of the rescue drone; the TD-LTE 4G private network has the advantages of high spectrum efficiency, low latency, mature technology and flexible uplink and downlink scheduling, full IP architecture, perfect QoS guarantee and high data transmission rate, etc. It is particularly suitable for scenarios that require rapid response and high-quality communication services, and provides an efficient and reliable communication solution for the field of emergency medical supplies transportation; therefore, based on the TD-LTE 4G private network, this embodiment can realize cross-regional remote signal transmission, realize blind spot-free communication in emergency rescue, and greatly improve the efficiency of emergency rescue; In this embodiment, at least two drone base stations are set up in the communication layer, at least one drone base station is used as the main base station, and the other drone base stations are used as auxiliary base stations. In a one-main-multiple-auxiliary mode, a TD-LTE 4G network is built as a rescue drone cluster private network; the drone base stations are all aerial LTE base stations based on drones, and wireless communication connections are established with LTE terminals carried by rescue drones; At present, the existing drone base stations are mainly equipped with a BBU (Base Band Unit) and an RRU (Remote Radio Unit). They use a combination of omnidirectional + directional antennas or a combination of directional + directional antennas to form a sector using multiple antennas to achieve coverage of a single base station signal in the target area. The power distribution of multiple antennas in the coverage area is not linear, and there is a user terminal RSRP (Reference Signal Ratio) ReceivingPower) fluctuates; at the same time, changes in the height and inclination of the drone also make it possible for "black holes" to exist at the edges of the coverage areas of multiple antennas; the drone's hovering coverage of the target area will also cause frequent switching of base station antennas in the target area, causing communication interruption, affecting the rescue effect. At the same time, too many antenna layouts will also affect the aerodynamic characteristics of the drone and reduce the flight time; therefore, in this embodiment, the drone base station includes a baseband control unit eBBU, a radio frequency remote processing unit eRRU and a directional antenna, and adopts an S1 configuration. At the same time, the drone hovering circle, the directional wide-beam antenna coverage circle and the coverage area circle are approximately concentric circles to achieve stable coverage of the base station signal; each drone base station is divided into areas in the rescue area, and a single antenna and single zone configuration are adopted to ensure that the drone base station in each zone does not switch sectors and antennas, thereby ensuring communication stability; Regarding the configuration of the drone base station, in this embodiment, the main base station is set at the dispatch center of the rescue area, and the auxiliary base station is set at other locations in the rescue area to ensure that each sub-area is covered by a drone air base station; in this embodiment, one drone base station is used as the central base station, and multiple other base stations are flexibly used to form a joint network in different areas to expand the signal coverage range. While realizing audio, video and data interconnection, it also maximizes the coverage range of the 4G private network; In order to measure the signal strength of each drone base station, this embodiment constructs a propagation loss model to systematically describe the characteristics and laws of radio wave propagation between the rescue drone and the drone base station; the traditional Close-in (CI) model, Floating Intercept (FI) model and Dual Slope (DS) model are the three commonly used general propagation loss models, which are applicable to the entire millimeter wave frequency band; in addition, some standard organizations have also proposed their own standardized propagation loss models, among which the most widely used ones are the 3rd Generation Partnership Project (3GPP) model proposed by the organization and the 5G Channel Model (5GCM) model proposed by the organization; The CI model takes shadow fading factors into consideration on the basis of the free space propagation loss model. The CI model adjusts the path loss exponent (PLE) by fitting with the measured data to adapt to different propagation environments. The CI model has been widely used in various radio wave propagation scenarios in recent decades due to its simplicity. However, as a traditional propagation loss model, the CI model only considers the situation of the ground communication system, and does not consider the influence of the height of the receiving antenna and the transmitting antenna on the propagation loss. For the drone aerial relay base station, the height of the drone base station and the rescue drone will change greatly during the transportation of materials. Therefore, the height of the drone is an important factor that cannot be ignored in the propagation of air-to-ground electromagnetic signals. Therefore, for the drone low-altitude base station, this embodiment proposes a propagation loss model between the rescue drone and the drone base station. The model retains its physical characteristics on the basis of the CI model and increases the influence of the drone height on the propagation loss index, which is expressed as:

