An emergency call method, system and device based on VONR technology

Abstract

The present application provides an emergency call method, system and device based on VONR technology, which relates to the field of emergency call technology, including: determining the initial deployment plan; conducting rapid aerial deployment of drone base stations; starting N drone base stations and enabling VONR technology; N drone base stations receiving and analyzing emergency call data in real time, and outputting N dynamic resource priority scheduling; 5G core network performing communication resource allocation; and performing emergency call guarantee by adopting N dynamic resource priority scheduling and dynamic resource allocation scheduling to synchronously execute dynamic traffic sharing and rescue dynamic task allocation of drone base stations. This application can solve the technical problem in the prior art that the quality of emergency communications is poor due to the inflexible allocation of communication resources in emergency situations, thereby affecting the efficiency of rescue. Through VONR technology and intelligent resource scheduling, the high quality and low latency of emergency communications are ensured, thereby improving the efficiency of rescue.

Description

Technical Field

[0001] The present application relates to the field of emergency call technology, and in particular to an emergency call method, system and device based on VONR technology. Background Art

[0002] Emergency calls refer to key communication means provided to disaster areas or disaster sites in order to ensure public safety and rescue work in emergency situations. They are usually used in post-disaster rescue, emergency response to sudden accidents, and resettlement of disaster victims. They aim to ensure that command centers, rescue teams, medical personnel, etc. can exchange information and instructions in a timely and effective manner under special circumstances. Existing emergency call systems mainly rely on ground base stations, satellite communications, radio and other technologies. When a disaster occurs, it is crucial to quickly restore communication connections and provide stable voice, video and data communications to reduce disaster losses and improve rescue efficiency. However, due to the diversity and urgency of rescue missions, communication resources are often tight, and priority resource allocation for different missions and different rescue teams cannot be achieved. When a large number of rescue personnel, command centers and ordinary people share limited network resources, the communication quality is prone to decline, especially in rescue missions that require high-speed, high-definition voice and video calls. Communication delays and poor quality may occur, thus affecting the coordination and command of rescue work.

[0003] In summary, the prior art has a technical problem that due to the inflexible allocation of communication resources in emergency situations, it is difficult to dynamically adjust the allocation of network resources according to real-time communication load and rescue needs, resulting in poor communication quality, thereby affecting rescue efficiency. Summary of the invention

[0004] The purpose of this application is to provide an emergency call method, system and equipment based on VONR technology to solve the technical problems in the prior art that due to the inflexible allocation of communication resources in emergency situations, it is difficult to dynamically adjust the allocation of network resources according to real-time communication load and rescue needs, resulting in poor communication quality, thereby affecting rescue efficiency.

[0005] In view of the above problems, the present application provides an emergency call method, system and device based on VONR technology.

[0006] In the first aspect, the present application provides an emergency call method based on VONR technology, which is implemented by an emergency call system based on VONR technology, wherein the emergency call method based on VONR technology includes: performing base station deployment analysis in the target rescue area according to the characteristics of the rescue scenario to obtain an initial deployment plan; performing rapid aerial deployment of drone base stations in the target rescue area according to the initial deployment plan to obtain N drone base stations for communication coverage of N rescue sub-areas in the target rescue area; after connecting the N drone base stations to the 5G core network via satellite, starting the N drone base stations and enabling VONR technology; the N drone base stations receive and analyze the emergency call data transmitted back by N groups of rescue participants in the N rescue sub-areas through VONR technology in real time, and output N dynamic resource priority scheduling; the 5G core network receives and allocates communication resources of the N drone base stations according to the N dynamic resource priority scheduling, and outputs dynamic resource allocation scheduling; by adopting the N dynamic resource priority scheduling and dynamic resource allocation scheduling, the dynamic traffic sharing of the N drone base stations and the dynamic task allocation of the N groups of rescue participants are synchronously executed to provide emergency call guarantee for the target rescue area.

[0007] In the second aspect, the present application also provides an emergency call system based on VONR technology, which is used to execute an emergency call method based on VONR technology as described in the first aspect, wherein the emergency call system based on VONR technology includes: a solution acquisition module, the solution acquisition module is used to perform base station deployment analysis in the target rescue area according to the characteristics of the rescue scenario, and obtain an initial deployment plan; a base station deployment module, the base station deployment module is used to perform rapid aerial deployment of drone base stations in the target rescue area according to the initial deployment plan, and obtain N drone base stations for communication coverage of N rescue sub-areas in the target rescue area; a base station startup module, the base station startup module is used to start the N drone base stations after connecting them to the 5G core network via satellite. UAV base stations are configured and VONR technology is enabled; a data receiving module is configured, wherein the data receiving module is configured to receive and analyze in real time the emergency call data transmitted back by the N groups of rescue participants in the N rescue sub-areas through the VONR technology by the N UAV base stations, and output N dynamic resource priority scheduling; a resource scheduling module is configured, wherein the resource scheduling module is configured to receive from the 5G core network and allocate communication resources to the N UAV base stations according to the N dynamic resource priority scheduling, and output a dynamic resource allocation scheduling; a task allocation module is configured to synchronously execute the dynamic traffic sharing of the N UAV base stations and the dynamic task allocation of the N groups of rescue participants by adopting the N dynamic resource priority scheduling and dynamic resource allocation scheduling, so as to provide emergency call guarantee for the target rescue area.

[0008] In the third aspect, the present application also provides an electronic device comprising: at least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the steps of an emergency call method based on VONR technology as described in any one of the first aspects above.

[0009] The multiple technical solutions provided in this application have at least the following technical effects or advantages:

[0010] An initial deployment plan is obtained by analyzing the base station deployment in the target rescue area according to the characteristics of the rescue scenario; UAV base stations are quickly deployed in the air in the target rescue area according to the initial deployment plan to obtain N UAV base stations for communication coverage of N rescue sub-areas in the target rescue area; after connecting the N UAV base stations to the 5G core network via satellite, the N UAV base stations are started and the VONR technology is enabled; the N UAV base stations receive and analyze the emergency call data sent back by the N groups of rescue participants in the N rescue sub-areas through the VONR technology in real time, and output N dynamic resource priority scheduling; the 5G core network receives and allocates communication resources to the N UAV base stations according to the N dynamic resource priority scheduling, and outputs a dynamic resource allocation scheduling; by adopting the N dynamic resource priority scheduling and dynamic resource allocation scheduling to synchronously execute the dynamic traffic sharing of the N UAV base stations and the dynamic task allocation of the N groups of rescue participants, emergency call guarantee is performed in the target rescue area. That is to say, by determining the initial deployment plan according to the characteristics of the rescue scenario, the drone base station is quickly deployed in the air in the rescue area, the drone base station receives and analyzes the emergency call data in real time, and the communication resources of the drone base station are allocated according to dynamic resource priority scheduling. According to rescue needs and resource conditions, the traffic allocation and task allocation are dynamically adjusted, and fast and flexible communication coverage and resource allocation can be achieved in the rescue scenario, thereby improving the rescue efficiency and communication quality in emergency situations.

[0011] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0013] Figure 1 A flowchart of an emergency call method based on VONR technology is provided for this application;

[0014] Figure 2 A flowchart of dynamic resource allocation and scheduling in an emergency call method based on VONR technology in this application;

[0015] Figure 3 A schematic diagram of the structure of an emergency call system based on VONR technology is provided for this application;

[0016] Figure 4 This is a schematic diagram of the structure of an exemplary electronic device of the present application.

[0017] Explanation of the reference numerals: solution acquisition module 11, base station deployment module 12, base station startup module 13, data receiving module 14, resource scheduling module 15, task allocation module 16, bus 300, receiver 301, processor 302, transmitter 303, memory 304, bus interface 305. DETAILED DESCRIPTION

[0018] This application provides an emergency call method, system and device based on VONR technology, which solves the technical problem in the prior art that due to the inflexible allocation of communication resources in emergency situations, it is difficult to dynamically adjust the allocation of network resources according to the real-time communication load and rescue needs, resulting in poor communication quality, thereby affecting the rescue efficiency. By determining the initial deployment plan according to the characteristics of the rescue scene, the drone base station is quickly deployed in the air in the rescue area, the drone base station receives and analyzes the emergency call data in real time, and the communication resources of the drone base station are allocated according to the dynamic resource priority scheduling. According to the rescue needs and resource conditions, the traffic allocation and task allocation are dynamically adjusted, and fast and flexible communication coverage and resource allocation can be achieved in the rescue scene, thereby improving the rescue efficiency and communication quality in emergency situations.

