Unmanned aerial vehicle swarm control system and method based on graph and numerical control integrated data link

Abstract

The invention discloses an unmanned aerial vehicle swarm control system and method based on a graph and numerical control integrated data link, and relates to the technical field of unmanned aerial vehicles, and the system comprises a data collection layer which is used for obtaining image data, spectrum data and environment data; the data processing layer is used for analyzing the received data and performing feature extraction on the analyzed data; generating a task instruction according to the extracted features; the decision execution layer is used for controlling an unmanned aerial vehicle swarm to execute a flight task according to the task instruction; and the data transmission layer is used for transmitting data and the task instruction to each layer through double communication channels. According to the invention, through the construction of double communication channels, the stability of data transmission is ensured, and meanwhile, through the application of the unmanned aerial vehicle swarm, the working efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and more particularly to an unmanned aerial vehicle swarm control system and method based on a graphics-based numerical control integrated data link. Background Art

[0002] With the development of modern agricultural technology, the demand for precision agriculture in the Chinese herbal medicine planting industry is growing. In the process of planting Chinese herbal medicine, every link from sowing, fertilization, irrigation to pest and disease control requires refined management to ensure the quality and yield of medicinal materials. Traditional operation methods rely on manual operation, which is not only inefficient but also difficult to achieve large-scale standardized production. In recent years, drone technology has been widely used in the agricultural field due to its high flexibility, wide coverage and good cost-effectiveness.

[0003] However, when a single UAV performs a task, its coverage is limited and its work efficiency is low. In addition, the Chinese herbal medicine planting area is relatively remote and the data transmission is unstable, which has caused great troubles for the application of UAV control systems. Therefore, how to ensure stable data transmission and improve work efficiency is an urgent problem that technicians in this field need to solve. Summary of the invention

[0004] In view of this, the present invention provides a UAV swarm control system and method based on an integrated graphics and numerical control data link, which overcomes the above-mentioned defects.

[0005] In order to achieve the above object, the present invention adopts the following technical solution:

[0006] A UAV swarm control system based on a graphics-based numerical control integrated data link, comprising:

[0007] Data acquisition layer, used to obtain image data, spectral data and environmental data;

[0008] The data processing layer is used to parse the received data, extract features from the parsed data, and generate task instructions based on the extracted features;

[0009] The decision-making execution layer is used to control the drone swarm to perform the flight mission according to the mission instructions;

[0010] The data transmission layer is used to transmit the data and the task instructions to each layer through the dual communication channels.

[0011] Optionally, the data transmission layer includes: a first transmission channel constructed based on LPWAN technology and a second transmission channel constructed by configuring multi-hop routing parameters on the basis of the first transmission channel.

[0012] Optionally, the data transmission layer is constructed as follows:

[0013] Determine network nodes based on scenario requirements and configure network devices for the network nodes;

[0014] Performing working parameter configuration on the network device to form a first transmission channel;

[0015] The second communication channel is formed based on the configuration of the routing protocol, routing strategy and communication parameters between the network nodes of the first transmission channel.

[0016] Optionally, the data processing layer includes a first data processing component, a second data processing component, a first decision component, a second decision component, a first storage component and a second storage component; the first data processing component, the first decision component and the first storage component are all deployed on the drone swarm; the second data processing component, the second decision component and the second storage component are all deployed on the ground control terminal; the first data processing component is used to perform preliminary processing and compression on the original collected data to obtain the collected data; the second data processing component is used to parse the collected data and flight control instructions, and extract features from the parsed data; the second decision component is used to generate the task instructions based on the extracted features; the first decision component is used to modify the task instructions based on real-time data; the first storage component is used to store the original collected data, the collected data and the first pre-stored data; the second storage component is used to store the collected data and the second pre-stored data.