[0032] in, It represents the propagation loss between the rescue drone and the drone base station, in dB; Indicates the operating frequency of the drone base station, in MHz; Indicates the distance between the rescue drone and the drone base station, in km; Indicates the flight altitude difference between the rescue drone and the drone base station; represents the base value of the propagation loss index; Represents the influence factor of flight altitude difference on propagation loss index; represents a Gaussian random variable with a mean of zero, which is used to represent the effect of shadow fading in the propagation loss model; According to the constructed propagation loss model and the preset maximum flight height difference between the rescue UAV and the UAV base station, the optimal operating frequency of the UAV base station is determined with the goal of minimizing the propagation loss between the rescue UAV and the UAV base station, so as to try to cover the maximum coverage area within the appropriate height difference range; In actual space, there are often various factors that affect the propagation of radio waves, and it is generally difficult to have true free space propagation conditions; when the base station power is the same, the level difference at the same distance has less loss in the low frequency band; at the same level communication distance, the low frequency band has the widest coverage; therefore, after calculation, the operating frequency of the drone base station in this embodiment is preferably 2G band recultivated 4G, and the low frequency band is used to maximize the coverage of the 4G network; Since the signal of the drone base station has high mobility during the rescue process, the signal frequency has Doppler shift, which will cause the receiving end to be unable to correctly demodulate the signal, affecting the communication quality. Especially when the speed is relatively high compared with the rescue drone, the impact of Doppler shift on communication quality becomes more serious; This embodiment will focus on high mobility scenarios and give the Doppler frequency shift of the communication signal between the rescue drone and the drone base station during movement. The calculation formula is expressed as:

[0033] Where c is the speed of light; The relative moving speed between the rescue drone and the drone base station; is the angle between the UAV base station and the moving direction of the rescue UAV; Calculate the Doppler frequency shift in real time according to the optimal operating frequency of the drone base station, and determine the optimal communication frequency band of the rescue drone according to the calculation result of the Doppler frequency shift; During the transportation of emergency medical supplies, the UAV base station (working at the optimal frequency of the current frequency band to ensure the widest coverage) is used as the reference anchor point. The relative moving speed between the rescue UAV and the UAV base station, as well as the angle between the moving directions of the two, are obtained in real time. The Doppler shift of the rescue UAV receiving signal is calculated in real time, and the threshold of the Doppler shift is set. If the Doppler shift of the current frequency band of the LTE terminal is too large, other frequency bands are replaced, and the optimal operating frequency of the base station is recalculated in the replaced frequency band, and the Doppler shift is calculated again; the above process is repeated until the Doppler shift is less than the threshold, thereby achieving high-quality rescue UAV data transmission; The data layer is used to perform real-time calculation and processing on the data transmitted by the communication layer; in this embodiment, the data layer includes an edge server, a cloud platform, a priori knowledge database and a decision support database; the edge server is used to perform real-time calculation and processing on the data transmitted by the communication layer; the cloud platform is used to store the data processing results of the edge server; the priori knowledge database is used to pre-store the priori knowledge required for edge computing; the decision support database is used to provide real-time decision support during the transportation of emergency medical supplies by the rescue drone; the priori knowledge database and the decision support database in the data layer are both deployed on the edge server; the edge server and the cloud platform both establish a wireless connection with the drone base station; The data transmitted by the communication layer include: management data, control data and perception data; management data include: distribution and transportation strategies of emergency medical supplies, and rescue drone route planning data; control data include flight control instructions of rescue drones; perception data include disaster victims’ perception data, environmental perception data, drone status perception data, drone scheduling execution perception data, drone flight route perception data and medical supplies status perception data collected by the equipment on the rescue drones; The application layer includes classification module, data visualization module and interactive analysis module; the classification module is used to classify the data on the cloud platform; the data visualization module is used to visualize the classified data; the interactive analysis module is used to provide data interaction and data analysis services for medical personnel; Before the transportation of emergency medical supplies, the application layer also establishes a communication connection with the external geographic information system and obtains map data and meteorological data of the rescue area. At the same time, it combines the emergency resource data, personnel organization data and emergency plan data of the rescue area to build an initial data set. Based on the initial data set, it conducts emergency event situation assessment, intelligent matching of emergency plans and material team deployment management, thereby obtaining initial management data; The application layer can determine the rescue danger level based on the identification and prediction results, and intuitively display the prediction and warning information to the medical team and rescue team through a visual interface, including real-time monitoring charts of the loading status of the rescue drone, visual display of analysis results, alarm prompts of abnormal situations, etc., to provide timely decision support for rescue personnel and help with job training for rescue personnel; at the same time, it can classify the information responded by the edge server according to user functional requirements, and perform data visualization, chart generation and decision support. At the same time, it provides an interactive interface between operators and the system, visualizes monitoring data, analysis results and decision support information, and helps rescue teams and others carry out flight missions efficiently and safely, effectively ensures real-time monitoring of data, and improves rescue efficiency and reliability; When performing emergency medical supplies transportation tasks, medical personnel upload the initial management data to the cloud platform through the application layer. The cloud platform automatically generates control data after parsing, and then sends the control data to the rescue drone through the drone base station for flight control; During the transportation of emergency medical supplies, the rescue drone uploads the perception data to the edge server in real time through the drone base station. The edge server calculates and processes the perception data based on the prior knowledge database and decision support database, and sends the processing results to the cloud platform for storage; The cloud platform then sends the stored data to the application layer in real time for medical staff to monitor and analyze in real time, and adjust the management data in real time to achieve closed-loop data control, such as Figure 3 As shown; From the perspective of improvements in the communication layer, this embodiment achieves large-scale, long-distance wireless private network coverage by setting up a drone base station of the TD-LTE 4G network standard, which can meet the communication needs of emergency medical supplies transportation; secondly, this embodiment constructs a dedicated propagation loss model to determine the optimal communication frequency to maximize the coverage range; in addition, this embodiment further considers the impact of Doppler frequency shift on communication quality when the drone moves at a high speed, determines the optimal communication frequency band, and improves the communication quality of the rescue drone to the greatest extent; On the whole, the system in this embodiment can be applied to the task of transporting medical supplies in major disasters, quickly delivering urgently needed supplies to disaster areas or hard-to-reach places, providing timely medical support to injured or sick people, and improving the effectiveness and speed of emergency rescue; secondly, the drone is equipped with sensors and identification technology, which can track and locate supplies, help the command department to grasp the use of materials in a timely manner, and thus scientifically dispatch, distribute and replenish supplies; in addition, the drone is equipped with various sensors, which can provide real-time aerial perspectives, data collection and transmission, help the command department make wise decisions, and provide effective support for rescue personnel.

