An emergency medical supplies transportation system based on drones

Abstract

The present invention provides an emergency medical supplies transportation system based on drones, including a resource layer, a communication layer, a data layer and an application layer arranged in sequence from the bottom to the top; the resource layer is used to provide hardware support for the transportation of emergency medical supplies; the communication layer is provided with a drone base station of the TD-LTE 4G network standard, which is used to provide wide-area coverage and high-quality wireless private network; the data layer is used to perform real-time calculation and processing of data transmitted by the communication layer; the application layer is used to provide application services for medical personnel; 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; secondly, the present invention constructs a dedicated propagation loss model to determine the optimal communication frequency, thereby expanding the signal coverage range; in addition, the present invention takes into account the influence of Doppler frequency shift on communication quality, and maximizes the communication quality of the rescue drone by determining the optimal communication frequency band in real time.

Description

Technical Field

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

[0002] In recent years, drones have played an increasingly important role in disaster reconnaissance, on-site assessment, monitoring and tracking, precise positioning, rescue assistance, supervision, and the delivery of relief supplies. In the aftermath of extreme disasters, where ground transportation is disrupted or closed, traditional human resources struggle to quickly reach disaster areas for rescue and disaster assessment. Drones, due to their portability and autonomous flight capabilities, are an ideal choice.

[0003] In the field of emergency medical rescue, it is generally believed that providing treatment within one hour of injury is the critical "golden window" for saving lives and reducing disability. However, in many areas of my country, insufficient communication and transportation, coupled with traffic congestion caused by accidents, significantly delays the arrival of medical personnel at the scene. Especially in mountainous areas and congested urban areas, on-site emergency response times often exceed two hours. Even if an ambulance is dispatched, it may not arrive at the scene in time to provide necessary medical assistance due to traffic and other factors, thus missing the optimal opportunity for treatment. Therefore, improving emergency response speed is crucial to improving the success rate of rescue.

[0004] On the one hand, drones can overcome terrain and environmental constraints, easily flying over damaged buildings, obstacles, and complex terrain. On the other hand, drone swarms can cover large areas in a short period of time, greatly improving search and rescue efficiency. Furthermore, drones can quickly identify closed roads and difficult rescue areas, and provide real-time image and video transmission, enabling command centers and rescue personnel to make rapid decisions. Therefore, building a drone-based material transportation system can quickly deliver supplies to accident sites within the golden rescue time, providing emergency assistance and thus improving the success rate of rescue operations.

[0005] However, when natural disasters occur, communication equipment is often damaged in the short term. Disaster-affected areas often face widespread communication outages, power outages, and road disruptions, making conventional emergency communication methods incapable of quickly restoring communications. Furthermore, ground base stations for rescue drones are limited, resulting in high deployment costs. Given the remote locations of some rescue operations, it's impossible to quickly achieve ultra-long-range communication coverage by deploying multiple ground base stations. Therefore, it's necessary to design a highly stable and reliable drone communication solution specifically for post-disaster scenarios to enhance the communication capabilities of rescue drones and, consequently, improve overall rescue efficiency. Summary of the Invention

[0006] In order to overcome the defect of the rescue drones in the above-mentioned prior art that they 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 solutions of the present invention are as follows:

[0008] An emergency medical supplies transportation system based on drones, comprising:

[0009] 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;

[0010] The communication layer includes a drone base station. The drone base station uses the TD-LTE 4G network standard, improves signal coverage by optimizing a preset propagation loss model, and improves communication quality by optimizing Doppler shift. The communication layer is used to provide wide-area coverage and high-quality wireless private networks.

[0011] 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 calculations 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; and the data layer is used to perform real-time calculations and processing on the data transmitted by the communication layer;

[0012] The application layer establishes a communication connection with the cloud platform and is used to provide application services to medical personnel;

[0013] The resource layer, communication layer, data layer and application layer are arranged in sequence from the bottom layer to the top layer.

[0014] Preferably, the equipment in the infrastructure resources includes:

[0015] Multispectral camera, used to obtain real-time image information within the rescue area and transmit live video in real time;

[0016] A laser tracking imager is used to acquire real-time hotspot information and radar point cloud information in the emergency medical supply loading area. The hotspot information is used to detect potential temperature anomalies in the medical supplies. The radar point cloud information is used to detect height differences in the emergency medical supply loading area to ensure uniform distribution of medical supplies during transportation and avoid unstable loading conditions.