[0017] Optionally, the first data processing component includes:

[0018] A first data receiving module, used for receiving the image data, the spectral data and the task instruction;

[0019] A first data preprocessing module, used to preprocess the image data and the spectral data respectively to obtain image preprocessing data and spectral preprocessing data;

[0020] an abnormality identification module, used for identifying the plant category and growth cycle according to the image preprocessing data, acquiring standard data according to the plant category and growth cycle, and judging whether there are abnormal data in the image preprocessing data and the spectral preprocessing data according to the standard data;

[0021] A transmission module, used for determining the data to be transmitted according to the abnormal data judgment structure, and transmitting the data to be transmitted to the ground control terminal based on the data layer;

[0022] The first data analysis module is used to analyze the mission instructions and extract flight-related features based on the analyzed data.

[0023] Optionally, the second data processing component includes:

[0024] A second data receiving module, used for receiving the collected data, the environmental data and the flight control instructions;

[0025] A second data preprocessing module is used to preprocess the environmental data to obtain environmental preprocessing data;

[0026] A first data analysis module, used for analyzing the collected data and the flight control instructions to obtain collected analysis data and control analysis data respectively;

[0027] The feature extraction module is used to extract feature data from the acquisition analysis data or the control analysis data to obtain acquisition features and control features.

[0028] Optionally, the second decision component includes:

[0029] A category recognition module, used to input the collected features into a pre-trained convolutional neural network for category judgment;

[0030] The initial solution generation module is used to generate a preliminary solution based on the classification results and expert experience;

[0031] a strategy generation module, for evaluating currently available UAV resources according to the preliminary plan or the control characteristics, and generating an execution plan according to the evaluation result;

[0032] The task instruction generation module is used to disassemble the execution plan to generate task instructions.

[0033] Optionally, the first decision component includes:

[0034] A flight control module controls the drone swarm to perform a flight mission according to the mission instructions;

[0035] Dynamic adjustment module, used to dynamically modify mission instructions according to the status data of the UAV and environmental change data during flight;

[0036] The data feedback module is used to send the revised mission instruction to the ground control center.

[0037] Optionally, the dynamic adjustment module includes:

[0038] A real-time data monitoring unit, used to obtain real-time status data of each drone in the drone swarm and environmental change data;

[0039] The strategy correction unit is used to input the real-time status data and the environmental change data into a pre-trained strategy generation model to generate a correction strategy.

[0040] A UAV swarm control method based on a graphics-based numerical control integrated data link, the specific steps are:

[0041] Obtaining flight control instructions, and parsing and feature extracting the flight control instructions;

[0042] Evaluate currently available drone resources based on the extracted features and generate data collection strategies based on the evaluation results;

[0043] Execute the data collection strategy to obtain collected data, and perform analysis and feature extraction on the collected data;

[0044] Generate a preliminary plan based on the extracted features and expert experience; evaluate currently available drone resources based on the preliminary plan, and generate an execution plan based on the evaluation results;

[0045] The execution plan is disassembled to generate task instructions, and the task instructions are sent to the corresponding drone.

[0046] It can be seen from the above technical solution that compared with the prior art, the present invention discloses a UAV swarm control system and method based on an integrated CNC data link, which ensures the stability of data transmission through the construction of dual communication channels, and improves work efficiency through the application of UAV swarms. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] 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 embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0048] Figure 1 A schematic diagram of the system structure provided by the present invention;

[0049] Figure 2 The present invention provides a flow chart of the method. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not 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.

[0051] This embodiment discloses a drone swarm control system based on a graphics-based numerical control integrated data link, such as Figure 1 As shown, including:

[0052] Data acquisition layer, used to obtain image data, spectral data and environmental data;

[0053] The data processing layer is used to parse the received data, extract features from the parsed data, and generate task instructions based on the extracted features;

[0054] The decision-making execution layer is used to control the drone swarm to perform flight missions according to mission instructions;

[0055] The data transmission layer is used to transmit data and task instructions to each layer through dual communication channels.

[0056] In one embodiment, the data acquisition layer includes an image collector, a multispectral sensor and a ground sensor; the image collector and the multispectral sensor are both carried on a drone swarm; the ground sensor is deployed in the planting area; and they are used to obtain image data, spectral data and environmental data respectively.

[0057] Furthermore, in this embodiment, the image collector adopts a high-precision camera; the ground sensors include a temperature sensor, a humidity sensor, a glass electrode pH probe, a conductivity sensor, and the like.

