Data processing method, system, equipment and medium for large-scale special-shaped drones

Abstract

The embodiment of the present invention provides a data processing method, system, device and medium for large-scale heterogeneous unmanned aerial vehicles, which relates to the technical field of unmanned aerial vehicles, wherein the method includes: receiving frame data and video data of unmanned aerial vehicles; calling data mapping relationships according to the aircraft type of the unmanned aerial vehicle, converting each parameter in the frame data of the unmanned aerial vehicle into corresponding preset parameters, and obtaining converted frame data, each aircraft type has its own corresponding data mapping relationship, and the data mapping relationship is used to convert the frame data of all aircraft types into unified preset parameters; converting the video data into a preset video format for unified output, and obtaining converted video data; outputting the converted frame data and converted video data of unmanned aerial vehicles of all aircraft types through a unified communication protocol. This scheme makes the output data more unified and standardized, avoids the problems of different names for the same parameter and inconsistent parameter units, and makes the output data easier to use.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular to a data processing method, system, equipment and medium for large-scale special-shaped unmanned aerial vehicles. Background Art

[0002] There are many types of drones on the market. Different manufacturers have different definitions for flight parameters, payload status parameters, payload business data (including video), and other data, and there is no unified standard in the industry. As a result, the same flight parameters, payload status parameters, payload business data, performance index parameters, etc. for different drones may have different names and inconsistent parameter measurement units. As a result, in subsequent data applications (for example, for monitoring special-shaped drones), each additional model requires program development, which makes drone data inconvenient to apply and the application process complicated. Summary of the invention

[0003] In view of this, an embodiment of the present invention provides a method for processing data of large-scale special-shaped drones to solve the technical problem that the data of large-scale special-shaped drones is not easy to apply in the prior art. The method includes:

[0004] For each drone in the large-scale heterogeneous drone group, receiving frame data and video data of the drone, wherein the frame data includes flight data and load parameters;

[0005] Calling a data mapping relationship according to the aircraft type of the UAV, and converting each parameter in the frame data of the UAV into a corresponding preset parameter according to the data mapping relationship to obtain the converted frame data, wherein each aircraft type corresponds to its own data mapping relationship, and the data mapping relationship is used to convert the frame data of all aircraft types into a unified preset parameter, wherein the preset parameter includes parameter type, data type, precision unit, and big-endian mode;

[0006] Converting the video data into a preset video format for unified output to obtain converted video data;

[0007] The converted frame data and the converted video data of all aircraft types of drones are output through a unified communication protocol.

[0008] The embodiment of the present invention also provides a data processing system for large-scale special-shaped UAVs to solve the technical problem that the data of large-scale special-shaped UAVs in the prior art is not easy to apply. The device includes:

[0009] A data receiving unit, for receiving frame data and video data of each drone in the large-scale heterogeneous drone group, wherein the frame data includes flight data and load parameters;

[0010] A parameter conversion unit is used to call a data mapping relationship according to the aircraft type of the UAV, and according to the data mapping relationship, convert each parameter in the frame data of the UAV into a corresponding preset parameter to obtain the converted frame data, wherein each aircraft type corresponds to its own data mapping relationship, and the data mapping relationship is used to convert the frame data of all aircraft types into a unified preset parameter, and the preset parameter includes a parameter type, a data type, a precision unit, and a big-endian mode;

[0011] A video conversion unit, used for converting the video data into a preset video format for unified output to obtain converted video data;

[0012] The data output unit is used to output the converted frame data and the converted video data of all aircraft types of drones through a unified communication protocol.

[0013] An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the processor implements any of the above-mentioned methods for processing data of large-scale heterogeneous drones, so as to solve the technical problem in the prior art that data of large-scale heterogeneous drones is inconvenient to apply.

[0014] An embodiment of the present invention also provides a computer-readable storage medium, which stores a computer program for executing any of the above-mentioned data processing methods for large-scale heterogeneous drones, so as to solve the technical problem in the prior art that the data of large-scale heterogeneous drones is not easy to apply.