[0017] The autonomous positioning bionic robotic arm is used to obtain images and position coordinates of emergency medical supplies loaded on a rescue drone, thereby further monitoring the status of the loaded medical supplies. The autonomous positioning bionic robotic arm is also equipped with an infrared sensor, which enables the robotic arm to autonomously grasp medical supplies. By equipping the bionic robotic arm with autonomous positioning capabilities, accurate material loading is ensured, and automated loading equipment reduces manual operations and improves loading efficiency.

[0018] The multi-sensor module includes a thermal imaging device and a GPS positioning device for real-time sensing and accurate positioning of trapped personnel. The multi-sensor module also includes temperature and humidity sensors for real-time sensing of the rescue area environment and the status of medical supplies. Based on the multi-sensor module, the present invention can achieve accurate sensing and positioning of trapped personnel, as well as monitoring the status of medical supplies and the environment, ensuring the safe delivery of supplies.

[0019] LTE terminal, used to achieve real-time wireless communication between the rescue drone and the drone base station.

[0020] 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. In a one-primary-multiple-secondary manner, a TD-LTE 4G network is built as a dedicated network for the rescue drone cluster.

[0021] Divide the rescue area into several sub-areas and rank them according to the severity of the disaster. Deploy the primary base station in the most important sub-area and deploy auxiliary base stations in other sub-areas.

[0022] 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 used in the private network, TD-LTE's call delay is relatively short, which is crucial for emergency communication scenarios that require rapid response; therefore, the present invention 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.

[0023] Preferably, the drone base stations are all aerial LTE base stations based on drones;

[0024] The drone base stations all include a baseband control unit (eBBU), a radio remote unit (eRRU), and directional antennas, all in S1 configuration. They also achieve stable coverage of drone base station signals by using the drone's hovering circle, the directional wide-beam antenna's coverage circle, and the coverage area circle as approximately concentric circles.

[0025] For the hardware configuration of a single drone base station, the present invention adopts eBBU and eRRU, which have better base station performance 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.

[0026] Preferably, the propagation loss model between the rescue drone and the drone base station is:

[0027]

[0028] 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; Indicates the base value of the propagation loss index; Indicates the impact factor of flight altitude difference on the propagation loss index; represents a Gaussian random variable with zero mean, which is used to represent the effect of shadow fading in the propagation loss model;

[0029] According to the propagation loss model and the preset maximum flight altitude difference between the rescue drone and the drone base station, with the goal of minimizing the propagation loss between the rescue drone and the drone base station, the optimal operating frequency of the drone base station is determined to expand the signal coverage range of the drone base station.

[0030] 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 :

[0031]

[0032] 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;

[0033] The Doppler frequency shift is calculated in real time according to the optimal operating 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, thereby improving the communication quality.

[0034] Preferably, the data transmitted by the communication layer includes: management data, control data and perception data;

[0035] The management data includes: distribution and transportation strategies for emergency medical supplies, and rescue drone route planning data;

[0036] The control data includes flight control instructions for the rescue drone;

[0037] The perception data includes the perception data of disaster victims, environmental perception data, drone status perception data, drone scheduling and execution perception data, drone flight route perception data and medical supplies status perception data collected by equipment on the rescue drone.

[0038] 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;

[0039] 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.

[0040] During the transportation of emergency medical supplies, rescue drones upload perception data to edge servers in real time through drone base stations. The edge servers calculate and process the perception data based on the prior knowledge database and decision support database, and send the processing results to the cloud platform for storage.

[0041] The cloud platform sends its 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 data management and control.

[0042] Through closed-loop data management and control, the present invention realizes a closed-loop process of "monitoring-analysis-rectification-issuance", thereby improving the accuracy and reliability of data and ensuring the stability and effectiveness of medical supplies transportation.

[0043] 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 transporting 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 construct an initial data set, 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.

[0044] Preferably, the application layer includes a classification module, a data visualization module and an interactive analysis module;

[0045] 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.

