Power grid indoor patrol method and device based on unmanned aerial vehicle, and electronic device

Abstract

The invention discloses a power grid indoor patrol method and device based on an unmanned aerial vehicle, and an electronic device, and relates to the technical field of unmanned aerial vehicle indoor patrol. The method comprises the following steps: in response to a received inspection request of a target power grid room, determining an inspection mode based on an inspection demand of the target power grid room; generating a target patrol route based on the parking position of the unmanned aerial vehicle and the patrol request; and controlling the unmanned aerial vehicle to inspect based on the inspection mode and the target inspection route. According to the method and the device, the technical problem that the continuity and the efficiency of the patrol work are influenced because the unmanned aerial vehicle possibly cannot complete a predetermined patrol task due to insufficient energy and cannot re-plan a patrol path or arrange on-line charging when necessary because of lack of detailed evaluation and management of energy parameters of the unmanned aerial vehicle in related technologies is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of indoor patrol using unmanned aerial vehicles (UAVs), and in particular to an indoor patrol method, device and electronic device for power grids based on unmanned aerial vehicles (UAVs). Background Art

[0002] Power grid facilities are often densely distributed in indoor environments such as conversion stations and distribution rooms, with complex responsibility areas. Traditional manual inspections face inconveniences and safety issues. Indoor environments are usually small, with insufficient light and many obstacles. The use of drones can effectively solve these problems and improve inspection efficiency and safety.

[0003] In the existing technical solutions, the drone may not be able to make the best decision according to the actual situation during the patrol process, such as adjusting the patrol frequency, planning obstacle avoidance paths or responding to emergencies. Due to the lack of real-time analysis of the safety distance during the drone patrol process, the drone may be unable to identify and avoid obstacles in time during flight, resulting in collision accidents. Due to the lack of a safety distance analysis module, the drone will not be able to dynamically adjust its flight trajectory to ensure safety, especially when patrolling in complex environments, which will significantly increase the risk of accidents. In addition, the existing technology lacks detailed evaluation and management of drone energy parameters, which may cause the drone to be unable to complete the scheduled patrol mission due to insufficient energy. As a result, the system cannot accurately predict the drone's endurance, nor can it re-plan the patrol path or arrange online charging when necessary, which in turn affects the continuity and efficiency of the patrol work.

[0004] No effective solution has been proposed yet for the above technical problems. Summary of the invention

[0005] The embodiments of the present invention provide a method, device and electronic device for indoor inspection of power grids based on drones, so as to at least solve the technical problem that the related technologies lack detailed evaluation and management of drone energy parameters, resulting in the drone being unable to complete the scheduled inspection tasks due to insufficient energy, and unable to re-plan the inspection route or arrange online charging when necessary, thereby affecting the continuity and efficiency of the inspection work.

[0006] According to one embodiment of the present invention, a method for indoor patrol of a power grid based on a drone is provided, the method comprising: in response to receiving a patrol request of a target power grid room, determining a patrol mode based on the patrol demand of the target power grid room, wherein the patrol request is generated based on a trigger operation of a patrol button in the target power grid room, or is generated based on a preset case occurring in the target power grid room; generating a target patrol route based on a parking position of the drone and the patrol request, wherein the target patrol route is a patrol route from a parking position to a button position of a patrol button, or is a patrol route from a parking position to a case occurrence position of a preset case; and controlling the drone to patrol based on the patrol mode and the target patrol route.

[0007] Optionally, generating a target patrol route based on the parking position and patrol request of the drone includes: generating a patrol start point and a patrol end point based on a trigger operation of a patrol button in the target power grid room in response to the patrol request, and generating a patrol start point and a patrol end point based on the three-dimensional model, the parking position and the button position of the target power grid room; or, generating a patrol start point and a patrol end point based on a preset case occurring in the target power grid room in response to the patrol request, and generating a patrol start point and a patrol end point based on the three-dimensional model, the parking position and the location where the case occurred; generating the target patrol route based on the patrol start point and the patrol end point.

[0008] Optionally, controlling the UAV to patrol based on the patrol mode and target patrol route includes: obtaining the flight parameters and environmental influencing factors of the UAV during the patrol process; determining the safe distance of the UAV during the patrol process based on the flight parameters and environmental influencing factors; and controlling the UAV to patrol based on the patrol mode, target patrol route and safe distance.

[0009] Optionally, the drone-based indoor power grid patrol method also includes: obtaining the number of abnormal patrols of the drone within a preset time period; determining the patrol frequency of the drone based on the relationship between the number of abnormal patrols and a preset safety patrol number threshold; and controlling the drone to patrol based on the patrol frequency.

[0010] Optionally, the drone-based indoor power grid inspection method also includes: during the drone inspection process, in response to the drone detecting the occurrence of a preset situation, video recording and photo taking of the location where the preset situation occurs, and recording the location coordinates of the preset situation.

[0011] Optionally, the drone-based indoor power grid inspection method also includes: obtaining energy parameters of the drone during the drone inspection process; and controlling the drone to inspect based on the energy parameters and a preset standard energy evaluation index.

[0012] According to one embodiment of the present invention, there is also provided a power grid indoor patrol device based on a drone, the device comprising: a determination module, for determining a patrol mode based on the patrol demand of the target power grid room in response to receiving a patrol request of the target power grid room, wherein the patrol request is generated based on a trigger operation of a patrol button in the target power grid room, or is generated based on a preset case occurring in the target power grid room; a generation module, for generating a target patrol route based on the parking position of the drone and the patrol request, wherein the target patrol route is a patrol route from the parking position to the button position of the patrol button, or is a patrol route from the parking position to the case occurrence location of a preset case; a control module, for controlling the drone to patrol based on the patrol mode and the target patrol route.

[0013] Optionally, the generation module is also used to generate, in response to a patrol request, based on a triggering operation of a patrol button in the target power grid room, a patrol starting point and a patrol end point based on the three-dimensional model, parking position and button position of the target power grid room; or, in response to a patrol request, based on a preset case occurring in the target power grid room, a patrol starting point and a patrol end point are generated based on the three-dimensional model, parking position and the location where the case occurred; and a target patrol route is generated based on the patrol starting point and patrol end point.

[0014] Optionally, the control module is also used to obtain the flight parameters and environmental influencing factors of the UAV during the patrol process; determine the safe distance of the UAV during the patrol process based on the flight parameters and environmental influencing factors; and control the UAV to patrol based on the patrol mode, target patrol route and safe distance.

[0015] According to one embodiment of the present invention, a computer-readable storage medium is further provided, in which a computer program is stored, wherein the computer program is configured to execute any of the above-mentioned drone-based indoor power grid inspection methods when running on a computer or a processor.

