Micro-service-based heterogeneous unmanned aerial vehicle flight control system design

Abstract

The invention discloses a micro-service-based heterogeneous unmanned aerial vehicle flight control system design, relates to the technical field of heterogeneous unmanned aerial vehicle flight control, and solves the problems that the operation difficulty is high due to the particularity of heterogeneous unmanned aerial vehicles; at present, many unmanned aerial vehicles in the market support one unmanned aerial vehicle to be equipped with one ground station and one management platform and face the technical problem that software of multiple ground stations is controlled at the same time. According to the method, heterogeneous unmanned aerial vehicle firmware is transformed and upgraded, a ground station distributed micro-service cluster framework is constructed, heterogeneous unmanned aerial vehicle data configuration and service access are carried out, a heterogeneous unmanned aerial vehicle operation platform is developed, and a heterogeneous unmanned aerial vehicle operation platform is constructed; and the task progress is adjusted according to the subscription data, and the behavior mode flight control operation is realized. The technical problem is solved.

Description

Technical Field

[0001] The present invention belongs to the field of heterogeneous UAV flight control, and specifically is a microservice-based heterogeneous UAV flight control system design. Background Art

[0002] With the development of low-altitude areas and drone technology, drones are increasingly being used in low-altitude areas, including agriculture, forestry, power inspection, urban safety, disaster relief, logistics and distribution, and many other fields. Drones are becoming more and more autonomous and intelligent, and can perform more complex tasks. Drones are gradually gaining different degrees of autonomous behavior control and target decision-making capabilities, and need to carry a variety of customized equipment to complete tasks in set scenarios. In the process of executing tasks, since heterogeneous drones are composed of many different types of drones, it is necessary to have a certain understanding and mastery of different types of drones, and the operation is relatively difficult.

[0003] A heterogeneous drone swarm refers to a group of drones of different types, each of which may have different performance parameters, load capacity, and flight characteristics. However, when current heterogeneous drones perform complex tasks, a single ground station plus a single heterogeneous drone cannot meet the problem of collaborative task execution, and when multiple heterogeneous drones perform a task at the same time, the particularity of heterogeneous drones will cause difficulty in operation and coordination. At the same time, many drones on the market currently support one drone equipped with one ground station and one management platform. If a group of heterogeneous drones are on a mission at the same time, it is necessary to control multiple platforms at the same time, which is particularly complicated to operate. Even though many open source ground station systems currently support multiple drones to access at the same time, the number of connected drones is still limited due to bandwidth limitations, and they also face the problem of simultaneous control of multiple ground station software. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a microservice-based heterogeneous UAV flight control system design, which is used to solve the problem that when the current heterogeneous UAVs perform complex tasks, a single ground station plus a single heterogeneous UAV cannot meet the collaborative task execution problem; and when multiple heterogeneous UAVs perform a task at the same time, the particularity of the heterogeneous UAVs will cause difficulty in operation and difficulty in simultaneous coordination; at the same time, many UAVs on the market currently support one UAV equipped with one ground station and one management platform. If a group of heterogeneous UAVs perform a mission at the same time, it is necessary to control multiple platforms at the same time, and the operation is particularly complicated; even if many open source ground station systems currently support multiple UAVs to access at the same time, due to bandwidth limitations, the number of accessed UAVs is still limited, and they also face the technical problem of simultaneous control of multiple ground station software.

[0005] To achieve the above objectives, the first aspect of the present invention provides a microservice-based heterogeneous UAV flight control system design, including:

[0006] S1, modify the firmware of heterogeneous drone payloads;

[0007] S2, upgrades the firmware of heterogeneous drone payloads;

[0008] S3, builds a distributed microservice cluster framework for ground stations;

[0009] S4, heterogeneous drone data configuration and service access;

[0010] S5, development of heterogeneous UAV operation platform;

[0011] S6, construction of heterogeneous UAV operation platform;

[0012] S7, adjusts the mission progress and implements behavioral mode flight control operations based on the subscription data.

[0013] Preferably, the modification of the heterogeneous UAV payload firmware includes:

[0014] Install Keil software development system;

[0015] Write firmware code based on Keil software development system; the code functions of writing firmware code include: sending instructions to the drone payload device through 4G / 5G, wireless, etc., the payload device sends command data to the drone through the serial port, and at the same time, the drone status data and video data are transmitted back to the ground station access submodule through the payload device, and finally compiled and tested;

[0016] Upgrade the device firmware through the MQTT protocol.