[0034] Example 3 This embodiment provides a network architecture applicable to the emergency medical supplies transportation system described in Embodiment 1 or 2.

[0035] In the specific implementation process, the existing UAV communication systems all use custom data composite transmission methods and point-to-point transmission architectures. Multiple data such as measurement and control data and user application data are complexly coupled, and the composite transmission data is bloated, presenting a "chimney-like" situation, which greatly increases the communication delay and greatly reduces the communication efficiency. The rescue area environment is harsh, and the transportation tasks are complex and changeable according to local conditions. Therefore, it is necessary to quickly reconstruct the data in the UAV base station in a targeted manner to meet the real-time requirements of emergency rescue scenarios. Based on this, this embodiment proposes a new network architecture for the wireless private network of the drone in Embodiment 1 or 2, such as Figure 4As shown in the figure, the entire system of this architecture is decoupled and divided into data plane, perception plane, control plane and management plane. The perception plane perceives the resource status of the underlying data plane, and learns and infers on the management plane, and finally generates a control strategy for emergency medical rescue needs, thus forming an emergency rescue intelligent management and control mechanism of "perception-decision-control"; the management plane uses abundant computing resources to further deploy intelligent training and inference engines of different scales and forms, analyzes the perceived rescue data and medical data, realizes the analysis, prediction and matching of rescue intentions and medical resource status, and forms an intelligent control strategy executable by the control plane; the control plane is the specific executor of the intelligent control strategy, and sends the corresponding action decisions to the data plane (rescue drone) through a standard interface, supporting the real-time and accurate issuance of strategies; 1) The perception plane analyzes, mines and predicts the status of disaster victims, flight safety status and medical supplies data, and realizes the perception of rescue dispatch execution, flight routes and service status. In order to make each rescue node programmable, its perception rules have been embedded in the device. Not only the flight status information of drones and the data flow information of medical supplies are measured, but also the information status, rescue characteristics and performance parameters flowing through each rescue node will be measured and reported to the perception plane through spontaneous measurement rules or receiving perception instructions issued by the perception plane. The active measurement method can more flexibly and accurately obtain the status information of various resources and the data flow information of medical supplies. In the active measurement, each rescue node actively sends a detection data packet to the network and analyzes the rescue behavior according to the changes in its characteristics affected by weather and other factors. 2) The data plane is mainly responsible for forwarding and processing various data packets in the entire system. It is based on the architecture of software-defined networks and builds a data plane for the transportation of medical rescue materials. In the data plane, its control rules are issued by the control plane through interfaces such as OpenFlow, and the intelligent recognition sensor completes the forwarding and processing of rescue data packets according to the control rules issued by the control plane; 3) The control plane connects the management plane and the data plane. Its main function is to map the control decisions of the intelligent management plane to the data plane. Through configuration management, the control plane sends the control strategies obtained by the management plane to achieve global control of the entire medical material transportation system. 4) The function of the management plane is to generate optimal control strategies such as global route planning and task scheduling based on the system's global situation map and corresponding rescue work needs; the management plane aggregates, stores and manages rescue situation information such as the status information collected by the perception plane and data stream information such as emergency medical supplies, extracts the global situation from it, and holographically represents the rescue situation from multiple dimensions to support the rescue upper plane's task formulation, rescue task execution, rescue control management, rescue result analysis and optimization and other functions; Based on the network architecture of this embodiment, it can effectively ensure the smooth operation of the drone wireless network built in Example 1 or 2, and can support more rescue drones to access at the same time without increasing additional hardware costs, thereby achieving more efficient transportation of emergency medical supplies.