[0046] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0047] The present invention provides an emergency medical supplies transportation system based on drones, comprising a resource layer, a communication layer, a data layer, and an application layer arranged in order from the bottom to the top; the resource layer is used to provide hardware support for the transportation of emergency medical supplies; 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 of data transmitted by the communication layer; and the application layer is used to provide application services for medical personnel.

[0048] Regarding improvements to the communication layer, the present invention achieves wide-area, long-distance wireless private network coverage by installing a TD-LTE 4G network standard drone base station, which can meet the communication needs of emergency medical supply transportation. Secondly, the present invention constructs a dedicated propagation loss model to determine the optimal communication frequency to maximize coverage. In addition, the present invention further considers the impact of Doppler frequency shift on communication quality when the drone moves at high speeds, and determines the optimal communication frequency band in real time, thereby maximizing the communication quality of the rescue drone.

[0049] Overall, the system of the present invention can be applied to the transportation of 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, helping the command department to grasp the usage status of supplies in a timely manner, so as to 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, helping the command department to make wise decisions and provide effective support for rescue personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a structural diagram of an emergency medical supplies transportation system based on drones provided in Example 1.

[0051] Figure 2 This is a structural diagram of an emergency medical supplies transportation system based on drones provided in Example 2.

[0052] Figure 3 This is the data closed-loop control flow chart provided in Example 2.

[0053] Figure 4 This is a network architecture diagram suitable for an emergency medical supplies transportation system provided in Example 3. DETAILED DESCRIPTION

[0054] The accompanying drawings are for illustrative purposes only and are not to be construed as limiting this patent;

[0055] In order to better illustrate this embodiment, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product size;

[0056] It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0057] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0058] Example 1

[0059] like Figure 1 As shown, this embodiment provides an emergency medical supplies transportation system based on drones, including:

[0060] 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;

[0061] The communication layer includes a drone base station. The drone base station uses the TD-LTE 4G network standard, improves signal coverage by optimizing a preset propagation loss model, and improves communication quality by optimizing Doppler shift. The communication layer is used to provide wide-area coverage and high-quality wireless private networks.

[0062] 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 calculations 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; and the data layer is used to perform real-time calculations and processing on the data transmitted by the communication layer;

[0063] The application layer establishes a communication connection with the cloud platform and is used to provide application services to medical personnel;

[0064] The resource layer, communication layer, data layer and application layer are arranged in sequence from the bottom layer to the top layer.

[0065] In the specific implementation process, post-disaster rescue is of the utmost importance. To address the challenges faced by the transportation of emergency medical supplies, such as widespread communication outages, power outages, and road disruptions, as well as the high cost of adding base stations for traditional operators, this embodiment provides a solution that uses drones as aerial base stations to achieve relay communications. This ensures that rescue drones can carry emergency medical supplies to disaster victims in a timely manner, achieving emergency rescue.

[0066] Regarding the selection of base station networks, since traditional operator base stations have difficulty covering 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 trunked private network.

[0067] The spectrum efficiency of TD-LTE private networks can reach 5-10 bps / Hz, which means that with the same spectrum resources, TD-LTE can provide higher data transmission rates. Furthermore, when using trunking applications in private networks, TD-LTE offers shorter call latency, which is crucial for emergency communication scenarios requiring rapid response.

[0068] TD-LTE private networks can support short-latency drone swarms, significantly improving command efficiency and rescue capabilities. More importantly, TD-LTE private networks feature Direct Mode Operation (DMO). Even in the event of core network and base station damage, this allows for intercom communication between command centers and rescue drone terminals, ensuring uninterrupted medical rescue operations and the reliable and stable transportation of medical supplies. Furthermore, TD-LTE's broadband multimedia video scheduling capabilities allow the application layer to further implement video upload, monitoring, distribution, and point-to-point calling, enabling anytime, anywhere visual command and expert consultation, further enhancing rescue capabilities.

[0069] 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 wireless connections are established between the rescue drones, edge servers, and cloud platforms and the drone base stations respectively. At the same time, the cloud platform is connected to the application layer.

[0070] 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 supply transportation and distribution strategy and the planned rescue drone route 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.