[0016] According to one embodiment of the present invention, there is also provided an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute any of the above-mentioned drone-based indoor power grid patrol methods.

[0017] According to one embodiment of the present invention, a computer program product is also provided, including a computer program, which implements any of the above-mentioned methods for indoor inspection of power grids based on drones when executed by a processor.

[0018] In an embodiment of the present invention, in response to receiving a patrol request from a target power grid room, a patrol mode is determined based on the patrol demand of the target power grid room, wherein the patrol request is generated based on a trigger operation of a patrol button in the target power grid room, or is generated based on a preset case occurring in the target power grid room; a target patrol route is generated based on the parking position of the drone and the patrol request, wherein the target patrol route is a patrol route from the parking position to the button position of the patrol button, or is a patrol route from the parking position to the case occurrence position of the preset case; and the drone is controlled to patrol based on the patrol mode and the target patrol route, so as to achieve the purpose of accurately determining the patrol mode of the drone, obtaining the flight parameters of the drone in real time, and calculating the minimum safety distance as well as the battery temperature, the remaining battery power and the average power consumption, thereby achieving the technical effect of improving the system's response capability to emergencies, ensuring the smoothness of the patrol process, and improving the continuity and stability of the patrol, thereby solving the technical problem that the drone may be unable to complete the scheduled patrol task due to insufficient energy, and cannot replan the patrol path or arrange online charging when necessary, thereby affecting the continuity and efficiency of the patrol work. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0020] Figure 1 is a flow chart of a method for indoor inspection of power grids based on a drone according to one embodiment of the present invention;

[0021] Figure 2 is a schematic diagram of system structure connection according to one embodiment of the present invention;

[0022] Figure 3 is a schematic diagram of the implementation steps according to one embodiment of the present invention;

[0023] Figure 4 It is a structural block diagram of a power grid indoor patrol device based on a drone according to one embodiment of the present invention. DETAILED DESCRIPTION

[0024] To facilitate understanding, some descriptions of concepts related to the embodiments of the present invention are exemplarily provided for reference.

[0025] As shown below:

[0026] Intelligent UAV patrol system: refers to a UAV system that integrates artificial intelligence, data processing and real-time monitoring technology, which is used to automatically patrol and inspect specific areas, such as indoor facilities of power grids. The system analyzes environmental conditions and task requirements, intelligently adjusts patrol modes and frequencies, and ensures that patrol tasks are completed efficiently and safely.

[0027] 3D coordinate autonomous navigation technology: A drone navigation technology that uses 3D modeling and coordinate systems to enable drones to accurately locate and plan paths in complex environments. By analyzing obstacles and environmental parameters in real time, drones can autonomously avoid obstacles, ensuring safety and accuracy when performing patrol missions.

[0028] Energy assessment and management module: An important component of the drone patrol system, used to monitor the drone's energy status in real time, including battery temperature, remaining power, and average power consumption. By evaluating these parameters, the system can predict the drone's endurance, plan a safe return path or adjust the patrol strategy in a timely manner, and avoid interrupting the mission due to insufficient energy.

[0029] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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 should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In the description of this embodiment, unless otherwise specified, the meaning of "multiple" is two or more. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] According to one embodiment of the present invention, an embodiment of a method for indoor inspection of a power grid based on a drone is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in an order different from that shown here.

[0032] The method embodiment can be executed in an electronic device including a memory and a processor, a similar control device or a system. Taking an electronic device as an example, the electronic device may include one or more processors and a memory for storing data. Optionally, the electronic device may also include a communication device and a display device for communication functions. It can be understood by a person of ordinary skill in the art that the above structural description is only illustrative and does not limit the structure of the above electronic device. For example, the electronic device may also include more or fewer components than the above structural description, or have a configuration different from the above structural description.

[0033] The processor may include one or more processing units. For example, the processor may include a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processing (DSP) chip, a microcontroller unit (MCU), a field-programmable gate array (FPGA), a neural network processor (NPU), a tensor processing unit (TPU), an artificial intelligence (AI) type processor, and the like. Among them, different processing units may be independent components or integrated into one or more processors. In some instances, the electronic device may also include one or more processors.

[0034] The memory can be used to store computer programs, for example, to store the computer program corresponding to the indoor patrol method of the power grid based on the drone in the embodiment of the present invention. The processor implements the indoor patrol method of the power grid based on the drone by running the computer program stored in the memory. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0035] The communication device is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the communication device includes a network adapter (network interface controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the communication device can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0036] The display device may be, for example, a touch screen type liquid crystal display (LCD) and a touch display (also referred to as a "touch screen" or "touch display screen"). The liquid crystal display may enable a user to interact with a user interface of a mobile terminal. In some embodiments, the mobile terminal has a graphical user interface (GUI), and a user may interact with the GUI by finger contact and / or gestures on a touch-sensitive surface, wherein the human-computer interaction functions here may optionally include the following interactions: creating web pages, drawing, word processing, making electronic documents, games, video conferencing, instant messaging, sending and receiving emails, call interfaces, playing digital videos, playing digital music and / or web browsing, etc. The executable instructions for executing the above human-computer interaction functions are configured / stored in a computer program product or a readable storage medium executable by one or more processors.

[0037] In this embodiment, a method for indoor inspection of a power grid based on a drone running on an electronic device is provided. Figure 1 is a flow chart of a method for indoor inspection of power grid based on drone according to one embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0038] Step S101, in response to receiving a patrol request from a target power grid room, determining a patrol mode based on the patrol demand of the target power grid room, wherein the patrol request is generated based on a trigger operation of a patrol button in the target power grid room, or based on a preset case occurring in the target power grid room;

[0039] In an embodiment of the present invention, the patrol request of the target power grid room can be understood as a request issued by the target power grid room to request a drone to conduct a patrol. Exemplarily, the patrol request of the target power grid room can be generated based on a patrol button trigger or based on a preset case occurring in the target power grid room. In the target power grid room, physical or virtual patrol buttons are provided. When the staff finds a situation that requires patrol or inspection, they can trigger these buttons to send a patrol request to the system, or they can be automatically triggered regularly to perform a predetermined patrol task. Preset cases refer to events that may occur in the target power grid room and require immediate patrol and response, such as equipment failures, fire alarms, or safety violations. When the system detects such events, it automatically triggers a patrol request and starts the emergency patrol mode. This is not limited here.

[0040] The inspection demand of the target power grid room can be understood as the specific demand of the target power grid room for drone inspection under specific time or conditions. For example, it is necessary to check whether the equipment is operating normally, whether there is water accumulation in the cable trench, whether the safety protection facilities are complete, etc. The inspection demand may change over time. For example, more frequent inspections may be required during equipment maintenance to ensure the stable operation of the equipment, which is not limited here.