[0036] The same or similar reference numerals correspond to the same or similar components; The terms used in the drawings to describe positional relationships are only used for illustrative purposes and should not be construed as limiting this patent; Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. An emergency medical supplies transportation system based on drones, characterized in that: include: The resource layer includes drone resources and infrastructure resources. The drone resources include several rescue drones. The infrastructure resources include several types of equipment installed on the rescue drones. The resource layer is used to provide hardware support for the transportation of emergency medical supplies. The communication layer includes a UAV base station, wherein the UAV base station adopts the TD-LTE 4G network standard, improves the signal coverage by optimizing a preset propagation loss model, and improves the communication quality by optimizing the Doppler frequency shift; the communication layer is used to provide wide-area coverage and high-quality wireless private network; The data layer includes an edge server, a cloud platform, a priori knowledge database and a decision support database; the edge server is used to perform real-time calculation and processing on the data transmitted by the communication layer; the cloud platform is used to store the data processing results of the edge server; the priori knowledge database is used to pre-store the priori knowledge required for edge computing; the decision support database is used to provide real-time decision support during the transportation of emergency medical supplies by rescue drones; the data layer is used to perform real-time calculation and processing on the data transmitted by the communication layer; The application layer establishes a communication connection with the cloud platform and is used to provide application services for medical personnel; The resource layer, communication layer, data layer and application layer are arranged in sequence from the bottom layer to the top layer.

2. The emergency medical supplies transportation system based on drone according to claim 1, characterized in that: The equipment in the infrastructure resources include: Multispectral camera, used to obtain real-time image information in the rescue area and transmit live video in real time; Laser tracking imager, used to obtain hot spot information and radar point cloud information in the emergency medical supplies loading area in real time; the hot spot information is used to detect potential temperature anomalies in medical supplies; the radar point cloud information is used to detect height differences in the emergency medical supplies loading area to ensure uniform distribution of medical supplies during transportation and avoid unstable loading conditions; The autonomous positioning bionic robotic arm is used to obtain the image and position coordinates of the emergency medical supplies loaded by the rescue drone, so as to further monitor the status information of the loaded medical supplies; the autonomous positioning bionic robotic arm is also provided with an infrared sensing device, based on which the robotic arm can autonomously grasp the medical supplies; The multi-sensor module includes a thermal imaging device and a GPS positioning device, which is used to sense and accurately locate the position of trapped people in real time; the multi-sensor module also includes a temperature and humidity sensor, which is used to sense the environment of the rescue area and the status of medical supplies in real time; LTE terminal, used to achieve real-time wireless communication between the rescue drone and the drone base station.