[0019] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0020] For example, please refer to the attached Figure 1 The present application provides an emergency call method based on VONR technology, wherein the emergency call method based on VONR technology specifically includes the following steps:

[0021] Step 1: Analyze the base station deployment in the target rescue area according to the characteristics of the rescue scenario and obtain the initial deployment plan.

[0022] Specifically, according to the characteristics of the rescue scenario, namely the terrain, scope, personnel distribution and signal coverage requirements of the rescue area, base stations are deployed in the target rescue area, that is, how to deploy base stations in the area is analyzed and planned in detail to obtain an initial deployment plan, including N rescue sub-areas and N initial communication resource configurations. The specific process of determining the plan is as follows: First, the terrain model of the target rescue area is obtained through the drone, which reflects the geographical characteristics of the target area. Receive geographic location information and rescue demand information uploaded by multiple rescue participants through VONR. Mark the geographic location information and rescue demand information on the target terrain model to obtain a dynamic rescue distribution map, which shows in detail which places need which rescue.

[0023] According to the standard communication coverage scale of the drone base station, that is, the maximum communication coverage range, the standard communication coverage scale of the drone base station is reduced by K times as a constraint condition for segmenting the dynamic rescue distribution map. The size of each dynamic rescue sub-model is a part of the coverage range of the drone base station. The entire dynamic rescue distribution map is divided into multiple smaller areas, each of which represents a dynamic rescue sub-model. Multiple dynamic rescue sub-models are managed and scheduled more finely according to the communication capabilities and rescue needs of the drone base station.

[0024] Perform task priority analysis on multiple dynamic rescue sub-models, and merge adjacent sub-models with similar priorities using the standard communication coverage scale as a constraint. Determine the priorities of the N rescue sub-areas after merging, determine their corresponding initial communication resource configuration, and communicate to form an initial deployment plan. By analyzing the characteristics of the rescue scenario, select the most suitable base station type and location to ensure that the communication needs of the rescue area are fully met, thereby improving the efficiency and response speed of the rescue operation.

[0025] Step 2: Rapidly deploy UAV base stations in the air in the target rescue area according to the initial deployment plan to obtain N UAV base stations for communication coverage of N rescue sub-areas in the target rescue area.

[0026] Specifically, according to the multiple rescue sub-areas and their communication needs determined by the initial deployment plan, the drone base station is quickly deployed in the target rescue area, and a drone base station is allocated to each sub-area, that is, N drone base stations, and N initial communication resource configurations are used to allocate drone communication resources. The drone base station is quickly located to the predetermined position to achieve communication coverage of the rescue sub-area. Through the automated flight control system and aerial path planning algorithm, the flight route and deployment time of the drone base station are calculated. The drone quickly flies to the target area according to the planned route and completes the deployment of the base station through precise landing or hovering.

[0027] After the base station is in place, the radio frequency planning tool is used to adjust the communication parameters (such as frequency band, power, etc.) of each drone base station to ensure that each rescue sub-area can obtain good communication coverage. For example, the transmission power of the base station is dynamically adjusted according to the different terrain to ensure that the coverage of the wireless signal meets the requirements. After the deployment is completed, the communication quality and network status of the drone base station are monitored in real time. If the communication needs of a certain area change (such as the addition of rescue personnel or emergency tasks), the mobility of the drone can be used to make quick adjustments. For example, if the communication load of a rescue sub-area is too high, more drone base stations are dispatched for additional coverage.

[0028] For example, suppose the target rescue area is a disaster-stricken city with an area of ​​10 square kilometers. In the post-disaster assessment, the area is divided into 5 rescue sub-areas (N=5), corresponding to different mission requirements, such as wounded treatment, material transportation, command and dispatch, etc. Rescue sub-area 1: wounded treatment, high mission priority, requires strong communication support; rescue sub-area 2: material transportation, medium mission priority, moderate communication requirements; rescue sub-area 3: command and dispatch, high mission priority, very large communication requirements; rescue sub-area 4: personnel evacuation, medium mission priority, high communication requirements; rescue sub-area 5: logistics support, low mission priority, low communication requirements. According to these requirements, the generated initial deployment plan is: rescue sub-area 1 and rescue sub-area 3 require stronger communication coverage, so two drone base stations will be deployed respectively; rescue sub-area 2, rescue sub-area 4 and rescue sub-area 5 deploy one drone base station to cover each area according to the mission and communication requirements. Drones are quickly deployed in the target area according to this plan. After aerial flight path planning, each drone base station arrives at the designated location within the expected time and automatically adjusts the signal strength according to the communication needs of the area to ensure stable communication in each area. Through precise deployment and communication needs analysis, each rescue sub-area can obtain the required communication resources, avoiding excessive coverage and waste of resources.

[0029] Step 3: After connecting the N drone base stations to the 5G core network via satellite, start the N drone base stations and enable VONR technology.

[0030] Specifically, the location of the drone base station may be in a remote area or disaster area that cannot be directly covered by the ground network. Satellite communication technology is used to connect N drone base stations to the 5G core network to achieve reliable communication. The drone base station is connected to the 5G core network on the ground through a satellite link, and communication signals are usually transmitted through satellite relay stations (such as low-Earth orbit satellites and geostationary orbit satellites) to ensure that even in the absence of ground base stations, communications can still be transmitted through air paths. The drone base station transmits data back to the 5G core network via a satellite link. After receiving the request from the drone base station, the 5G core network completes further data processing and resource scheduling, ensuring that all communication data of the drone base station can be smoothly connected to the 5G network and achieve efficient resource management.

[0031] The communication center of N rescue sub-areas is located to determine the location of each planned base station to ensure that the base station can cover the entire sub-area and provide the best communication quality. The communication resources are divided according to different task priorities or regional requirements, and different communication resources are given different priorities. Each drone base station is configured with a priority slice so that high-priority areas can obtain more communication resource guarantees.

[0032] Once the satellite link is established, the next step is to start the drone base station, which automatically configures the communication parameters, including wireless frequency band, signal strength, etc., while the drone is flying to adapt to different environmental requirements. The drone base station needs to control the hovering state of the aircraft to ensure that the drone can stably cover the target area. The drone hovers at the set planned base station location and has sufficient power to support its communication work. The drone base station will automatically configure communication resources and frequency bands based on the instructions and network requirements obtained from the 5G core network. The drone base station will dynamically adjust its signal coverage to ensure that the communication needs of the entire rescue area are met.

[0033] With VONR technology enabled, the drone base station is connected to the 5G core network via a satellite link and communicates with the IMS in the 5G core network. When a rescuer or participant initiates a voice call, the base station forwards it to the 5G network for processing through VONR technology, and obtains sufficient resources (such as bandwidth and latency guarantee) from the 5G core network to make the voice call. Voice data is transmitted in the form of IP packets to ensure data integrity and real-time transmission. The use of VONR technology provides higher quality and lower latency voice communications, allowing rescuers to communicate clearly and in real time, thereby improving rescue efficiency.

[0034] Step 4: The N drone base stations receive and analyze in real time the emergency call data transmitted back by the N groups of rescue participants in the N rescue sub-areas through the VONR technology, and output N dynamic resource priority scheduling.

[0035] Specifically, N groups of rescue participants collect rescue information through rescue terminal equipment to obtain N groups of emergency data. Each group of emergency data includes audio data stream, video data stream, participant location and location environment data, etc. The audio data stream, video data stream, participant location and location environment data are analyzed through the rescue task analysis module built into the rescue terminal equipment to determine the complexity of the task. The emergency data of the task complexity is transmitted back through VONR technology to the corresponding drone base station. The drone base station receives and processes the emergency call data transmitted back through VONR technology in real time, and outputs N dynamic resource priority scheduling according to the needs and urgency of the N task complexities, that is, dynamically allocates communication resources according to the needs and urgency of the rescue task, and determines which tasks are the most urgent and which require more communication resources. According to the N dynamic resource priority scheduling, the N drone base stations transfer the emergency data of their respective areas according to the complexity of the task. The base station will optimize the channel resources to ensure that areas with higher task complexity (such as medical emergency rescue) obtain more bandwidth. By receiving and analyzing emergency call data in real time, data quality can be improved, so that the situation at the rescue site can be more accurately conveyed to the command center and other rescue teams, ensuring that the rescue mission receives corresponding resource support based on its urgency and improving resource utilization efficiency.

[0036] Step 5: The 5G core network receives and allocates communication resources to the N drone base stations according to the N dynamic resource priority scheduling, and outputs the dynamic resource allocation scheduling.

