A method and system for cooperative inspection and scheduling of unmanned aerial vehicles based on GIS

Abstract

The application relates to the field of unmanned planes, in particular to a GIS-based unmanned plane cooperative inspection scheduling method and system, which comprises a task management module, a GIS data management module, an intelligent scheduling module, an unmanned plane control module, a data acquisition module and a warning module; the task management module is used for issuing initial task information or emergency task information to the intelligent scheduling module; the GIS data management module is used for storing relevant data of various inspection positions; the intelligent scheduling module is used for generating an inspection path of an inspection unmanned plane and an emergency inspection route of an emergency unmanned plane; the unmanned plane control module is used for controlling the inspection unmanned plane or the emergency unmanned plane; the data acquisition module is used for shooting various images required by tasks; and the warning module is used for acquiring risk indexes of various inspection positions. The scheme measures the regional risk by comprehensively considering various factors, is favorable for improving the accuracy of judgment and improving the intelligent degree of the system.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicles (UAVs), and more particularly to a GIS-based UAV collaborative inspection and scheduling method and system. Background Technology

[0002] Currently, drones are increasingly being used to inspect target areas in fields such as power lines, oil pipelines, forest fire prevention, and traffic monitoring, in order to improve operational efficiency and reduce labor costs.

[0003] For example, the prior art disclosed in CN117572806A is an automatic homing control system for a GIS geographic monitoring drone, relating to the field of geographic monitoring drone technology. This GIS geographic monitoring drone automatic homing control system includes: a flight control module, an automatic battery swapping and charging module, an automatic repair module, an image acquisition module, a positioning module, a data collection module, a server terminal module, and a data processing module. The flight control module is signal-connected to the drone body; the automatic battery swapping and charging module is located inside the drone's homing nest; the data collection module and data processing module form the server terminal module; the positioning module and image acquisition module are both installed on the drone body; and the image acquisition module is wirelessly connected to the data collection module and data processing module. The system determines patrol points based on the area of ​​the geographical location to be patrolled and then flies the drone to each patrol point one by one.

[0004] Another typical example is the prior art disclosed in CN108717301B, which discloses a GIS-based drone plant protection system and method. This GIS-based drone plant protection system includes a drone and a monitoring terminal. The drone is equipped with a positioning unit, a depth camera, and a flight execution unit. The monitoring terminal includes a GIS processing unit, a manual control unit, an automatic control unit, and a control mode switching unit. The depth camera collects depth point cloud data of the farmland and sends it to the GIS processing unit. The GIS processing unit analyzes the depth point cloud data and displays the areas of the farmland where crops are planted on a GIS map.

[0005] Let's look at another prior art, such as CN112218051A, which discloses a GIS-based method for monitoring unmanned aerial vehicles (UAVs) in the sea area. The method includes the following steps: on the GIS system, obtaining relevant parameter fields for UAV telemetry and control through configuration files; for a specific UAV, receiving real-time video data from the UAV and telemetry data generated and stored by the flight control system.

[0006] For video data, a grayscale projection-based method is used to achieve fast motion estimation of video frames, forming video motion coding; for telemetry data, based on the UAV payload status data and payload status data parsed from the telemetry data, the motion patterns of the UAV and payload are encoded with time as the axis to obtain telemetry motion coding; thus achieving synchronization between telemetry data and video data.

[0007] Existing drone dispatching systems often lack intelligent support and cannot flexibly adjust according to dynamic factors such as regional risks. In order to solve the common problems in this field, this invention was made. Summary of the Invention

[0008] The purpose of this invention is to address the shortcomings of current methods by proposing a GIS-based collaborative inspection and scheduling method and system for unmanned aerial vehicles (UAVs).