[0058] In one embodiment, the data transmission layer includes: a first transmission channel constructed based on LPWAN technology and a second transmission channel constructed by configuring multi-hop routing parameters based on the first transmission channel.

[0059] Furthermore, the data transmission layer also includes:

[0060] Wireless communication protocol component, used to support multiple wireless communication protocols to ensure compatibility with different models of drones;

[0061] The signal strength monitoring component is used to monitor the quality of the transmission channel and request resending instructions when necessary.

[0062] In one embodiment, the steps of constructing the data transmission layer are:

[0063] Determine network nodes based on scenario requirements and configure network devices for network nodes;

[0064] Configuring working parameters of the network device to form a first transmission channel;

[0065] A routing protocol, a routing strategy and communication parameters between network nodes are configured based on the first transmission channel to form a second communication channel.

[0066] Furthermore, the construction method of the data transmission layer is specifically as follows:

[0067] Determine the network nodes based on the geographical data of the planting site and the type of the plant; and select the terminal network nodes from the determined network nodes as needed;

[0068] Build a LoRa network based on the selected network nodes and set parameters for the LoRa network, such as the spreading factor (SF) and other LoRa modulation parameters;

[0069] Configure routing protocols, routing strategies, and communication parameters between nodes based on the LoRa network, such as communication frequency, data packet format, etc.

[0070] After the configuration is completed, the LoRa network is tested and adjusted to obtain the data transmission layer.

[0071] In one embodiment, the data processing layer includes a first data processing component, a second data processing component, a first decision component, a second decision component, a first storage component and a second storage component; the first data processing component, the first decision component and the first storage component are all arranged on the drone swarm; the second data processing component, the second decision component and the second storage component are all arranged on the ground control terminal; the first data processing component is used to perform preliminary processing and compression on the original collected data to obtain the collected data; the second data processing component is used to parse the collected data and flight control instructions, and extract features from the parsed data; the second decision component is used to generate task instructions based on the extracted features; the first decision component is used to modify the task instructions based on real-time data; the first storage component is used to store the original collected data, the collected data and the first pre-stored data; the second storage component is used to store the collected data and the second pre-stored data.

[0072] In one embodiment, the first data processing component includes:

[0073] A first data receiving module, used for receiving image data, spectral data and task instructions;

[0074] A first data preprocessing module, used to preprocess the image data and the spectrum data respectively to obtain image preprocessing data and spectrum preprocessing data;

[0075] An abnormality recognition module is used to identify the plant category and growth cycle according to the image preprocessing data, obtain standard data according to the plant category and growth cycle, and determine whether there is abnormal data in the image preprocessing data and the spectral preprocessing data according to the standard data;

[0076] A transmission module, used for determining the data to be transmitted according to the abnormal data judgment structure, and transmitting the data to be transmitted to the ground control terminal based on the data layer;

[0077] The first data analysis module is used to analyze the mission instructions and extract flight-related features (such as starting position, destination, flight altitude, flight speed, etc.) based on the analyzed data.

[0078] Furthermore, the first data preprocessing module includes an image preprocessing unit and a spectral data preprocessing unit. The image preprocessing unit is used to remove noise, perform color correction, grayscale and other processing on the collected original image data to obtain preprocessed data; the spectral data preprocessing unit is used to correct and normalize the collected spectral data to obtain preprocessed data.

[0079] The abnormality recognition module includes a plant recognition unit and an abnormality judgment unit. The plant recognition unit, based on a plant recognition model trained using a convolutional neural network (CNN), determines the type and growth stage of the plant according to the preprocessed image data; the abnormality judgment unit determines whether there are abnormal data in the preprocessed image data and spectral data according to pre-stored standard images and normal spectral range values.

[0080] The transmission module includes a transmission data processing unit and a transmission data sending unit. The transmission data processing unit is used to organize the data to be transmitted according to the abnormal data judgment result, and compress the data to be transmitted using a compression algorithm to generate transmission data; the transmission data sending unit transmits the transmission data to the ground control terminal through the data layer.

[0081] Furthermore, the data to be transmitted includes: if there is no abnormal data, the recognition result and all the data; if there is abnormal data, the recognition result and the abnormal data are included.