[0037] Specifically, N dynamic resource priority scheduling refers to dynamically adjusting the communication resources required by each drone base station according to the real-time rescue needs and task complexity. This scheduling is based on the specific needs of the rescue site, such as personnel density, communication load, and the urgency of the task, so as to allocate appropriate communication resources to each drone base station. The N initial communication resource configurations of the N rescue sub-areas are extracted from the initial deployment plan, and the deviations of the N dynamic resource priority scheduling and the N initial communication resource configurations are calculated to calculate the deviation between the actual resource allocation and the initial plan to understand whether the resource allocation is executed according to the initial plan or whether it needs to be adjusted according to the actual situation. The deviation vector reflects the actual resource allocation of each drone base station relative to the initial communication resource configuration. The deviation vectors of the N resource scheduling deviations are used to group the N drone base stations, and the drone base stations are divided into two groups: the first base station combination and the second base station combination. A drone base station with a negative resource scheduling deviation, that is, insufficient communication resources; a drone base station with a positive resource scheduling deviation, that is, redundant communication resources.

[0038] The 5G core network uses the deviation of N resource scheduling deviations to adjust the communication resource allocation between the first base station combination and the second base station combination. According to the resource requirements of each drone base station, the communication resources are transferred from the base station with redundant resources (the second base station combination) to the base station with insufficient resources (the first base station combination) to achieve the purpose of load balancing, and output dynamic resource allocation scheduling, including the communication resource configuration of each drone base station, such as bandwidth, priority, and delay control. Through dynamic resource allocation scheduling, the quality and efficiency of emergency communications are significantly improved, ensuring that all rescue personnel, equipment and drone base stations can communicate stably and avoid communication interruptions caused by insufficient bandwidth or excessive delay.

[0039] Step 6: By adopting the N dynamic resource priority scheduling and dynamic resource allocation scheduling, the dynamic traffic sharing of the N drone base stations and the dynamic task allocation of the N groups of rescue participants are synchronously executed to provide emergency call guarantee for the target rescue area.

[0040] Specifically, dynamic resource priority scheduling refers to allocating different communication resources to each drone base station based on the real-time communication needs of each drone base station and the priority of emergency tasks, taking into account factors such as task urgency, personnel density, and communication needs. Dynamic resource allocation scheduling means that on the basis of dynamic resource priority scheduling, the core network converts these priorities into specific communication resource configurations (such as bandwidth, latency, etc.), and dynamically allocates them to drone base stations based on these configurations. Each base station will obtain the required communication resources based on its priority and specific needs, and the allocation of these resources will be adjusted at any time according to real-time needs.

[0041] Dynamic traffic sharing refers to the reasonable distribution of communication traffic among multiple base stations based on the real-time load of each drone base station to avoid overloading a certain base station and reducing communication quality. It also adjusts the data traffic distribution between drone base stations to ensure that network congestion is minimized. Dynamic task allocation refers to the allocation of rescue tasks to corresponding rescue participants based on the actual situation and task priority of each rescue sub-area, taking into account factors such as the skills, equipment resources, and geographical location of the rescuers to ensure that each participant can participate in the appropriate task. Based on the urgency and complexity of the rescue mission, the core network will assign a priority to each task, and tasks with higher priority will receive more resources and support.

[0042] By synchronously executing dynamic traffic sharing and dynamic task allocation, emergency calls are ensured to proceed smoothly. Even in an emergency, all rescue tasks, resources and communication needs can be effectively coordinated and supported. Ensure that all rescue participants (including drones, ground rescue teams, command centers, etc.) can maintain smooth communication and transmit key information (such as video, audio, location, etc.) in a timely manner. Through effective task allocation and resource scheduling, ensure that each task can be processed and executed quickly to avoid delays caused by poor communication or improper task allocation. Through real-time monitoring and scheduling, ensure that all resources (communication bandwidth, rescue personnel, equipment, etc.) are used in an optimized manner to avoid waste and maximize rescue results.

[0043] Furthermore, step one of the present application includes: starting redundant drones to scan the target rescue area to obtain a target terrain model; receiving multiple real-time location information and multiple rescue demand information uploaded by multiple rescue participants through VONR technology; using the multiple real-time location information as a guide, fitting the multiple rescue demand information to the target terrain model to generate a dynamic rescue distribution map; interactively obtaining the standard communication coverage scale of the drone base station, and using 1 / K of the standard communication coverage scale as a model segmentation constraint to divide the dynamic rescue distribution map into multiple dynamic rescue sub-models; performing task priority analysis on the multiple dynamic rescue sub-models, and using the standard communication coverage scale as a constraint, merging adjacent sub-models according to the analysis results to obtain the initial deployment plan, wherein the initial deployment plan includes N rescue sub-areas and N initial communication resource configurations.

[0044] Specifically, redundant drones, that is, extra backup drones, are used as supplements during normal operations to improve overall reliability. In rescue missions, redundant drones can ensure the continuity and stability of the mission. Even if one drone fails, other drones can still continue to work. The target rescue area refers to the geographical area where rescue is required. When a disaster occurs, it is usually an affected area, such as the site of an earthquake, flood or other disaster. Use a drone equipped with high-precision sensors (such as LiDAR or high-definition cameras) to scan the target rescue area and obtain a target terrain model, that is, a geospatial data model of the rescue area, which contains the terrain features of the area, such as the undulations of the ground, the location and height of obstacles, roads, etc., reflecting the geographical characteristics of the target area.

[0045] Multiple rescuers upload their location information and rescue needs (such as the number of injured, urgently needed medical equipment, etc.) through VONR technology. For example, rescuers upload their location (latitude and longitude) and the type of rescue required (medical care, food, etc.) through 5G mobile phones. VONR technology is a voice communication technology based on 5G New Radio (NR), which allows high-quality, low-latency voice and data transmission on 5G networks, and is suitable for real-time communication in complex environments, such as disaster emergency rescue. The command center receives GPS location and rescue demand information from different rescue teams to allocate resources in a timely manner.

[0046] According to multiple real-time location information, multiple rescue demand information is fitted to the target terrain model, that is, each position on the target terrain model is matched with the corresponding rescue demand, and a dynamic rescue distribution map is obtained, which reflects the resource demand, priority and resource distribution of each position in the rescue area, which is helpful for the reasonable allocation of rescue tasks. For example, if a serious cluster of wounded appears in a certain area, the area with dense wounded is marked on the map, and the places where medical resources need to be quickly transported are marked. Fitting refers to the process of matching data with a model or function. Here, it refers to combining rescue demand information with the terrain model to more accurately reflect the location and terrain environment of the rescue demand.

[0047] Interact with the drone base station system to obtain data on its standard communication coverage, that is, the maximum area that the drone base station can effectively cover. It is the range in which the drone base station can effectively provide communication services, which is usually affected by communication signal strength, bandwidth and environmental factors. Reduce the standard communication coverage scale of the drone base station by K times as a constraint for segmenting the dynamic rescue distribution map. The size of each dynamic rescue sub-model is part of the coverage of the drone base station. The entire dynamic rescue distribution map is divided into multiple smaller areas, each of which represents a dynamic rescue sub-model. Multiple dynamic rescue sub-models are managed and scheduled more finely according to the communication capabilities and rescue needs of the drone base station.

[0048] According to the terrain characteristics and rescue needs (such as personnel density, communication needs, emergency tasks, etc.) of each dynamic rescue sub-model, the task priority corresponding to each dynamic rescue sub-model is determined to obtain multiple task priorities. According to the preset priority deviation, the adjacent sub-models are merged according to the task priority scores with the smallest difference to obtain N rescue sub-areas. According to the N rescue sub-areas, multiple rescue participants are divided into N groups of rescue participants. In the case of merging multiple dynamic rescue sub-models, the final regional priority is determined by selecting the highest priority among them to obtain N regional priorities. According to the task priority of each rescue sub-area, by traversing the communication resource configuration table, the communication resource configuration that best suits the needs of the area is selected to generate an initial deployment plan, including N rescue sub-areas and N initial communication resource configurations. Through the real-time location information and rescue needs, the allocation of rescue resources is quickly adjusted and optimized. Combined with the task priority analysis, communication resources can be effectively utilized and resources can be allocated to the most needed places in the shortest time, thereby shortening the rescue time.