[0017] It should be noted that since heterogeneous UAVs can be of different models, specifications or functions, they may have different performance parameters, load capacity and flight characteristics. Therefore, the load firmware of the flight control module of the heterogeneous UAV must be modified to ensure the consistency of the operating standards and complete the unified entrance of the heterogeneous UAV group to the ground station access submodule; there is a complex secondary development process for the modification of the load firmware of heterogeneous UAVs; since the firmware protocol content used by UAVs of different manufacturers is different, and there is source code protection between manufacturers, it cannot be completely achieved by modifying the manufacturer's UAV firmware, so it can be achieved through load firmware modification; the UAV payload equipment includes modules such as communication and navigation, which are responsible for receiving and forwarding information from the ground station access submodule. The load firmware connects to each UAV through serial communication, and then interacts with the internal firmware of the UAV through the serial communication protocol content provided by each manufacturer, realizing the uplink and downlink data transmission and image transmission functions.

[0018] Preferably, the upgrading of the heterogeneous UAV payload firmware includes:

[0019] Generate a new ".hex" or ".bin" file by opening the project in Keil MDK and compiling it for the purpose of upgrading. The purpose of upgrading includes: fixing errors or implementing new functions.

[0020] Connect the drone to the firmware upgrade mode through a debugger, including J-Link, ST-Link or OpenOCD.

[0021] Use Keil's "Download / Programming" function to burn the new firmware into the drone's memory.

[0022] It should be noted that the new firmware refers to a binary file (usually in ".hex" or ".bin" format) compiled by Keil MDK that contains improvements or updates.

[0023] Preferably, the construction of a distributed microservice cluster framework for a ground station includes:

[0024] Agree on Mavlink protocol, develop and compile ground station code, and establish serial port or network communication module;

[0025] Use low-code frameworks to develop servers and deploy distributed microservices;

[0026] Debug uplink and downlink data links to ensure the stability of data transmission;

[0027] Use Docker and Kubernetes for containerized deployment and management, and build MQTT and Rest communication mechanisms.

[0028] It should be noted that the agreed Mavlink protocol is used to communicate with drones;

[0029] The use of a low-code framework to complete server-side development ensures smooth issuance and feedback of heterogeneous drone operation instructions;

[0030] The MQTT and REST communication mechanisms are established to achieve efficient data interaction and task coordination between microservices and ground station access submodules and heterogeneous UAV operation platforms.

[0031] Preferably, the heterogeneous drone data configuration and service access include:

[0032] Install and calibrate the drone's hardware components and set the drone's related parameters; the drone's hardware components include: flight controller, sensors, compass, gyroscope, accelerometer, etc.; related parameters include: flight mode, battery / power module settings, safety configuration, motor / servo check, etc.;

[0033] Register the drone as an independent service in the microservice cluster through the MQTT protocol;

[0034] Use REST API to realize data interaction between ground station and heterogeneous UAV operation platform;

[0035] Synchronize the status data and service configuration of all drones to the microservice cluster in real time.

[0036] It should be noted that the drone is registered as an independent service in the microservice cluster through the MQTT protocol to ensure that it can communicate with the ground station in both directions, subscribe to mission instructions and send back status data;

[0037] Status data and service configuration refer to the real-time information and pre-set parameters generated by the drone during operation; the status data includes the drone's position, speed, altitude, battery level, sensor readings (such as compass, gyroscope, accelerometer, etc.) and flight status (such as whether it is in manual, automatic or return mode), which reflect the current operating status of the drone; service configuration includes the drone's mission settings, flight mode, safety mechanisms (such as geo-fencing, obstacle avoidance system), motor / servo parameters, communication protocols, etc., which determine the behavior and function of the drone; this information is synchronized to the microservice cluster in real time to ensure that the ground station and other services can obtain the latest drone status and adjust and manage tasks as needed.