[0082] Furthermore, the first data parsing module of this embodiment adopts a method that combines natural language processing and regular expressions to parse task instructions and extract relevant features. Specifically: natural language processing technology is used to perform preliminary analysis on task instructions to extract semantic information of task instructions; and then the preliminary extracted semantic information is accurately matched based on a predefined regular expression pattern.

[0083] In one embodiment, the second data processing component includes:

[0084] A second data receiving module, used for receiving collected data, environmental data and flight control instructions;

[0085] A second data preprocessing module is used to preprocess the environmental data and obtain environmental preprocessing data;

[0086] The second data analysis module is used to analyze the collected data and the flight control instructions to obtain collected analysis data and control analysis data respectively;

[0087] The feature extraction module is used to extract feature data from the acquisition analysis data or the control analysis data to obtain acquisition features and control features.

[0088] Furthermore, the second data preprocessing module is used to perform cleaning, standardization, time alignment and other processing on the environmental data.

[0089] Furthermore, the feature extraction module includes a feature extraction unit and a feature selection unit, wherein the feature extraction unit includes an image feature extraction subunit, a spectral feature extraction subunit, and an environmental feature extraction subunit; the image feature extraction subunit extracts image features through a pre-stored algorithm, such as: using the Canny algorithm to extract edge information in the image, and then using the LBP algorithm to extract texture features, etc.; the spectral feature extraction subunit extracts spectral features according to the spectral index; the environmental feature extraction subunit is used to perform data feature response according to the standard deviation; the feature selection unit selects features that are highly correlated with the plant state through correlation analysis.

[0090] In one embodiment, the second decision component includes:

[0091] Category recognition module, used for classification based on the pre-trained convolutional neural network (CNN) of extracted features;

[0092] The initial solution generation module is used to generate a preliminary solution based on the classification results and historical data;

[0093] The strategy generation module is used to evaluate the currently available drone resources based on the preliminary plan and generate an execution plan based on the evaluation results;

[0094] The task instruction generation module is used to generate specific task instructions based on the task allocation results.

[0095] Furthermore, the classification of the category recognition module is to classify the objects of the extracted features, such as identifying pests and diseases, weeds, etc., and output the recognition results, including the location coordinates, category, quantity and other information of the objects.

[0096] Furthermore, the strategy generation module is used to evaluate the currently available UAV resources based on the preliminary plan (task type, target area, distance, etc.), including the number, type, current location, power status, etc. of UAVs, and determine the optimal task allocation plan according to a predetermined algorithm, so that the task execution efficiency is maximized and the UAV resources are reasonably utilized.

[0097] Furthermore, the strategy generation module includes:

[0098] Resource monitoring unit, used to monitor the status information of all drones in the drone swarm in real time, including but not limited to the number, type, current location, power status, etc. of drones;

[0099] A resource matching unit, used to match drones according to the preliminary plan;

[0100] An intelligent assessment unit, used to assess currently available UAV resources based on the integrated information;

[0101] The allocation algorithm unit is used to determine the optimal task allocation plan according to a preset algorithm.

[0102] The resource matching unit includes:

[0103] The task priority calculation subunit is used to calculate the task priority according to factors such as the urgency and importance of the task;

[0104] The resource demand prediction subunit is used to predict the type and number of drones required to execute the preliminary plan;

[0105] The mission matching subunit is used to match the UAV that is most suitable to execute the preliminary plan.

[0106] The allocation algorithm unit includes:

[0107] The load balancing algorithm subunit is used to ensure that the load of the drones is evenly distributed to avoid overloading a single drone;

[0108] The energy consumption minimization algorithm subunit is used to select the UAV with the least energy consumption to perform the task;

[0109] The time window optimization algorithm subunit is used to optimize the allocation plan according to the current position of the UAV and the execution time window of the task.

