CN118333613A - Unmanned aerial vehicle power inspection risk detection method and system - Google Patents

Abstract

The invention is applicable to the field of unmanned aerial vehicle inspection, and provides an unmanned aerial vehicle power inspection risk detection method and system, wherein the method comprises the following steps: acquiring navigation path information of a target unmanned aerial vehicle; extracting magnetic field information of electrical equipment in the navigation path information of the target unmanned aerial vehicle; identifying magnetic field information of the electrical equipment, and generating an avoidance instruction; generating path correction information based on the avoidance instruction and the path information of the target unmanned aerial vehicle; in the flight process of the target unmanned aerial vehicle, acquiring real-time magnetic field induction information and real-time wind field information; identifying real-time magnetic field induction information and generating first aviation diameter calibration information; identifying real-time wind field information and generating second aviation diameter calibration information; based on the first path calibration information and the first path calibration information, an intervention adjustment instruction is generated, so that composite risk avoidance judgment can be realized, firstly, the magnetic field information of the electrical equipment in the unmanned aerial vehicle path information is pre-judged, and then, in the actual flight inspection process, the real-time magnetic field induction information and the real-time wind field information are synchronously acquired and identified.

Description

A UAV power inspection risk detection method and system

Technical Field

The present invention belongs to the field of computers, and in particular relates to a method and system for detecting risks of unmanned aerial vehicle power inspections.

Background technique

Drone power inspection refers to the use of drones to conduct regular or irregular inspections and maintenance of power facilities to ensure the safe and efficient operation of the facilities. Compared with traditional manual inspections, this inspection method has higher efficiency, lower costs and lower safety risks.

During power inspections, drones can be equipped with high-definition cameras, infrared thermal imagers, lidars and other equipment to inspect power facilities. Drones can fly along transmission lines to check for potential risks and threats to the lines and to drones. However, since drones are manually controlled, when line problems are discovered or the drone is threatened by the fuselage, the drone has often flown out of or into the risk area, affecting the accuracy of the drone's operations and its service life. To address the above issues, it is urgent to develop a drone power inspection risk detection method and system.

Summary of the invention

The purpose of the embodiments of the present invention is to provide a method and system for detecting risks of power inspection by unmanned aerial vehicles, aiming to solve the problems raised in the above-mentioned background technology.

The embodiment of the present invention is implemented as follows: on the one hand, a method for detecting risks of power inspection by a drone, the method comprising:

Obtain the flight path information of the targeted UAV;

Extract the magnetic field information of electrical equipment from the flight path information of the targeted UAV;

Identify the magnetic field information of electrical equipment and generate avoidance instructions;

Generate path correction information based on avoidance instructions and target drone path information;

During the flight of the targeted drone, real-time magnetic field sensing information and real-time wind field information are collected;

Identify real-time magnetic field sensing information and generate first path calibration information;

Identify real-time wind field information and generate second path calibration information;

An intervention adjustment instruction is generated based on the first path calibration information and the second path calibration information.

As a further solution of the present invention, the identifying of the magnetic field information of the electrical equipment and generating the avoidance instruction specifically includes:

Import magnetic field information of several electrical devices;

Determine whether the magnetic field information of several electrical devices is greater than a magnetic field strength threshold;

If the magnetic field information of several electrical devices is greater than the magnetic field strength threshold;

Mark and store obstacle avoidance positioning information of electrical equipment whose magnetic field information is greater than the magnetic field strength threshold;

Based on the obstacle avoidance positioning information, a first obstacle avoidance compensation value is generated.

As a further solution of the present invention, the generating of the path correction information based on the avoidance instruction and the target UAV path information specifically includes:

Import obstacle avoidance positioning information;

Identify the time nodes of obstacle avoidance positioning information in the UAV path information;

generating an obstacle avoidance advance time based on the first obstacle avoidance compensation value;

An obstacle avoidance path is generated based on the target UAV path information and obstacle avoidance advance time.

As a further solution of the present invention, the identifying of real-time magnetic field sensing information and generating first path calibration information specifically includes:

Get real-time magnetic field sensing information;

Determine whether the real-time magnetic field sensing information is greater than the magnetic field strength threshold;

If the real-time magnetic field sensing information is greater than the magnetic field strength threshold;

generating a second obstacle avoidance compensation value;

Based on the second obstacle avoidance compensation value, a first flight path adjustment strategy is calculated and generated.

As a further solution of the present invention, the identifying of real-time wind field information and generating second path calibration information specifically includes:

Get real-time wind field information;

Extracting real-time wind information from real-time wind field information;

Determine whether the real-time wind speed information is greater than the wind speed threshold;

If the real-time wind speed information is greater than the wind speed threshold;

generating a third obstacle avoidance compensation value;

Based on the third obstacle avoidance compensation value, a second flight path adjustment strategy is calculated and generated.

