CN116419162A - Non-networked UAV tracking and monitoring method and system based on new generation core network - Google Patents

Abstract

The invention discloses a method and system for tracking and monitoring a non-networked UAV based on a new generation of communication network. Communication cell information; according to the communication cell information, determine the target base station corresponding to each passing point; each target base station will feed back the obtained monitoring information to the core network; the core network processes the monitoring information to obtain the current monitoring information corresponding to The identity information of the drone; when the predicted identity information of the drone matches the basic information, the position, flight altitude, flight speed and monitored time information of the drone corresponding to the monitoring information will be fed back to the user terminal. According to the above method, according to the model of the unnetworked UAV specified by the user, corresponding tracking and monitoring information can be fed back to the user, so as to effectively reduce the tracking and monitoring cost of the non-networked UAV for private users.

Description

Non-networked UAV tracking and monitoring method and system based on new generation core network

technical field

The invention relates to the technical field of unmanned aerial vehicle monitoring, in particular to a method and system for tracking and monitoring unnetworked unmanned aerial vehicles based on a new generation core network.

Background technique

With the popularization of 5G network, the integration of 5G network and drone technology has brought new opportunities to the application of drones, greatly enriching the application fields and service scenarios of drones, such as agriculture, electric power, water conservancy , transportation, public security, fire protection, etc., have brought new changes to the lives of the public. For UAVs with network communication capabilities, the current wide-coverage cellular network can be directly used to track their flight routes to ensure the UAVs can successfully complete their missions and be safe during flight.

However, there are still some drones on the market that lack internet connectivity or drones that do not need to be connected to the Internet for data transmission during missions. When using such drones to perform missions, if the flight path cannot be tracked, there may be certain security risks.

In this regard, nowadays, based on the 5G communication perception integration technology, taking advantage of the mass deployment of 5G base stations, the effective detection of low-altitude drones through 5G base stations has been realized. Compared with traditional drone detection technology, this technology has a large-scale continuous coverage. , low cost, easy deployment and many other advantages, it can provide effective monitoring means for low-altitude supervision departments, and the detection radius can reach more than 1000 meters.

However, in the prior art, the method of monitoring drones based on 5G base stations can only monitor whether there are drones within the communication service range of the base station, but cannot obtain the model of the drone. Therefore, for drone system service providers, it is impossible to track and monitor the flight status of their own non-networked drones based on 5G base stations, and can only track them through other high-cost monitoring methods.

Contents of the invention

The purpose of the present invention is to provide a new generation of core network-based non-networked UAV tracking and monitoring based on the communication base station to track and monitor the flight status of the non-networked UAV and automatically know the identity of the monitored UAV methods and systems.

In order to achieve the above object, the present invention discloses a new-generation communication network-based non-networked UAV tracking and monitoring method, the communication network includes a core network and a base station communicatively connected to the core network, and the tracking and monitoring method includes :

The core network generates communication cell information corresponding to the UAV to be monitored based on the basic information of the UAV to be monitored;

According to the communication cell information, determine the target base station corresponding to each passing point, and the target base station is provided with a monitoring camera device;

Each of the target base stations feeds back the obtained monitoring information to the core network, and the monitoring information includes the position, flight height, flight speed and photos of the drone;

The core network processes the monitoring information based on a preset prediction model to obtain the identity information of the UAV corresponding to the current monitoring information;

When the predicted identity information of the UAV matches the basic information, the UAV's position, flight altitude, flight speed and monitored time information corresponding to the monitoring information are fed back to the user terminal.

Preferably, the method for generating the communication cell information includes:

The core network receives the basic information through the user terminal;

Mapping the area range and the via points in the expected flight path to a communication network to generate the communication cell information.

Preferably, the basic information also includes the start time of the detected UAV mission, the way points in the expected flight route, the flight speed and the flight altitude; the area range includes the geographical area covered by the mission and each the distance between the passing points;

The core network also estimates the time period for the UAV to reach each way point according to the area range, start time, way point, and flight speed of the task;

The target base station regularly enters a monitoring state according to the time period information, so as to obtain the monitoring information.

Preferably, the prediction model is constructed based on an intelligent neural network model.

