Fusion processing method and device for positioning data of unmanned aerial vehicle, and medium

Abstract

The invention discloses an unmanned aerial vehicle positioning data fusion processing method and device and a medium, and belongs to the technical field of unmanned aerial vehicles. The method comprises the following steps: acquiring packet header data in a positioning data packet uploaded by an unmanned aerial vehicle; wherein the packet header data at least comprises an unmanned aerial vehicle model and a positioning signal type; according to the unmanned aerial vehicle model and the positioning signal type, generating an analysis algorithm corresponding to the positioning data packet, and analyzing the positioning data packet through the analysis algorithm to obtain a positioning signal of the unmanned aerial vehicle; determining a positioning signal database corresponding to the unmanned aerial vehicle according to the model of the unmanned aerial vehicle, and adding the analyzed positioning signal to the positioning signal database; and in the positioning signal database, performing space-time sorting on the positioning signals in the positioning signal database by using a data fusion algorithm so as to generate a flight path corresponding to the unmanned aerial vehicle according to a space-time sorting result. According to the invention, real-time monitoring and checking of the flight position and the flight path of the unmanned aerial vehicle are realized by analyzing different types of positioning signals.

Description

Technical Field

[0001] The present application relates to the field of drone technology, and in particular to a method, device and medium for fusion processing of drone positioning data. Background Art

[0002] When general aviation aircraft, such as drones, are in flight, controllers need to obtain the aircraft's flight dynamics in real time, including the aircraft's longitude, latitude, and altitude, in order to better provide flight services for the aircraft.

[0003] At present, the main technical means for controllers to obtain the flight trajectory of drones are: the monitoring device obtains the flight signal data packet based on the Automatic Dependent Surveillance-Broadcast (ADS-B) uploaded by the drone; or the monitoring device obtains the flight signal data packet of the Beidou positioning short message uploaded by the drone; or the monitoring device obtains the flight signal data packet based on 4G / 5G transmitted by the drone; or the monitoring device obtains the flight signal data packet of the secondary radar positioning uploaded by the drone. The drone is aligned with these received data packets to obtain the flight trajectory of the drone.

[0004] However, due to the characteristics of the drone's operating mission, its flight scenes are mainly in areas such as forests and mountains where it is difficult for humans to observe. This makes it impossible for the monitoring equipment to receive ADS-B flight data packets, Beidou positioning short message flight signal data packets, 4G / 5G flight signal data packets, and secondary radar positioning flight signal data packets in real time, resulting in the inability to monitor the drone's flight trajectory and flight position in real time. In addition, existing monitoring equipment can usually only analyze one type of flight signal separately, which also leads to the monitoring equipment being unable to obtain the drone's continuous and stable flight trajectory in real time. Summary of the invention

[0005] The embodiments of the present application provide a method, device and medium for fusion processing of drone positioning data to solve at least one of the above-mentioned technical problems.

[0006] In the first aspect, an embodiment of the present application provides a method for fusion processing of unmanned aerial vehicle positioning data, the method comprising: obtaining header data in a positioning data packet uploaded by a unmanned aerial vehicle; wherein the header data includes at least a unmanned aerial vehicle model and a positioning signal type; generating a parsing algorithm corresponding to the positioning data packet according to the unmanned aerial vehicle model and the positioning signal type, and parsing the positioning data packet through the parsing algorithm to obtain the positioning signal of the unmanned aerial vehicle; determining a positioning signal database corresponding to the unmanned aerial vehicle according to the unmanned aerial vehicle model, and adding the parsed positioning signal to the positioning signal database; in the positioning signal database, using a data fusion algorithm to perform spatiotemporal sorting of the positioning signals in the positioning signal database, so as to generate a flight trajectory corresponding to the unmanned aerial vehicle according to the result of the spatiotemporal sorting.

[0007] In a possible implementation of the present application, a parsing algorithm corresponding to the positioning data packet is generated according to the UAV model and the positioning signal type, including: determining the parsing area corresponding to the positioning data packet in a preset parsing database according to the positioning signal type; obtaining a number of algorithm statements corresponding to the positioning data packet in the parsing area according to the UAV model, and determining the execution order corresponding to the number of algorithm statements; generating the parsing algorithm through the number of algorithm statements and their corresponding execution order.