[0009] To overcome the shortcomings of the prior art, the present invention adopts the following technical solution:

[0010] A GIS-based UAV collaborative inspection and scheduling system is characterized by comprising a task management module, a GIS data management module, an intelligent scheduling module, a UAV control module, a data acquisition module, and an early warning module. The task management module sends initial task information or emergency task information to the intelligent scheduling module. The GIS data management module stores relevant data for each inspection location. The intelligent scheduling module generates inspection paths for inspection UAVs based on the information provided by the GIS data management module and the initial task information, or generates emergency inspection routes for emergency UAVs based on the emergency task information. The UAV control module controls the inspection UAVs according to the inspection paths generated by the intelligent scheduling module, or controls the emergency UAVs according to the emergency inspection routes. The data acquisition module captures various images required for the task during the flight of the inspection UAVs. The early warning module obtains risk indicators for each inspection location based on the data collected by the data acquisition module and determines whether an early warning signal needs to be sent to the task management module based on the risk indicators. The early warning signal is used by the task management module to select and send emergency task information to the intelligent scheduling module.

[0011] Furthermore, the task management module includes an input unit, a display unit, and a task configuration unit. The input unit is used to receive user instructions, the display unit is used to display the analysis results of the early warning module, and the task configuration unit is used to set the target area, task type, time requirements, etc. of the inspection task.

[0012] Furthermore, the GIS data management module includes a geographic information storage unit and a network connection unit. The geographic information storage unit is used to store and manage remote sensing images and topographic maps, and the network connection unit is used to receive third-party data such as real-time meteorological information and traffic information, and to mark and update the remote sensing images and topographic maps based on the third-party data.

[0013] Furthermore, the intelligent scheduling module includes a drone library, a drone resource management unit, and a path planning unit. The drone library is used to store drones, the drone resource management unit is used to manage the power of each drone and its position in the drone library, and the path planning unit is used to generate inspection paths for each drone based on the information stored in the GIS data management module and the initial task information.

[0014] Furthermore, the UAV control module includes an instruction parsing unit, a motion control unit, and an attitude adjustment unit. The instruction parsing unit is used to parse the training path obtained by the path planning unit and send corresponding control signals to the motion control unit and the attitude adjustment unit. The motion control unit is used to control the UAV to move according to the control signals. The attitude adjustment unit is used to control the UAV to take pictures at the designated inspection position according to the control signals. The data acquisition module includes a camera, which is installed on the UAV and is used to capture images of the inspection position.

[0015] Furthermore, the early warning module includes an image preprocessing unit, an image recognition unit, a risk judgment model, a calculation unit, and an alarm unit. The image preprocessing unit is used to magnify and reduce noise in the captured image. The image recognition unit is used to identify the object type and size in each part of the preprocessed image. The risk judgment model is used to obtain corresponding risk parameters based on the recognized image. The calculation unit is used to calculate risk indicators based on the recognition results of the image recognition unit, the risk parameters obtained by the risk judgment model, and past records. When the risk indicator is greater than the risk indicator threshold, the alarm unit sends an early warning signal to the task management module.

[0016] A GIS-based UAV collaborative inspection and scheduling method, comprising the following steps:

[0017] S1, The task management module sends initial task information to the intelligent scheduling module;

[0018] S2, the intelligent scheduling module generates the inspection path of the inspection drone based on the initial task information and the information provided by the GIS data management module;

[0019] S3, the drone control module controls the inspection drone to perform inspections according to the generated inspection path, and the data acquisition module takes pictures of the target at the designated inspection location.

[0020] S4, the early warning module obtains risk indicators based on the data collected by the data acquisition module, and determines whether the risk indicators are greater than the risk indicator threshold. If not, the process ends; otherwise, the corresponding area of ​​the risk indicator is considered a high-risk area, and the next step is executed.

[0021] S5, the inspection drone continues to inspect along the original inspection path. The early warning module sends a signal to the task management module, which then sends emergency task information to the intelligent scheduling module. The intelligent scheduling module generates an emergency inspection route based on the high-risk areas obtained by the early warning module. The drone control module controls the emergency drone to reach the high-risk area according to the emergency inspection route. The emergency drone collects various data from the high-risk area and feeds it back to the user.