[0041] The patrol mode can be understood as the operation mode adopted by the drone when performing patrol tasks, including: scheduled patrol: patrol at preset time intervals for daily inspection and maintenance. On-demand patrol: patrol according to the real-time needs of the target power grid room or the requirements of the staff, and flexibly respond to specific inspection needs. Emergency patrol: When an emergency occurs in the target power grid room, such as fire, equipment failure, etc., the system immediately starts the drone for patrol to quickly assess the on-site situation and take corresponding measures. Exemplarily, each request patrol button is set in the designated power grid room. When the staff triggers a request patrol button, the control center plans the patrol route from the drone parking place to the request patrol button, and sends the planned patrol route to the drone platform, and then conducts on-demand patrol. When an emergency occurs in the designated power grid room, the control center receives the alarm information of the designated power grid room, and then plans the patrol route from the drone parking place to the emergency situation, and sends the planned patrol route to the drone platform, thereby realizing emergency patrol. After the designated drone performs on-demand patrols and emergency patrols, the time point corresponding to the designated drone's current scheduled patrol time interval is used as the starting time point for the scheduled patrol, so that the designated drone can perform subsequent scheduled patrols. There is no restriction here.

[0042] Preset cases can be understood as abnormal situations or emergencies preset in the target power grid room. When these events are triggered, drones are required to patrol and handle them immediately. Preset cases include but are not limited to: Equipment failure: such as automatic switch tripping, transformer abnormality, etc. Fire alarm: When smoke or fire is detected, the system automatically triggers the drone to conduct emergency patrols to assess the fire and assist in extinguishing the fire. Safety violations: such as unauthorized entry, illegal stacking of items, etc., which may affect the safe operation of the power grid room.

[0043] In response to receiving a patrol request from a target power grid room, determining a patrol mode based on the patrol requirements of the target power grid room can be understood as the system intelligently selecting or adjusting the patrol operation mode of the drone based on the patrol request signal received from a specific power grid room and the current conditions and requirements of the power grid room.

[0044] In an embodiment of the present invention, the patrol strategy is automatically adjusted according to the real-time needs and environmental changes of the power grid room, thereby improving the efficiency, safety and response speed of the patrol. For example, when a device failure is detected, the system can immediately initiate an emergency patrol, quickly locate the problem and record detailed information to help staff deal with it in a timely manner and avoid greater losses. During routine maintenance, scheduled patrols can ensure continuous monitoring of equipment, detect potential problems in a timely manner, and reduce the occurrence of failures. Through intelligent patrol mode selection, effective management and maintenance of power grid indoor facilities can be achieved.

[0045] Step S102, generating a target patrol route based on the parking position of the drone and the patrol request, wherein the target patrol route is a patrol route from the parking position to the button position of the patrol button, or a patrol route from the parking position to the case occurrence position of a preset case;

[0046] In the embodiments of the present invention, the parking position of the drone can be understood as the default position of the drone when it is not performing a mission, which can be a charging station, a maintenance area, or a safe standby area. The selection of the parking position usually takes into account the energy supply, safe storage, and rapid deployment requirements of the drone, which are not limited here.

[0047] The target patrol route can be understood as the flight path of the drone from its parking location to a specific destination. The specific destination depends on the nature of the patrol request. It can be the location of a specific patrol button in the power grid room, or the location where a preset case occurs, such as the specific location of equipment failure, the starting point of a fire, etc., which are not limited here.

[0048] Generating a target patrol route based on the parking position and patrol request of the UAV can be understood as the system intelligently planning a flight path from the parking position to the designated patrol destination based on the current parking position information of the UAV and the specific patrol request.

[0049] In the embodiment of the present invention, through intelligent analysis and planning, the system can provide accurate and safe flight guidance for the UAV, whether it is daily on-demand inspection or rapid response in emergency situations, it can effectively improve the efficiency and safety of UAV inspections and ensure the normal operation and maintenance of indoor facilities of the power grid.

[0050] Step S103, controlling the UAV to patrol based on the patrol mode and the target patrol route.

[0051] In the embodiment of the present invention, controlling the drone to patrol based on the patrol mode and the target patrol route can be understood as guiding and managing the drone's patrol activities through a pre-set drone operation mode and a precisely planned flight path to ensure that it completes the patrol mission inside the power grid efficiently and safely.

[0052] In the embodiment of the present invention, by flexibly adjusting the patrol mode and accurately planning the flight route, the UAV can adapt to various patrol needs while ensuring the efficiency and safety of the patrol, effectively improving the management level and response efficiency of the power grid room facilities.

[0053] In an embodiment of the present invention, the patrol strategy is automatically adjusted according to the real-time needs and environmental changes of the power grid room, thereby improving the efficiency, safety and response speed of the patrol. For example, when a device failure is detected, the system can immediately start an emergency patrol, quickly locate the problem and record detailed information, helping staff to deal with it in time and avoid greater losses. During routine maintenance, scheduled patrols can ensure continuous monitoring of equipment, timely discover potential problems, and reduce the occurrence of failures. Through intelligent patrol mode selection, effective management and maintenance of power grid indoor facilities can be achieved. Through intelligent analysis and planning, the system can provide accurate and safe flight guidance for drones, whether in daily on-demand patrols or rapid response in emergency situations, it can effectively improve the efficiency and safety of drone patrols and ensure the normal operation and maintenance of power grid indoor facilities. By flexibly adjusting the patrol mode and accurately planning the flight route, the drone can adapt to various patrol needs while ensuring the efficiency and safety of the patrol, effectively improving the management level and response efficiency of the power grid room facilities.

[0054] Through the above steps, in response to receiving a patrol request from the target power grid room, a patrol mode is determined based on the patrol demand of the target power grid room, wherein the patrol request is generated based on the trigger operation of the patrol button in the target power grid room, or is generated based on the occurrence of a preset case in the target power grid room; a target patrol route is generated based on the parking position of the drone and the patrol request, wherein the target patrol route is a patrol route from the parking position to the button position of the patrol button, or is a patrol route from the parking position to the occurrence position of the preset case; the drone is controlled to patrol based on the patrol mode and the target patrol route, so as to achieve the purpose of accurately determining the patrol mode of the drone, obtaining the flight parameters of the drone in real time, and calculating the minimum safety distance as well as the battery temperature, the remaining battery power and the average power consumption, thereby achieving the technical effect of improving the system's response capability to emergencies, ensuring the smoothness of the patrol process, and improving the continuity and stability of the patrol, thereby solving the technical problem that the drone may be unable to complete the scheduled patrol task due to insufficient energy, and cannot replan the patrol path or arrange online charging when necessary, thereby affecting the continuity and efficiency of the patrol work.