[0038] Preferably, the development of the heterogeneous UAV operating platform includes:

[0039] Set the core functions of the platform; the core functions include: drone parameter import, information management, task grouping, task assignment, real-time monitoring and task adjustment;

[0040] Build the interface through Vue.js combined with the interface component library; the interface component library includes: Element UI or AntDesign;

[0041] Data interaction between the front-end and back-end of the platform is realized through RESTful API, and MQTT or Kafka is integrated to realize real-time communication between drones and the platform;

[0042] The drone information, mission data and flight data are stored in the database; drone information includes: model, status, battery power, etc.; mission data includes: mission ID, creation time, execution progress, etc.; flight data includes: location, speed, altitude, etc.;

[0043] Display the real-time location and flight trajectory of the drone through third-party map services; display the flight data of the drone by using chart libraries; third-party map services include: Google Maps or Amap; chart libraries include: ECharts or Chart.js;

[0044] Automatic execution of tasks is achieved by defining a workflow engine; wherein the defined workflow engine includes: Zeebe or Camunda.

[0045] It should be noted that Vue.js is used in combination with component libraries such as Element UI or Ant Design to quickly build an interface, ensuring that users can easily view and manage the status and task progress of multiple drones; real-time data push is achieved through WebSocket technology, ensuring that the user interface can instantly update the status changes of drones. In the end, an operating platform front end with good user experience is obtained;

[0046] Data interaction between the front-end and back-end is achieved through RESTful API, and MQTT or Kafka is integrated to achieve real-time communication between the drone and the platform, ensuring the timely delivery of mission instructions and status data, and ultimately obtaining a stable and efficient back-end service cluster;

[0047] Integrate third-party map services to display the real-time location and flight trajectory of the drone, helping users to intuitively monitor the execution of tasks; use chart libraries to display the flight data of the drone, helping users analyze the execution effect of tasks;

[0048] By defining a workflow engine, we can realize the automated execution of tasks. For example, when a drone completes a task, it will automatically assign the next task, or automatically trigger emergency measures (such as returning home or landing) when encountering an abnormal situation. Ultimately, we will get an intelligent and automated task scheduling system.

[0049] Preferably, the heterogeneous UAV operating platform is constructed, including:

[0050] Database deployment;

[0051] Dependent service deployment; Dependent service deployment includes: Docker deployment, Docker Comprose deployment, Redis deployment, JDK deployment, Minio deployment, Rocketmq deployment, Kafka deployment, Kibana deployment, Solr deployment, Elasticsearch deployment, and Memcached deployment;

[0052] System service deployment; among them, system service deployment includes: nacos service deployment, authentication service deployment, user and role management service deployment.

[0053] Preferably, the step of adjusting the task progress and implementing the behavior mode flight control operation according to the subscription data includes:

[0054] Obtain subscription data; and adjust mission progress and implement behavioral mode flight control operations based on the subscription data.

[0055] Preferably, the obtaining of subscription data includes:

[0056] The real-time status data of each drone is obtained based on the ground station or operating platform, and the real-time status data of each drone is transmitted to the back-end service of the platform in real time; the real-time status data includes: the drone's position, speed, altitude, battery power, sensor readings and mission execution status.

[0057] It should be noted that the sensors include a compass, a gyroscope, an accelerometer, etc.; the mission execution status includes: whether it deviates from the scheduled route, encounters obstacles, etc.

[0058] Preferably, the step of adjusting the mission progress and implementing the behavior mode flight control operation based on the subscription data includes:

[0059] Task progress adjustment:

[0060] After receiving the real-time status data, the backend service uses preset rules or machine learning models to determine whether the execution of the current task meets expectations;

[0061] Dynamically adjust task parameters based on real-time data analysis results;

[0062] When the drone encounters an emergency, it will suspend or terminate the current mission and trigger emergency measures; emergency situations include: low battery, communication interruption, sensor failure, etc.

[0063] Behavioral flight control:

[0064] Configure several behavior modes for the drone; the behavior modes include: automatic cruise, obstacle avoidance mode, fixed-point hovering, emergency return, etc.

[0065] Dynamically switch the drone's behavior mode based on real-time status data.

[0066] It should be noted that the system can determine whether the execution of the current task is in line with expectations through preset rules or machine learning models. For example, if the drone deviates from the scheduled route, the system can automatically detect this deviation and calculate the corrected path. In addition, the platform can also analyze the battery power of the drone, predict the remaining flight time, and trigger the return or landing command in advance.