[0022] Furthermore, the early warning module acquires risk indicators through the following steps:

[0023] S41, the image preprocessing unit enlarges and reduces noise in the images captured by the data acquisition module;

[0024] S42, the image recognition unit identifies the object type and size of each part in the preprocessed image;

[0025] S43, The risk assessment model obtains risk parameters based on the identified image;

[0026] S44, the calculation unit calculates the risk index based on the risk parameters, the recognition results of the image recognition unit, the risk parameters, and past records.

[0027] The beneficial effects achieved by this invention are: 1. By combining the recognition results of the image recognition unit, the judgment results of the risk judgment model, and past data to calculate the risk index, it is beneficial to comprehensively measure regional risks from various aspects, improve the accuracy of judgment, and enhance the intelligence level of the system.

[0028] 2. Two types of drones are used for the operation. By using inspection drones for inspection, it is beneficial to make a preliminary judgment on the risk of the area through images, which can be applied to most work tasks. When the risk is high, emergency drones are dispatched to collect detailed data. This allows staff to combine the collected data with the original image data to make a detailed judgment on the risk of the area and confirm the actual possibility of the risk occurring. Attached Figure Description

[0029] The invention will be further understood from the following description taken in conjunction with the accompanying drawings. The components in the drawings are not necessarily drawn to scale, but rather the emphasis is on illustrating the principles of the embodiments. In different views, the same reference numerals designate the same parts.

[0030] Figure 1 This is a schematic diagram of the structure of the present invention.

[0031] Figure 2 This is a flowchart of the process of the present invention.

[0032] Figure 3 This is a flowchart illustrating how the early warning module of the present invention acquires risk indicators.

[0033] Figure 4 This is a graph showing the relationship between historical data parameters, the total number of evaluation results, and the reasonable number of evaluation results. Detailed Implementation

[0034] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Furthermore, the accompanying drawings of the present invention are for simple illustrative purposes only and are not depictions of actual dimensions; this is stated beforehand. The following embodiments will further describe the relevant technical content of the present invention in detail, but the disclosed content is not intended to limit the scope of protection of the present invention.

[0035] Example 1: According to Figure 1 , Figure 2 , Figure 3 and Figure 4This embodiment provides a GIS-based UAV collaborative inspection and scheduling system, including a task management module, a GIS data management module, an intelligent scheduling module, a UAV control module, a data acquisition module, and an early warning module. The task management module is used to send initial task information or emergency task information to the intelligent scheduling module. The GIS data management module is used to store relevant data for each inspection location. The intelligent scheduling module generates inspection paths for inspection UAVs based on the information provided by the GIS data management module and the initial task information, or generates emergency inspection routes for emergency UAVs based on the emergency task information. The UAV control module controls inspection UAVs according to the inspection paths generated by the intelligent scheduling module, or controls emergency UAVs according to the emergency inspection routes. The data acquisition module captures various images required for the task during the flight of the inspection UAVs. The early warning module obtains risk indicators for each inspection location based on the data collected by the data acquisition module, and determines whether an early warning signal needs to be sent to the task management module based on the risk indicators. The early warning signal is used by the task management module to select and send emergency task information to the intelligent scheduling module.

[0036] Furthermore, the task management module includes an input unit, a display unit, and a task configuration unit. The input unit is used to receive user instructions, the display unit is used to display the analysis results of the early warning module, and the task configuration unit is used to set the target area, task type, time requirements, etc. of the inspection task.

[0037] Furthermore, the GIS data management module includes a geographic information storage unit and a network connection unit. The geographic information storage unit is used to store and manage remote sensing images and topographic maps, and the network connection unit is used to receive third-party data such as real-time meteorological information and traffic information, and to mark and update the remote sensing images and topographic maps based on the third-party data.

[0038] Furthermore, the intelligent scheduling module includes a drone library, a drone resource management unit, and a path planning unit. The drone library is used to store drones, the drone resource management unit is used to manage the power of each drone and its position in the drone library, and the path planning unit is used to generate inspection paths for each drone based on the information stored in the GIS data management module and the initial task information.