[0008] In a possible implementation of the present application, after generating the parsing algorithm, the method further includes: storing the parsing algorithm, and storing the correspondence between the parsing algorithm and the positioning signal type and the drone model; when a positioning data packet carrying the positioning signal type and the drone model is received again, directly calling the parsing algorithm; and, when an update operation is detected for any algorithm statement in the preset parsing database, triggering an update instruction to update the stored parsing algorithm.

[0009] In a possible implementation of the present application, after determining the positioning signal database corresponding to the drone according to the drone model, the method further includes: obtaining an identification code and location information carried in the positioning signal; wherein the identification code is used to distinguish the positioning signal type, and the positioning signal type includes at least one or more of the following: ADS-B signal, Beidou short message, 4G / 5G signal and secondary radar signal; according to the identification code, determining a reference positioning signal corresponding to the positioning signal in the positioning signal database; wherein the reference positioning signal is a positioning signal at a previous timestamp of the timestamp corresponding to the positioning signal; obtaining the location information carried in the reference positioning signal; wherein the location information includes the longitude, latitude and altitude of the drone; determining a first flight position of the drone according to the location information carried in the positioning signal, and determining a second flight position of the drone according to the location information carried in the reference positioning signal; comparing the first flight position with the second flight position; if the first flight position is before the second flight position, determining the positioning signal as a valid positioning signal, otherwise, determining the positioning signal as an invalid positioning signal; adding the valid positioning signal to the positioning signal database.

[0010] In one possible implementation of the present application, after adding the valid positioning signal to the positioning signal database, the method also includes: in the positioning signal data, converting different types of positioning signals into a common data structure, in which the drone model is used as the starting data and the positioning signal type is used as the second data, and then the longitude, latitude and altitude of the drone are written in sequence.

[0011] In a possible implementation of the present application, in the positioning signal database, a data fusion algorithm is used to perform spatiotemporal sorting of the positioning signals in the positioning signal database, including: sorting the positioning signals based on the timestamp corresponding to each positioning signal; when the number of positioning signals at any timestamp is greater than 1, the multiple positioning signals at any timestamp are fused by a data fusion algorithm according to a preset weight; after the fusion process is completed, a three-dimensional coordinate system is established with time, longitude and latitude as coordinate axes, and the positioning signal is added to the three-dimensional coordinate system according to the timestamp; each positioning signal is identified in the three-dimensional coordinate system with a drone symbol, and all drone identifiers are connected with a dotted line to obtain the corresponding flight trajectory of the drone.

[0012] In a possible implementation of the present application, in the positioning signal database, the positioning signals in the positioning signal database are sorted in time and space using a data fusion algorithm, and also include: grouping the positioning signals in the positioning signal database according to the positioning signal type; after the grouping is completed, establishing a three-dimensional coordinate system corresponding to any group with time, longitude and latitude as coordinate axes; adding the positioning signals in any group to the three-dimensional coordinate system, and identifying each positioning signal with a drone symbol; using dotted lines to connect all drone identifiers in the three-dimensional coordinate system to obtain the corresponding flight trajectory of the drone.

[0013] In a possible implementation of the present application, after generating the flight trajectory corresponding to the drone, the method also includes: receiving the latest positioning signal sent by the drone in real time; determining the corresponding three-dimensional coordinate system based on the type of the latest positioning signal, and adding the latest positioning signal to the three-dimensional coordinate system, the type of the latest positioning signal including at least one of ADS-B signal, Beidou short message, 4G / 5G signal and secondary radar signal; in the three-dimensional coordinate system, rendering the drone logo corresponding to the latest positioning signal in different colors until the next positioning signal is received.

[0014] In the second aspect, an embodiment of the present application also provides a fusion processing device for drone positioning data, the device comprising: a processor; and a memory on which executable code is stored, and when the executable code is executed, the processor executes a fusion processing method for drone positioning data as described in any of the above embodiments.