The present invention also discloses a new-generation core network-based non-networking UAV tracking and monitoring system. The communication network includes a core network and a base station communicatively connected to the core network. The tracking and monitoring system includes Cell generation module, base station positioning module, analysis and processing module, judgment module and feedback module in the network;

The cell generation module is used to generate communication cell information corresponding to the UAV to be monitored according to the basic information of the UAV to be monitored; the basic information includes the identity of the UAV and the area range and Waypoints in the intended flight path;

The base station positioning module is used to determine the target base station corresponding to each passing point according to the communication cell information, and the target base station is provided with a camera device;

The analysis and processing module is configured to process the monitoring information fed back by the target base station based on a preset prediction model, so as to obtain the identity information of the UAV corresponding to the current monitoring information; the monitoring information includes The position, flight altitude, flight speed and image of the man-machine;

The judging module is used to judge whether the identity information of the drone predicted by the analyzing and processing module matches the basic information;

The feedback module is configured to feed back the position, flight height, flight speed and monitored time information of the drone corresponding to the monitoring information to the user terminal when the judgment result of the judgment module is yes.

Preferably, the cell generating module maps the area range and the passing points in the expected flight route to a communication network to generate the communication cell information.

Preferably, the basic information also includes the start time of the detected UAV mission, the way points in the expected flight route, the flight speed and the flight altitude; the area range includes the geographical area covered by the mission and each the distance between the passing points;

The core network is also provided with a time estimation module, and the time estimation module is used to estimate the time period for the drone to reach each way point according to the area range, start time, way points, and flight speed of the task;

The target base station regularly enters a monitoring state according to the time period information, so as to obtain the monitoring information.

Preferably, the prediction model is constructed based on an intelligent neural network model.

The invention also discloses a non-networked UAV tracking and monitoring system, which includes:

one or more processors;

memory;

and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, the programs including for performing the non-networked wireless Instructions for human-machine tracking and monitoring methods.

The present invention also discloses a computer-readable storage medium, which includes a computer program, and the computer program can be executed by a processor to complete the above-mentioned non-networked UAV tracking and monitoring method.

Compared with the prior art, the above-mentioned technical solution of the present invention is based on the base station of the new generation communication network to sense the drones within its communication service range, and intelligently analyze the monitoring information obtained by the base station through the data analysis function of the core network, To obtain the identity information of the currently monitored UAV, so as to provide users with differentiated UAV tracking and monitoring services, that is, according to the model of the non-networked UAV specified by the user, provide the user with relevant feedback. Corresponding tracking and monitoring information to effectively reduce the tracking and monitoring costs of non-networked UAVs for private users.

Description of drawings

FIG. 1 is a flowchart of a method for tracking and monitoring a non-networked UAV in an embodiment of the present invention.

Fig. 2 is a flow chart of execution in the core network of the non-networked UAV tracking and monitoring method in the embodiment of the present invention.

Detailed ways

In order to describe the technical content, structural features, achieved goals and effects of the present invention in detail, the following will be described in detail in conjunction with the embodiments and accompanying drawings.

This embodiment discloses a method for tracking and monitoring non-networked UAVs based on a new generation of communication network, which is used to track and monitor low-altitude UAVs based on communication base stations, and automatically analyze the monitored UAVs. model. The new generation communication network in this embodiment includes the 5GS system and the next generation communication system compatible with the 5GS system. Taking the 5GS system as an example, it includes a core network and a base station communicating with the core network.

As shown in Figure 1, the tracking and monitoring method in the present embodiment includes the following steps:

S1: The core network generates communication cell information corresponding to the UAV to be monitored based on the basic information of the UAV to be monitored. way point;

S2: According to the communication cell information, determine the target base station corresponding to each passing point, the target base station is provided with a camera device, and the camera device is used to take images of drones within the communication service range of the base station;

S3: Each target base station feeds back the obtained monitoring information to the core network. The monitoring information includes the position, flight altitude, flight speed and image of the drone;

S4: The core network processes the monitoring information based on the preset prediction model to obtain the identity information of the UAV corresponding to the current monitoring information;

S5: When the identity information of the UAV predicted in the above step S4 matches the basic information, feed back the position, flight altitude, flight speed and monitored time information of the UAV corresponding to the monitoring information to user terminal. Since the drone is in motion, the position of the drone in this embodiment refers to the corresponding position at the moment when the target base station senses the drone.