[0067] Dynamically adjust task parameters based on real-time data analysis results; for example, if a drone encounters cloud cover during a surveying and mapping mission, the platform can adjust the camera’s shooting angle or flight altitude to ensure image quality; if a drone encounters traffic congestion during a logistics delivery mission, the platform can re-plan the delivery route to avoid delays;

[0068] When the drone encounters an emergency, the current mission is suspended or terminated, and emergency measures are triggered; for example, the platform can order the drone to return immediately or land in a safe area to avoid potential risks;

[0069] Each behavior mode corresponds to different flight logic and control strategies; for example, in "obstacle avoidance mode", the drone will automatically avoid obstacles ahead based on sensor data; in "emergency return" mode, the drone will immediately return to the take-off point and land safely;

[0070] Dynamically switch the drone's behavior mode based on real-time status data; for example, when the drone approaches a predetermined target area, the platform can switch it from "auto cruise" mode to "fixed-point hovering" mode to ensure that the drone can accurately stay at the target location; if the drone encounters strong winds or air currents, the platform can switch to "stable flight" mode to enhance the drone's wind resistance.

[0071] Compared with the prior art, the present invention has the following beneficial effects:

[0072] 1. The present invention transforms and upgrades the firmware of heterogeneous UAVs, builds a distributed microservice cluster framework for ground stations, configures data and accesses services for heterogeneous UAVs, develops and builds a heterogeneous UAV operating platform, adjusts task progress and implements behavioral mode flight control operations according to subscription data, thereby solving the problem that a single ground station plus a single heterogeneous UAV cannot meet the requirements of collaborative task execution when UAVs perform complex tasks, and that when multiple heterogeneous UAVs perform a task at the same time, the operation is difficult and coordination is difficult due to the particularity of the heterogeneous UAVs.

[0073] 2. Currently, many drones on the market support one drone equipped with one ground station and one management platform. If a group of heterogeneous drones are required to perform missions at the same time, multiple platforms need to be controlled at the same time, which makes the operation very complicated. Even though many open source ground station systems currently support the simultaneous access of multiple drones, the number of connected drones is still limited due to bandwidth limitations, and the problem of simultaneous control of multiple ground station software is also faced. The present invention transforms the ground station into a ground station access submodule through secondary development through the framework of distributed microservices. In this way, one microservice is equipped with a ground station access submodule, and then the distributed microservice only interacts with one heterogeneous drone operation platform, which solves the problem of complex control operations when a large number of heterogeneous drone groups participate in missions. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0075] Figure 1 A schematic diagram of the design method steps of an embodiment of the present invention;

[0076] Figure 2 This is a schematic diagram of the ground station distributed microservice cluster framework flow according to an embodiment of the present invention. DETAILED DESCRIPTION

[0077] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0078] See also Figure 1 The first embodiment of the present invention provides a microservice-based heterogeneous UAV flight control system design, including:

[0079] S1, modify the firmware of heterogeneous drone payloads;

[0080] S2, upgrades the firmware of heterogeneous drone payloads;

[0081] S3, builds a distributed microservice cluster framework for ground stations;

[0082] S4, heterogeneous drone data configuration and service access;

[0083] S5, development of heterogeneous UAV operation platform;

[0084] S6, construction of heterogeneous UAV operation platform;

[0085] S7, adjusts the mission progress and implements behavioral mode flight control operations based on the subscription data.

[0086] Modify the firmware of heterogeneous drone payloads, including:

[0087] Install Keil software development system;

[0088] Write firmware code based on Keil software development system; the code functions of writing firmware code include: sending instructions to the drone payload device through 4G / 5G, wireless, etc., the payload device sends command data to the drone through the serial port, and at the same time, the drone status data and video data are transmitted back to the ground station access submodule through the payload device, and finally compiled and tested;

[0089] Upgrade the device firmware through the MQTT protocol.

[0090] Upgrade the firmware of heterogeneous drone payloads, including:

[0091] Generate a new ".hex" or ".bin" file by opening the project in Keil MDK and compiling it for the purpose of upgrading. The purpose of upgrading includes: fixing errors or implementing new functions.

[0092] Connect the drone to the firmware upgrade mode through a debugger, including J-Link, ST-Link or OpenOCD.