[0071] During the transportation of emergency medical supplies, rescue drones upload perception data to edge servers in real time through drone base stations. The edge servers calculate and process the perception data based on the prior knowledge database and decision support database, and send the processing results to the cloud platform for storage.

[0072] The cloud platform then sends the stored data to the application layer in real time for medical staff to monitor and analyze, adjust and manage data in real time, and achieve closed-loop data control.

[0073] The system provided in this embodiment can implement a closed-loop process of "monitoring-analysis-rectification-distribution", thereby improving the accuracy and reliability of data and ensuring the stability and effectiveness of medical supply transportation.

[0074] 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.

[0075] Example 2

[0076] like Figure 2 As shown, this embodiment provides an emergency medical supplies transportation system based on drones, including:

[0077] 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;

[0078] The communication layer includes a drone base station. The drone base station adopts the TD-LTE 4G network standard, improves signal coverage by optimizing a preset propagation loss model, and improves communication quality by optimizing Doppler shift. The communication layer is used to provide wide-area coverage and high-quality wireless private networks. In this embodiment, when the coverage area is limited, the drone base station can also use a 5G network with faster speed, shorter latency, and larger bandwidth.

[0079] 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 calculations 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; and the data layer is used to perform real-time calculations and processing on the data transmitted by the communication layer;

[0080] The application layer establishes a communication connection with the cloud platform and is used to provide application services to medical personnel;

[0081] The resource layer, communication layer, data layer and application layer are arranged in order from the bottom to the top;

[0082] The equipment in the infrastructure resources include:

[0083] Multispectral camera, used to obtain real-time image information within the rescue area and transmit live video in real time;

[0084] A laser tracking imager is used to acquire real-time hotspot information and radar point cloud information in the emergency medical supply loading area. The hotspot information is used to detect potential temperature anomalies in the medical supplies. The radar point cloud information is used to detect height differences in the emergency medical supply loading area to ensure uniform distribution of medical supplies during transportation and avoid unstable loading conditions.

[0085] The autonomous positioning bionic robotic arm is used to obtain images and position coordinates of emergency medical supplies loaded on a rescue drone, thereby further monitoring the status of the loaded medical supplies. The autonomous positioning bionic robotic arm is also equipped with an infrared sensor, which enables the robotic arm to autonomously grasp medical supplies. By equipping the bionic robotic arm with autonomous positioning capabilities, accurate material loading is ensured, and automated loading equipment reduces manual operations and improves loading efficiency.

[0086] The multi-sensor module includes a thermal imaging device and a GPS positioning device for real-time sensing and accurate positioning of trapped personnel. The multi-sensor module also includes temperature and humidity sensors for real-time sensing of the rescue area environment and the status of medical supplies. Based on the multi-sensor module, this embodiment can achieve accurate sensing and positioning of trapped personnel, as well as monitoring the status of medical supplies and the environment, ensuring the safe delivery of supplies.

[0087] LTE terminal, used to achieve real-time wireless communication between the rescue drone and the drone base station;

[0088] 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 dedicated network for the rescue drone cluster;

[0089] Divide the rescue area into several sub-areas and rank them according to the severity of the disaster. Deploy the primary base station in the most important sub-area and deploy auxiliary base stations in other sub-areas.

[0090] 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;

[0091] The drone base stations are all aerial LTE base stations based on drones;

[0092] The drone base stations all include a baseband control unit (eBBU), a radio remote unit (eRRU), and directional antennas, all in S1 configuration. They also achieve stable coverage of drone base station signals by using the drone's hovering circle, the directional wide-beam antenna's coverage circle, and the coverage area circle as approximately concentric circles.

[0093] For the hardware configuration of a single drone base station, this embodiment uses eBBU and eRRU, which provide better base station performance to meet the higher data rates and more complex network requirements within the rescue area. At the same time, a single antenna configuration is used in a single sub-area to avoid antenna switching at the base station and ensure communication quality.