The tracking and monitoring method based on the above structure is based on the perception of unmanned aerial vehicles within its communication service range based on the 5G base station, and intelligently analyzes the monitoring information obtained by the base station through the data analysis function of the core network to obtain the currently monitored unmanned aerial vehicles. The identity information of the drone can provide users with differentiated tracking and monitoring services for non-networked drones, that is, according to the model of the drone specified by the user, the corresponding tracking and monitoring information will be fed back to the user to effectively reduce the Tracking and monitoring costs of non-networked drones for private users.

Specifically, the method for generating communication cell information includes:

The core network receives basic information through the user terminal;

Area ranges and via points in the intended flight path are mapped into the communication network to generate communication cell information. The communication cell in this embodiment is the basic unit of the communication service area. In a communication service network, a base station includes a cell, and a cell includes a sector. A base station can be a 360-degree omni-directional cell, or it can be divided into multiple cells.

Further, the basic information also includes information such as the start time of the detected drone's mission, the waypoints in the expected flight route, the flight speed, and the flight altitude. The area range in this embodiment includes the geographic area covered by the execution task and the distance between various passing points.

In this regard, the core network also estimates the time period for the drone to reach each way point based on information such as the area where the mission is performed, the start time, the way point, and the flight speed. The target base station regularly enters the monitoring state according to the time period information to obtain monitoring information. In this embodiment, when the core network sends a monitoring request to the target base station, the estimated time period information is sent to the corresponding target base station at the same time, and the target base station regularly monitors the low-altitude environment within its service range according to the time period information. Therefore, it can effectively reduce the energy consumption of the target base station. In addition, when the base station does not monitor the UAV within the timing period sent by the core network, the core network feeds back abnormal report information to the user terminal.

Furthermore, the prediction model is built based on the intelligent neural network model. For this predictive model, it is trained based on a massive data set, with the UAV's position, flight height, flight speed, UAV's photo or video and other information as feature indicators, and the UAV's model as a label to form a supervisory model. The data is trained by machine learning algorithms, and different model evaluation indicators are selected to evaluate the model effect to obtain a model with excellent performance.

As shown in Figure 2, the implementation process of the tracking and monitoring method for non-networked drones provided by the above-mentioned embodiment is as follows:

01. The UAV system service provider (USS) initiates an event report request to the network function NF in the core network. The request message includes the event ID (non-networked UAV route tracking), report condition (monitored UAV Report the status when it is expected to arrive at the passing point, and report the abnormal status of the drone if it is not detected within the time period expected to arrive at the passing point), for the model ID, model, start time of the mission, and expected flight route of the drone to be monitored Passing point, flight speed, flight altitude, area range and other information;

02. NF authorizes the request of USS;

03. NF responds to USS;

04. NF maps the information of the area and way points of the expected flight route of the UAV into communication cell information, and estimates the time when the UAV arrives at each way point based on the start time, flight speed, and area range of the UAV's mission. segment information, and then forward the above information (communication cell information, time segment information expected to arrive at each passing point, and information in the request initiated by the USS in step 01) to the network data analysis function NWDAF;

05. NWDAF initiates a monitoring request to the access and mobility management function AMF, and the monitoring request message includes the monitoring event ID, the drone’s machine, model, communication cell, and the expected arrival time at each passing point, etc.;

06. AMF determines the RAN (target base station) of each passing point according to the communication cell information in the monitoring request, and forwards the monitoring request to each RAN. The monitoring request message received by the RAN includes the monitoring event ID, the UAV’s expected arrival at the Time period information of RAN service scope;

07. RANs respond to AMF;

08. AMF responds to NWDAF;

09. NWDAF responds to NF;

10. Each RAN monitors the UAV based on wireless signals within its service area. When the RAN monitors the UAV within the expected time period of reaching the RAN service area, the RAN reports the monitored UAV information to the AMF. The monitoring information includes the UAV's position, flight altitude, flight speed, and photos or videos; when the RAN does not monitor the UAV within the time period expected to reach the RAN's service range, the RAN will report the abnormal information;

11. AMF forwards the monitoring information/abnormal information reported by RAN to NWDAF;

12. NWDAF analyzes the monitoring information through the built-in pre-trained prediction model, outputs the model of the drone, and matches the model of the drone with the request information provided by the USS to judge the drone. Whether it is the target that USS requires monitoring.