[0093] Use Keil's "Download / Programming" function to burn the new firmware into the drone's memory.

[0094] See also Figure 2 , build a distributed microservice cluster framework for ground stations, including:

[0095] Agree on Mavlink protocol, develop and compile ground station code, and establish serial port or network communication module;

[0096] Use low-code frameworks to develop servers and deploy distributed microservices;

[0097] Debug uplink and downlink data links to ensure the stability of data transmission;

[0098] Use Docker and Kubernetes for containerized deployment and management, and build MQTT and Rest communication mechanisms.

[0099] Heterogeneous drone data configuration and service access, including:

[0100] Install and calibrate the drone's hardware components and set the drone's related parameters; the drone's hardware components include: flight controller, sensors, compass, gyroscope, accelerometer, etc.; related parameters include: flight mode, battery / power module settings, safety configuration, motor / servo check, etc.;

[0101] Register the drone as an independent service in the microservice cluster through the MQTT protocol;

[0102] Use REST API to realize data interaction between ground station and heterogeneous UAV operation platform;

[0103] Synchronize the status data and service configuration of all drones to the microservice cluster in real time.

[0104] Heterogeneous UAV operating platform development, including:

[0105] Set the core functions of the platform; the core functions include: drone parameter import, information management, task grouping, task assignment, real-time monitoring and task adjustment;

[0106] Build the interface through Vue.js combined with the interface component library; the interface component library includes: Element UI or AntDesign;

[0107] Data interaction between the front-end and back-end of the platform is realized through RESTful API, and MQTT or Kafka is integrated to realize real-time communication between drones and the platform;

[0108] The drone information, mission data and flight data are stored in the database; drone information includes: model, status, battery power, etc.; mission data includes: mission ID, creation time, execution progress, etc.; flight data includes: location, speed, altitude, etc.;

[0109] Display the real-time location and flight trajectory of the drone through third-party map services; display the flight data of the drone by using chart libraries; third-party map services include: Google Maps or Amap; chart libraries include: ECharts or Chart.js;

[0110] Automatic execution of tasks is achieved by defining a workflow engine; wherein the defined workflow engine includes: Zeebe or Camunda.

[0111] Heterogeneous UAV operation platform construction, including:

[0112] Database deployment;

[0113] Dependent service deployment; Dependent service deployment includes: Docker deployment, Docker Comprose deployment, Redis deployment, JDK deployment, Minio deployment, Rocketmq deployment, Kafka deployment, Kibana deployment, Solr deployment, Elasticsearch deployment, and Memcached deployment;

[0114] System service deployment; among them, system service deployment includes: nacos service deployment, authentication service deployment, user and role management service deployment.

[0115] Adjust mission progress and implement behavioral flight control operations based on subscription data, including:

[0116] Obtain subscription data; and adjust mission progress and implement behavioral mode flight control operations based on the subscription data.

[0117] Get subscription data, including:

[0118] The real-time status data of each drone is obtained based on the ground station or operating platform, and the real-time status data of each drone is transmitted to the back-end service of the platform in real time; the real-time status data includes: the drone's position, speed, altitude, battery power, sensor readings and mission execution status.

[0119] Adjust mission progress and implement behavioral flight control operations based on subscription data, including:

[0120] Task progress adjustment:

[0121] After receiving the real-time status data, the backend service uses preset rules or machine learning models to determine whether the execution of the current task meets expectations;

[0122] Dynamically adjust task parameters based on real-time data analysis results;

[0123] When the drone encounters an emergency, it will suspend or terminate the current mission and trigger emergency measures; emergency situations include: low battery, communication interruption, sensor failure, etc.

[0124] Behavioral flight control:

[0125] Configure several behavior modes for the drone; the behavior modes include: automatic cruise, obstacle avoidance mode, fixed-point hovering, emergency return, etc.

[0126] Dynamically switch the drone's behavior mode based on real-time status data.