[0094] In addition, in this embodiment, the drone base station can also add the drone self-organizing network LR-WIFI (long-range WIFI) protocol function on the basis of TD-LTE wireless communication, so as to facilitate WIFI communication between various drone base stations on site and realize real-time synchronization and sharing of data of each base station;

[0095] The propagation loss model between the rescue drone and the drone base station is:

[0096]

[0097] 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; Indicates the base value of the propagation loss index; Indicates the impact factor of flight altitude difference on the propagation loss index; represents a Gaussian random variable with zero mean, which is used to represent the effect of shadow fading in the propagation loss model;

[0098] Based on the propagation loss model and the preset maximum flight altitude 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, thereby expanding the signal coverage range of the drone base station.

[0099] When the rescue drone is moving, the Doppler frequency shift of the communication signal between it and the drone base station is :

[0100]

[0101] 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;

[0102] Calculate the Doppler frequency shift in real time according to the optimal operating frequency of the UAV base station, and determine the optimal communication frequency band of the rescue UAV according to the calculation result of the Doppler frequency shift to improve the communication quality;

[0103] The data transmitted by the communication layer includes: management data, control data and perception data;

[0104] The management data includes: distribution and transportation strategies for emergency medical supplies, and rescue drone route planning data;

[0105] The control data includes flight control instructions for the rescue drone;

[0106] The perception data includes the perception data of disaster victims, 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 drone;

[0107] 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.

[0108] 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. 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;

[0109] 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.

[0110] During the transportation of emergency medical supplies, rescue drones upload perception data to edge servers in real time through drone base stations. The edge servers calculate and process the perception data based on the prior knowledge database and decision support database, and send the processing results to the cloud platform for storage.

[0111] The cloud platform sends its 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 data management and control.

[0112] This embodiment implements a closed-loop process of "monitoring-analysis-rectification-distribution" through closed-loop data management and control, thereby improving the accuracy and reliability of data and ensuring the stability and effectiveness of medical supply transportation.

[0113] The application layer includes a classification module, a data visualization module and an interactive analysis module;

[0114] 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.

[0115] 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 to the top;

[0116] The resource layer provides hardware support for the transportation of emergency medical supplies. It includes multiple rescue drones, each equipped with the following equipment: a multispectral camera, a laser tracking imager, an autonomous positioning bionic robotic arm, multiple sensors, and an LTE terminal.

[0117] The resource layer can obtain relevant data on the loading status of drones; 1) Rapidly obtain various types of image information in the area through multispectral cameras, and realize real-time live broadcast of on-site video; based on the accumulation of big data, support vector machines can be further used to realize rapid extraction and monitoring of aerial survey and remote sensing information, radar optoelectronic linkage control and rescue drone target classification and identification; 2) Rapidly identify hot spots in the medical supplies loading area through laser tracking imagers to help detect potential abnormalities in medical supplies, such as abnormal heating, uneven temperature, etc., and detect height differences in the medical supplies loading area 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 of loaded medical supplies and, with the help of infrared sensing devices and bionic robotic arms, autonomously grasp objects. Combined with rescue drones, they provide systematic delivery services to achieve intelligent, autonomous, three-dimensional, rapid, and seamless instant delivery logistics. 4) Through multiple sensors, they can achieve real-time perception and precise positioning of trapped people. At the same time, they can also achieve real-time perception and monitoring of the environment in the rescue area and the temperature and humidity of medical supplies, and further establish safety response mechanisms. For example, when a harsh environment or abnormal temperature and humidity of medical supplies are detected, the rescue drone automatically returns and reloads the medical supplies, ensuring the safe and accurate delivery of the supplies. 5) Wireless communication between the rescue drone and the drone base station is achieved through the LTE terminal, and the TD-LTE wireless network between the LTE terminal and the drone base station is set up in conjunction with the LTE terminal.

[0118] The communication layer is equipped with a TD-LTE 4G network standard drone base station for data transmission of rescue drones. The TD-LTE 4G private network has the advantages of high spectrum efficiency, low latency, mature and flexible uplink and downlink scheduling, all-IP architecture, comprehensive QoS guarantees, and high data transmission rates. It is particularly suitable for scenarios requiring rapid response and high-quality communication services, providing an efficient and reliable communication solution for the transportation of emergency medical supplies. Therefore, based on the TD-LTE 4G private network, this embodiment can realize cross-regional long-distance signal transmission, achieve blind spot communication during emergency rescue, and greatly improve the efficiency of emergency rescue.