13. NWDAF sends the analysis results to the NF. If the analysis results show that the monitored UAV is the U UAV in the USS service request, it will report the status information of the UAV. The report information includes the UAV’s position, flight Altitude, flight speed, time when the UAV was detected, etc.; if the analysis result is no, or when NWDAF receives abnormal information in step 11, it will report the abnormal status;

14. The NF forwards the analysis results to the USS.

According to the above service process, with the help of 5GS, USS can obtain flight route tracking information and abnormal information during the mission execution process of non-networked drones, so as to make decision-making adjustments based on the information reported results.

The present invention also discloses a new-generation core network-based non-networked UAV tracking and monitoring system. The communication network includes a core network and a base station communicatively connected to the core network. The tracking and monitoring system includes The cell generation module, the base station positioning module, the analysis and processing module, the judgment module and the feedback module in the system.

The cell generation module is used to generate communication cell information corresponding to the UAV to be monitored according to the basic information of the mission performed by the UAV to be monitored. The basic information includes the identity of the drone, the range of the area where the task is performed, and the waypoints in the expected flight route.

The base station positioning module is used to determine the target base station corresponding to each passing point according to the communication cell information, and the target base station is provided with a camera device.

The analysis and processing module is configured to process the monitoring information fed back by the target base station based on a preset prediction model, so as to obtain the identity information of the UAV corresponding to the current monitoring information; the monitoring information includes The position, flight altitude, flight speed and image of the man-machine.

The judging module is used to judge whether the identity information of the UAV predicted by the analyzing and processing module matches the basic information.

The feedback module is configured to feed back the position, flight height, flight speed and monitored time information of the drone corresponding to the monitoring information to the user terminal when the judgment result of the judgment module is yes.

Further, the cell generating module maps the area range and the passing points in the expected flight route to a communication network, so as to generate the communication cell information.

Further, the basic information also includes the start time of the detected drone's mission, the waypoints in the expected flight route, the flight speed and the flight altitude. The area range includes the geographical area covered by the execution task and the distance between various passing points.

The core network is also provided with a time estimation module, and the time estimation module is used to estimate the time period for the UAV to reach each way point according to the area range, start time, way points, and flight speed of the task. The target base station regularly enters a monitoring state according to the time period information, so as to obtain the monitoring information.

In addition, it should be noted that the working principle and working method of the non-networked UAV tracking and monitoring system in this embodiment are detailed in the above-mentioned non-networked UAV tracking and monitoring method, and will not be repeated here.

The present invention also discloses another non-networked UAV tracking and monitoring system, which includes one or more processors, memory and one or more programs, wherein one or more programs are stored in the memory and configured To be executed by the one or more processors, the program includes instructions for executing the above-mentioned non-networked UAV tracking and monitoring method. The processor may adopt a general-purpose central processing unit (Central Processing Unit, CPU), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits for executing related programs to realize The functions to be performed by the modules in the non-networked UAV tracking and monitoring system in the embodiment of the present application, or the method for tracking and monitoring the non-networked UAV in the method embodiment of the present application.

The present invention also discloses a computer-readable storage medium, which includes a computer program, and the computer program can be executed by a processor to complete the above-mentioned non-networked UAV tracking and monitoring method. The computer-readable storage medium may be any available medium that can be accessed by a computer, or a data storage device such as a server or a data center integrated with one or more available media. The available medium may be a read-only memory (read-only memory, ROM), or a random access memory (random access memory, RAM), or a magnetic medium, for example, a floppy disk, a hard disk, a magnetic tape, a magnetic disk, or an optical medium, for example, A digital versatile disc (digital versatile disc, DVD), or a semiconductor medium, for example, a solid state disk (solid state disk, SSD).

The embodiment of the present application also discloses a computer program product or computer program, where the computer program product or computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device executes the above-mentioned non-networked UAV tracking and monitoring method.

What is disclosed above is only a preferred embodiment of the present invention, and of course it cannot limit the scope of rights of the present invention. Therefore, equivalent changes made according to the patent scope of the present invention still fall within the scope of the present invention.