[0127] For example, an agricultural technology company plans to use multiple heterogeneous drones of different models to spray pesticides on large areas of farmland. These drones come from different manufacturers and have different performance parameters and payload capacities. In order to ensure efficient execution of the task, the company needs to develop a unified ground station operation platform that can centrally manage and monitor all drones in real time, and dynamically adjust the task progress and flight mode according to the specific conditions of the farmland. The details are as follows:

[0128] Step 1: Modify the firmware of heterogeneous drone payloads;

[0129] 1. Install Keil software development system: Keil MDK is installed in the development environment to write and compile the drone payload firmware code.

[0130] 2. Write firmware code: Based on Keil MDK, the development team wrote firmware code to implement the following functions:

[0131] The instructions are sent to the drone payload device through the 4G / 5G network, and the payload device then forwards the instruction data to the drone through the serial port.

[0132] The drone's status data (such as location, speed, altitude, battery level, etc.) and video data are transmitted back to the ground station access submodule through the payload device.

[0133] 3. Compile and test: After completing the code writing, compile and test to ensure that the firmware can work properly and meet the task requirements.

[0134] 4.MQTT protocol upgrade: In order to ensure the remote upgrade function of the firmware, the MQTT protocol is integrated, allowing the new firmware to be pushed to the drone payload device through the cloud.

[0135] Step 2: Upgrade the firmware of heterogeneous drone payloads;

[0136] 1. Generate new firmware: Open the project file in Keil MDK, modify the firmware code and compile to generate a new ".hex" or ".bin" file according to the needs of fixing errors and implementing new functions.

[0137] 2. Connect to the debugger: Connect the drone via the J-Link debugger and enter the firmware upgrade mode.

[0138] 3. Burn the new firmware: Use Keil's "Download / Programming" function to burn the new firmware into the drone's memory to ensure that the drone can run the latest firmware version.

[0139] Step 3: Build a distributed microservice cluster framework for ground stations;

[0140] 1. Agreed Mavlink protocol: The communication between the ground station and the drone adopts the Mavlink protocol to ensure the standardization and compatibility of data transmission.

[0141] 2. Develop and compile ground station code: Write the ground station code, establish the serial port and network communication modules, and ensure a stable connection with the drone.

[0142] 3. Deploy distributed microservices: Use low-code frameworks (such as Spring Boot) to develop the server side and deploy multiple microservices, including drone management services, task management services, data collection services, etc.

[0143] 4. Containerized deployment: Containerized deployment is performed through Docker and Kubernetes to ensure high availability and elastic expansion of microservices. MQTT and REST communication mechanisms are built to achieve efficient data interaction and task coordination between microservices and ground station access submodules and heterogeneous drone operation platforms.

[0144] Step 4: Heterogeneous drone data configuration and service access;

[0145] 1. Hardware installation and calibration: The hardware components of each drone (such as flight controller, sensors, compass, gyroscope, accelerometer, etc.) are installed and calibrated to ensure that it can accurately perceive environmental information.

[0146] 2. Parameter setting: Set the flight mode, battery monitoring, safety mechanism and other parameters of the drone to ensure that the drone can operate safely during flight.

[0147] 3. Service registration: Register each drone as an independent service in the microservice cluster through the MQTT protocol to ensure that it can communicate with the ground station in both directions, subscribe to mission instructions and send back status data.

[0148] 4. Data synchronization: The status data and service configuration of all drones are synchronized to the microservice cluster in real time, ensuring that the ground station and other services can obtain the latest drone status and adjust and manage tasks as needed.

[0149] Step 5: Development of heterogeneous UAV operating platform;

[0150] 1. Core function settings: The core functions of the platform include drone parameter import, information management, task grouping, task assignment, real-time monitoring and task adjustment. Users can manage multiple drones in batches through the platform, monitor the execution of tasks in real time, and adjust the progress of tasks as needed.

[0151] 2. Front-end development: An intuitive and easy-to-use user interface was built using Vue.js combined with the Element UI component library, allowing users to easily view and manage the status and mission progress of multiple drones.

[0152] 3. Back-end development: Data interaction between the front-end and back-end is realized through RESTful API, and the MQTT protocol is integrated to realize real-time communication between the drone and the platform to ensure the timely delivery of mission instructions and status data.

[0153] 4. Data storage: Store drone information, mission data and flight data in the database to support mission creation, allocation and monitoring.

[0154] 5. Visual display: Integrate third-party map services (such as Amap) to display the real-time location and flight trajectory of the drone, helping users to intuitively monitor the execution of tasks. Use the ECharts chart library to display the flight data of the drone, helping users analyze the execution effect of the task.