[0119] 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 primary base station, and the other drone base stations are used as auxiliary base stations. In this way, a TD-LTE 4G network is built as a dedicated network for the rescue drone cluster. The drone base stations are all aerial LTE base stations based on drones, and establish wireless communication connections with LTE terminals carried by rescue drones.

[0120] Currently, 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 non-linear, and there is a user terminal RSRP (Reference Signal Ratio) difference. Receiving Power) fluctuates; at the same time, changes in the height and inclination of the drone also make it possible for "black holes" to appear at the edges of the coverage areas of multiple antennas; the drone's hovering coverage of the target area will also lead to 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, reducing 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, adopting the S1 configuration, and adopting the drone hovering circle, the directional wide-beam antenna coverage circle and the coverage area circle as approximately concentric circles to achieve stable coverage of the base station signal; each drone base station is divided into areas within the rescue area, and a single antenna and single area configuration is adopted to ensure that the drone does not switch sectors or antennas at the base station in each area, thereby ensuring communication stability;

[0121] Regarding the configuration of drone base stations, in this embodiment, the primary base station is set up at the dispatch center of the rescue area, and the secondary base stations are set up at other locations in the rescue area, ensuring that each sub-area is covered by a drone aerial 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 achieving audio, video, and data interconnection, it also maximizes the coverage of the 4G private network.

[0122] To measure the signal strength of each drone base station, this embodiment constructs a propagation loss model to systematically describe the characteristics and patterns of radio wave propagation between rescue drones and drone base stations. The traditional Close-in (CI) model, Floating Intercept (FI) model, and Dual Slope (DS) model are three commonly used general propagation loss models applicable to the entire millimeter wave frequency band. In addition, some standards organizations have also proposed their own standardized propagation loss models. The most widely used are the 3rd Generation Partnership Project (3GPP) model and the 5G Channel Model (5GCM) model.

[0123] The CI model takes shadow fading into account based on the free-space propagation loss model. The CI model adjusts the path loss exponent (PLE) by fitting measured data to adapt to different propagation environments. Due to its simplicity, the CI model has been widely used in various radio wave propagation scenarios in recent decades. However, as a traditional propagation loss model, the CI model only considers the situation of ground communication systems and does not consider the impact of the height of the receiving and transmitting antennas on propagation loss. For drone aerial relay base stations, the altitudes of the drone base stations and rescue drones will vary significantly during the transportation of supplies. Therefore, the altitude of the drone is an important factor that cannot be ignored in the propagation of air-to-ground electromagnetic signals. Therefore, for low-altitude drone base stations, this embodiment proposes a propagation loss model between a rescue drone and a drone base station. Based on the CI model, this model retains its physical characteristics and adds the impact of the drone altitude on the propagation loss exponent, expressed as:

[0124]

[0125] 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; Indicates the base value of the propagation loss index; Indicates the impact factor of flight altitude difference on the propagation loss index; represents a Gaussian random variable with zero mean, which is used to represent the effect of shadow fading in the propagation loss model;

[0126] Based on the constructed propagation loss model and the preset maximum flight altitude 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 achieve the maximum coverage area within the appropriate altitude difference range;

[0127] In real space, various factors often affect radio wave propagation, making true free-space propagation difficult to achieve. When base station power is the same and the level difference is the same at the same distance, the low-frequency band has less loss. For the same level communication distance, the low-frequency band has the widest coverage. Therefore, after calculation, the preferred operating frequency of the drone base station in this embodiment is the 2G band, which is used to re-cultivate 4G, maximizing the 4G network coverage using the low-frequency band.

[0128] Because the signal from the drone base station is highly mobile during the rescue process, the signal frequency has Doppler shift. This Doppler shift can cause the receiving end to be unable to correctly demodulate the signal, affecting communication quality. This is especially serious when the speed relative to the rescue drone is high.