[0110] Furthermore, the task instruction generation module includes: a task segmentation unit and a task generation unit, wherein the task segmentation unit is used to decompose a large task into multiple subtasks, each subtask is assigned to one or a group of drones for execution and determines the task sequence and execution time window of each drone, and its structure includes:

[0111] The task splitting subunit is used to decompose the execution plan into several subtasks according to the key information of the task (the dependency between each subtask and the importance and urgency of the task);

[0112] The subtask allocation subunit is used to intelligently allocate subtasks to one or a group of drones based on the deep learning algorithm according to the drone's type, location, power and other status information;

[0113] The execution plan subunit is used to determine the order and time window for the drone to execute subtasks.

[0114] The task generation unit is used to generate specific task instructions based on the task allocation results, including detailed information such as flight route, operation parameters (such as spraying amount, speed, etc.), package these instructions and send them to the corresponding UAV through the data link system. Its structure includes:

[0115] Parameter optimization subunit, used to automatically optimize operating parameters (based on historical data), such as spraying volume, speed, etc., according to the task type and environmental conditions;

[0116] The flight route generation subunit is used to generate the optimal flight route according to the mission requirements and the current position of the UAV;

[0117] The instruction packaging subunit is used to package the optimized operating parameters and flight routes into data packets suitable for transmission, and transmit them to the UAV through the data layer;

[0118] The feedback receiving subunit is used to receive feedback information after the UAV performs a mission and adjust subsequent mission instructions based on the feedback.

[0119] In another embodiment, the second decision component further includes: a multi-task parallel processing module, whose structure is specifically as follows:

[0120] Task prioritization unit, used to prioritize tasks according to their importance and urgency;

[0121] Resource allocation unit, used to reasonably allocate UAV resources in multi-task scenarios;

[0122] A conflict detection unit, used to detect and prevent conflicts between multiple tasks;

[0123] The mission coordination unit is used to coordinate the execution of missions when multiple UAVs work together.

[0124] Furthermore, the conflict detection unit also includes:

[0125] A time-space conflict detection subunit, used to detect possible task conflicts within the same time and space;

[0126] The task rescheduling subunit is used to reschedule the task execution order when a conflict is detected.

[0127] In one embodiment, the first decision component includes:

[0128] The flight control module controls the drone swarm to perform flight missions according to mission instructions;

[0129] Dynamic adjustment module, used to dynamically modify mission instructions according to the status data of the UAV and environmental change data during flight;

[0130] The data feedback module is used to send the revised mission instructions to the ground control center.

[0131] In one embodiment, the flight control module includes:

[0132] The flight path management unit is used to manage the flight path of the UAV according to the content of the mission instructions, monitor the position of the UAV in real time, compare it with the preset path, and adjust the flight route to keep it on the predetermined route.

[0133] The flight attitude control unit is used to adjust flight altitude, speed and other parameters according to mission requirements and control the attitude (pitch, yaw, roll angle) of the drone to ensure that the drone maintains a stable flight attitude.

[0134] The sensor data processing unit is used to read data from various sensors installed on the drone (such as GPS, IMU, barometer, etc.) and adjust flight control parameters according to the sensor data, such as adjusting the flight direction, speed, etc.

[0135] The automatic obstacle avoidance unit is used to detect obstacles ahead using laser radar, ultrasonic sensors and other equipment, and adjust the flight path based on the obstacle information to avoid collisions.

[0136] The mission execution monitoring unit is used to monitor the status of the drone during the mission execution (such as power, communication status, location, speed, altitude, etc.). During the mission execution, the status of the drone is monitored in real time to ensure the smooth progress of the mission.

[0137] In one embodiment, the dynamic adjustment module includes:

[0138] A real-time data monitoring unit, used to obtain real-time status data of each drone in the drone swarm and environmental change data;

[0139] The strategy correction unit is used to input the real-time status data and environmental change data into the pre-trained strategy generation model to generate a correction strategy.

[0140] The first storage component and the second storage component are used to store and save data, specifically: classify the data according to categories, use a suitable compression algorithm to compress large files to reduce the occupied space, and save the compressed data to a suitable storage medium.