[0049] Furthermore, the present application also includes the following steps: extracting a first terrain feature and a first rescue requirement set from a first dynamic rescue sub-model, wherein the first rescue requirement set is composed of a first personnel density, a first communication requirement set and a first emergency task set; performing task priority evaluation based on the first terrain feature and the first rescue requirement set to obtain a first task priority; and so on, obtaining multiple task priorities of the multiple dynamic rescue sub-models; presetting a priority deviation, and using the priority deviation as a constraint, merging adjacent sub-models according to the multiple task priorities to obtain N rescue sub-areas, wherein the N rescue sub-areas divide the multiple rescue participants into the N groups of rescue participants; performing maximum value calls based on the task priority composition of the N rescue sub-areas to obtain N regional priorities; using the N regional priorities to traverse the communication resource configuration table to obtain the N initial communication resource configurations; and associatively storing the N rescue sub-areas and N initial communication resource configurations to obtain the initial deployment plan.

[0050] Specifically, a sub-model is randomly selected from multiple dynamic rescue sub-models as the first dynamic rescue sub-model. The first terrain feature and the first rescue demand set of the first dynamic rescue sub-model are extracted. The first terrain feature refers to the geographical environment feature of the target rescue area, such as the undulation of the terrain, the distribution of buildings, the road network, etc. The terrain feature affects the execution mode of the rescue operation. For example, the complexity of the roads in the mountainous area and the densely built areas may affect the movement of rescue personnel and the transportation of materials. The first rescue demand set refers to the rescue resources required in the area, including but not limited to the density of personnel, communication requirements, and emergency task sets. The density of personnel refers to the number of personnel per unit area. When a disaster occurs, the higher the density of personnel in the disaster-stricken area, the more urgent the demand for rescue resources in the area. The communication demand set includes the type and quantity of demand for communication resources in a specific rescue area, such as the requirements for communication bandwidth, delay and availability. The emergency task set refers to various tasks that need to be performed in the rescue sub-area, such as the treatment of the wounded, the transportation of materials, the evacuation of disaster victims, etc. Each task is assigned a priority according to its urgency and importance.

[0051] The task priority is evaluated according to the first terrain feature and the first rescue demand set, and factors such as terrain features, personnel density, communication requirements and emergency tasks are analyzed to obtain the corresponding first task priority. For example, if the personnel density in a certain area is very high and there are many wounded, the task priority of this area is higher. If the communication demand in a certain area is urgent, the communication task in this area will also be given a higher priority. Through task priority scoring, it is ensured that the most urgent tasks are handled first. By analogy, multiple task priorities are obtained for multiple dynamic rescue sub-models. Each dynamic rescue sub-model will be evaluated for task priority according to its specific terrain features and rescue demand set, so as to determine a task priority for each sub-model.

[0052] The preset priority deviation refers to the deviation range allowed in task priority evaluation and resource allocation, which is used to tolerate some small adjustments so as to make resource scheduling more flexible, especially in emergency situations. According to the preset priority deviation, the resource merger of adjacent rescue sub-areas is adjusted. For example, two adjacent sub-areas may be merged into a larger area because of similar task priorities. Through the merger, resources can be scheduled more efficiently and the repeated waste of resources between multiple areas can be avoided. Through the merger, N rescue sub-areas are formed, each of which contains multiple originally independent rescue sub-models, which are adjacent and have a small difference in task priorities. According to the N rescue sub-areas, multiple rescue participants are divided into N groups of rescue participants, and each group of rescue participants is responsible for the corresponding area.

[0053] Analyze the task priorities of N rescue sub-areas, and select the highest priority in each rescue sub-area as the regional priority through the maximum call method. For example, a rescue sub-area is the merger of two dynamic rescue sub-models, which are high priority and medium priority respectively. The high priority is selected as the priority of the rescue sub-area. When two or more adjacent dynamic rescue sub-models are merged, the priority of each sub-model may be different. Through the maximum call method, the sub-model with the highest priority will be selected as the regional priority of the entire merged rescue sub-area. The highest priority determines that more resources need to be allocated to the area first. If the merged area has a higher priority, related communication resources, materials and other supports will also be prioritized, thereby ensuring that the most urgent rescue tasks can be completed efficiently.

[0054] The communication resource configuration table is traversed by N regional priorities to determine the initial communication resource configuration for each rescue sub-area. The communication resource configuration table is a table containing various communication resource configuration options so that the appropriate communication resource configuration can be selected according to the task priority. The N rescue sub-areas and the N initial communication resource configurations are associated and stored to form an initial deployment plan, including multiple rescue sub-areas, the task priority of each sub-area, and the communication resource configuration. For example, areas with high task priorities will give priority to the deployment of drone base stations and rescue personnel, while areas with low task priorities will receive relatively less resource support. By combining terrain features, rescue demand sets, and real-time information, the task priority of each area is accurately evaluated. Through the merging of adjacent sub-models and the flexible adjustment of priority deviations, resources are dynamically adjusted according to actual needs, so that each rescue sub-area can obtain the most suitable communication resource configuration according to its needs, avoid resource waste, and improve rescue efficiency.

[0055] Furthermore, step three of the present application includes: locating the communication center of the N rescue sub-areas to obtain N planned base station locations; controlling the N drone base stations to hover at the N planned base station locations; configuring N priority slices for the N drone base stations according to the N regional priorities; after connecting the N drone base stations to the 5G core network via satellite, loading the N priority slices into the 5G core network; in the 5G core network, after allocating communication resources of the N drone base stations based on the N initial communication resource configurations with the N priority slices as constraints, starting the N drone base stations and enabling VONR technology.

[0056] Specifically, according to the communication needs and task priorities of each rescue sub-area, the appropriate base station location is selected to ensure the best communication coverage. Communication center positioning refers to determining the location of the communication center of each rescue sub-area. The communication center is usually the point where the communication needs are most concentrated or the most important in the rescue sub-area, such as the rescue command center or the area with dense disaster victims. Based on the communication center positioning, the optimal location of the base station is planned for each rescue sub-area to ensure that the base station can most effectively cover the rescue sub-area it is responsible for and provide the best communication quality. Based on the relevant data of each rescue sub-area, including geographical features, task priorities, and communication needs, the objectives and constraints of the optimization problem are defined. For example, the location of the communication center should be selected to maximize the communication coverage of the entire sub-area, minimize signal transmission delay, and maximize communication quality as the goal. Constraints include communication coverage, feasibility of base station location (such as avoiding no-fly zones or high-risk areas), and specific needs and available resources of each rescue sub-area (for example, flight altitude, power limit, etc.).

[0057] According to geographic information and communication needs, several candidate locations are preliminarily determined, such as the center point of the rescue sub-area, densely populated areas, medical facilities and other key areas. Select appropriate optimization algorithms, such as genetic algorithm, particle swarm optimization, K-means clustering algorithm, simulated annealing algorithm, etc., to search for the optimal communication center location among the candidate locations. By selecting an optimization algorithm (such as genetic algorithm, particle swarm optimization, etc.), the optimal communication center location is searched among the candidate locations. In this process, multiple factors such as signal coverage, task priority, bandwidth requirements, communication quality, etc. are considered, and each candidate location is evaluated using an objective function. For example, using the particle swarm optimization (PSO) algorithm, multiple candidate base station locations are selected near the center of area A, area B, and area C, and optimized according to factors such as communication needs and task priority, and finally the base station location of each area is determined. According to the output results of the algorithm, the best communication center location is selected, and the specific parameters of the base station, such as communication frequency band, transmission power, coverage radius, etc., are determined.

[0058] Each drone base station hovers according to the planned base station location and starts to provide communication coverage, while allocating communication resources and configuring the base station's spectrum, bandwidth, channels, etc. through the radio resource management system (RRM). After the base station location is selected, the drone will hover at the specified location, ensuring that each base station can be optimized according to the specific needs of its coverage area. A priority slice is configured for each drone base station based on N regional priorities to ensure that critical rescue missions have sufficient network resources. Priority slicing is a network resource allocation strategy that allocates specific network resources to different tasks or services based on different priorities. Rescue sub-areas with high priorities will be allocated more network resources (such as more bandwidth or lower latency).