[0015] In a third aspect, an embodiment of the present application further provides a non-volatile computer storage medium on which computer instructions are stored, and when the computer instructions are executed, a method for fusion processing of drone positioning data as described in any of the above embodiments is implemented.

[0016] Due to the adoption of the above technical solution, this application has the following beneficial effects:

[0017] This application realizes the real-time reception of ADS-B signals, Beidou short messages, 4G / 5G signals and / or secondary radar signals by parsing positioning data packets of different signal types. Even if there is a situation where one signal cannot be received, other signals can be parsed through the parsing process in this application, thereby ensuring that the positioning data of the drone can be obtained in real time. Afterwards, the parsed positioning data is added to the positioning signal database, and the positioning signal is fused and processed in the positioning signal database through a data fusion algorithm to generate the flight trajectory of the drone, so that the monitoring equipment or monitoring personnel can monitor the flight trajectory of the drone in real time. At the same time, when a new positioning data packet arrives, the positioning signal corresponding to the new positioning data packet will be added to the flight trajectory and marked with different colors, so that the monitoring equipment or monitoring personnel can monitor the real-time location information of the drone. In short, this application ensures that the positioning data packet of the drone can be received in real time by implementing the parsing of multiple positioning signals, generates a flight trajectory based on the positioning data packet, and realizes the monitoring of the flight trajectory and real-time position of the drone. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 A flow chart of a method for fusion processing of drone positioning data provided in an embodiment of the present application;

[0020] Figure 2 A schematic diagram of the internal structure of a fusion processing device for drone positioning data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0022] At present, there is no effective means to complete the flight monitoring task of drones because of their slow flight speed, low altitude and complex and diverse operating environment. This application proposes a method, device and medium for fusion processing of drone positioning data, which can effectively monitor the real-time flight position and view the flight trajectory by fusion processing of different types of positioning signals in different drone operating scenarios and geographical environments, and provide an effective tool for air traffic controllers to grasp the flight dynamics of drones in real time.

[0023] In addition, a method for fusion processing of drone positioning data proposed in an embodiment of the present application is executed by a server corresponding to the drone or a server of a monitoring system / equipment corresponding to the drone.

[0024] The technical solution proposed in the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0025] Figure 1 A flowchart of a method for fusion processing of drone positioning data provided in an embodiment of the present application. Figure 1 As shown, the positioning data fusion processing method in this application at least includes the following execution steps:

[0026] Step 101: Obtain the packet header data in the positioning data packet uploaded by the drone.

[0027] During the flight of the drone, the positioning data packet will be uploaded in real time or at intervals. After receiving the positioning data packet uploaded by the drone, the header data is extracted. In one example, the header data of the positioning data packet includes at least the drone model and the positioning signal type. The drone model here can also be the drone ID, which is used to identify which drone uploaded the data. The positioning signal type is used to distinguish what type of positioning signal is carried in the positioning data uploaded by the drone at this time. The positioning signal type includes at least one or more of the ADS-B signal, Beidou short message, 4G / 5G signal and secondary radar signal, and the positioning signal includes at least the longitude, latitude, altitude of the drone and the upload timestamp of the positioning data packet.

[0028] Step 102: Generate a parsing algorithm corresponding to the positioning data packet according to the drone model and the positioning signal type to parse the positioning data packet and obtain the positioning signal of the drone.

[0029] After obtaining the drone model and positioning signal type through the packet header data of the positioning data packet, a parsing algorithm for the positioning data packet is generated, and the positioning data packet is parsed using the generated parsing algorithm to obtain the positioning signal of the drone. In this process, the present application can parse various types of positioning data packets through the generated parsing algorithm, thereby avoiding the disadvantage that the traditional technology center can usually only process one type of signal. In this way, the real-time acquisition of the drone positioning signal can be guaranteed. Even if a certain type of positioning signal fails to be uploaded, it can be uploaded through other types of positioning signals, thereby achieving the continuity of the drone positioning signal upload, providing a guarantee for the subsequent acquisition of a continuous flight trajectory.