[0015] Compared with the prior art, the beneficial effects that can be achieved by at least one of the above-mentioned technical solutions adopted in the embodiments of this specification include at least: it is proposed that after receiving the frame data and video data of each drone, a data mapping relationship is called according to the aircraft type of the drone, and according to the data mapping relationship, each parameter in the frame data of the drone is converted into a corresponding preset parameter to obtain the converted frame data, so as to realize the conversion of the frame data of drones of each aircraft type into a unified preset parameter, that is, a unified standard preset parameter, and realize the conversion of the frame data of drones of each aircraft type into data of a unified standard; and the video data of drones of each aircraft type is converted into video data of a preset video format with a unified output, and finally, the converted frame data and the converted video data of drones of all aircraft types are output through a unified communication protocol. The frame data and video data of drones of various aircraft types are converted into data of unified standard and output through a unified communication protocol, making the output data more unified and standardized, avoiding problems such as different names for the same parameter and inconsistent parameter measurement units, making the output data easier to use; and making it possible to arbitrarily add drones of different models to a large-scale group of heterogeneous drones without affecting the unification and standardization of the output data, thereby avoiding the development of application programs. The output data can be widely and conveniently used in different application scenarios, improving the application promotion of the output data. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a flow chart of a data processing method for large-scale special-shaped UAVs provided by an embodiment of the present invention;

[0018] Figure 2 It is a schematic diagram of an initialization process for creating a data mapping relationship provided by an embodiment of the present invention;

[0019] Figure 3 is a network schematic diagram of a drone data monitoring system provided by an embodiment of the present invention;

[0020] Figure 4 It is a schematic diagram of a process of standardized output of UAV flight data and load parameters provided by an embodiment of the present invention;

[0021] Figure 5 It is a schematic diagram of a process of receiving and standardizing UAV video data provided by an embodiment of the present invention;

[0022] Figure 6 is a structural block diagram of a computer device provided by an embodiment of the present invention;

[0023] Figure 7 It is a structural block diagram of a data processing system for large-scale special-shaped unmanned aerial vehicles provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0025] The following describes the implementation methods of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The present application can also be implemented or applied through other different specific implementation methods, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, in the absence of conflict, the following embodiments and the features in the embodiments can be combined with each other. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work belong to the scope of protection of the present application.

[0026] In an embodiment of the present invention, a method for processing data of large-scale special-shaped drones is provided, such as Figure 1 As shown, the method includes:

[0027] Step S101: for each drone in the large-scale heterogeneous drone group, receiving frame data and video data of the drone, wherein the frame data includes flight data and load parameters (for example, optoelectronic load state parameters and radar load state parameters);

[0028] Step S102: calling a data mapping relationship according to the aircraft type of the UAV, and converting each parameter in the frame data of the UAV into a corresponding preset parameter according to the data mapping relationship to obtain the converted frame data, wherein each aircraft type corresponds to its own data mapping relationship, and the data mapping relationship is used to convert the frame data of all aircraft types into a unified preset parameter, wherein the preset parameter includes parameter type, data type, precision unit, and big-endian mode;

[0029] Step S103: converting the video data into a preset video format for unified output to obtain converted video data;

[0030] Step S104: outputting the converted frame data and the converted video data of all aircraft-type UAVs through a unified communication protocol.

[0031] Depend on Figure 1 As can be seen from the process shown, in an embodiment of the present invention, it is proposed that after receiving the frame data and video data of each drone, a data mapping relationship is called according to the aircraft type of the drone (the data mapping relationship includes a mapping relationship between various parameters in the flight data and corresponding preset parameters, and also includes a mapping relationship between various parameters in the load parameters and corresponding preset parameters). According to the data mapping relationship, each parameter in the frame data of the drone is converted into a corresponding preset parameter to obtain the converted frame data, so that the frame data of drones of each aircraft type are converted into a unified preset parameter, that is, a unified standard preset parameter, so that the frame data of drones of each aircraft type are converted into data of a unified standard; and the video data of drones of each aircraft type are converted into video data of a preset video format of a unified output. Finally, the converted frame data and the converted video data of drones of all aircraft types are output through a unified communication protocol. The frame data and video data of drones of various aircraft types are converted into data of unified standard and output through a unified communication protocol, making the output data more unified and standardized, avoiding problems such as different names for the same parameter and inconsistent parameter measurement units, making the output data easier to use; and making it possible to arbitrarily add drones of different models to a large-scale group of heterogeneous drones without affecting the unification and standardization of the output data, thereby avoiding the development of application programs. The output data can be widely and conveniently used in different application scenarios, improving the application promotion of the output data.