[0129] 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:

[0130]

[0131] 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;

[0132] Calculate the Doppler frequency shift in real time based on the optimal operating frequency of the drone base station, and determine the optimal communication frequency band of the rescue drone based on the calculation result of the Doppler frequency shift;

[0133] During the transportation of emergency medical supplies, the UAV base station (operating at the optimal frequency in the current frequency band to ensure the widest coverage) is used as the reference anchor point. By obtaining the relative movement speed between the rescue UAV and the UAV base station in real time, as well as the angle between their movement directions, the Doppler shift of the rescue UAV's received signal is calculated in real time, and a Doppler shift threshold is set. If the Doppler shift of the LTE terminal's current frequency band is too large, another frequency band is switched, and the optimal operating frequency of the base station is recalculated within the switched frequency band, and the Doppler shift is calculated again. This process is repeated until the Doppler shift is less than the threshold, thus achieving high-quality data transmission from the rescue UAV.

[0134] The data layer is used to perform real-time calculations and processing on 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 calculations and processing on 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 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 wireless connections with the drone base station.

[0135] The data transmitted by the communication layer includes: management data, control data and perception data; management data includes: distribution and transportation strategies of emergency medical supplies, as well as rescue drone route planning data; control data includes flight control instructions for rescue drones; perception data includes perception data of disaster victims, environmental perception data, drone status perception data, drone scheduling and execution perception data, drone flight route perception data and medical supply status perception data collected by equipment on rescue drones;

[0136] 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. The interactive analysis module is used to provide data interaction and data analysis services for medical personnel.

[0137] Before the transportation of emergency medical supplies, the application layer also establishes a communication connection with an 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 situation assessment, intelligent matching of emergency plans, and material team deployment management, thereby obtaining initial management data.

[0138] 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 rescue drone's loading status, visual display of analysis results, alarm prompts for 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, visually presenting monitoring data, analysis results and decision support information, helping rescue teams and others to carry out flight missions efficiently and safely, effectively ensuring real-time monitoring of data, and improving rescue efficiency and reliability;

[0139] 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 analysis, and then sends the control data to the rescue drone through the drone base station for flight control;

[0140] During the transportation of emergency medical supplies, rescue drones upload perception data to edge servers in real time through drone base stations. The edge servers calculate and process the perception data based on the prior knowledge database and decision support database, and send the processing results to the cloud platform for storage.

[0141] 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 control of data, such as Figure 3 As shown;

[0142] From the perspective of improvements at the communication layer, this embodiment achieves wide-area, long-distance wireless private network coverage by setting up drone base stations using the TD-LTE 4G network standard, which can meet the communication needs of emergency medical supply transportation. Secondly, this embodiment constructs a dedicated propagation loss model to determine the optimal communication frequency to maximize coverage. In addition, this embodiment further considers the impact of Doppler frequency shift on communication quality when the drone moves at high speeds, determines the optimal communication frequency band, and maximizes the communication quality of the rescue drone.

[0143] Overall, the system in this embodiment can be applied to the transportation of 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, helping the command department to promptly understand the usage status of supplies, so as to 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, helping the command department make wise decisions and provide effective support to rescue personnel.

[0144] Example 3

[0145] This embodiment provides a network architecture applicable to the emergency medical supplies transportation system described in Embodiment 1 or 2.

[0146] In the specific implementation process, the existing drone communication systems all use customized data composite transmission methods and point-to-point transmission architectures. Multiple data types, such as measurement and control data and user application data, are complexly coupled, resulting in bloated composite transmission data and a "chimney-like" situation, which greatly increases communication latency and reduces 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 drone base station in a targeted manner to meet the real-time requirements of emergency rescue scenarios.

[0147] Based on this, this embodiment proposes a new network architecture for the UAV wireless private network in embodiment 1 or 2, such as Figure 4As shown in the figure, the entire system of this architecture is decoupled and divided into the data plane, perception plane, control plane, and management plane. The perception plane perceives the resource status of the underlying data plane, and conducts learning and reasoning on the management plane, ultimately generating 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 utilizes abundant computing resources to further deploy intelligent training and reasoning engines of different scales and forms to analyze the perceived rescue data and medical data, realize the analysis, prediction, and matching of rescue intentions and medical resource status, and form 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 real-time and accurate delivery of strategies.