[0155] 6. Task automation: By defining a workflow engine (such as Zeebe), the automated execution of tasks is achieved. For example, when a drone completes a task, the platform will automatically assign the next task; if the drone encounters an abnormal situation (such as low battery), the platform will trigger emergency measures (such as return home).

[0156] Step 6: Build a heterogeneous UAV operation platform;

[0157] 1. Database deployment: MySQL was selected as the database to store drone information, mission data, and flight data to ensure data consistency and integrity.

[0158] 2. Dependent service deployment: Dependent services such as Redis cache service, RabbitMQ message queue, ELK log collection system, etc. are deployed to ensure high performance and stability of the system.

[0159] 3. System service deployment: Nacos service discovery tools, authentication services, user and role management services and other system services are deployed through Docker and Kubernetes to ensure the security and scalability of the platform.

[0160] Step 7: Adjust mission progress and implement behavioral mode flight control operations based on subscription data;

[0161] 1. Get subscription data: The platform subscribes to the real-time status data of each drone (such as location, speed, altitude, battery level, sensor readings, etc.) through the MQTT protocol, and transmits this data to the backend service in real time.

[0162] 2. Task progress adjustment:

[0163] Dynamically adjust task parameters: The platform dynamically adjusts task parameters based on real-time data analysis results. For example, if the drone encounters excessive wind speed when performing a pesticide spraying task, the platform will automatically adjust the flight altitude to ensure the spraying effect.

[0164] Pause or terminate the mission: If the drone encounters an emergency (such as low battery or communication interruption), the platform will immediately pause or terminate the current mission and trigger emergency measures (such as return or landing).

[0165] 3. Behavioral mode flight control:

[0166] Predefined behavior modes: The platform configures a variety of behavior modes for drones, such as "auto cruise", "obstacle avoidance mode", "fixed point hovering", "emergency return", etc. Each behavior mode corresponds to different flight logic and control strategies.

[0167] Dynamically switch behavior modes: Based on real-time status data, the platform can dynamically switch the behavior mode of the drone. For example, when the drone approaches the edge of a farmland, the platform will switch it from "auto cruise" mode to "fixed point hovering" mode to ensure that the drone can stay accurately at the target location; if the drone encounters strong winds or air currents, the platform will switch to "stable flight" mode to enhance the drone's wind resistance.

[0168] Practical application scenarios:

[0169] In a specific agricultural plant protection mission, the platform dispatched multiple drones of different models to spray pesticides. The mission path and spraying parameters of each drone are dynamically adjusted according to the specific conditions of the farmland. For example, when drone A encountered excessive wind speed during the mission, the platform automatically adjusted its flight altitude to ensure that the spraying effect was not affected; when the battery power of drone B was lower than the threshold, the platform immediately ordered it to return to avoid potential risks. In addition, the platform dynamically switched the behavior mode of the drone based on real-time data to ensure that it can fly safely in complex farmland environments. In the end, all drones successfully completed the mission, and the platform recorded detailed flight data and mission execution status for subsequent analysis and optimization.

[0170] It can be seen from the above examples that the present invention can centrally manage and monitor multiple drones of different models in real time. The platform can not only dynamically adjust the task progress according to the subscription data, but also implement behavioral mode flight control operations according to real-time status data to ensure the safety and efficiency of the task. This solution not only improves the flexibility and adaptability of agricultural plant protection tasks, but also enhances the autonomous decision-making ability of drones in complex environments, providing strong support for the development of agricultural science and technology.

[0171] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A design of a heterogeneous UAV flight control system based on microservices, characterized by: include: S1, transform the firmware of heterogeneous drone payloads; S2, upgrades the firmware of heterogeneous drone payloads; S3, builds a distributed microservice cluster framework for ground stations; S4, heterogeneous drone data configuration and service access; S5, development of heterogeneous UAV operation platform; S6, construction of heterogeneous UAV operation platform; S7, adjusts the mission progress and implements behavioral mode flight control operations based on the subscription data.

2. According to the design of a heterogeneous UAV flight control system based on microservices in claim 1, it is characterized in that: The modification of the heterogeneous UAV payload firmware includes: Install Keil software development system; Write firmware code based on Keil software development system; Upgrade the firmware of the device via the MQTT protocol.