[0032] In specific implementation, in order to convert the frame data of drones of various aircraft types into data of a unified standard, this embodiment proposes to implement data conversion based on the data mapping relationship corresponding to each aircraft type, for example,

[0033] According to the data mapping relationship, each parameter in the frame data of the drone is converted into a corresponding preset parameter to obtain the converted frame data, including:

[0034] For each parameter in the frame data, judging whether the parameter is consistent with the corresponding preset parameter according to the data mapping relationship, and if not, converting the parameter into the corresponding preset parameter according to the conversion method pre-stored in the data mapping relationship; and / or

[0035] It is determined whether there is a parameter item corresponding to the preset parameter in the frame data according to the data mapping relationship. If not, the preset parameter is calculated based on other parameters in the frame data according to a calculation method pre-stored in the data mapping relationship.

[0036] In a specific implementation, the preset parameters are calculated based on other parameters in the frame data according to the calculation method pre-stored in the data mapping relationship, including:

[0037] If the preset parameter is infrared focal length, the infrared focal length is calculated by the following formula according to the calculation method pre-stored in the data mapping relationship:

[0038]

[0039] Among them, w represents the image width, imagemeta represents the pixel, f represents the infrared focal length, fov represents the field of view, lsb represents the field of view resolution, and PI represents π.

[0040] In specific implementation, in order to realize convenient and accurate data conversion based on the data mapping relationship, the above data mapping relationship can be realized in the form of a data table. For example, each aircraft type corresponds to its own data mapping relationship table, and the data mapping relationship table includes multiple parameter items, specifically including unified preset parameters corresponding to each parameter in the frame data of the drone, and the preset parameters include parameter type, data type, precision unit, big-endian and small-endian modes, etc. Ultimately, through the respective data mapping relationship tables corresponding to each aircraft type, the frame data of each aircraft type is converted into a unified preset parameter, so as to realize the unification and standardization of the frame data of each aircraft type.

[0041] When implementing it, Figure 2 As shown, the data mapping relationship of each aircraft type can be configured by configuring the visual interface, for example,

[0042] (1) The data type, big and small endian, and precision unit of the standardized flight parameters (i.e., the multiple preset parameters corresponding to the flight parameters, as shown in Table 1), the optoelectronic load state parameters (i.e., the multiple preset parameters corresponding to the optoelectronic state parameters, as shown in Table 2), and the radar load state parameters (i.e., the multiple preset parameters corresponding to the radar state parameters, as shown in Table 3) are configured through a visual interface (e.g., a GUI module), and the configured standardized frame parameters (i.e., the above-mentioned standardized flight parameters, optoelectronic load state parameters, and radar load state parameters) are stored in a database.

[0043] Table 1

[0044]

[0045] Table 2

[0046]

[0047] Table 3

[0048]

[0049] (2) Create a receiving aircraft of type A through a visual interface (such as a GUI module), configure the communication protocol and port for accessing the data of type A drone, and only support UDP multicast and RTSP protocols. If you need to support other protocols, you can customize them. And configure the parameters of each type of frame of the drone of type A (i.e., flight parameters, including multiple parameter items). The example is as follows:

[0050] Table 4

[0051]

[0052] Examples of optoelectronic load state parameters and radar load state parameters are similar to the above-mentioned examples of flight parameters, and are omitted here.

[0053] (3) Based on the aircraft type, establish a data mapping relationship between the flight parameters of the drone and the corresponding preset parameters. For example, establish a mapping relationship table that maps the flight parameters of aircraft type A to the standard preset parameters. As shown in Table 5, the flight parameters include multiple parameter items, and the preset parameters also have multiple items. The mapping relationship table defines the mapping relationship between the parameter items included in the flight parameters and the corresponding preset parameters. In addition, the mapping relationship table also includes a conversion method, which is used to realize the conversion between the parameters in the flight parameters and the corresponding preset parameters. For example, the "high altitude" in the flight parameters is inconsistent with the preset parameter "high air pressure", and the "high altitude" needs to be calculated and converted into "high air pressure" through the conversion method.