[0148] 1) The perception plane analyzes, mines, and predicts data on the status of disaster victims, flight safety, and medical supplies, enabling awareness of situations such as rescue dispatch execution, flight routes, and service status. To make each rescue node programmable, perception rules have been embedded in the device. This not only measures drone flight status information and medical supply data flow information, but also measures the information status, rescue characteristics, and performance parameters flowing through each rescue node and reports them to the perception plane through spontaneous measurement rules or receiving perception commands issued by the perception plane. Active measurement enables more flexible and accurate acquisition of resource status information and medical supply data flow information. In active measurement, each rescue node actively sends detection data packets to the network and analyzes rescue behavior based on changes in characteristics affected by weather and other factors.

[0149] 2) The data plane is primarily responsible for forwarding and processing various data packets throughout the system. Based on the software-defined network architecture, it constructs the data plane for transporting medical aid supplies. In the data plane, control rules are published by the control plane through interfaces such as OpenFlow. Intelligent recognition sensors forward and process rescue data packets according to the control rules published by the control plane.

[0150] 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 distributes the control policies obtained by the management plane to achieve global control of the entire medical supply transportation system.

[0151] 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 requirements. The management plane aggregates, stores, and manages rescue situation information such as status information collected by the perception plane and data stream information such as emergency medical supplies, extracting the global situation from it and holographically representing the rescue situation from multiple dimensions to support the rescue upper-level plane's functions such as task formulation, rescue task execution, rescue control management, and rescue result analysis and optimization.

[0152] 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.

[0153] The same or similar reference numerals correspond to the same or similar components;

[0154] The terms used in the drawings to describe positional relationships are for illustrative purposes only and should not be construed as limiting this patent;

[0155] Obviously, the above embodiments of the present invention are merely examples for the purpose of clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection 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 drone base station. The drone base station uses the TD-LTE 4G network standard, improves signal coverage by optimizing a preset propagation loss model, and improves communication quality by optimizing Doppler shift. The communication layer is used to provide wide area coverage and high-quality wireless private network; 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 dedicated network for the rescue drone cluster; Divide the rescue area into several sub-areas and rank them according to the severity of the disaster. Deploy the primary base station in the most important sub-area and deploy auxiliary base stations in other sub-areas. 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 remote unit (eRRU), and directional antennas, all in S1 configuration. They also achieve stable coverage of drone base station signals by using the drone's hovering circle, the directional wide-beam antenna's coverage circle, and the coverage area circle as approximately concentric circles. 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; Indicates the base value of the propagation loss index; Indicates the impact factor of flight altitude difference on the propagation loss index; represents a Gaussian random variable with zero mean, which is used to represent the effect of shadow fading in the propagation loss model; Based on the propagation loss model and the preset maximum flight altitude 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, thereby expanding 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 : 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 UAV base station, and determine the optimal communication frequency band of the rescue UAV 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 for the rescue drone; The perception data includes the perception data of disaster victims, 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 drone; 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 calculations 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; and the data layer is used to perform real-time calculations and processing on the data transmitted by the communication layer; 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, rescue drones upload perception data to edge servers in real time through drone base stations. The edge servers calculate and process the perception data based on the prior knowledge database and decision support database, and send the processing results to the cloud platform for storage. The cloud platform sends its 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 data management and control. The application layer establishes a communication connection with the cloud platform and is used to provide application services to 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 drones according to claim 1, characterized in that: The equipment in the infrastructure resources include: Multispectral camera, used to obtain real-time image information within the rescue area and transmit live video in real time; A laser tracking imager is used to acquire real-time hotspot information and radar point cloud information in the emergency medical supply loading area. The hotspot information is used to detect potential temperature anomalies in the medical supplies. The radar point cloud information is used to detect height differences in the emergency medical supply 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 images and position coordinates of emergency medical supplies loaded on a rescue drone, so as to further monitor the status of the loaded medical supplies. The autonomous positioning bionic robotic arm is also equipped with an infrared sensor, which enables the robotic arm to autonomously grasp the medical supplies. The multi-sensor module includes a thermal imaging device and a GPS positioning device for real-time sensing and accurate positioning of trapped people. The multi-sensor module also includes temperature and humidity sensors for real-time sensing of the rescue area environment and the status of medical supplies. 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 drones according to claim 1, 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 transporting 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 construct an initial data set. Based on the initial data set, emergency event situation assessment, intelligent matching of emergency plans and material team deployment management are performed, thereby obtaining initial management data.

4. 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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