[0059] The drone base station is connected to the 5G network via satellite, and the satellite is used as a relay station for ground communication to ensure the transmission of communication traffic. Through satellite relay, the drone base station is guaranteed to communicate with the 5G core network, and resource configuration and scheduling are performed. After the drone base station is successfully connected to the 5G core network, the priority slices previously configured for each base station are loaded into the 5G core network. The 5G core network can manage and allocate network resources based on these priority slices. Inside the 5G core network, the communication resource allocation of the drone base station is constrained and guided according to the N priority slices previously configured. The 5G core network will allocate corresponding network resources such as bandwidth, frequency and power according to the priority of the rescue sub-area served by each drone base station. After the communication resource allocation is completed, all drone base stations are started and VONR technology is enabled. VONR technology is used to select appropriate communication links and network nodes according to the network environment during the flight of the drone base station. Through VONR, it is ensured that the drone base station can maintain a stable communication connection even during the movement, and automatically switch to the best network path. When the drone base station arrives at the planned location and completes the communication resource allocation, the 5G core network starts the base station to provide communication services. Through precise communication resource allocation and task priority slicing, each rescue sub-area can obtain the required communication resources, improve communication efficiency, and ensure the smooth progress of the mission.

[0060] Furthermore, step four of the present application includes: the first rescue participant collects rescue information through the first rescue terminal device to obtain first emergency data, wherein the first emergency data includes a first audio data stream, a first video data stream, a first participant position and a first position environmental data; the first audio data stream, the first video data stream and the first position environmental data are analyzed by a rescue task analysis module built into the first rescue terminal device, and a first task complexity is output; the first rescue terminal device transmits the first emergency data of the first task complexity back to the first drone base station through VONR technology; and so on, the N groups of rescue participants transmit N groups of task complexities to the N drone base stations through VONR technology; the N drone base stations receive and analyze the N groups of task complexities, and output the N dynamic resource priority scheduling; the N groups of emergency data of the N groups of rescue participants are transferred by the N drone base stations with the N dynamic resource priority scheduling as constraints.

[0061] Specifically, one group is randomly selected from N groups of rescue participants as the first rescue participant. The first rescue participant collects audio, video and location information through the rescue terminal device. For example, the camera and sensor worn by the rescuer capture the on-site environment and sound, such as the thick smoke at the fire scene or the collapsed buildings in the disaster area. Through the integrated GPS module, its geographical location is recorded in real time. The first emergency data, that is, the data collected by the rescue terminal device, is obtained, including the first audio data stream, the first video data stream, the first participant's location and the first location environment data. The first audio data stream refers to the sound data at the rescue scene, which may include background noise, personnel conversations, and sound signals at the post-disaster scene; the first video data stream refers to the video data transmitted back from the scene, which can present the scene in real time, such as the actions of the rescuers, the real-time situation at the disaster site, etc.; the first participant's location refers to the real-time geographical location information of the rescuers, which is usually obtained through technologies such as GPS and LBS (location-based services); the first location environment data includes the surrounding environment information, such as temperature, humidity, air quality, etc., which may affect the rescue action.

[0062] The rescue task analysis module built into the first rescue terminal device analyzes the collected audio data stream, video data stream and location environment data to identify key information and task complexity in the rescue scene. The analysis of audio data stream mainly relies on audio processing technology, and uses the features in the audio signal (such as explosion sound, fire burning sound, people shouting, etc.) to determine the urgency of the disaster. Video data stream analysis uses image recognition technology (such as computer vision) to process and analyze on-site images to analyze whether there are significant changes in the image, such as fire, collapsed buildings, traffic jams, etc., to determine the urgency of the task. The crowd density in a specific area is calculated through image recognition technology to determine whether the personnel are too dense, thereby increasing the complexity of the rescue task. Environmental data (such as temperature, humidity, air pressure, etc.) can reflect the actual situation on the scene, including temperature, humidity, toxic gas monitoring, etc., to evaluate the complexity of the task. After integrating audio, video and environmental data, the task analysis module assigns different weights to audio, video and environmental data according to the importance of different data, and the weighted sum is used to obtain the first task complexity.

[0063] The first rescue terminal device transmits the first task complexity back to the first drone base station through VONR technology to ensure that the data is quickly transmitted back to the base station and transmitted to the command center through the 5G network. Similarly, the above analysis is also performed on other rescue participants in the N groups of rescue participants to obtain N groups of task complexity, and the N groups of task complexity are transmitted back to the corresponding drone base station.

[0064] The drone base station receives and analyzes the task complexity data from different rescue participants, and performs dynamic resource scheduling based on these data to determine N dynamic resource priority scheduling. The real-time resource allocation of each dynamic resource priority scheduling is extracted, and the audio data stream and video data stream are dynamically enhanced according to the real-time resource allocation. The enhanced audio and video data streams will replace the original emergency data to obtain the updated N groups of emergency data. The N groups of emergency data that have been enhanced and pre-processed are transferred by N drone base stations according to the constraints of N dynamic resource priority scheduling, ensuring that the enhanced data can be effectively transmitted to the destination, such as the rescue command center or other rescue teams. The drone base station will optimize channel resources to ensure that areas with higher task complexity (such as medical emergency rescue) obtain more bandwidth, while areas with lower task complexity are allocated less resources. In other words, the drone base station will reasonably arrange the data transfer process according to the task urgency and resource requirements of each rescue team to ensure that key data can be transmitted first. Through real-time analysis and dynamic scheduling of task complexity, it ensures that important tasks have sufficient communication resources, and ensures that high-priority tasks (such as emergency medical rescue, command and dispatch, etc.) have more bandwidth and resource support, thereby improving the efficiency and success rate of the entire rescue operation.

[0065] Furthermore, the present application also includes the following steps: extracting the first real-time resource allocation of the first rescue participant from the N dynamic resource priority scheduling; after dynamically enhancing the first audio data stream and the first video data stream according to the first real-time resource allocation, using the enhancement result to replace the first emergency data to obtain first updated data; and so on, after enhancing and pre-processing the N groups of emergency data of the N groups of rescue participants, they are transferred by the N drone base stations with the N dynamic resource priority scheduling as constraints.

[0066] Specifically, the first real-time resource allocation information of the first rescue participant is extracted from N dynamic resource priority scheduling, including parameters such as bandwidth allocation, signal strength, and communication priority. The audio and video data streams may be affected by environmental noise, bandwidth limitations, or data packet loss, and need to be dynamically enhanced, that is, to improve the quality of the audio and video streams so that the data can express the rescue scene more clearly and efficiently during transmission. The first audio data stream and the first video data stream are dynamically enhanced according to the first real-time resource allocation. The enhancement of the audio data stream includes technical processing such as noise suppression and voice clarity improvement. The noise suppression technology reduces the interference of background noise, and the voice clarity improvement technology ensures the accurate communication of rescue instructions and information. The enhancement of the video data stream includes image clarity improvement, video brightness adjustment in low-light environments, frame rate improvement, etc.

[0067] Through signal processing algorithms (such as spectral subtraction, Wiener filtering, etc.), the interference of environmental noise is reduced and the clarity of speech signals is improved. Combined with AI speech enhancement technology, such as deep learning models (such as convolutional neural networks, CNN), the speech quality in noisy environments is further improved. Use deep neural networks to enhance the intelligibility of speech signals, remove unnecessary background noise, and enhance the high-frequency part of speech, making speech clearer in complex environments. Utilize super-resolution algorithms, through deep convolutional networks, to enhance the clarity of video images, improve resolution, and enhance low-quality video streams. For example, for videos in low-light environments, apply image enhancement technology (such as a low-light image enhancement model based on deep learning) to improve the quality of videos in low-light environments, making the video brighter and more visible in dim scenes.

[0068] The enhanced audio and video data streams will replace the original first emergency data, that is, the enhanced data will replace the low-quality data that may be affected by insufficient bandwidth or environmental noise. For example, the video stream may be blurred, and the enhanced video stream will clearly show the details of the rescue site (such as the location of the injured, the trapped situation, etc.), providing accurate information for decision-making. The emergency data of the other N groups of rescue participants are enhanced in the same way. The data of each group of rescue participants will be enhanced accordingly through the dynamic resource priority scheduling results to ensure that the audio and video data of all participants have good quality during the transmission process. All enhanced data are transferred by N drone base stations. The drone base station forwards this data to the 5G core network via satellite or other communication methods for use by the command center or other rescue personnel. By dynamically enhancing the audio and video data streams, the quality of emergency data is significantly improved, making the on-site situation clearer. Especially in noisy or low-light environments, the enhanced audio and video streams can help the command center obtain accurate on-site data in real time, thereby improving decision-making efficiency and response speed.

[0069] Further, as attached Figure 2 As shown, step five of the present application includes: extracting N initial communication resource configurations of the N rescue sub-areas from the initial deployment plan; performing deviation calculation on the N dynamic resource priority scheduling and the N initial communication resource configurations to obtain N resource scheduling deviations; dividing the N UAV base stations into a first base station combination and a second base station combination according to the deviation vectors of the N resource scheduling deviations; the 5G core network performs communication resource load balancing between the first base station combination and the second base station combination according to the deviation amounts of the N resource scheduling deviations, and outputs the dynamic resource allocation schedule.