[0039] Specifically, the path planning unit generates inspection paths for the UAV using existing path planning algorithms (TSP, A-star, etc.).

[0040] Furthermore, the UAV control module includes an instruction parsing unit, a motion control unit, and an attitude adjustment unit. The instruction parsing unit is used to parse the training path obtained by the path planning unit and send corresponding control signals to the motion control unit and the attitude adjustment unit. The motion control unit is used to control the UAV to move according to the control signals. The attitude adjustment unit is used to control the UAV to take pictures at the designated inspection position according to the control signals. The data acquisition module includes a camera, which is installed on the UAV and is used to capture images of the inspection position.

[0041] Furthermore, the early warning module includes an image preprocessing unit, an image recognition unit, a risk judgment model, a calculation unit, and an alarm unit. The image preprocessing unit is used to magnify and reduce noise in the captured image. The image recognition unit is used to identify the object type and size in each part of the preprocessed image. The risk judgment model is used to obtain corresponding risk parameters based on the recognized image. The calculation unit is used to calculate risk indicators based on the recognition results of the image recognition unit, the risk parameters obtained by the risk judgment model, and past records. When the risk indicator is greater than the risk indicator threshold, the alarm unit sends an early warning signal to the task management module.

[0042] Specifically, the image recognition unit uses existing image recognition models (such as VGG, SSD, and U-Net) to identify the types of objects in the image, such as mountains, vegetation, and humans. Specifically, the risk assessment model (SVR, CNN, and XGBoost, etc.) is configured by those skilled in the art based on the tasks the drone is intended to perform. This risk assessment model is trained by those skilled in the art based on the tasks the drone is intended to perform. For example, if the goal is to analyze the risk of landslides in various locations, the identified content is mountains. By taking images of mountains, the model takes the image after image recognition as input and outputs risk parameters. The range of values ​​for the risk parameters is... The range is 0 to 5, with larger parameters indicating a higher probability of risk. To analyze the risk of landslides in various locations, the training set consists of images taken before landslides occurred at locations from various sources, as well as ordinary mountain images. Experts assign scores (0 to 5, with larger values ​​indicating a higher likelihood of landslides) to these training set images based on experience, thus obtaining the corresponding risk assessment model. Similarly, to analyze the risk of pest infestations, the identified content is trees, and the risk parameter also ranges from 0 to 5. Experts assign scores (0 to 5, with larger values ​​indicating more severe pest infestations) to the training set images containing trees with pests.

[0043] Specifically, the risk index threshold is set by those skilled in the art based on the severity of the disaster corresponding to different images in the training set. For example, if the analysis content is insect pests, images with a score greater than 3 in the training images are generally considered by staff to be serious, while images with a score less than or equal to 3 are generally considered by staff to be minor, then the threshold can be set to 3. As another example, if the analysis content is wildfires, images with a score greater than 4 in the training images are generally considered by staff to be prone to wildfires, while images with a score less than or equal to 4 are generally considered by staff to be unlikely to wildfires, then the threshold can be set to 4. The risk index threshold is the boundary value for the occurrence of an accident. When the risk index is greater than the risk index threshold, it is considered that the inspection location is more likely to have the corresponding accident, and vice versa.

[0044] A GIS-based UAV collaborative inspection and scheduling method, comprising the following steps:

[0045] S1, The task management module sends initial task information to the intelligent scheduling module;

[0046] S2, the intelligent scheduling module generates the inspection path of the inspection drone based on the initial task information and the information provided by the GIS data management module;

[0047] S3, the drone control module controls the inspection drone to perform inspections according to the generated inspection path, and the data acquisition module takes pictures of the target at the designated inspection location.

[0048] S4, the early warning module obtains risk indicators based on the data collected by the data acquisition module, and determines whether the risk indicators are greater than the risk indicator threshold. If not, the process ends; otherwise, the corresponding area of ​​the risk indicator is considered a high-risk area, and the next step is executed.