[0054] Table 5

[0055]

[0056] (4) For aircraft type A, a data mapping relationship table (including selecting the corresponding conversion algorithm) is established and saved, which maps the optoelectronic load state parameters of the UAV to the corresponding preset parameters, as shown in Table 6.

[0057] Table 6

[0058]

[0059] Specifically, in aircraft type A, there is no "infrared focal length value" parameter in the optoelectronic load parameters, but there is "infrared focal length value" in the standardized preset parameters, so it is necessary to rely on other parameters for calculation according to the conversion method. For example,

[0060] The algorithm for calculating infrared focal length is as follows:

[0061] Input: field of view fov (in degrees), field of view resolution lsb (data accuracy),

[0062] Calculation method:

[0063]

[0064] Among them, w represents the image width, imagemeta represents the pixel, and f represents the infrared focal length.

[0065] (5) A one-to-one mapping relationship table (including selecting the corresponding conversion algorithm) is established between the radar load parameters of aircraft type A and the preset parameters of the standard radar load, and is saved, which is similar to Table 6 and the example is omitted here.

[0066] In specific implementation, after all configurations are completed, the operation starts to receive data from the drone of aircraft type A. According to the communication protocol and port number entered by the aircraft of aircraft type A, a communication thread is automatically created to receive data from drone A.

[0067] In a specific implementation, the conversion of the video data is achieved by the following methods, for example, the video data is converted into a preset video format for unified output to obtain the converted video data, including:

[0068] Performing frame structure analysis on the video data received through different communication protocols, and extracting video data contents respectively;

[0069] The video data content is converted into a preset video format for unified output, wherein the preset video format for unified output includes a unified resolution, bit rate, and encoding format.

[0070] In order to improve the efficiency of data conversion during implementation, a distributed deployment + message queue approach is proposed to handle high-concurrency data reception and data publishing. For example,

[0071] Receive the frame data and video data of the drone, including:

[0072] Receiving the frame data and the video data of the drones in the large-scale heterogeneous drone group through a plurality of distributedly deployed devices, wherein each device receives the frame data and the video data of the drones of a specified aircraft type;

[0073] Each device inputs the received frame data and video data of each drone into the Kafka message queue to trigger data conversion of the frame data and video data in the Kafka message queue.

[0074] For example, using distributed deployment, the deployment of multiple data access modules (or devices) is planned according to the type and number of access aircraft. Multiple data access modules (or devices) can be deployed on different machines, and the data access modules (or devices) can be configured to receive specific types of aircraft.

[0075] The Kafka message queue is used to process high throughput. The Kafka message queue is deployed in clusters to ensure data integrity and throughput. The topic is designed with the drone type + drone ID as the topic name.

[0076] Specifically, the converted frame data and the converted video data of all aircraft types of drones are output through a unified communication protocol, including:

[0077] Multiple servers are deployed in a distributed manner, and each server creates multiple data processing threads. The converted frame data and the converted video data of drones of all aircraft types are concurrently output through a unified communication protocol through the multiple data processing threads on each server, wherein the number of data processing threads created by each server is the maximum number of threads that each server can process obtained through stress testing.

[0078] For example, the data publishing module sends the converted and standardized data of the drone in a distributed way + multi-threaded way. The data publishing module is deployed on each server. The data publishing module creates multiple data processing threads according to the number of topics to be processed, and each thread processes the data of one topic.

[0079] Use distributed deployment and plan the number of servers required for the data publishing module based on the number of message topics and the capacity of each machine to process topics.

[0080] Specifically, the Jmeter tool can be used to simulate multiple drones periodically writing data to each server, and the maximum amount of drone data that each server can process is calculated, and the data is used as the maximum number of threads that each server can process.

[0081] For example, use the Jmeter tool to simulate multiple aircraft periodically (drone frame data sending period) writing data to message queues topic A, topic B, topic C, etc., perform stress testing on a single server where the data publishing module is located, and calculate the number of aircraft that a server can handle. Based on the number of aircraft that a single server can handle, estimate the number of publishing servers that are ultimately required.