[0055] Optionally, in step S102, generating a target patrol route based on the parking position of the drone and the patrol request may include the following execution steps:

[0056] In response to a patrol request generated based on a trigger operation of a patrol button in a target power grid room, a patrol start point and a patrol end point are generated based on a three-dimensional model of the target power grid room, a parking position and a button position; or,

[0057] In response to a patrol request, a patrol start point and a patrol end point are generated based on a preset case occurring in a target power grid room, and based on a three-dimensional model, a parking location, and a location where the case occurred;

[0058] Generate a target tour route based on the tour start point and tour end point.

[0059] In the embodiment of the present invention, the three-dimensional model of the target power grid room can be understood as being created by three-dimensional modeling technology, and a comprehensive and accurate digital representation of the indoor spatial structure, equipment layout and environmental characteristics of the target power grid room is made, and the three-dimensional model of the target power grid room is modeled in a one-to-one ratio according to the size and layout of the actual power grid room. Exemplarily, the three-dimensional model is not only used for real-time path planning and obstacle avoidance of drones, but also provides a visualization tool for facility management and maintenance of the power grid room, which helps staff to more intuitively understand the internal conditions of the power grid room and conduct effective management and decision-making, which is not limited here.

[0060] In response to the patrol request, the patrol start point and patrol end point are generated based on the trigger operation of the patrol button in the target power grid room. It can be understood that when a specific area or equipment needs to be inspected, the staff or the automatic monitoring system can trigger the corresponding patrol button, thereby generating a patrol request. After receiving the patrol request, the system will use the pre-built three-dimensional model of the power grid room to determine the parking position of the drone and the button position that needs to be patrolled. In the three-dimensional model, the parking position is identified as the starting point of the drone, and the triggered patrol button position is regarded as the end point. The system will plan a safe and feasible flight path based on the starting point and end point, combined with the distribution of obstacles in the three-dimensional model, to ensure that the drone can fly smoothly from the parking position to the button position and complete the patrol task, which is not restricted here.

[0061] In response to the patrol request, the patrol start point and patrol end point are generated based on the preset case in the target power grid room, and the three-dimensional model, parking position and the location where the case occurred. It can be understood that the system continuously monitors various key indicators and equipment status in the target power grid room, and once a preset abnormal case is detected, such as equipment failure, safety alarm, environmental abnormality, etc., a patrol request is immediately generated. The system will call the pre-established three-dimensional model data of the power grid room to provide a basis for the path planning of the drone. The patrol start point is the current parking position of the drone, and the patrol end point is the specific location where the case occurred. According to the location information of the preset case, combined with the obstacle distribution and safety distance requirements in the three-dimensional model, the system plans the optimal flight route from the parking position of the drone to the location where the case occurred, which is not restricted here.

[0062] Generating a target patrol route based on the patrol start point and patrol end point can be understood as follows: first, the system needs to clearly identify the current parking position of the drone as the starting point, and the patrol position determined based on the patrol request (such as patrol button trigger or preset case occurrence) as the end point. Using the pre-built three-dimensional model, the system can fully understand the spatial layout, equipment distribution, obstacle location, and flight restrictions in the power grid room, and use an optimized path planning algorithm to calculate the optimal flight path from the start point to the end point, which is not restricted here.

[0063] In the embodiment of the present invention, each patrol request button is set in the designated power grid room. When a staff member triggers a patrol request button, the control center plans a patrol route from the drone parking place to the patrol request button, and sends the planned patrol route to the drone platform, and then conducts patrol on demand. When an emergency occurs in the designated power grid room, the control center receives the alarm information of the designated power grid room, and then plans a patrol route from the drone parking place to the emergency place, and sends the planned patrol route to the drone platform, thereby realizing emergency patrol. The power grid indoor patrol system based on engineering drones can intelligently and efficiently generate and execute patrol tasks, ensuring that drones can safely and quickly reach the patrol destination in the complex and changeable power grid indoor environment, improving the accuracy and efficiency of patrols, and reducing potential safety risks.

[0064] Optionally, in step S103, controlling the drone to patrol based on the patrol mode and the target patrol route may include the following execution steps:

[0065] Obtain the flight parameters and environmental influencing factors of the UAV during the patrol process;

[0066] Determine the safe distance during the drone patrol based on flight parameters and environmental factors;

[0067] Control the drone to patrol based on patrol mode, target patrol route and safety distance.

[0068] In the embodiment of the present invention, the flight parameters can be understood as indicators used to describe the state and capability of the drone during flight. For example, the flight parameters include the drone diameter D, the drone flight speed v, the drone reaction time t, the drone acceleration a, the sensor accuracy sen, and the environmental impact factor k, which are not limited here.

[0069] Environmental factors can be understood as external conditions that may be encountered during the drone patrol process. These factors affect the flight safety and efficiency of the drone. For example, the main environmental factors include: ambient temperature T1, wind speed V f The number of obstacles C corresponding to the flight altitude of the drone is not limited here.

[0070] The safety distance can be understood as the minimum distance interval set during the drone patrol process to avoid collision with obstacles or other safety risks. The calculation of the safety distance is based on the flight parameters of the drone and the current environmental factors to ensure that the drone can maintain a sufficient distance from any obstacles when performing tasks to prevent collision or adverse effects of environmental factors. For example, the minimum safety distance can be expressed as D safe , no restriction is given here.

[0071] Obtaining the flight parameters and environmental factors of the drone during the patrol process can be understood as obtaining the drone diameter and drone reaction time corresponding to the specified drone from the control center, obtaining the sensor accuracy from the infrared ultrasonic sensor specification manual according to the infrared ultrasonic sensor equipped by the specified drone, and calculating the environmental impact factor k corresponding to the current environment of the specified grid room according to the environmental impact factors that affect the flight of the specified drone in the specified grid room, and obtaining the flight speed and acceleration corresponding to the current specified drone through the speed sensor equipped in the specified drone. Environmental factors include ambient temperature, wind speed, and the number of obstacles corresponding to the drone's flight altitude, which are recorded as T1 and V respectively. f and C, by calculating the formula: k = T1*π1+V f *π2+C*π3, and the environmental impact factor k corresponding to the current environment of the designated power grid room is obtained, where π1, π2, and π3 are the weight factors corresponding to the ambient temperature, the weight factor corresponding to the wind speed, and the weight factor corresponding to the number of obstacles corresponding to the flight altitude of the UAV, respectively. The number of obstacles corresponding to the ambient temperature, wind speed, and flight altitude of the UAV can be detected by using temperature sensors, wind speed sensors, and the camera device in the designated power grid room in combination with image recognition technology, which are not restricted here.

[0072] The safe distance determined by the flight parameters and environmental factors during the UAV patrol process can be understood as: safe =D+D t +(t*v+sen)*k, get the minimum safety distance D corresponding to the specified UAV during the patrol process safe , where D t It is expressed as the safety distance corresponding to the specified drone acceleration, calculated by Get the safety distance D corresponding to the specified drone acceleration t , no restriction is given here.