3. According to the design of a heterogeneous UAV flight control system based on microservices in claim 1, it is characterized in that: The upgrade of the heterogeneous UAV payload firmware includes: Generate a new ".hex" or ".bin" file by opening the project in KeilMDK and compiling it for the purpose of upgrading. The purpose of upgrading includes: fixing errors or implementing new functions. Connect the drone to the firmware upgrade mode through a debugger, including J-Link, ST-Link or OpenOCD. Use Keil's "Download / Programming" function to burn the new firmware into the drone's memory.

4. According to the design of a heterogeneous UAV flight control system based on microservices in claim 1, it is characterized in that: The construction of the ground station distributed microservice cluster framework includes: Agree on Mavlink protocol, develop and compile ground station code, and establish serial port or network communication module; Use low-code frameworks to develop servers and deploy distributed microservices; Debug uplink and downlink data links to ensure the stability of data transmission; Use Docker and Kubernetes for containerized deployment and management, and build MQTT and Rest communication mechanisms.

5. According to the design of a heterogeneous UAV flight control system based on microservices in claim 1, it is characterized in that: The heterogeneous drone data configuration and service access include: Install and calibrate the drone's hardware components and set the drone's relevant parameters; Register the drone as an independent service in the microservice cluster through the MQTT protocol; Use REST API to realize data interaction between ground station and heterogeneous UAV operation platform; Synchronize the status data and service configuration of all drones to the microservice cluster in real time.

6. According to the design of a heterogeneous UAV flight control system based on microservices in claim 1, it is characterized in that: The heterogeneous UAV operation platform development includes: Set the core functions of the platform; the core functions include: drone parameter import, information management, task grouping, task assignment, real-time monitoring and task adjustment; Build the interface through Vue.js combined with the interface component library; the interface component library includes: Element UI or AntDesign; Data interaction between the front-end and back-end of the platform is realized through RESTful API, and MQTT or Kafka is integrated to realize real-time communication between drones and the platform; Store drone information, mission data, and flight data in a database; Display the real-time location and flight trajectory of the drone through third-party map services; display the flight data of the drone by using chart libraries; third-party map services include: Google Maps or Amap; chart libraries include: ECharts or Chart.js; Automatic execution of tasks is achieved by defining a workflow engine; wherein the defined workflow engine includes: Zeebe or Camunda.

7. According to the design of a heterogeneous UAV flight control system based on microservices in claim 1, it is characterized in that: The heterogeneous UAV operation platform is constructed, including: Database deployment; Dependent service deployment; Dependent service deployment includes: Docker deployment, Docker Comprose deployment, Redis deployment, JDK deployment, Minio deployment, Rocketmq deployment, Kafka deployment, Kibana deployment, Solr deployment, Elasticsearch deployment, and Memcached deployment; System service deployment; among them, system service deployment includes: nacos service deployment, authentication service deployment, user and role management service deployment.

8. The design of a heterogeneous UAV flight control system based on microservices according to claim 1 is characterized in that: The step of adjusting the task progress and implementing the behavior mode flight control operation according to the subscription data includes: Obtain subscription data; and adjust mission progress and implement behavioral mode flight control operations based on the subscription data.

9. The design of a heterogeneous UAV flight control system based on microservices according to claim 8 is characterized in that: The obtaining of subscription data includes: The real-time status data of each drone is obtained based on the ground station or operating platform, and the real-time status data of each drone is transmitted to the back-end service of the platform in real time; the real-time status data includes: the drone's position, speed, altitude, battery power, sensor readings and mission execution status.

10. The design of a heterogeneous UAV flight control system based on microservices according to claim 8 is characterized in that: The step of adjusting the mission progress and implementing the behavior mode flight control operation based on the subscription data includes: Task progress adjustment: After receiving the real-time status data, the backend service uses preset rules or machine learning models to determine whether the execution of the current task meets expectations; Dynamically adjust task parameters based on real-time data analysis results; When the drone encounters an emergency, it will suspend or terminate the current mission and trigger emergency measures; Behavioral flight control: Configure several behavior modes for the drone; Dynamically switch the drone's behavior mode based on real-time status data.

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