[0082] In specific implementation, the unified and standardized data output by the above-mentioned large-scale heterogeneous UAV data processing method can be used in monitoring systems or other application systems. For example, taking the monitoring system as an example, Figure 3 As shown, the monitoring system includes monitoring software, data link terminal, data access and standardization software and streaming media server to form a local area network through an Ethernet switch. Specifically,

[0083] (1) Monitoring software: consists of GUI module and data receiving module.

[0084] Data receiving module: used to receive the converted unified and standardized UAV flight parameters, optoelectronic load status parameters, and radar load status parameters output through data access and standardized software UDP multicast.

[0085] GUI module: used to complete human-computer interaction. The interface displays the standardized UAV flight parameters, optoelectronic load status data, and radar load status data received by the data receiving module; and pulls video from the streaming media server for playback.

[0086] (2) Data access and standardization software: It consists of GUI module, data receiving module, parameter calculation module and data publishing module.

[0087] GUI module: used for configuration of standardized frames; configuration of access to drone data frames and related parameters; selection of parameter conversion algorithms.

[0088] Data receiving module: used to receive data frames of various special-shaped drones such as type A drone, type B drone, type C drone, etc. from the data link terminal (the data link terminal receives data frames and streaming media data of various special-shaped drones such as type A drone, type B drone, type C drone, etc.), and verify the data frames;

[0089] Data publishing module: used to read the received data frames, call the parameter calculation module to convert the parameters of the connected drone into standardized parameters; reorganize the standardized data frames and publish them through UDP multicast.

[0090] Parameter calculation module: a function library for parameter standardization calculation.

[0091] (3) Streaming media server software: It consists of RSTP simple server module, data receiving module and data conversion module.

[0092] RSTP simple server module: streaming media server, publishing live video;

[0093] Data receiving module: used to receive streaming media data pushed by various special-shaped drones such as type A drone, type B drone, type C drone, etc. using different streaming media communication protocols from the data link terminal, extract video data and push it to the data conversion module;

[0094] Data conversion module: used to convert streaming media data of different formats pushed by different types of aircraft into unified encoding, unified resolution, and unified bit rate, and then push them to the RTSP streaming media server.

[0095] In the specific implementation, the process of converting and standardizing the flight data and load parameters of the UAV is as follows: Figure 4 As shown, the following steps are included:

[0096] (1) Drone A sends flight parameters to the data receiving module of the data access and standardization software.

[0097] (2) After the data receiving module of the data access and standardization software receives the flight parameter data, it verifies the data frame according to the configured frame verification method. The data frames that pass the verification are sent to the message queue (such as the Kafka message queue). The data frames that do not pass the verification are discarded and logged.

[0098] (3) The data publishing module of the data access and standardization software monitors the message queue. After listening to the data frame, it parses the parameters and reads the data mapping table of the corresponding model from the database. According to the conversion method in the mapping table, it calls the calculation method in the calculation function library of the parameter calculation module to convert the parameters into standardized preset parameters. After all the parameters are converted, the standard data frame with unified parameters is assembled according to the format of the standard frame. After the assembly is completed, the frame is sent through UDP multicast.

[0099] (4) The data receiving module of the monitoring software receives the standardized frame data and sends it to the GUI module of the monitoring software for display.

[0100] In the specific implementation, the process of converting and standardizing the video data of the drone is as follows: Figure 5 As shown, the following steps are included:

[0101] (1) Multiple types of drones send videos to the streaming media server. The data receiving module of the streaming media server starts different threads to receive video data of different types of drones according to the receiving protocols and ports pre-configured for different models. For example: the streaming protocol used by aircraft type A is RTSP protocol, then an RTSP protocol thread is created to receive streaming data from type A drone; the streaming protocol used by aircraft type B is UDP, then a UDP protocol thread is created to receive streaming data from aircraft type B aircraft.

[0102] (2) After receiving the video data, the data receiving module parses the data packet to extract the video data. The same video data transmitted by UDP protocol has different frame structures due to different aircraft types, for example:

[0103] Type A aircraft uses UDP multicast. The frame structure of Type A drone video is shown in Table 7 below:

[0104] Standard H265 code stream.