[0070] Specifically, N initial communication resource configurations for N rescue sub-areas are referred to from the initial deployment plan, that is, the initial communication resource configuration information for the N rescue sub-areas is extracted from the initial deployment plan, including the type, quantity, coverage, etc. of communication resources required for each sub-area. Dynamic resource priority scheduling refers to the process of dynamically adjusting resource allocation according to real-time conditions (such as task complexity, communication requirements, etc.). Initial communication resource configuration refers to the communication resource allocation plan pre-set for each rescue sub-area based on the rescue scenario and needs at the beginning of the rescue operation. Deviation calculation is performed on N dynamic resource priority scheduling and N initial communication resource configurations to obtain N resource scheduling deviations, and the difference between the preliminary configuration and the actual demand is analyzed. The deviation may be due to factors such as changes in actual network load, changes in demand caused by emergencies, or the initial configuration fails to accurately consider the special needs of certain areas.

[0071] According to the deviation vectors of N resource scheduling deviations, N drone base stations are divided into the first base station combination and the second base station combination, and the deviation vectors of N resource scheduling deviations are used to group the N drone base stations. The deviation vector reflects the actual resource allocation of each drone base station relative to the initial communication resource configuration. The first base station combination is responsible for areas with relatively insufficient communication resources, that is, drone base stations with negative resource scheduling deviations. Communication resources are insufficient to meet current rescue needs. These areas have large resource demands and may require more bandwidth and processing power due to a surge in demand or high network load. The second base station combination is responsible for redundant areas, that is, drone base stations with positive resource scheduling deviations. Communication resources are redundant and can be used to support areas with insufficient resources. These areas have relatively few resource demands and low communication loads, and may have excess communication resources.

[0072] According to the calculated deviations of the N resource scheduling deviations, the 5G core network performs communication resource load balancing between the first base station combination and the second base station combination, sharing the burden of the overloaded base station to the redundant base station to ensure that the communication in all areas will not be affected. Communication resource load balancing refers to adjusting resource allocation in the network to ensure that all users or devices can obtain fair and effective services. In the rescue scenario, it means adjusting the communication resources of the drone base station to meet the rescue needs of different areas. Transferring communication resources from the base station with redundant resources (the second base station combination) to the base station with insufficient resources (the first base station combination) to achieve the purpose of load balancing. Through this load balancing process, the 5G core network outputs a dynamic resource allocation scheduling plan, which details how much communication resources should be allocated to each drone base station to ensure that all rescue sub-areas can get enough support. Through the calculation of resource scheduling deviations, the communication resource requirements and actual differences in each area are accurately understood, so as to avoid waste of resources or insufficient resources, ensure stability, and avoid performance degradation or communication interruption of some base stations due to excessive load.

[0073] Furthermore, the present application also includes the following steps: the 5G core network calls historical allocation records to obtain historical resource allocation scheduling, wherein the timestamp difference between the historical resource allocation scheduling and the dynamic resource allocation scheduling satisfies a preset resource scheduling window; the deviation between the historical resource allocation scheduling and the dynamic resource allocation scheduling is calculated based on the Pearson correlation coefficient to obtain a scheduling deviation coefficient; if the scheduling deviation coefficient does not meet the preset scheduling deviation threshold, the base station deployment in the target rescue area is updated by updating the rescue scenario characteristics to obtain an updated deployment plan; N deployment flight trajectories of the N drone base stations are generated according to the updated deployment plan and the division deviation of the N rescue sub-areas; according to the updated deployment plan and the N deployment flight trajectories, the aerial deployment of the N drone base stations is updated in the target rescue area.

[0074] Specifically, the 5G core network is the center of the 5G communication system, responsible for data routing, processing and connection with external networks. The core network manages the communication resources of all base stations and performs dynamic resource scheduling according to demand. Historical resource allocation scheduling refers to resource allocation scheduling based on previous scheduling records, which includes the allocation of resources of each base station within a certain time range in the past. By calling historical data, it can help to make more accurate resource allocation. The 5G core network obtains historical resource allocation scheduling through historical allocation record calls. In order to ensure the relevance of the referenced historical data to the current situation, the 5G core network only considers historical data whose timestamp difference with the timestamp of the current dynamic resource allocation scheduling is within the preset resource scheduling window. The preset resource scheduling window is a time range, such as data within the last 24 hours or 48 hours, depending on the specific rescue scenario and needs.

[0075] Dynamic resource allocation scheduling is the process of dynamically adjusting resource allocation according to current resource needs. It is scheduled based on real-time data traffic, task priority and other parameters to ensure that the communication needs of emergency rescue are met in a timely manner. The timestamp difference refers to the difference between two timestamps, which is used to determine the time difference between historical resource allocation scheduling and dynamic resource allocation scheduling to ensure that the comparison is within the appropriate time window.

[0076] The Pearson correlation coefficient is a statistical method used to measure the strength of the linear relationship between two variables. The Pearson correlation coefficient is used to calculate the deviation between the historical resource allocation scheduling and the dynamic resource allocation scheduling. First, the mean of the historical resource allocation scheduling and the dynamic resource allocation scheduling is calculated, and then the covariance of the historical resource allocation scheduling and the dynamic resource allocation scheduling is calculated to indicate the direction and degree of change of the two variables. If the historical resource allocation scheduling and the dynamic resource allocation scheduling increase or decrease at the same time, the covariance is positive; if one increases and the other decreases, the covariance is negative. Then the standard deviation of the historical resource allocation scheduling and the dynamic resource allocation scheduling is calculated to measure the degree of discreteness of each variable, that is, the average distance of the data point from the mean. The Pearson correlation coefficient is obtained based on the covariance and standard deviation, which indicates the degree of linear correlation between the historical resource allocation scheduling and the dynamic resource allocation scheduling. The specific calculation process involves covariance / (the product of the standard deviation of the historical resource allocation scheduling and the standard deviation of the dynamic resource allocation scheduling).

[0077] The preset dispatch deviation threshold is set according to the specific needs and expectations of the rescue operation, and is used to determine whether the correlation between historical data and the current situation is strong enough. If the dispatch deviation coefficient does not meet the preset dispatch deviation threshold, that is, there is a large difference between historical resource allocation and dynamic resource allocation, it is impossible to accurately predict future resource needs. It is necessary to update the rescue scenario characteristics, re-evaluate the geographical environment of the rescue area, communication requirements, distribution of rescue teams and other factors, and obtain an updated deployment plan.

[0078] Reposition or adjust the position of the drone base station according to the deviation between the updated deployment plan and the division of N rescue sub-areas. Update the deployment flight trajectory of the drone base station to ensure that the base station moves quickly to the optimal position and improve the rescue response speed. Plan a flight trajectory for each drone base station so that it can move from its current position to the new deployment position, ensuring that the drone base station can accurately reach the area that needs to be covered according to the updated deployment plan. Redeploy the drone base station in the target rescue area according to the updated deployment plan and N deployment flight trajectories. The drone base station will be quickly deployed to the new location to ensure that communication resources are evenly distributed and cover all required rescue sub-areas. By calculating the deviation between historical data and dynamic scheduling, accurately evaluate whether the resource scheduling is reasonable. If the deviation coefficient is too large, it means that there is a problem with resource allocation, and update the base station deployment plan in time for optimization. Through base station deployment updates and flight trajectory adjustments, a more flexible deployment method can be achieved to ensure that each area can get the required resource support and improve overall communication efficiency.

[0079] In summary, the emergency call method based on VONR technology provided by this application has the following technical effects:

[0080] An initial deployment plan is obtained by analyzing the base station deployment in the target rescue area according to the characteristics of the rescue scenario; UAV base stations are quickly deployed in the air in the target rescue area according to the initial deployment plan to obtain N UAV base stations for communication coverage of N rescue sub-areas in the target rescue area; after connecting the N UAV base stations to the 5G core network via satellite, the N UAV base stations are started and the VONR technology is enabled; the N UAV base stations receive and analyze the emergency call data sent back by the N groups of rescue participants in the N rescue sub-areas through the VONR technology in real time, and output N dynamic resource priority scheduling; the 5G core network receives and allocates communication resources to the N UAV base stations according to the N dynamic resource priority scheduling, and outputs a dynamic resource allocation scheduling; by adopting the N dynamic resource priority scheduling and dynamic resource allocation scheduling to synchronously execute the dynamic traffic sharing of the N UAV base stations and the dynamic task allocation of the N groups of rescue participants, emergency call guarantee is performed in the target rescue area. That is to say, by determining the initial deployment plan according to the characteristics of the rescue scenario, the drone base station is quickly deployed in the air in the rescue area. The drone base station receives and analyzes emergency call data in real time, and allocates communication resources of the drone base station according to dynamic resource priority scheduling. According to rescue needs and resource conditions, traffic allocation and task allocation are dynamically adjusted. Fast and flexible communication coverage and resource allocation can be achieved in the rescue scenario, thereby improving the rescue speed and communication quality in emergency situations.