[0030] In a possible implementation of the present application, the generation process of the parsing algorithm is as follows: a parsing database is pre-stored in the system, and the parsing data contains parsing algorithm statements for various types of data packets, and these algorithm statements are stored in different parsing areas. The present application uses the aforementioned determined positioning signal type to find the corresponding parsing area in the parsing database, and each parsing area can correspond to the parsing of different types of signals. For example, when the positioning signal type corresponding to the positioning data packet is a Beidou satellite signal, when generating the parsing algorithm, it is necessary to find the parsing area corresponding to the Beidou satellite signal in the pre-set parsing database. After that, according to the drone model corresponding to the positioning data packet, several algorithm statements used for interpretation and the execution order of several algorithm statements are determined in the parsing area. Of course, here, according to the different forms of storing the parsing algorithm in the parsing database, some have separate parsing statements, some store parsing blocks, and the corresponding parsing blocks can also be extracted according to the drone model. Finally, the parsing algorithm is generated through several algorithm statements and their execution order, or parsing blocks.

[0031] Furthermore, the generated parsing algorithm is used to parse the positioning data packet to obtain the positioning signal of the drone. It should be noted that the parsing process of the positioning data packet here can be implemented by the existing parsing algorithm, and the embodiments of the present application are not repeated here. The core point of the parsing process of the present application is to implement the parsing of multiple types of data packets through the combination of algorithm statements, so that the monitoring device or system can receive multiple types of positioning signals, thereby realizing continuous flight trajectory monitoring of the drone.

[0032] In one possible implementation of the present application, in order to avoid frequent generation of parsing algorithms and to improve the parsing efficiency of positioning data packets in the future, after obtaining the parsing algorithm, the parsing algorithm will be stored, and the correspondence between the parsing algorithm and the positioning signal type and the drone model will also be stored. In this way, in the subsequent data packet reception process, if a data packet carrying the positioning signal type and drone model in the corresponding relationship is received again, the stored parsing algorithm can be directly called to parse the data packet, thereby avoiding the need to execute the parsing algorithm generation process again.

[0033] Furthermore, the algorithm statements in the preset parsing database are usually updated. To ensure the timeliness of the stored parsing algorithm and the efficiency of parsing data packets using the parsing algorithm, when an algorithm statement update is detected in the parsing database, if the updated statement is included in the parsing algorithm, then the present application will also synchronously update the corresponding algorithm statement in the existing parsing algorithm. It should be noted that the synchronous update process here can be implemented through the existing data update algorithm, and the embodiments of the present application will not be elaborated here.

[0034] Step 103: Determine the corresponding positioning signal database according to the drone model, and add the parsed positioning signal to the positioning signal database.

[0035] After the positioning data packet is parsed using the generated parsing algorithm to obtain the positioning signal, the positioning signal is stored in the corresponding positioning signal database. The positioning signal database here is a database corresponding to the UAV model, that is, a database corresponding to the current UAV. This makes it easier to generate the flight trajectory of the UAV based on the positioning signal in the database.

[0036] In a possible implementation of the present application, before storing the positioning signal in the positioning signal database, it is necessary to determine whether the positioning signal is a valid positioning signal. Only when it is a valid positioning signal, the positioning signal is allowed to be stored, so as to ensure the accuracy of the positioning signal stored in the positioning signal database, thereby ensuring the accuracy of the flight trajectory subsequently generated based on the positioning signal database. Specifically, first determine the identification code and location information carried in the positioning signal. The identification code is used to distinguish the type of the positioning signal. The location information includes at least the longitude, latitude and altitude of the drone. Then, according to the identification code, obtain the positioning signal at the previous timestamp of the corresponding timestamp of the positioning signal in the positioning signal database, and record it as a reference positioning signal. For example, if the timestamp of the current Beidou satellite signal is 13:20, then obtain the Beidou satellite signal at the previous timestamp, such as 13:15, in the positioning signal database as a reference positioning signal. The first flight position of the UAV is determined by the position information in the current positioning signal, and the second flight position of the UAV is determined by referring to the position information in the positioning signal. If the first flight position is after the second flight position, it means that the current positioning signal may not reflect the current real-time position of the UAV due to reasons such as signal transmission delay. At this time, the current positioning signal is determined as an invalid positioning signal and is eliminated, that is, it is not allowed to be added to the positioning signal database. If the first flight position is before the second flight position, that is, the first flight position is the next position of the second flight position, it means that the current positioning signal is a valid positioning signal, and the valid positioning signal is added to the positioning signal database. Therefore, it is ensured that the signals in the positioning signal data can reflect the actual flight trajectory of the UAV when arranged in chronological order.