[0141] In one embodiment, the ground control terminal also includes a human-computer interaction component, wherein the human-computer interaction component includes a data display module, a command input module, a data processing module and an alarm module; wherein the data display module is used to display historical data, planning maps and real-time data, etc., and the historical data includes information such as the past mission records of the drone swarm, the flight trajectory of the drone and the details of the executed missions; the planning map includes the geographical data of the planting site (topography, water source distribution, etc.), obstacles (buildings, electric poles, etc.) and the starting position, flight route, target area and other information of the drone swarm; the real-time data includes the real-time position, status (such as power level, flight altitude, speed, etc.) and collected data (such as image data, etc.) of the drone; and also includes environmental data (such as temperature, humidity, soil moisture, etc.) collected by pre-set sensors.

[0142] The command input module is used to receive the overall flight mission instructions (such as starting a new flight mission, stopping the current mission, etc.), mission parameters (mission area, flight altitude, flight path (manually drawn or automatically planned path), flight mission type (monitoring, spraying, planting), monitoring frequency, etc.), emergency control instructions (one-key return, emergency shutdown) and other data input by the operator.

[0143] The data processing module is used to convert and verify the data format, specifically: converting the acquired raw data into charts, map marks, etc., so that the operator can understand and analyze it. After the operator enters the command, the HMI will verify the input to ensure that it is in the correct format and logical, and provide immediate feedback, such as confirming that the command has been received or prompting that the input is wrong.

[0144] Further, the steps for manually drawing a flight path are:

[0145] Determine the starting and ending locations of the flight mission based on the flight mission type, geographic data of the target area (topography, climatic conditions, etc.), and the current location of the drone;

[0146] Identify key points (buildings, trees, high-voltage lines and other obstacles) and mark them on the map;

[0147] Use map tools or hand-drawing to draw one or more possible flight paths between the starting point and the end point, optimize multiple flight paths based on flight time, and save the optimized flight paths.

[0148] The alarm module is used to identify abnormal conditions of the drone or system (such as low battery, communication interruption, etc.) and send out warning signals or pop-up notifications to remind the operator.

[0149] This embodiment, on the other hand, also discloses a drone swarm control method based on a graphics-based numerical control integrated data link, such as Figure 2 As shown, the specific steps are:

[0150] Obtain flight control instructions, and parse and extract features from them;

[0151] Evaluate currently available drone resources based on the extracted features and generate data collection strategies based on the evaluation results;

[0152] Execute data collection strategies to obtain collected data, and perform analysis and feature extraction on the collected data;

[0153] Generate a preliminary plan based on the extracted features and expert experience; evaluate the currently available drone resources based on the preliminary plan, and generate an execution plan based on the evaluation results;

[0154] The execution plan is disassembled to generate task instructions, and the task instructions are sent to the corresponding UAV.

[0155] Furthermore, agricultural experts or managers input flight control instructions for fertilizing a certain herb through the ground control terminal, then determine the destination area based on the planning map, generate a data collection strategy based on the area range, and dispatch drones to collect data based on the data collection strategy; judge the growth stage of the herb based on the returned data, and determine the execution plan based on environmental data and expert experience, such as the flight path, altitude, speed, and fertilization mode (uniform spreading, spot application, etc.), and split the execution plan, such as the area each drone is responsible for, the fertilization method, etc.

[0156] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0157] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A drone swarm control system based on a graphics and numerical control integrated data link, characterized in that: include: Data acquisition layer, used to obtain image data, spectral data and environmental data; The data processing layer is used to parse the received data and extract features from the parsed data; And generate task instructions according to the extracted features; The decision-making execution layer is used to control the drone swarm to perform the flight mission according to the mission instructions; The data transmission layer is used to transmit the data and the task instructions to each layer through the dual communication channels.

2. The UAV swarm control system based on the integrated data link of graphics and numerical control according to claim 1 is characterized in that: The data transmission layer includes: a first transmission channel constructed based on LPWAN technology and a second transmission channel constructed by configuring multi-hop routing parameters on the basis of the first transmission channel.

3. The UAV swarm control system based on the integrated data link of graphics and numerical control according to claim 1 is characterized in that: The method for constructing the data transmission layer is as follows: Determine network nodes based on scenario requirements and configure network devices for the network nodes; Performing working parameter configuration on the network device to form a first transmission channel; The second communication channel is formed based on the first transmission channel configuring the routing protocol, routing strategy and communication parameters between the network nodes.