[0081] Embodiment 2, based on the same inventive concept as the emergency call method based on VONR technology in the aforementioned embodiment 1, the present application also provides an emergency call system based on VONR technology, please refer to the attached Figure 3The emergency call system based on VONR technology includes: a solution acquisition module 11, the solution acquisition module 11 is used to perform base station deployment analysis in the target rescue area according to the characteristics of the rescue scenario, and obtain an initial deployment plan. A base station deployment module 12, the base station deployment module 12 is used to perform rapid aerial deployment of drone base stations in the target rescue area according to the initial deployment plan, and obtain N drone base stations for communication coverage of N rescue sub-areas in the target rescue area. A base station startup module 13, the base station startup module 13 is used to connect the N drone base stations to the 5G core network via satellite, start the N drone base stations and enable VONR technology. A data receiving module 14, the data receiving module 14 is used for the N drone base stations to receive and analyze the emergency call data transmitted back by the N groups of rescue participants in the N rescue sub-areas through VONR technology in real time, and output N dynamic resource priority scheduling. A resource scheduling module 15, the resource scheduling module 15 is used for the 5G core network to receive and allocate communication resources of the N drone base stations according to the N dynamic resource priority scheduling, and output dynamic resource allocation scheduling. The task allocation module 16 is used to synchronously execute the dynamic traffic sharing of the N drone base stations and the dynamic task allocation of the N groups of rescue participants by adopting the N dynamic resource priority scheduling and dynamic resource allocation scheduling, so as to provide emergency call guarantee for the target rescue area.

[0082] Furthermore, the solution acquisition module 11 in the emergency call system based on VONR technology is also used to: start redundant drones to scan the target rescue area to obtain a target terrain model; receive multiple real-time location information and multiple rescue demand information uploaded by multiple rescue participants through VONR technology; use the multiple real-time location information as a guide to fit the multiple rescue demand information to the target terrain model to generate a dynamic rescue distribution map; interactively obtain the standard communication coverage scale of the drone base station, and use 1 / K of the standard communication coverage scale as a model segmentation constraint to divide the dynamic rescue distribution map into multiple dynamic rescue sub-models; perform task priority analysis on the multiple dynamic rescue sub-models, and use the standard communication coverage scale as a constraint to merge adjacent sub-models according to the analysis results to obtain the initial deployment plan, wherein the initial deployment plan includes N rescue sub-areas and N initial communication resource configurations.

[0083] Furthermore, the solution acquisition module 11 in the emergency call system based on VONR technology is also used to: extract a first terrain feature and a first rescue requirement set from a first dynamic rescue sub-model, wherein the first rescue requirement set is composed of a first personnel density, a first communication requirement set and a first emergency task set; perform task priority evaluation based on the first terrain feature and the first rescue requirement set to obtain a first task priority; and so on, obtain multiple task priorities of the multiple dynamic rescue sub-models; preset a priority deviation, and use the priority deviation as a constraint to merge adjacent sub-models according to the multiple task priorities to obtain N rescue sub-areas, wherein the N rescue sub-areas divide the multiple rescue participants into the N groups of rescue participants; perform maximum value calls based on the task priority composition of the N rescue sub-areas to obtain N regional priorities; use the N regional priorities to traverse the communication resource configuration table to obtain the N initial communication resource configurations; associate and store the N rescue sub-areas and N initial communication resource configurations to obtain the initial deployment plan.

[0084] Furthermore, the base station startup module 13 in the emergency call system based on VONR technology is also used to: locate the communication center of the N rescue sub-areas to obtain N planned base station locations; control the N drone base stations to hover at the N planned base station locations; configure N priority slices for the N drone base stations according to the N regional priorities; after connecting the N drone base stations to the 5G core network via satellite, load the N priority slices to the 5G core network; in the 5G core network, after allocating communication resources of the N drone base stations based on the N initial communication resource configurations with the N priority slices as constraints, start the N drone base stations and enable VONR technology.

[0085] Furthermore, the data receiving module 14 in the emergency call system based on VONR technology is also used for: the first rescue participant collects rescue information through the first rescue terminal device to obtain first emergency data, wherein the first emergency data includes a first audio data stream, a first video data stream, a first participant position and a first position environment data; the first audio data stream, the first video data stream and the first position environment data are analyzed by a rescue task analysis module built into the first rescue terminal device, and a first task complexity is output; the first rescue terminal device transmits the first emergency data of the first task complexity back through VONR technology to the first drone base station; and so on, the N groups of rescue participants transmit N groups of task complexities to the N drone base stations through VONR technology; the N drone base stations receive and analyze the N groups of task complexities, and output the N dynamic resource priority scheduling; the N groups of emergency data of the N groups of rescue participants are transferred by the N drone base stations with the N dynamic resource priority scheduling as constraints.

[0086] Furthermore, the data receiving module 14 in the emergency call system based on VONR technology is also used to: extract the first real-time resource allocation of the first rescue participant from the N dynamic resource priority scheduling; after dynamically enhancing the first audio data stream and the first video data stream according to the first real-time resource allocation, use the enhancement result to replace the first emergency data to obtain first updated data; and so on, after the N groups of emergency data of the N groups of rescue participants are enhanced and pre-processed, they are transferred by the N drone base stations with the N dynamic resource priority scheduling as constraints.

[0087] Furthermore, the resource scheduling module 15 in the emergency call system based on VONR technology is also used to: extract N initial communication resource configurations of the N rescue sub-areas from the initial deployment plan; perform deviation calculations on the N dynamic resource priority scheduling and the N initial communication resource configurations to obtain N resource scheduling deviations; divide the N drone base stations into a first base station combination and a second base station combination according to the deviation vectors of the N resource scheduling deviations; the 5G core network performs communication resource load balancing between the first base station combination and the second base station combination according to the deviation amounts of the N resource scheduling deviations, and outputs the dynamic resource allocation schedule.

[0088] Furthermore, the resource scheduling module 15 in the emergency call system based on VONR technology is also used for: the 5G core network performs a historical allocation record call to obtain a historical resource allocation schedule, wherein the timestamp difference between the historical resource allocation schedule and the dynamic resource allocation schedule satisfies a preset resource scheduling window; the deviation between the historical resource allocation schedule and the dynamic resource allocation schedule is calculated based on the Pearson correlation coefficient to obtain a scheduling deviation coefficient; if the scheduling deviation coefficient does not meet the preset scheduling deviation threshold, the base station deployment in the target rescue area is updated by updating the rescue scenario characteristics to obtain an updated deployment plan; N deployment flight trajectories of the N drone base stations are generated according to the updated deployment plan and the division deviation of the N rescue sub-areas; and the aerial deployment of the N drone base stations is updated in the target rescue area according to the updated deployment plan and the N deployment flight trajectories.

[0089] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. Figure 1 The emergency call method based on VONR technology and the specific examples in Embodiment 1 are also applicable to the emergency call system based on VONR technology in this embodiment. Through the above detailed description of the emergency call method based on VONR technology, those skilled in the art can clearly know the emergency call system based on VONR technology in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0090] Embodiment three, based on the inventive concept of an emergency call method based on VONR technology in the aforementioned embodiment one, the present application also provides an electronic device, comprising: at least one processor; a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the steps of an emergency call method based on VONR technology described in any one of the aforementioned embodiments one.

[0091] Attached Figure 4 This is a schematic diagram of the structure of an exemplary electronic device of this application. Figure 4In the embodiment, the bus architecture is represented by bus 300, which may include any number of interconnected buses and bridges, and bus 300 connects various circuits including one or more processors represented by processor 302 and memory represented by memory 304. Bus 300 may also connect various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same element, namely a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 302 is responsible for managing bus 300 and general processing, while memory 304 may be used to store data used by processor 302 when performing operations.