[0037] Furthermore, in order to facilitate the generation of subsequent UAV flight trajectories, after the positioning signal is added to the positioning signal database, the positioning signal will be adjusted to a unified data format: UAV model / UAV ID-positioning signal type-UAV longitude-UAV latitude-UAV altitude.

[0038] Step 104: In the positioning signal database, use a data fusion algorithm to perform spatiotemporal sorting on the positioning signals in the positioning signal database, so as to generate a flight trajectory corresponding to the UAV according to the result of the spatiotemporal sorting.

[0039] This application generates the flight trajectory of the drone based on several positioning signals in the positioning signal database, which has the following two implementation methods:

[0040] Method 1: Generate a flight trajectory by integrating all positioning signals.

[0041] Specifically, based on the timestamps corresponding to the positioning signals in the positioning signal database, all the positioning signals in the database are time-sorted. If the number of positioning signals at any timestamp is 1, there is no need to fuse the positioning signals at that timestamp. If the number of positioning signals at any timestamp is greater than 1, it means that the monitoring system or monitoring device has received at least two different types of positioning signals at that timestamp. At this time, it is necessary to fuse the multiple positioning signals at that timestamp so that there is only one positioning signal at one timestamp. In one example, the fusion process here can be performed by assigning different weights to different types of positioning signals, and using a weighted summation scheme to fuse at least two positioning signals into one positioning signal. It can also use an existing data fusion algorithm to fuse at least two positioning signals into one positioning signal. The present application does not limit the fusion method, as long as a positioning signal can be obtained in the end.

[0042] Furthermore, after the fusion process is completed, a three-dimensional coordinate system is established with time, longitude and latitude as coordinate axes, and then the positioning signals are sequentially added to the three-dimensional coordinate system according to the corresponding timestamps of the positioning signals. In the three-dimensional coordinate system, each added positioning signal is identified with a drone symbol, that is, each time a positioning signal is added to the three-dimensional coordinate system, a drone identifier is added to the corresponding coordinate position of the positioning signal, and all drone identifiers in the coordinate system are connected by dotted lines to obtain the corresponding flight trajectory of the drone, thereby realizing the monitoring of the drone flight trajectory.

[0043] Method 2: Generate multiple flight trajectories using different types of positioning signals.

[0044] The monitoring equipment or monitoring system may receive and parse multiple types of positioning signals. Therefore, in the positioning signal database, all positioning signals can be grouped according to the type of positioning signal, and one group corresponds to one type. Then, for each group, a corresponding three-dimensional coordinate system is established with time, longitude and latitude as coordinate axes. Then, the positioning signals in each group are added to the three-dimensional coordinate system in the order of timestamps, and each positioning signal is marked with a drone symbol. Finally, all drone marks in the three-dimensional coordinate system are connected with dotted lines to obtain the corresponding flight trajectory of the drone.

[0045] In this way, corresponding flight trajectories can be generated for different types of positioning signals. On the one hand, the flight trajectory generation process is simplified while ensuring that the flight trajectory can be monitored. On the other hand, different types of positioning signals can be compared horizontally to determine which type of positioning signal can better reflect the actual drone position or flight trajectory.

[0046] In a possible implementation of the present application, no matter which of the above two methods is used to generate the flight trajectory of the drone, the monitoring of the flight trajectory of the drone can be achieved. In addition, the monitoring of the real-time position of the drone can also be achieved by using a three-dimensional coordinate system. Specifically, after receiving and parsing the latest positioning signal generated by the drone, the corresponding three-dimensional coordinate system is determined according to the type of the latest positioning signal, and the latest positioning signal is added to the three-dimensional coordinate system. When adding, it is preferred to render the drone logo corresponding to the latest positioning signal in different colors so that the user can see the latest drone position at a glance when monitoring the real-time position of the drone. After receiving and parsing the updated positioning signal, the different rendering color is adjusted to the same color as other drone logos, and the drone logo corresponding to the updated positioning signal is rendered in a different color. That is, in the three-dimensional coordinate system, in addition to displaying the flight trajectory of the drone, there is also a logo with a color different from other drone logos. The position information corresponding to the logo of different colors reflects the real-time position of the drone, thereby realizing the monitoring of the real-time position of the drone.