[0105] Table 7

[0106]

[0107] Type B aircraft uses UDP multicast. The frame structure of Type B drone video is shown in Table 8 below:

[0108] Table 8

[0109]

[0110] Type C aircraft uses UDP multicast. The frame structure of Type C drone video is shown in Table 9 below:

[0111] Table 9

[0112]

[0113] For type A aircraft, the data content of the received UDP data packet is extracted and sent to the transcoding module.

[0114] For type B aircraft, the received UDP data packet is extracted, the frame is analyzed and deconstructed, the 4-1023 data content is extracted and sent to the transcoding module.

[0115] For type C aircraft, the received UDP data packet is extracted, the frame structure is analyzed, the 201-1023 data content is extracted, and sent to the transcoding module.

[0116] (3) After receiving the video data, the data conversion module converts the video streaming media into a unified H265 encoding through FFMEPG software (the video encoding of different types of aircraft is different, there are H264, H265 and other encoding formats), converts it to the same resolution of 1080p (the video resolution of different types of aircraft is also different, there are 1080p, 720p, and 480), converts it to the same bit rate of 2Mb / s, and pushes the converted data to the RTSP simple server through RTSP.

[0117] (4) RTSP simple server provides video services to external monitoring or application systems.

[0118] In specific implementation, a database may be created to store the above data mapping relationship table and related aircraft parameter tables, for example,

[0119] (1) The basic information of the drone is shown in Table 10.

[0120] Table 10

[0121]

[0122] (2) The preset parameter table of the standardized flight parameters of the UAV is shown in Table 11.

[0123] Table 11

[0124]

[0125] (3) The preset parameter table of UAV standardized payload-photoelectric parameters is shown in Table 12.

[0126] Table 12

[0127]

[0128] (4) The preset parameter table of UAV standardized payload-radar parameters is shown in Table 13.

[0129] Table 13

[0130]

[0131] (5) The mapping table of UAV flight parameters (i.e. flight parameters) and standardized flight parameters (i.e. preset parameters) is shown in Table 14.

[0132] Table 14

[0133]

[0134] In this embodiment, a computer device is provided, such as Figure 6 As shown, it includes a memory 601, a processor 602 and a computer program stored in the memory and executable on the processor. The memory 601 and the processor 602 are connected to an internal bus of a computer device. When the processor executes the computer program, any of the above-mentioned methods for processing data of large-scale heterogeneous UAVs is implemented.

[0135] Specifically, the computer device may be a computer terminal, a server or a similar computing device.

[0136] In this embodiment, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program for executing any of the above-mentioned methods for processing data of large-scale heterogeneous drones.

[0137] Specifically, computer-readable storage media include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer-readable storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable storage media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0138] Based on the same inventive concept, a data processing device for large-scale heterogeneous drones is also provided in an embodiment of the present invention, as described in the following embodiments. Since the principle of solving the problem by the data processing device for large-scale heterogeneous drones is similar to the data processing method for large-scale heterogeneous drones, the implementation of the data processing device for large-scale heterogeneous drones can refer to the implementation of the data processing method for large-scale heterogeneous drones, and the repeated parts will not be repeated. As used below, the term "unit" or "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0139] Figure 7 is a structural block diagram of a data processing device for large-scale special-shaped UAVs according to an embodiment of the present invention. Figure 7 As shown, the device comprises:

[0140] The data receiving unit 701 is used to receive frame data and video data of each drone in the large-scale heterogeneous drone group, wherein the frame data includes flight data and load parameters;

[0141] The parameter conversion unit 702 is used to call the data mapping relationship according to the aircraft type of the UAV, and convert each parameter in the frame data of the UAV into a corresponding preset parameter according to the data mapping relationship to obtain the converted frame data, wherein each aircraft type corresponds to its own data mapping relationship, and the data mapping relationship is used to convert the frame data of all aircraft types into a unified preset parameter, and the preset parameter includes parameter type, data type, precision unit, and big-endian mode;

[0142] The video conversion unit 703 is used to convert the video data into a preset video format for unified output to obtain converted video data;

[0143] The data output unit 704 is used to output the converted frame data and the converted video data of all aircraft types of drones through a unified communication protocol.