[0049] S5, the inspection drone continues to inspect along the original inspection path. The early warning module sends a signal to the task management module, which then sends emergency task information to the intelligent scheduling module. The intelligent scheduling module generates an emergency inspection route based on the high-risk areas obtained by the early warning module. The drone control module controls the emergency drone to reach the high-risk area according to the emergency inspection route. The emergency drone collects various data from the high-risk area and feeds it back to the user.

[0050] Specifically, by dispatching emergency drones to collect data from high-risk areas without altering the patrol routes of existing inspection drones, it is beneficial for these drones to continue patrolling other areas. When multiple high-risk areas exist, relying solely on a single inspection drone is insufficient to collect sufficient data from each area (such as soil samples). Utilizing multiple emergency drones to collect data from high-risk areas separately increases the amount of data that can be collected. The intervention of emergency drones also enhances the overall system's data collection and risk warning capabilities.

[0051] Specifically, the main function of the inspection drone is image capture. By capturing images of various inspection locations, it can be applied to various risk identifications, such as landslides and wildfires, and has high versatility. The main function of the emergency drone is to collect detailed data required for a specific risk assessment. For example, for landslides, it is necessary to collect corresponding soil samples; for pests, it is necessary to collect corresponding leaf samples; and for wildfires, it is necessary to detect the smoke concentration in the air.

[0052] Furthermore, the early warning module acquires risk indicators through the following steps:

[0053] S41, the image preprocessing unit enlarges and reduces noise in the images captured by the data acquisition module;

[0054] S42, the image recognition unit identifies the object type and size of each part in the preprocessed image;

[0055] S43, The risk assessment model obtains risk parameters based on the identified image;

[0056] S44, the calculation unit calculates the risk index based on the risk parameters, the recognition results of the image recognition unit, the risk parameters, and past records.

[0057] Specifically, the risk indicator can be calculated using the following formula:

[0058]

[0059] Wherein, FX is the risk indicator, CS is the risk parameter obtained by the risk assessment model, Q is the image accuracy parameter, F is the historical data parameter, used to characterize the reliability of the historical assessment results of the risk assessment model, and w1 is the balance coefficient, used to adjust the weight of the image accuracy parameter in the calculation. This weight is set by those skilled in the art according to the image accuracy required for the type of risk to be assessed, ranging from 0.3 to 0.7 (the common weight value range is 0 to 1, but this scheme sets the weight to 0.7 to 0.3 to avoid being too extreme). If image quality is very critical in risk assessment (such as high-resolution soil images being crucial in the judgment of landslides), then w1 is larger (e.g., w1 = 0.7), assigning a higher weight. Q is given a higher weight if the image quality has little impact on risk assessment (e.g., judging the risk of tree collapse is easy even if the image quality is poor); w2 is the floating range adjustment coefficient, used to control the uncertainty range of the risk indicator and reflect the reliability of the prediction. The value of this coefficient is 0.5 or 1. This coefficient can be set by a person skilled in the art according to the type of risk to be assessed. If the risk type being assessed is more harmful, such as wildfire, the coefficient is set to 1 to expand the floating range to cover extreme cases. If the risk type being assessed is more harmful, such as insect pests, the coefficient is set to 0.5 to reduce the floating range to provide a more accurate prediction. ε is to avoid the minimum value of 0 in the denominator, and its value is generally taken as 0.001.

[0060] e is a natural constant, ver is the averaging function, L is the standard deviation of the Laplacian gradient of the image (the larger the deviation, the better the image quality), which is obtained by the image recognition unit through OpenCV, l is the lower limit threshold of the gradient difference, which is set by those skilled in the art according to the required image accuracy, M is the number of pixels per row of the processed image, N is the number of pixels per column of the processed image, and XS a Let xs be the grayscale value of the a-th pixel. a is the convolutional smoothed pixel value corresponding to the a-th pixel, and NUM is the number of pixels in the image that involve unrecognizable object types.