[0047] Of course, in this implementation, the latest positioning signal analyzed can also be fused and added to the three-dimensional coordinate system in method 1, so that the real-time position of the drone can be monitored.

[0048] The above is a method embodiment of the present application. In addition, the present application also provides a fusion processing device for drone positioning data, the structure of which is as follows: Figure 2 shown.

[0049] Figure 2 This is a schematic diagram of the internal structure of a fusion processing device for drone positioning data provided in an embodiment of the present application. Figure 2 As shown, the device in the present application includes: a processor; and a memory connected to the processor through a bus, on which executable code is stored. When the executable code is executed, the processor executes: obtaining header data in a positioning data packet uploaded by a drone; wherein the header data includes at least a drone model and a positioning signal type; generating a parsing algorithm corresponding to the positioning data packet according to the drone model and the positioning signal type, and parsing the positioning data packet through the parsing algorithm to obtain a positioning signal of the drone; determining a positioning signal database corresponding to the drone according to the drone model, and adding the parsed positioning signal to the positioning signal database; in the positioning signal database, using a data fusion algorithm to perform spatiotemporal sorting on the positioning signals in the positioning signal database, so as to generate a flight trajectory corresponding to the drone according to the result of the spatiotemporal sorting.

[0050] In addition, the present application also provides a non-volatile computer storage medium having computer instructions stored thereon, which when executed can achieve: obtaining header data in a positioning data packet uploaded by a drone; wherein the header data includes at least a drone model and a positioning signal type; generating a parsing algorithm corresponding to the positioning data packet according to the drone model and the positioning signal type, and parsing the positioning data packet through the parsing algorithm to obtain the positioning signal of the drone; determining a positioning signal database corresponding to the drone according to the drone model, and adding the parsed positioning signal to the positioning signal database; in the positioning signal database, using a data fusion algorithm to perform spatiotemporal sorting of the positioning signals in the positioning signal database, so as to generate a flight trajectory corresponding to the drone according to the result of the spatiotemporal sorting.

[0051] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0052] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0053] The above is only the embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for fusion processing of drone positioning data, characterized in that: The method comprises: Obtaining header data in a positioning data packet uploaded by the drone; wherein the header data at least includes the drone model and the positioning signal type; Generate a parsing algorithm corresponding to the positioning data packet according to the UAV model and the positioning signal type, and parse the positioning data packet through the parsing algorithm to obtain the positioning signal of the UAV; According to the drone model, determine the positioning signal database corresponding to the drone, and add the parsed positioning signal to the positioning signal database; In the positioning signal database, a data fusion algorithm is used to perform spatiotemporal sorting on the positioning signals in the positioning signal database, so as to generate a flight trajectory corresponding to the UAV according to the result of the spatiotemporal sorting.

2. The method for fusion processing of drone positioning data according to claim 1, characterized in that: According to the drone model and positioning signal type, a parsing algorithm corresponding to the positioning data packet is generated, including: Determine, according to the positioning signal type, a resolution area corresponding to the positioning data packet in a preset resolution database; According to the drone model, a plurality of algorithm statements corresponding to the positioning data packet are obtained in the parsing area, and an execution order corresponding to the plurality of algorithm statements is determined; The analysis algorithm is generated through the several algorithm statements and their corresponding execution order.

3. The method for fusion processing of drone positioning data according to claim 2, characterized in that: After generating the parsing algorithm, the method further comprises: Storing the analysis algorithm, and storing the correspondence between the analysis algorithm, the positioning signal type, and the drone model; When a positioning data packet carrying the positioning signal type and the UAV model is received again, the parsing algorithm is directly called; And, when an update operation is detected for any algorithm statement in the preset parsing database, an update instruction is triggered to update the stored parsing algorithm.