Claims (10)

1. The non-networking unmanned aerial vehicle tracking and monitoring method based on a new generation communication network, wherein the communication network comprises a core network and a base station in communication connection with the core network, and the method is characterized by comprising the following steps:

the core network generates communication cell information corresponding to the unmanned aerial vehicle to be monitored according to the basic information of the unmanned aerial vehicle to be monitored for executing tasks; the basic information comprises the identity of the unmanned aerial vehicle, the range of the area for executing the task and the route points in the expected flight route;

determining target base stations corresponding to all the passing points according to the communication cell information, wherein the target base stations are provided with image pick-up devices;

each target base station feeds back the obtained monitoring information to the core network, wherein the monitoring information comprises the position, flight height, flight speed and images of the unmanned aerial vehicle;

the core network processes the monitoring information based on a preset prediction model to obtain identity information of the unmanned aerial vehicle corresponding to the current monitoring information;

and when the predicted identity information of the unmanned aerial vehicle is matched with the basic information, feeding back the position, the flying height, the flying speed and the monitored time information of the unmanned aerial vehicle corresponding to the monitoring information to the user terminal.

2. The non-networked unmanned aerial vehicle tracking and monitoring method based on the new generation core network according to claim 1, wherein the method for generating the communication cell information comprises the following steps:

the core network receives the basic information through the user terminal;

mapping the area range and the passing points in the expected flight path into a communication network to generate the communication cell information.

3. The new generation core network-based non-networked unmanned aerial vehicle tracking and monitoring method according to claim 2, wherein the basic information further comprises the start time of the detected unmanned aerial vehicle to perform a task, the route points in the expected flight route, the flight speed and the flight altitude; the area range comprises a geographical area covered by the execution task and the distance between all the passing points;

the core network also estimates the time period of the unmanned aerial vehicle reaching each path point according to the area range for executing the task, the starting time, the path point and the flying speed;

and the target base station enters a monitoring state at regular time according to the time period information so as to obtain the monitoring information.

4. The non-networked unmanned aerial vehicle tracking and monitoring method based on the new generation core network according to claim 1, wherein the prediction model is constructed based on an intelligent neural network model.

5. The non-networking unmanned aerial vehicle tracking and monitoring system based on the new generation core network is characterized in that the tracking and monitoring system comprises a cell generating module, a base station positioning module, an analysis processing module, a judging module and a feedback module which are arranged in the core network;

the cell generation module is used for generating communication cell information corresponding to the unmanned aerial vehicle to be monitored according to the basic information of the unmanned aerial vehicle to be monitored for executing tasks; the basic information comprises the identity of the unmanned aerial vehicle, the range of the area for executing the task and the route points in the expected flight route;

the base station positioning module is used for determining target base stations corresponding to all the passing points according to the communication cell information, and the target base stations are provided with image pick-up devices;

the analysis processing module is used for processing the monitoring information fed back by the target base station based on a preset prediction model so as to obtain the identity information of the unmanned aerial vehicle corresponding to the current monitoring information; the monitoring information comprises the position, the flying height, the flying speed and the image of the unmanned aerial vehicle;

the judging module is used for judging whether the identity information of the unmanned aerial vehicle predicted by the analysis processing module is matched with the basic information;

and the feedback module is used for feeding back the position, the flight height, the flight speed and the monitored time information of the unmanned aerial vehicle corresponding to the monitoring information to the user terminal when the judging result of the judging module is yes.

6. The new generation core network based non-networked unmanned aerial vehicle tracking and monitoring system of claim 5, wherein the cell generation module maps the area coverage and the waypoints in the intended flight path into a communication network to generate the communication cell information.

7. The new generation core network-based non-networked unmanned aerial vehicle tracking and monitoring system of claim 6, wherein the basic information further comprises a start time of the detected unmanned aerial vehicle to perform a task, a waypoint in an expected flight path, a flight speed, and a flight altitude; the area range comprises a geographical area covered by the execution task and the distance between all the passing points;

the core network is also provided with a time estimation module which is used for estimating the time period of the unmanned aerial vehicle reaching each path point according to the area range for executing the task, the starting time, the path point and the flying speed;

and the target base station enters a monitoring state at regular time according to the time period information so as to obtain the monitoring information.

8. The non-networked unmanned aerial vehicle tracking and monitoring method based on the new generation core network according to claim 5, wherein the prediction model is constructed based on an intelligent neural network model.

9. A non-networked unmanned aerial vehicle tracking monitoring system, comprising:

one or more processors;

a memory;

and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs comprising instructions for performing the non-networked drone tracking monitoring method of any of claims 1 to 4.

10. A computer readable storage medium comprising a computer program executable by a processor to perform the non-networked drone tracking monitoring method of any one of claims 1 to 4.

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