[0061] num represents the total number of risk parameters obtained from past assessments of the risk assessment model. Those skilled in the art will periodically review and evaluate the risk parameters assessed by the risk assessment model. Reasonable risk parameters will be used as a new training set to train the model, thereby continuously improving the model's judgment ability. The preferred review frequency is once a week, but it can also be adjusted by those skilled in the art according to the actual application. right represents the number of risk parameters that the staff considers reasonable out of the num assessment results, and e is a natural constant.

[0062] The following is the procedure required to calculate risk indicators:

[0063]

[0064]

[0065] return round(fx_base,3),round(fx_lower,3),round(fx_upper,3)

[0066] def_calculate_q(self,image_path,l=30):

[0067] """

[0068] Calculate the image quality parameter Q

[0069] :param image_path: Image path

[0070] :paraml: Gradient difference lower limit threshold (default 30)

[0071] """

[0072] #1. Read the image and convert it to grayscale.

[0073] img=cv2.imread(image_path,cv2.IMREAD_GRAYSCALE)

[0074] ifimgis None:

[0075] raise ValueError("Unable to read image file")

[0076] m,n = img.shape

[0077] #2. Calculate the Laplace gradient

[0078] laplacian=cv2.Laplacian(img,cv2.CV_64F)

[0079] L = np.std(laplacian) # Gradient standard deviation

[0080] #3. Smoothing Processing (Gaussian Filter)

[0081] smoothed=cv2.GaussianBlur(img,(5,5),0)

[0082] #4. Calculate pixel differences

[0083] diff=np.abs(img.astype(float)-smoothed.astype(float))

[0084] avg_diff=np.sum(diff) / (m*n*255)

[0085] #5. Number of unrecognized pixels (assuming threshold processing)

[0086] _,binary=cv2.threshold(img,127,255,cv2.THRESH_BINARY)

[0087] num_unrecognized=np.sum(binary==0)

[0088] #6. Comprehensive Calculation of Q

[0089]

[0090] like Figure 4 As shown, Figure 4 This is a graph showing the relationship between historical data parameters, the total number of evaluation results (num), and the reasonable number of evaluation results (right).

[0091] The beneficial effects of this solution are: 1. By combining the recognition results of the image recognition unit, the judgment results of the risk judgment model, and past data to calculate risk indicators, it is beneficial to comprehensively measure regional risks from various factors, improve the accuracy of judgment, and enhance the intelligence level of the system.

[0092] 2. Two types of drones are used for the operation. By using inspection drones for inspection, it is beneficial to make a preliminary judgment on the risk of the area through images, which can be applied to most work tasks. When the risk is high, emergency drones are dispatched to collect detailed data. This allows staff to combine the collected data with the original image data to make a detailed judgment on the risk of the area and confirm the actual possibility of the risk occurring.

[0093] Example 2: This example should be understood as including all the features of any of the foregoing examples, and further improving upon them. It also includes a method for predicting future risks in various regions and adjusting inspection shifts based on these risks. This method is applicable when the risk index is greater than a risk index threshold and the user, after receiving data collected by the emergency drone, determines that no manual intervention in the region is temporarily necessary (i.e., the situation is not particularly serious). This determination refers to manual judgment. The method calculates the predicted risk change rate index for a given region using the following formula:

[0094]

[0095] Where YC is the predicted risk change rate index, used to measure the predicted change of risk indicators. The larger the index, the greater the predicted change. fx is the predicted risk index, and I is the number of data points collected by the emergency drone that require user judgment and analysis. i MIN is the parameter representing the degree of excess for the i-th data point. i MAX represents the lower limit of the recommended range for the degree parameter of the i-th data point. i CS represents the upper limit of the recommended range for the degree parameter of the i-th data point. i Let be the actual value of the i-th data point. The recommended numerical range is set by those skilled in the art based on experience, known constants, and existing experimental data (e.g., for sandy soil, according to the "Code for Geotechnical Investigation" (GB 50021), when the soil moisture content is greater than 30%, drainage and reinforcement measures are required. At this time, it is considered that there is a high risk of landslides, with an upper limit of 30% and a lower limit of 0%). FX is the currently acquired risk indicator, X is the number of previously acquired risk indicators for this area, and day x Let FX be the number of days between the x-th historically acquired risk indicator and the current value, where e is a natural constant. x This is the xth risk indicator obtained in the past.