3. The emergency medical supplies transportation system based on drone according to claim 1, characterized in that: At least two drone base stations are set in the communication layer, at least one drone base station is used as the main base station, and the other drone base stations are used as auxiliary base stations. In a one-main-multiple-auxiliary mode, a TD-LTE 4G network is built as a rescue drone cluster dedicated network; The rescue area is divided into several sub-areas and ranked according to the degree of disaster. The main base station is deployed in the most important sub-area, and the auxiliary base stations are deployed in other sub-areas.

4. The emergency medical supplies transportation system based on drone according to claim 3 is characterized in that: The drone base stations are all aerial LTE base stations based on drones; The drone base stations all include a baseband control unit eBBU, a radio frequency remote processing unit eRRU and a directional antenna, all of which adopt the S1 configuration. At the same time, the drone hovering circle, the directional wide-beam antenna coverage circle and the coverage area circle are approximately concentric circles to achieve stable coverage of the drone base station signal.

5. The emergency medical supplies transportation system based on drones according to claim 3 or 4, characterized in that: The propagation loss model between the rescue drone and the drone base station is: in, It represents the propagation loss between the rescue drone and the drone base station, in dB; Indicates the operating frequency of the drone base station, in MHz; Indicates the distance between the rescue drone and the drone base station, in km; Indicates the flight altitude difference between the rescue drone and the drone base station; represents the base value of the propagation loss index; Represents the influence factor of flight altitude difference on propagation loss index; represents a Gaussian random variable with a mean of zero, which is used to represent the effect of shadow fading in the propagation loss model; According to the propagation loss model and the preset maximum flight height difference between the rescue drone and the drone base station, the optimal operating frequency of the drone base station is determined with the goal of minimizing the propagation loss between the rescue drone and the drone base station, and the signal coverage range of the drone base station is expanded.

6. The emergency medical supplies transportation system based on drones according to claim 5, characterized in that: When the rescue drone is moving, the Doppler frequency shift of the communication signal between it and the drone base station is : Where c is the speed of light; The relative moving speed between the rescue drone and the drone base station; is the angle between the UAV base station and the moving direction of the rescue UAV; The Doppler frequency shift is calculated in real time according to the optimal working frequency of the UAV base station, and the optimal communication frequency band of the rescue UAV is determined according to the calculation result of the Doppler frequency shift to improve the communication quality.

7. The emergency medical supplies transportation system based on drone according to claim 1, characterized in that: The data transmitted by the communication layer includes: management data, control data and perception data; The management data includes: distribution and transportation strategies for emergency medical supplies, and rescue drone route planning data; The control data includes flight control instructions of the rescue drone; The perception data includes disaster-affected personnel perception data, environmental perception data, drone status perception data, drone scheduling execution perception data, drone flight route perception data and medical supplies status perception data collected by equipment on the rescue drone.

8. The emergency medical supplies transportation system based on drones according to claim 7, characterized in that: The prior knowledge database and decision support database in the data layer are both deployed on the edge server; the edge server and the cloud platform both establish wireless connections with the drone base station; Medical personnel upload the management data to the cloud platform through the application layer and generate control data, which is then sent to the rescue drone through the drone base station for flight control; During the transportation of emergency medical supplies, the rescue drone uploads the perception data to the edge server in real time through the drone base station. The edge server calculates and processes the perception data based on the prior knowledge database and decision support database, and sends the processing results to the cloud platform for storage; The cloud platform sends the stored data to the application layer in real time for medical personnel to monitor and analyze in real time, as well as adjust and manage data in real time to achieve closed-loop control of data.

9. The emergency medical supplies transportation system based on drones according to claim 7, characterized in that: The application layer establishes a communication connection with an external geographic information system, and obtains map data and meteorological data of the rescue area before the transportation of emergency medical supplies. At the same time, it constructs an initial data set in combination with the emergency resource data, personnel organization data and emergency plan data of the rescue area. Based on the initial data set, emergency event situation assessment, intelligent matching of emergency plans and material team deployment management are performed to obtain initial management data.

10. The emergency medical supplies transportation system based on drones according to claim 1, characterized in that: The application layer includes a classification module, a data visualization module and an interactive analysis module; The classification module is used to classify the data on the cloud platform; the data visualization module is used to visualize the classified data; and the interactive analysis module is used to provide data interaction and data analysis services for medical personnel.

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