[0144] In one embodiment, a parameter conversion unit is used to determine, for each parameter in the frame data, whether the parameter is consistent with a corresponding preset parameter according to the data mapping relationship; if not, convert the parameter into a corresponding preset parameter according to a conversion method pre-stored in the data mapping relationship; and / or determine, according to the data mapping relationship, whether there is a parameter item corresponding to the preset parameter in the frame data; if not, calculate the preset parameter based on other parameters in the frame data according to a calculation method pre-stored in the data mapping relationship.

[0145] In one embodiment, the parameter conversion unit is used to calculate the infrared focal length by the following formula according to the calculation method pre-stored in the data mapping relationship if the preset parameter is the infrared focal length:

[0146]

[0147] Wherein: w represents image width, imagemeta represents pixel, f represents infrared focal length, fov represents field of view angle, lsb represents field of view angle resolution, and PI represents π.

[0148] In one embodiment, a video conversion unit is used to perform frame structure analysis on the video data received through different communication protocols and extract the video data content respectively; convert the video data content into a preset video format for unified output, wherein the preset video format for unified output includes a unified resolution, bit rate and encoding format.

[0149] In one embodiment, a data receiving unit is used to receive the frame data and the video data of the drones in the large-scale heterogeneous drone group through multiple distributed devices, wherein each device receives the frame data and video data of drones of a specified aircraft type; each device inputs the received frame data and video data of each drone into a kafka message queue to trigger data conversion of the frame data and video data in the kafka message queue.

[0150] In one embodiment, the data output unit is used to distribute and deploy multiple servers, each server creates multiple data processing threads, and concurrently outputs the converted frame data and the converted video data of all types of drones through a unified communication protocol through the multiple data processing threads on each server, wherein the number of data processing threads created by each server is the maximum number of threads that each server can handle obtained through stress testing.

[0151] In one embodiment, the data output unit is also used to simulate multiple drones to periodically write data to each server through the Jmeter tool, calculate the maximum number of drone data that each server can process, and use the data as the maximum number of threads that each server can process.

[0152] The embodiment of the present invention achieves the following technical effects: after receiving the frame data and video data of each drone, a data mapping relationship is called according to the aircraft type of the drone, and each parameter in the frame data of the drone is converted into a corresponding preset parameter according to the data mapping relationship to obtain the converted frame data, so as to realize that the frame data of drones of each aircraft type are all converted into a unified preset parameter, that is, a unified standard preset parameter, and realize that the frame data of drones of each aircraft type are converted into unified standard data; and the video data of drones of each aircraft type are converted into video data of a preset video format of a unified output, and finally, the converted frame data and the converted video data of drones of all aircraft types are output through a unified communication protocol. The frame data and video data of drones of each aircraft type are converted into unified standard data and output through a unified communication protocol, so that the output data is more unified and standardized, and the problems of different names of the same parameter and inconsistent parameter measurement units are avoided, so that the output data is more convenient for application; and when drones of different models are arbitrarily added to a large-scale heterogeneous drone group, the unification and standardization of the output data are not affected, thereby avoiding the development of application programs, and the output data can be widely and conveniently used in different application scenarios, thereby improving the application promotion of the output data.

[0153] Obviously, those skilled in the art should understand that the modules or steps of the above-mentioned embodiments of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. In this way, the embodiments of the present invention are not limited to any specific combination of hardware and software.

[0154] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the embodiments of the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for processing data of large-scale special-shaped drones, characterized in that: include: For each drone in the large-scale heterogeneous drone group, receiving frame data and video data of the drone, wherein the frame data includes flight data and load parameters; Calling a data mapping relationship according to the aircraft type of the UAV, and converting each parameter in the frame data of the UAV into a corresponding preset parameter according to the data mapping relationship to obtain the converted frame data, wherein each aircraft type corresponds to its own data mapping relationship, and the data mapping relationship is used to convert the frame data of all aircraft types into a unified preset parameter, wherein the preset parameter includes parameter type, data type, precision unit, and big-endian mode; Converting the video data into a preset video format for unified output to obtain converted video data; Outputting the converted frame data and the converted video data of all aircraft types of drones through a unified communication protocol; According to the data mapping relationship, each parameter in the frame data of the drone is converted into a corresponding preset parameter to obtain the converted frame data, including: For each parameter in the frame data, judging whether the parameter is consistent with the corresponding preset parameter according to the data mapping relationship, and if not, converting the parameter into the corresponding preset parameter according to the conversion method pre-stored in the data mapping relationship; and / or Determining whether there is a parameter item corresponding to the preset parameter in the frame data according to the data mapping relationship, and if not, calculating the preset parameter based on other parameters in the frame data according to a calculation method pre-stored in the data mapping relationship; The video data is converted into a preset video format for unified output to obtain converted video data, including: Performing frame structure analysis on the video data received through different communication protocols, and extracting video data contents respectively; The video data content is converted into a preset video format for unified output, wherein the preset video format for unified output includes a unified resolution, bit rate, and encoding format.