[0096] Specifically, when the predicted risk change rate is greater than or equal to 10% (set by those skilled in the art according to the required accuracy; when higher accuracy is required, this value can be appropriately reduced, and when lower accuracy is required, this value can be appropriately increased), it is considered that the risk is likely to become more serious, and the inspection shifts of the inspection drones need to be increased to cope with emergencies. When the predicted risk change rate is less than 10% and greater than or equal to 0, it is considered that the risk is less likely to become more serious, and there is no need to adjust the inspection shifts of the drones. When the predicted risk change rate is less than 0, it is considered that the risk is likely to decrease, and there is no need to adjust the inspection shifts of the drones.

[0097] The beneficial effects of this embodiment are as follows: By introducing a predicted risk change rate index, this solution enables the system to dynamically predict future risk trends based on historical risk data and multi-dimensional data fed back by emergency drones. When the predicted change rate exceeds a threshold (e.g., 10%), the number of inspection shifts is automatically increased, achieving proactive risk management and avoiding the time lag problem of responding only after a disaster occurs.

[0098] The above-disclosed content is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Therefore, all equivalent technical changes made based on the description and drawings of the present invention are included within the scope of protection of the present invention. Furthermore, the elements therein can be updated as technology develops. The above units are merely examples, and those skilled in the art can adopt corresponding units according to actual needs when implementing this solution.