4. The method for fusion processing of drone positioning data according to claim 1, characterized in that: After determining the positioning signal database corresponding to the drone according to the drone model, the method further includes: Obtaining an identification code and location information carried in the positioning signal; wherein the identification code is used to distinguish the type of the positioning signal, and the positioning signal type includes at least one or more of the following: ADS-B signal, Beidou short message, 4G / 5G signal, and secondary radar signal; According to the identification code, determining a reference positioning signal corresponding to the positioning signal in the positioning signal database; wherein the reference positioning signal is a positioning signal at a previous timestamp of a timestamp corresponding to the positioning signal; Acquire the location information carried in the reference positioning signal; wherein the location information includes the longitude, latitude and altitude of the UAV; Determine a first flight position of the UAV according to the position information carried in the positioning signal, and determine a second flight position of the UAV according to the position information carried in the reference positioning signal; comparing the first flight position and the second flight position; If the first flight position is before the second flight position, the positioning signal is determined to be a valid positioning signal; otherwise, the positioning signal is determined to be an invalid positioning signal; The valid positioning signal is added to the positioning signal database.

5. The method for fusion processing of drone positioning data according to claim 4, characterized in that: After adding the valid positioning signal to the positioning signal database, the method further includes: In the positioning signal data, different types of positioning signals are converted into a general data structure, in which the drone model is used as the starting data, the positioning signal type is used as the second data, and then the longitude, latitude and altitude of the drone are written in sequence.

6. The method for fusion processing of drone positioning data according to claim 1, characterized in that: In the positioning signal database, using a data fusion algorithm to perform spatiotemporal sorting of the positioning signals in the positioning signal database includes: Sort the positioning signals based on the timestamps corresponding to the positioning signals; When the number of positioning signals at any timestamp is greater than 1, a plurality of positioning signals at any timestamp are fused by a data fusion algorithm according to a preset weight; After the fusion process is completed, a three-dimensional coordinate system is established with time, longitude and latitude as coordinate axes, and the positioning signal is added to the three-dimensional coordinate system according to the timestamp; Each positioning signal is identified in the three-dimensional coordinate system by a drone symbol, and all drone identifiers are connected by a dotted line to obtain the corresponding flight trajectory of the drone.

7. The method for fusion processing of drone positioning data according to claim 1, characterized in that: In the positioning signal database, using a data fusion algorithm to perform spatiotemporal sorting of the positioning signals in the positioning signal database, further comprising: Grouping the positioning signals in the positioning signal database according to the positioning signal type; After the grouping is completed, a three-dimensional coordinate system corresponding to any group is established with time, longitude and latitude as coordinate axes; Adding the positioning signals in any one of the groups to the three-dimensional coordinate system, and marking each of the positioning signals with a drone symbol; All the UAV identifiers in the three-dimensional coordinate system are connected by dotted lines to obtain the flight trajectory corresponding to the UAV.

8. The method for fusion processing of drone positioning data according to claim 7, characterized in that: After generating the flight trajectory corresponding to the drone, the method further includes: Receive the latest positioning signal sent by the drone in real time; Determine a corresponding three-dimensional coordinate system according to the type of the latest positioning signal, and add the latest positioning signal to the three-dimensional coordinate system, wherein the type of the latest positioning signal includes at least one of an ADS-B signal, a Beidou short message, a 4G / 5G signal, and a secondary radar signal; In the three-dimensional coordinate system, the drone logo corresponding to the latest positioning signal is rendered in different colors until the next positioning signal is received.

9. A fusion processing device for unmanned aerial vehicle positioning data, characterized in that: The device comprises: processor; and a memory having executable codes stored thereon, which, when executed, enables the processor to execute a method for fusion processing of drone positioning data as described in any one of claims 1 to 8.

10. A non-volatile computer storage medium, characterized in that: Computer instructions are stored thereon, and when the computer instructions are executed, a method for fusion processing of unmanned aerial vehicle positioning data as described in any one of claims 1 to 8 is implemented.

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