[0073] Controlling the drone to patrol based on patrol mode, target patrol route and safety distance can be understood as using 3D modeling technology to perform 3D modeling on the designated power grid room, thereby obtaining a 3D model of the designated power grid room, setting a 3D coordinate axis in the 3D model of the designated power grid room, and obtaining the patrol path of each designated drone according to the patrol starting point and patrol end point of the designated drone before the designated drone patrols according to a certain patrol mode. When the drone platform receives the patrol task sent by the control center, the patrol task includes patrol mode, flight parameters and patrol path, and starts the designated drone to patrol. When the designated drone encounters an obstacle during the patrol process, the distance between the designated drone and the obstacle is detected by an infrared ultrasonic sensor. When the distance between the designated drone and the obstacle is less than or equal to the minimum safety distance, the designated drone stops moving forward, first maintains the current position in the air, and then moves to the left, right, upward, backward or downward according to the set avoidance speed. After the designated drone successfully avoids the obstacle, it returns to the original designated drone patrol path according to the set avoidance speed, thereby completing the patrol of the designated drone in the designated power grid room, which is not restricted here.

[0074] In the embodiment of the present invention, by acquiring flight parameters and environmental influencing factors in real time, the system can dynamically calculate the safety distance to ensure the flight safety of the drone during the patrol process. Based on the safety distance, the system controls the drone to fly safely according to the planned patrol mode and target route, and flexibly adjusts the flight path to deal with possible emergencies, such as the temporary appearance of obstacles or changes in the energy status of the drone. As a result, the drone can complete the patrol mission efficiently and safely, while ensuring the accuracy and completeness of data collection.

[0075] Optionally, the indoor inspection method for power grid based on drone also includes the following execution steps:

[0076] Get the number of abnormal patrols of the drone within the preset time period;

[0077] The patrol frequency of the drone is determined based on the relationship between the number of abnormal patrols and the preset threshold of the number of safe patrols;

[0078] Control the drone to patrol based on the patrol frequency.

[0079] In the embodiment of the present invention, the preset time period can be understood to refer to a fixed time interval set for evaluating the number of abnormal patrols of the drone, such as one day, one week or one month. The selection of this time period is usually based on the operation cycle of the power grid room, the frequency of equipment inspection and historical data analysis, with the purpose of monitoring abnormal conditions within a specific period, so as to evaluate the safety status of the power grid room and the health status of the equipment, which is not limited here.

[0080] The number of abnormal patrols can be understood as the number of additional patrols that the drone needs to perform within a preset time period due to detection of preset cases such as equipment failure, security alarms, environmental abnormalities, etc., which are not restricted here.

[0081] The preset safety patrol frequency threshold can be understood as a standard value set based on the historical data of the power grid room and the routine maintenance requirements of the equipment. It is used to determine whether abnormal patrol situations are frequent enough to require action. It is not restricted here.

[0082] The patrol frequency of the drone can be understood as the number of times the drone performs patrol tasks in a unit of time. For example, the patrol frequency can be 2 times per hour, 4 times per day, or 10 times per week, etc., which is not limited here.

[0083] Obtaining the number of abnormal patrols of the drone within a preset time period can be understood as the system counting and recording the number of additional patrol tasks performed by the drone within a specific time period due to the detection of abnormal conditions indoors in the power grid.

[0084] Determining the patrol frequency of the drone based on the relationship between the number of abnormal patrols and the preset safety patrol number threshold can be understood as optimizing the patrol plan of the drone based on the frequency of abnormal events detected in the power grid room. Exemplarily, the abnormal patrol status corresponding to the specified power grid room in the set time period is obtained from the database. When the number of abnormal patrols of the specified power grid room in the set time period is greater than the set safety patrol number threshold, the patrol frequency of the specified drone is adjusted, and the frequency of scheduled patrols is increased by 50% in the future set time period. When the number of abnormal patrols of the specified power grid room in the set time period is less than or equal to the set safety patrol number threshold, the frequency of scheduled patrols is reduced by 50% in the future set time period. There is no restriction here.

[0085] Controlling drones to patrol based on patrol frequency can be understood as using pre-set or dynamically adjusted patrol frequencies to guide and dispatch drones to perform patrol tasks. Exemplarily, abnormal patrol conditions include but are not limited to equipment failure, water accumulation in cable trenches, and missing safety protection facilities. A 50% increase in the frequency of scheduled patrols means increasing the number of patrols by 50% on the basis of the original patrol frequency. For example, the original frequency is 2 times / hour, and after an increase of 50%, it is calculated as follows: 2+(2×50%)=3 times / hour. The number of abnormal patrols refers to the number of times a designated drone patrols when an abnormal patrol condition occurs. An emergency in a designated power grid room refers to situations such as fires and injuries to staff, which are not limited here.

[0086] In the embodiment of the present invention, by monitoring the relationship between the number of abnormal patrols and the safety threshold, the patrol frequency of the drone is dynamically adjusted to achieve intelligent monitoring of the indoor conditions of the power grid. Through the above steps, potential safety hazards can be discovered and handled in a timely manner, and the patrol strategy can be adjusted according to the actual operating status of the power grid room, avoiding unnecessary waste of resources, and improving the efficiency of equipment maintenance and the overall safety of the power system.

[0087] Optionally, the indoor inspection method for power grid based on drone also includes the following execution steps:

[0088] During the drone patrol process, in response to the drone detecting the occurrence of a preset situation, a video recording and a photo shooting are performed at the location where the preset situation occurs, and the coordinates of the location where the preset situation occurs are recorded.

[0089] In the embodiment of the present invention, the preset conditions can be understood as various abnormalities that may be encountered during the inspection process or conditions that require special attention. By way of example, the preset conditions may include equipment failure, safety hazards, environmental abnormalities, etc., which are not limited here.

[0090] During the drone inspection process, in response to the drone detecting a preset situation, the location where the preset situation occurs is recorded with video and photographed, and the coordinates of the location where the preset situation occurs are recorded. It can be understood that when the designated drone is inspecting the designated power grid indoors, the patrol video and patrol images are recorded and photographed through the equipped high-definition camera. When the designated drone detects an abnormal situation during the inspection process, the abnormal situation image is photographed in the abnormal situation area through the high-definition camera, and the coordinates of the location where the abnormal situation occurs are recorded. When the designated drone is blocked when photographing the image of the abnormal situation area, an early warning prompt is issued through a pop-up window on the display page of the control center. Abnormal situations include but are not limited to staff operating equipment according to incorrect operations and illegally stacking goods, which are not restricted here.