2. The data processing method for large-scale special-shaped UAVs according to claim 1, characterized in that: Calculating preset parameters based on other parameters in the frame data according to the calculation method pre-stored in the data mapping relationship includes: If the preset parameter is infrared focal length, the infrared focal length is calculated by the following formula according to the calculation method pre-stored in the data mapping relationship: Among them, w represents the image width, imagemeta represents the pixel, f represents the infrared focal length, fov represents the field of view, lsb represents the field of view resolution, and PI represents π.

3. The data processing method for large-scale special-shaped UAVs according to any one of claims 1 to 2, characterized in that: Receive the frame data and video data of the drone, including: Receiving the frame data and the video data of the drones in the large-scale heterogeneous drone group through a plurality of distributedly deployed devices, wherein each device receives the frame data and the video data of the drones of a specified aircraft type; Each device inputs the received frame data and video data of each drone into the Kafka message queue to trigger data conversion of the frame data and video data in the Kafka message queue.

4. The data processing method for large-scale special-shaped UAVs according to any one of claims 1 to 2, characterized in that: Outputting the converted frame data and the converted video data of all aircraft types of drones through a unified communication protocol, including: Multiple servers are deployed in a distributed manner, and each server creates multiple data processing threads. The converted frame data and the converted video data of drones of all aircraft types are concurrently output through a unified communication protocol through the multiple data processing threads on each server, wherein the number of data processing threads created by each server is the maximum number of threads that each server can process obtained through stress testing.

5. The data processing method for large-scale special-shaped UAVs as claimed in claim 4 is characterized in that: Also includes: The Jmeter tool is used to simulate multiple drones to periodically write data to each server, and the maximum amount of drone data that each server can process is calculated, and this data is used as the maximum number of threads that each server can process.

6. A data processing system for large-scale special-shaped drones, characterized in that: include: A data receiving unit, for receiving frame data and video data of each drone in the large-scale heterogeneous drone group, wherein the frame data includes flight data and load parameters; A parameter conversion unit is used to call a data mapping relationship according to the aircraft type of the UAV, and according to the data mapping relationship, convert each parameter in the frame data of the UAV into a corresponding preset parameter to obtain the converted frame data, wherein each aircraft type corresponds to its own data mapping relationship, and the data mapping relationship is used to convert the frame data of all aircraft types into a unified preset parameter, and the preset parameter includes a parameter type, a data type, a precision unit, and a big-endian mode; A video conversion unit, used for converting the video data into a preset video format for unified output to obtain converted video data; A data output unit, used to output the converted frame data and the converted video data of all aircraft types of drones through a unified communication protocol; The parameter conversion unit is used to determine, for each parameter in the frame data, whether the parameter is consistent with the corresponding preset parameter according to the data mapping relationship, and if not, convert the parameter into the corresponding preset parameter according to the conversion method pre-stored in the data mapping relationship; and / or determine, according to the data mapping relationship, whether there is a parameter item corresponding to the preset parameter in the frame data, and if not, calculate the preset parameter based on other parameters in the frame data according to the calculation method pre-stored in the data mapping relationship; The video conversion unit is used to perform frame structure analysis on the video data received through different communication protocols and extract the video data content respectively; convert the video data content into a preset video format for unified output, and the preset video format for unified output includes a unified resolution, bit rate and encoding format.

7. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the data processing method for large-scale special-shaped drones described in any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program for executing the data processing method for large-scale heterogeneous drones according to any one of claims 1 to 5.

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