Claims

1. A GIS-based unmanned aerial vehicle cooperative inspection scheduling system, characterized in that, The task management module is used for issuing initial task information or emergency task information to the intelligent scheduling module, the GIS data management module is used for storing relevant data of each inspection position, the intelligent scheduling module generates an inspection path of an inspection unmanned aerial vehicle according to information provided by the GIS data management module and the initial task information or generates an emergency inspection route of an emergency unmanned aerial vehicle according to the emergency task information, the unmanned aerial vehicle control module is used for controlling the inspection unmanned aerial vehicle according to the inspection path generated by the intelligent scheduling module or controlling the emergency unmanned aerial vehicle according to the emergency inspection route, the data acquisition module is used for shooting various images required by a task in the flight process of the inspection unmanned aerial vehicle, and the early warning module is used for obtaining a risk index of each inspection position according to acquisition data of the data acquisition module and judging whether an early warning signal needs to be sent to the task management module according to the risk index. The early warning signal is used for the task management module to select and issue emergency task information to the intelligent scheduling module, a risk index threshold is a boundary value of an accident, when the risk index is greater than the risk index threshold and a user judges that manual intervention is not required for a region after receiving each item of data collected by the emergency unmanned aerial vehicle, a predicted risk change rate index of each region is calculated, and whether the inspection shift of the unmanned aerial vehicle needs to be adjusted is judged according to the size of the predicted risk change rate index. 2.The GIS-based unmanned aerial vehicle cooperative inspection scheduling system according to claim 1, characterized in that, The task management module includes an input unit, a display unit and a task configuration unit, the input unit is used for receiving a user instruction, the display unit is used for an analysis result of the early warning module, and the task configuration unit is used for setting contents including but not limited to a target region of an inspection task, a task type and a time requirement. 3.The GIS-based unmanned aerial vehicle cooperative inspection scheduling system of claim 1, wherein, The GIS data management module includes a geographic information storage unit and a network connection unit, the geographic information storage unit is used for storing remote sensing images and topographic maps, and the network connection unit is used for receiving real-time meteorological information and traffic information third-party data and marking and updating the remote sensing images and the topographic maps according to the third-party data. 4.The GIS-based unmanned aerial vehicle cooperative inspection scheduling system of claim 1, wherein, The intelligent scheduling module includes an unmanned aerial vehicle warehouse, an unmanned aerial vehicle resource management unit and a path planning unit, the unmanned aerial vehicle warehouse is used for storing unmanned aerial vehicles, the unmanned aerial vehicle resource management unit is used for managing the power of each unmanned aerial vehicle and the position of the unmanned aerial vehicle in the unmanned aerial vehicle warehouse, and the path planning unit is used for generating an inspection path of each unmanned aerial vehicle according to information stored in the GIS data management module and initial task information. 5.The GIS-based unmanned aerial vehicle cooperative inspection scheduling system of claim 4, wherein, The unmanned aerial vehicle control module comprises an instruction analysis unit, a movement control unit and a posture adjustment unit, the instruction analysis unit is used for analyzing the training path acquired by the path planning unit and sending a corresponding control signal to the movement control unit and the posture adjustment unit, the movement control unit is used for controlling the unmanned aerial vehicle to move according to the control signal, and the posture adjustment unit is used for controlling the unmanned aerial vehicle to take pictures at the specified inspection position according to the control signal, and the data acquisition module comprises a camera, the camera is arranged on the unmanned aerial vehicle, and the camera is used for taking pictures of the inspection position. 6.The GIS-based unmanned aerial vehicle cooperative inspection scheduling system of claim 1, wherein, The early warning module comprises an image preprocessing unit, an image recognition unit, a risk judgment model, a calculation unit and an alarm unit, the image preprocessing unit is used for magnifying and denoising the taken pictures, the image recognition unit is used for identifying the object types and sizes of each part in the preprocessed pictures, the risk judgment model is used for acquiring corresponding risk parameters according to the identified pictures, the calculation unit is used for calculating a risk index according to the identification result of the image recognition unit, the risk parameters acquired by the risk judgment model and the past records, and when the risk index is greater than a risk index threshold, the alarm unit sends a prewarning signal to the task management module. 7.A GIS-based unmanned aerial vehicle cooperative inspection scheduling method, applied to the GIS-based unmanned aerial vehicle cooperative inspection scheduling system of claim 1, characterized in that, The method comprises the following steps: S1, the task management module issues initial task information to the intelligent scheduling module; S2, the intelligent scheduling module generates an inspection path of the inspection unmanned aerial vehicle according to the initial task information and information provided by the GIS data management module; S3, the unmanned aerial vehicle control module controls the inspection unmanned aerial vehicle to perform inspection according to the generated inspection path, and the data acquisition module takes target pictures at the specified inspection position; S4, the early warning module acquires a risk index according to the acquisition data of the data acquisition module, judges whether the risk index is greater than a risk index threshold, if not, ends, otherwise, considers that the corresponding area of the risk index is a high-risk area, and performs the next step; S5, the inspection unmanned aerial vehicle continues to perform inspection according to the original inspection path, the early warning module sends a signal to the task management module, the task management module issues emergency task information to the intelligent scheduling module, the intelligent scheduling module generates an emergency inspection route according to the high-risk area acquired by the early warning module, the unmanned aerial vehicle control module controls the emergency unmanned aerial vehicle to arrive at the high-risk area according to the emergency inspection path, and the emergency unmanned aerial vehicle collects various data of the high-risk area and feeds back to the user. 8.The GIS-based cooperative inspection scheduling method of unmanned aerial vehicles according to claim 7, wherein, The early warning module acquires the risk index, which comprises the following steps: S41, the image preprocessing unit magnifies and denoises the taken pictures of the data acquisition module; S42, the image recognition unit identifies the object types and sizes of each part in the preprocessed pictures; S43, the risk judgment model acquires risk parameters according to the identified pictures; S44, the calculation unit calculates a risk index according to the risk parameters, the identification result of the image recognition unit, the risk parameters and the past records.

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