[0091] In the embodiment of the present invention, the drone inspection system can effectively collect detailed information about abnormal conditions in the power grid room, including visual records and precise geographic locations, providing key data support for rapid response, problem diagnosis and maintenance decisions. This intelligent process enables drone inspections to not only detect abnormalities, but also provide rich on-site information through video and image recordings, greatly enhancing the practicality of the monitoring system and its ability to respond to emergencies.

[0092] Optionally, the indoor inspection method for power grid based on drone also includes the following execution steps:

[0093] During the drone patrol process, obtain the energy parameters of the drone;

[0094] The drone is controlled to conduct patrols based on energy parameters and preset standard energy assessment indexes.

[0095] In the embodiment of the present invention, energy parameters can be understood to refer to those indicators that directly affect the flight performance and endurance of the drone. For example, energy parameters include battery temperature, remaining battery power and average power consumption of the drone, which are not limited here.

[0096] The preset standard energy evaluation index can be understood as a quantitative indicator used to evaluate whether the current energy status of the drone is suitable for performing a specific task. For example, the standard control energy evaluation index corresponding to the specified drone during flight is obtained from the database. The preset standard control energy evaluation index includes the standard control battery temperature T', the standard control remaining battery power E' and the standard control drone average power consumption W'. By calculating the formula The energy evaluation index α corresponding to the designated UAV during the patrol process is obtained, where T, E, and W represent the battery temperature, remaining battery power, and average power consumption of the designated UAV during the patrol mission, respectively, and μ1, μ2, and μ3 represent the weight factors corresponding to the set battery temperature, the remaining battery power, and the average power consumption of the UAV, respectively. Among them, the values ​​of μ1, μ2, and μ3 are all greater than 0 and less than 1. In addition, the battery temperature directly affects the performance and endurance of the battery. The set temperature range is, for example, 0°C to 40°C. When it is below 20°C, the weight factor is set to 0.3, 20°C to 30°C to 0.5, and 30°C to 0.7, to reflect the negative impact of increased temperature on battery life. The remaining battery power affects the flight time of the drone. When it is above 70%, the weight factor is 0.2, 40% to 70% to 0.4, and below 40% to 0.6, to ensure that safe return is given priority when the battery is insufficient. The average power consumption of the drone affects the flight distance and time. When it is set to 3-5W, a weight of 0.2 is arbitrarily selected, 0.5 is selected for 5-7W, and 0.8 is selected for more than 7W, to ensure that the flight plan can be adjusted in time when the power consumption is high. The weight factor corresponding to the set battery temperature, the weight factor corresponding to the remaining battery power, and the weight factor corresponding to the average power consumption of the drone are obtained, which are not restricted here.

[0097] During the drone patrol process, obtaining the energy parameters of the drone can be understood as obtaining the battery temperature and remaining battery power corresponding to the designated drone from the designated drone parking place, obtaining the power consumption corresponding to the drone battery power at each stage from the database, and obtaining the number of power consumption stages corresponding to the remaining battery power of the designated drone according to the remaining battery power corresponding to the designated drone, accumulating the power consumption corresponding to the drone battery power at each stage, and dividing the obtained result by the number of power consumption stages corresponding to the remaining battery power of the designated drone, thereby obtaining the average power consumption corresponding to the designated drone. For example, assuming that when the battery power is 100%, 70%, 40% and 20%, the power consumption is 5 watts, 6 watts, 7 watts and 10 watts respectively, then according to the remaining battery power of the designated drone, assuming that the remaining power is 60%, then the stage where the power 60% falls between 70% and 40%, according to the assumed conditions, the average power consumption of the designated drone should be the accumulation of 5 watts, 6 watts and 7 watts, and divided by the number of stages 3, and the result obtained is the average power consumption of the designated drone, which is not limited here.

[0098] Controlling the drone to patrol based on energy parameters and preset standard energy evaluation index can be understood as: during the drone patrol, the system continuously monitors the drone's energy parameters, including but not limited to battery temperature, remaining power, average power consumption, etc., and compares the real-time energy parameters with the preset standard energy evaluation index. By continuously monitoring the comparison of energy parameters and standard evaluation index, the system can predict the drone's energy trend, plan subsequent flight routes or missions in advance, and ensure that the drone can still have enough energy to return safely or perform emergency avoidance operations after completing the patrol mission.

[0099] In the embodiment of the present invention, controlling drone patrols based on energy parameters and preset standard energy evaluation indexes is a key capability in modern drone management systems. Through real-time monitoring, intelligent evaluation and flexible adjustment, it ensures the smooth execution of drone patrol tasks, improves energy utilization efficiency, and effectively avoids safety risks during flight.

[0100] Figure 2 is a schematic diagram of a system structure connection according to one embodiment of the present invention. Figure 2As shown, the present application provides a power grid indoor patrol system based on an engineering drone, the system comprising: a patrol mode analysis module, a safety distance analysis module, an abnormal situation processing module, a drone energy management module, an early warning terminal, a control center, a drone platform and a database. The control center comprises a patrol mode analysis module, a safety distance analysis module and an abnormal situation processing module, the control center is respectively connected to the drone platform and the database, the drone platform comprises a drone energy management module and an early warning terminal, and the drone platform is connected to the database. Specifically, the patrol mode analysis module is used to analyze the patrol mode adopted by the designated drone when patrolling according to the specific needs corresponding to the designated power grid room, and the patrol mode includes scheduled patrol, on-demand patrol and emergency patrol. In the safety distance analysis module, by obtaining the flight parameters of the drone in real time and calculating the minimum safety distance, and thereby analyzing the corresponding flight process of the designated drone during the patrol process, it is conducive to ensuring that the drone maintains a safe interval with obstacles during the patrol process, effectively preventing the drone from colliding during the patrol process, and ensuring the integrity of the equipment and patrol data. When the flight distance of the drone and the distance from the obstacle are lower than the safety range, the system will automatically issue an alarm and take avoidance measures to ensure the smoothness of the patrol process. The abnormal situation processing module is used to analyze the abnormal situation of the designated drone during the patrol process. The drone platform includes drone energy management and early warning terminals. The drone energy management module is used to obtain the energy parameters corresponding to the current designated drone when the designated drone is patrolling, and then plan the patrol path of the designated drone. The early warning terminal is used to issue an early warning prompt when the designated drone encounters an abnormal situation during the patrol process.