4. The UAV swarm control system based on the integrated data link of graphics and numerical control according to claim 1 is characterized in that: The data processing layer includes a first data processing component, a second data processing component, a first decision component, a second decision component, a first storage component and a second storage component; the first data processing component, the first decision component and the first storage component are all arranged on the drone swarm; the second data processing component, the second decision component and the second storage component are all arranged on the ground control terminal; the first data processing component is used to perform preliminary processing and compression on the original collected data to obtain the collected data; the second data processing component is used to parse the collected data and flight control instructions, and extract features from the parsed data; the second decision component is used to generate the task instruction according to the extracted features; the first decision component is used to modify the task instruction according to real-time data; the first storage component is used to store the original collected data, the collected data and the first pre-stored data; The second storage component is used to store the collected data and the second pre-stored data.

5. The drone swarm control system based on the integrated data link of graphics and numerical control according to claim 4 is characterized in that: The first data processing component comprises: A first data receiving module, used for receiving the image data, the spectral data and the task instruction; A first data preprocessing module, used to preprocess the image data and the spectral data respectively to obtain image preprocessing data and spectral preprocessing data; an abnormality identification module, used for identifying the plant category and growth cycle according to the image preprocessing data, acquiring standard data according to the plant category and growth cycle, and judging whether there are abnormal data in the image preprocessing data and the spectral preprocessing data according to the standard data; A transmission module, used for determining the data to be transmitted according to the abnormal data judgment structure, and transmitting the data to be transmitted to the ground control terminal based on the data layer; The first data analysis module is used to analyze the mission instructions and extract flight-related features based on the analyzed data.

6. The drone swarm control system based on the integrated data link of graphics, numerical control and control according to claim 4 is characterized in that: The second data processing component comprises: A second data receiving module, used for receiving the collected data, the environmental data and the flight control instructions; A second data preprocessing module is used to preprocess the environmental data to obtain environmental preprocessing data; A first data analysis module, used for analyzing the collected data and the flight control instructions to obtain collected analysis data and control analysis data respectively; The feature extraction module is used to extract feature data from the acquisition analysis data or the control analysis data to obtain acquisition features and control features.

7. The UAV swarm control system based on the integrated data link of graphics and numerical control according to claim 4 is characterized in that: The second decision-making component includes: A category recognition module, used to input the collected features into a pre-trained convolutional neural network for category judgment; The initial solution generation module is used to generate a preliminary solution based on the classification results and expert experience; a strategy generation module, for evaluating currently available UAV resources according to the preliminary plan or the control characteristics, and generating an execution plan according to the evaluation result; The task instruction generation module is used to disassemble the execution plan to generate task instructions.

8. The UAV swarm control system based on the integrated data link of graphics, numerical control and control according to claim 4 is characterized in that: The first decision-making component includes: A flight control module controls the drone swarm to perform a flight mission according to the mission instructions; Dynamic adjustment module, used to dynamically modify mission instructions according to the status data of the UAV and environmental change data during flight; The data feedback module is used to send the revised mission instruction to the ground control center.

9. The UAV swarm control system based on the integrated data link of graphics, numerical control and control according to claim 8 is characterized in that: The dynamic adjustment module comprises: A real-time data monitoring unit, used to obtain real-time status data of each drone in the drone swarm and environmental change data; The strategy correction unit is used to input the real-time status data and the environmental change data into a pre-trained strategy generation model to generate a correction strategy.

10. A method for controlling a drone swarm based on a graphics-computer-controlled integrated data link, characterized in that: The specific steps are: Obtaining flight control instructions, and parsing and feature extracting the flight control instructions; Evaluate currently available drone resources based on the extracted features and generate data collection strategies based on the evaluation results; Execute the data collection strategy to obtain collected data, and perform analysis and feature extraction on the collected data; Generate preliminary solutions based on extracted features and expert experience; and evaluating currently available drone resources based on the preliminary plan, and generating an implementation plan based on the evaluation results; The execution plan is disassembled to generate task instructions, and the task instructions are sent to the corresponding drone.

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