[0092] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0093] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. An emergency call method based on VONR technology, characterized in that: include: Analyze the base station deployment in the target rescue area according to the rescue scenario characteristics and obtain the initial deployment plan; Performing rapid aerial deployment of UAV base stations in the target rescue area according to the initial deployment plan to obtain N UAV base stations for communication coverage of N rescue sub-areas in the target rescue area; After connecting the N drone base stations to the 5G core network via satellite, start the N drone base stations and enable the VONR technology; The N drone base stations receive and analyze in real time the emergency call data transmitted back by the N groups of rescue participants in the N rescue sub-areas through the VONR technology, and output N dynamic resource priority scheduling; The 5G core network receives and allocates communication resources to the N drone base stations according to the N dynamic resource priority scheduling, and outputs a dynamic resource allocation schedule; By adopting the N dynamic resource priority scheduling and dynamic resource allocation scheduling to synchronously execute the dynamic traffic sharing of the N drone base stations and the dynamic task allocation of the N groups of rescue participants, emergency call guarantee is performed in the target rescue area; The N drone base stations receive and analyze in real time the emergency call data transmitted back by the N groups of rescue participants in the N rescue sub-areas through the VONR technology, and output N dynamic resource priority scheduling, including: The first rescue participant collects rescue information through the first rescue terminal device to obtain first emergency data, wherein the first emergency data includes a first audio data stream, a first video data stream, a first participant position and first position environment data; Analyze the first audio data stream, the first video data stream and the first location environment data by a rescue task analysis module built into the first rescue terminal device, and output a first task complexity; The first rescue terminal device transmits the first emergency data of the first mission complexity back to the first drone base station through the VONR technology; By analogy, the N groups of rescue participants transmit N groups of task complexity back to the N drone base stations through VONR technology; The N drone base stations receive and analyze the N groups of task complexities, and output the N dynamic resource priority scheduling; The N groups of emergency data of the N groups of rescue participants are transferred by the N drone base stations subject to the N dynamic resource priority scheduling constraints; The 5G core network receives and allocates communication resources to the N drone base stations according to the N dynamic resource priority scheduling, and outputs a dynamic resource allocation schedule, including: The 5G core network performs a historical allocation record call to obtain a historical resource allocation schedule, wherein a timestamp difference between the historical resource allocation schedule and the dynamic resource allocation schedule satisfies a preset resource scheduling window; Calculate the deviation between the historical resource allocation scheduling and the dynamic resource allocation scheduling based on the Pearson correlation coefficient to obtain a scheduling deviation coefficient; If the scheduling deviation coefficient does not meet the preset scheduling deviation threshold, the base station deployment in the target rescue area is updated by updating the rescue scenario characteristics to obtain an updated deployment plan; Generate N deployment flight trajectories of the N UAV base stations according to the updated deployment plan and the division deviation of the N rescue sub-areas; According to the updated deployment plan and the N deployment flight trajectories, the aerial deployment of the N UAV base stations is updated in the target rescue area.

2. The emergency call method based on VONR technology as claimed in claim 1, characterized in that: The N groups of emergency data of the N groups of rescue participants are transferred by the N drone base stations with the N dynamic resource priority scheduling as a constraint, before which, it includes: Extracting a first real-time resource allocation for the first rescue participant from the N dynamic resource priority scheduling; After dynamically enhancing the first audio data stream and the first video data stream according to the first real-time resource allocation, the first emergency data is replaced with the enhanced result to obtain first updated data; By analogy, after enhanced preprocessing, the N groups of emergency data of the N groups of rescue participants are transferred by the N drone base stations based on the N dynamic resource priority scheduling constraints.

3. The emergency call method based on VONR technology as claimed in claim 2, characterized in that: The 5G core network receives and allocates communication resources to the N drone base stations according to the N dynamic resource priority scheduling, and outputs a dynamic resource allocation schedule, including: Extracting N initial communication resource configurations of the N rescue sub-areas from the initial deployment plan; Calculating deviations between the N dynamic resource priority scheduling and the N initial communication resource configurations to obtain N resource scheduling deviations; Dividing the N drone base stations into a first base station group and a second base station group according to the deviation vectors of the N resource scheduling deviations; The 5G core network performs communication resource load balancing between the first base station combination and the second base station combination according to the deviation amounts of the N resource scheduling deviations, and outputs the dynamic resource allocation scheduling.

4. The emergency call method based on VONR technology as claimed in claim 1, characterized in that: Analyze the base station deployment in the target rescue area based on the characteristics of the rescue scenario and obtain the initial deployment plan, including: Starting redundant UAVs to scan the target rescue area to obtain a target terrain model; Receive multiple real-time location information and multiple rescue demand information uploaded by multiple rescue participants through VONR technology; Based on the multiple real-time location information, the multiple rescue demand information is fitted to the target terrain model to generate a dynamic rescue distribution map; Interactively obtain a standard communication coverage scale of the UAV base station, and use 1 / K of the standard communication coverage scale as a model segmentation constraint to divide the dynamic rescue distribution map into multiple dynamic rescue sub-models; Performing task priority analysis on the multiple dynamic rescue sub-models, and taking the standard communication coverage scale as a constraint, merging adjacent sub-models according to the analysis results to obtain the initial deployment plan, wherein the initial deployment plan includes N rescue sub-areas and N initial communication resource configurations.

5. The emergency call method based on VONR technology as claimed in claim 4, characterized in that: Performing task priority analysis on the multiple dynamic rescue sub-models, and taking the standard communication coverage scale as a constraint, merging adjacent sub-models according to the analysis results to obtain the initial deployment plan, including: Extracting a first terrain feature and a first rescue requirement set from a first dynamic rescue sub-model, wherein the first rescue requirement set consists of a first personnel density, a first communication requirement set, and a first emergency task set; Performing a task priority evaluation according to the first terrain feature and the first rescue requirement set to obtain a first task priority; By analogy, multiple task priorities of the multiple dynamic rescue sub-models are obtained; Preset a priority deviation, and use the priority deviation as a constraint to merge adjacent sub-models according to the plurality of task priorities to obtain N rescue sub-areas, wherein the N rescue sub-areas divide the plurality of rescue participants into the N groups of rescue participants; Performing a maximum value call according to the task priority composition of the N rescue sub-areas to obtain N regional priorities; Using the N regional priorities to traverse the communication resource configuration table to obtain the N initial communication resource configurations; The N rescue sub-areas and the N initial communication resource configurations are stored in association to obtain the initial deployment plan.

6. The emergency call method based on VONR technology as claimed in claim 5, characterized in that: After the N drone base stations are connected to the 5G core network via satellite, the N drone base stations are started and the VONR technology is enabled, including: Positioning the communication centers of the N rescue sub-areas to obtain the locations of N planned base stations; Controlling the N UAV base stations to hover at the N planned base station positions; configuring N priority slices for the N drone base stations according to the N regional priorities; After connecting the N drone base stations to the 5G core network via satellite, the N priority slices are loaded into the 5G core network; After the 5G core network allocates communication resources for the N drone base stations based on the N initial communication resource configurations with the N priority slices as constraints, the N drone base stations are started and the VONR technology is enabled.

7. An emergency call system based on VONR technology, characterized in that: Steps for implementing the emergency call method based on VONR technology as described in any one of claims 1 to 6, wherein the emergency call system based on VONR technology comprises: A solution acquisition module, which is used to perform base station deployment analysis in a target rescue area according to rescue scenario characteristics and obtain an initial deployment solution; A base station deployment module, the base station deployment module is used to perform rapid aerial deployment of UAV base stations in the target rescue area according to the initial deployment plan, and obtain N UAV base stations for communication coverage of N rescue sub-areas in the target rescue area; A base station startup module, wherein the base station startup module is used to start the N drone base stations and enable the VONR technology after connecting the N drone base stations to the 5G core network via satellite; A data receiving module, wherein the N drone base stations receive and analyze the emergency call data transmitted back by the N groups of rescue participants in the N rescue sub-areas through the VONR technology in real time, and output N dynamic resource priority scheduling; A resource scheduling module, wherein the resource scheduling module is used for the 5G core network to receive and allocate communication resources to the N drone base stations according to the N dynamic resource priority scheduling, and output a dynamic resource allocation schedule; A task allocation module is used to synchronously execute the dynamic traffic sharing of the N drone base stations and the dynamic task allocation of the N groups of rescue participants by adopting the N dynamic resource priority scheduling and dynamic resource allocation scheduling, so as to provide emergency call guarantee for the target rescue area.

8. An electronic device, comprising: at least one processor; a memory communicatively coupled to the at least one processor; In which, the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the steps of an emergency call method based on VONR technology as described in any one of claims 1 to 6.

Images