[0101] In a specific embodiment, the patrol path of a designated UAV is planned, and the specific analysis process is as follows: when the UAV platform receives a patrol task issued by the control center, the total patrol distance corresponding to the patrol path in the patrol task is obtained from the database, and each patrol distance corresponds to each energy assessment index threshold. The energy assessment index corresponding to the designated UAV during the patrol process is compared with the energy assessment index threshold corresponding to the total patrol distance. If the energy assessment index corresponding to the designated UAV during the patrol process is greater than the energy assessment index threshold corresponding to the total patrol distance, it indicates that the designated UAV cannot meet the patrol task of the total patrol distance, and then the patrol simulation of the designated UAV is performed in the three-dimensional model of the designated power grid room. The total distance of the patrol path corresponding to the designated UAV is shortened through the patrol simulation, thereby realizing the planning of the patrol path of the designated UAV.

[0102] It should be noted that the total patrol distance refers to the sum of the round-trip distances from the parking place to the patrol target point. Through the patrol simulation of the specified UAV, the shortest patrol path for the specified UAV to fly from the parking place to the patrol target point is obtained, so as to achieve the purpose of shortening the total distance of the corresponding patrol path of the specified UAV through the patrol simulation.

[0103] The embodiment of the present application uses the energy evaluation module. When the drone is patrolling, the battery temperature, remaining battery power and average power consumption can be obtained in real time, and then the patrol path can be reasonably planned, which is conducive to avoiding patrol interruptions caused by insufficient battery power and improving the continuity and stability of the patrol. Before the patrol task begins, the system will calculate the total patrol distance and dynamically adjust the strategy according to the energy evaluation index of the current drone to ensure that the drone completes the task with sufficient energy. The early warning terminal is used to issue an early warning prompt when the designated drone encounters an abnormal situation during the patrol process. In the abnormal situation processing module of the embodiment of the present invention, by performing real-time analysis of abnormal situations during the patrol process, it is conducive to providing timely solutions. When the drone encounters abnormal situations during the patrol, such as equipment erroneous operation or accumulation of illegal items, it can quickly identify and record the specific location of the abnormal situation and shoot evidence. In the process of using three-dimensional modeling technology, a one-to-one modeling of the power grid room is performed, which is conducive to providing accurate spatial references for drones to ensure the accuracy of autonomous navigation. In addition, the application of three-dimensional coordinate axis settings helps drones to perform more efficient path planning in complex environments and avoid safety hazards caused by errors in flight routes.

[0104] The power grid indoor patrol system and method based on engineering drones provided in the present application, during the patrol mode analysis process, accurately determines the drone patrol mode, such as scheduled patrol, on-demand patrol and emergency patrol, by real-time analysis of the specific needs of the power grid room. The flexible mode selection mechanism is conducive to adjusting the patrol frequency according to actual conditions, and improves the system's response capability to emergencies. For example, when the number of abnormal patrols within a set time period exceeds a safety threshold, the system can automatically increase the patrol frequency, thereby ensuring that abnormal situations can be handled in a timely manner and reducing potential risks.

[0105] In the safety distance analysis module of the embodiment of the present application, by acquiring the flight parameters of the drone in real time and calculating the minimum safety distance, it is helpful to ensure that the drone maintains a safe distance from obstacles during the patrol process, effectively prevent the drone from colliding during the patrol process, and ensure the integrity of equipment and inspection data. When the flight distance of the drone and the distance from the obstacle is lower than the safe range, the system will automatically issue an alarm and take avoidance measures to ensure a smooth patrol process.

[0106] The embodiment of the present application uses an energy assessment module. When the drone is patrolling, it can obtain the battery temperature, remaining battery power and average power consumption in real time, and then reasonably plan the patrol route, which is conducive to avoiding patrol interruptions caused by insufficient battery power and improving the continuity and stability of the patrol. Before the patrol task begins, the system will calculate the total patrol distance and dynamically adjust the strategy according to the current energy assessment index of the drone to ensure that the drone completes the task with sufficient energy.

[0107] In the abnormal situation processing module of the embodiment of the present application, by performing real-time analysis of abnormal situations during the patrol process, it is helpful to provide timely solutions. When the UAV encounters an abnormal situation during the patrol, such as incorrect operation of the equipment or accumulation of illegal items, it can quickly identify and record the specific location of the abnormal situation and photograph the evidence. In the process of using three-dimensional modeling technology, a one-to-one modeling of the power grid room is performed, which is helpful to provide the UAV with accurate spatial reference to ensure the accuracy of autonomous navigation. In addition, the application of three-dimensional coordinate axis settings helps the UAV to perform more efficient path planning in complex environments and avoid safety hazards caused by errors in the flight route.

[0108] Figure 3 is a schematic diagram of the implementation steps according to one embodiment of the present invention, such as Figure 3 As shown, the present application also provides a method for indoor power grid inspection based on engineering drones, including: (1) Inspection mode analysis: According to the specific needs of the designated power grid room, the inspection mode adopted by the designated drone during the inspection is analyzed. (2) Safety distance analysis: Real-time acquisition of the flight parameters corresponding to the designated drone during the inspection process, and then calculation of the minimum safety distance corresponding to the designated drone during the inspection process, and thereby analysis of the flight process corresponding to the designated drone during the inspection process. (3) Abnormal situation handling: Analysis of abnormal situations during the inspection process of the designated drone. (4) Drone energy management: When the designated drone receives the inspection task, the battery energy parameters corresponding to the current designated drone are obtained, and then the inspection path of the designated drone is planned. (5) Early warning prompt: When the designated drone encounters an abnormal situation during the inspection process, an early warning prompt is issued.

[0109] The power grid indoor patrol system and method based on engineering drones provided in the present application, during the patrol mode analysis process, accurately determines the drone patrol mode, such as scheduled patrol, on-demand patrol and emergency patrol, by real-time analysis of the specific needs of the power grid room. The flexible mode selection mechanism is conducive to adjusting the patrol frequency according to actual conditions, and improves the system's response capability to emergencies. For example, when the number of abnormal patrols within a set time period exceeds a safety threshold, the system can automatically increase the patrol frequency, thereby ensuring that abnormal situations can be handled in a timely manner and reducing potential risks.

[0110] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0111] In this embodiment, a drone-based indoor patrol device for a power grid is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and will not be repeated hereafter. As used below, the term "module" may be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0112] Figure 4 is a structural block diagram of a power grid indoor patrol device based on a drone according to one embodiment of the present invention, such as Figure 4 As shown, a power grid indoor patrol device 400 based on a drone is taken as an example, and the device includes: a determination module 401, which is used to determine a patrol mode based on the patrol demand of the target power grid room in response to receiving a patrol request of the target power grid room, wherein the patrol request is generated based on a trigger operation of a patrol button in the target power grid room, or is generated based on a preset case occurring in the target power grid room; a generation module 402, which is used to generate a target patrol route based on the parking position of the drone and the patrol request, wherein the target patrol route is a patrol route from the parking position to the button position of the patrol button, or is a patrol route from the parking position to the case occurrence position of the preset case; a control module 403, which is used to control the drone to patrol based on the patrol mode and the target patrol route.

[0113] Optionally, the generation module 402 is also used to generate a patrol start point and a patrol end point in response to a patrol request based on a trigger operation of a patrol button in the target power grid room, based on the three-dimensional model, parking position and button position of the target power grid room; or, generate a patrol start point and a patrol end point in response to a patrol request based on a preset case occurring in the target power grid room, based on the three-dimensional model, parking position and the location where the case occurred; and generate a target patrol route based on the patrol start point and the patrol end point.

[0114] Optionally, the control module 403 is also used to obtain the flight parameters and environmental influencing factors of the UAV during the patrol process; determine the safe distance of the UAV during the patrol process based on the flight parameters and environmental influencing factors; and control the UAV to patrol based on the patrol mode, target patrol route and safe distance.

[0115] It should be noted that the above modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0116] An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored. The computer program is configured to execute the steps of any of the above method embodiments when running on a computer or a processor.

[0117] Optionally, in this embodiment, the computer-readable storage medium may be configured to store a computer program for performing the following steps:

[0118] Step S101, in response to receiving a patrol request from a target power grid room, determining a patrol mode based on the patrol demand of the target power grid room, wherein the patrol request is generated based on a trigger operation of a patrol button in the target power grid room, or based on a preset case occurring in the target power grid room;

[0119] Step S102, generating a target patrol route based on the parking position of the drone and the patrol request, wherein the target patrol route is a patrol route from the parking position to the button position of the patrol button, or a patrol route from the parking position to the case occurrence position of a preset case;

[0120] Step S103, controlling the UAV to patrol based on the patrol mode and the target patrol route.

[0121] Optionally, in this embodiment, the above-mentioned computer-readable storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store computer programs.

[0122] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0123] Optionally, in this embodiment, the processor in the electronic device may be configured to run a computer program to perform the following steps:

[0124] Step S101, in response to receiving a patrol request from a target power grid room, determining a patrol mode based on the patrol demand of the target power grid room, wherein the patrol request is generated based on a trigger operation of a patrol button in the target power grid room, or based on a preset case occurring in the target power grid room;

[0125] Step S102, generating a target patrol route based on the parking position of the drone and the patrol request, wherein the target patrol route is a patrol route from the parking position to the button position of the patrol button, or a patrol route from the parking position to the case occurrence position of a preset case;

[0126] Step S103, controlling the UAV to patrol based on the patrol mode and the target patrol route.

[0127] An embodiment of the present invention further provides a computer program product, including a computer program, which implements the steps in any of the above method embodiments when executed by a processor.

[0128] Optionally, in this embodiment, the computer program in the above computer program product may be configured to perform the following steps when executed by a processor:

[0129] Step S101, in response to receiving a patrol request from a target power grid room, determining a patrol mode based on the patrol demand of the target power grid room, wherein the patrol request is generated based on a trigger operation of a patrol button in the target power grid room, or based on a preset case occurring in the target power grid room;

[0130] Step S102, generating a target patrol route based on the parking position of the drone and the patrol request, wherein the target patrol route is a patrol route from the parking position to the button position of the patrol button, or a patrol route from the parking position to the case occurrence position of a preset case;

[0131] Step S103, controlling the UAV to patrol based on the patrol mode and the target patrol route.

[0132] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0133] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0134] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0135] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0136] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0137] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0138] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, disk or optical disk, etc., various media that can store program codes.

[0139] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for indoor inspection of power grid based on drone, characterized in that: The method comprises: In response to receiving a patrol request from a target power grid room, determining a patrol mode based on the patrol demand of the target power grid room, wherein the patrol request is generated based on a trigger operation of a patrol button in the target power grid room, or is generated based on a preset case occurring in the target power grid room; Generate a target patrol route based on the parking position of the drone and the patrol request, wherein the target patrol route is a patrol route from the parking position to the button position of the patrol button, or a patrol route from the parking position to the location where the case of the preset case occurs; The unmanned aerial vehicle is controlled to perform patrol based on the patrol mode and the target patrol route.

2. The method according to claim 1, characterized in that The generating of the target patrol route based on the parking position of the UAV and the patrol request comprises: In response to the patrol request being generated based on a trigger operation of a patrol button in the target power grid room, a patrol start point and a patrol end point are generated based on the three-dimensional model of the target power grid room, the parking position and the button position; or, In response to the patrol request, based on the occurrence of the preset case in the target power grid room, the patrol start point and the patrol end point are generated based on the three-dimensional model, the parking position and the location where the case occurs; The target tour route is generated based on the tour starting point and the tour end point.

3. The method according to claim 1, characterized in that The controlling the UAV to patrol based on the patrol mode and the target patrol route includes: Obtaining flight parameters and environmental influencing factors of the UAV during the patrol process; Determine the safe distance during the patrol of the UAV based on the flight parameters and the environmental influencing factors; The unmanned aerial vehicle is controlled to perform patrol based on the patrol mode, the target patrol route and the safety distance.

4. The method according to claim 1, characterized in that: The method further comprises: Obtain the number of abnormal patrols of the drone within a preset time period; Determining the patrol frequency of the drone based on the magnitude relationship between the number of abnormal patrols and a preset threshold number of safe patrols; The drone is controlled to perform patrol based on the patrol frequency.

5. The method according to claim 1, characterized in that The method further comprises: During the drone patrol process, in response to the drone detecting the occurrence of a preset situation, a video recording and a photo shooting are performed on the location where the preset situation occurs, and the coordinates of the location where the preset situation occurs are recorded.

6. The method according to claim 1, characterized in that The method further comprises: During the patrol process of the drone, obtaining energy parameters of the drone; The UAV is controlled to patrol based on the energy parameters and a preset standard energy evaluation index.

7. A power grid indoor patrol device based on drone, characterized in that: The device comprises: A determination module, configured to determine a patrol mode based on a patrol demand of the target power grid room in response to receiving a patrol request of the target power grid room, wherein the patrol request is generated based on a trigger operation of a patrol button in the target power grid room, or is generated based on a preset case occurring in the target power grid room; A generating module, configured to generate a target patrol route based on the parking position of the drone and the patrol request, wherein the target patrol route is a patrol route from the parking position to the button position of the patrol button, or a patrol route from the parking position to the location where the case of the preset case occurs; A control module is used to control the UAV to patrol based on the patrol mode and the target patrol route.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program is configured to execute the drone-based indoor power grid patrol method described in any one of claims 1 to 6 when running on a computer or a processor.

9. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the indoor inspection method for power grid based on a drone as described in any one of claims 1 to 6.

10. An electronic device comprising a memory and a processor, characterized in that: The memory stores a computer program, and the processor is configured to run the computer program to execute the drone-based indoor patrol method for power grids as described in any one of claims 1 to 6.

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