As a further solution of the present invention, on the other hand, a UAV power inspection risk detection system is provided, the system comprising:

An acquisition module is used to obtain the path information of the targeted UAV;

An extraction module is used to extract the magnetic field information of electrical equipment from the flight path information of the targeted UAV;

A first identification module, used to identify the magnetic field information of the electrical equipment;

A first generating module, used to generate an evasion instruction;

The second generation module is used to generate path correction information based on the avoidance instruction and the target UAV path information;

The first acquisition module is used to collect real-time magnetic field sensing information during the flight of the targeted UAV;

The second acquisition module is used to collect real-time wind field information during the flight of the targeted UAV;

A second identification module is used to identify real-time magnetic field sensing information;

A third generating module, used to generate first path calibration information;

A third identification module is used to identify real-time wind field information;

A fourth generating module, used to generate second path calibration information;

The fifth generating module is used to generate an intervention adjustment instruction.

As a further solution of the present invention, the first identification module specifically includes:

An import unit, used to import magnetic field information of several electrical devices;

A first judging unit, used to judge whether the magnetic field information of a plurality of electrical devices is greater than a magnetic field strength threshold;

A marking storage unit is used to mark and store obstacle avoidance positioning information of the electrical devices whose magnetic field information is greater than the magnetic field strength threshold if the magnetic field information of the electrical devices is greater than the magnetic field strength threshold;

The first generating unit is used to generate a first obstacle avoidance compensation value.

As a further solution of the present invention, the second identification module specifically includes:

A first acquisition unit, used to acquire real-time magnetic field sensing information;

A second judgment unit is used to judge whether the real-time magnetic field sensing information is greater than a magnetic field intensity threshold;

A second generating unit, configured to generate a second obstacle avoidance compensation value if the real-time magnetic field sensing information is greater than a magnetic field strength threshold;

The first calculation generating unit is used to calculate and generate a first flight path adjustment strategy based on the second obstacle avoidance compensation value.

As a further solution of the present invention, the third identification module specifically includes:

A second acquisition unit is used to acquire real-time wind field information;

An extraction unit, used for extracting real-time wind force information from real-time wind field information;

A third judgment unit is used to judge whether the real-time wind force information is greater than a wind force threshold;

A third generating unit, configured to generate a third obstacle avoidance compensation value if the real-time wind force information is greater than the wind force threshold;

The second calculation and generation unit is used to calculate and generate a second flight path adjustment strategy based on the third obstacle avoidance compensation value.

An embodiment of the present invention provides a method and system for detecting risks in unmanned aerial vehicle power inspections. The method and system can realize a composite risk avoidance judgment in the process of unmanned aerial vehicle power inspections. First, the magnetic field information of electrical equipment in the unmanned aerial vehicle path information is pre-judged, and then in the actual flight inspection process, the real-time magnetic field sensing information and real-time wind field information are synchronously collected and identified. After the risk is discovered, intervention adjustment instructions are generated in a timely manner, the corresponding compensation values are calculated and generated, the flight trajectory is adjusted, and the stability of the power inspection is ensured.

BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1 is a main flow chart of a UAV power inspection risk detection method.

FIG2 is a flow chart of identifying magnetic field information of electrical equipment and generating avoidance instructions in a UAV power inspection risk detection method.

FIG3 is a flow chart of generating path correction information based on avoidance instructions and targeted UAV path information in a UAV power inspection risk detection method.

FIG4 is a flow chart of identifying real-time magnetic field sensing information and generating first path calibration information in a UAV power inspection risk detection method.

FIG5 is a flow chart of identifying real-time wind field information and generating second path calibration information in a UAV power inspection risk detection method.

FIG6 is a main structure diagram of a UAV power inspection risk detection system.

FIG. 7 is a structural block diagram of a first identification module in a UAV power inspection risk detection system.

FIG8 is a structural block diagram of a second identification module in a UAV power inspection risk detection system.

FIG9 is a structural block diagram of the third identification module in the UAV power inspection risk detection system.

Detailed ways

In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

The present invention provides a UAV power inspection risk detection method and system, which solves the technical problems in the background technology.

As shown in FIG1 , a main flow chart of a UAV power inspection risk detection method provided by an embodiment of the present invention is provided. The UAV power inspection risk detection method includes:

Step S100: Acquire the target UAV path information;

Step S200: extracting the magnetic field information of the electrical equipment in the target UAV path information;

Step S300: Identify the magnetic field information of the electrical equipment and generate an avoidance instruction;

Step S400: Generate path correction information based on the avoidance instruction and the target UAV path information;

Step S500: During the flight of the targeted UAV, real-time magnetic field sensing information and real-time wind field information are collected;

Step S600: identifying real-time magnetic field sensing information and generating first flight path calibration information;

Step S700: identifying real-time wind field information and generating second path calibration information;

Step S800: generating an intervention adjustment instruction based on the first path calibration information and the first path calibration information;

When this embodiment is applied, before the targeted UAV takes off for inspection, the path information is preset, and the targeted UAV will fly according to the path information. During the navigation of the targeted UAV along the path information, there are various electrical equipment and lines, and the electrical equipment and lines will generate a certain magnetic field, which will cause certain interference to the UAV. A number of magnetic field sensors and wind field sensors are installed on the targeted UAV to monitor the surrounding magnetic field strength in real time. After the targeted UAV path information is obtained, the magnetic field information of the electrical equipment in the targeted UAV path information is extracted. There may be various electrical equipment in the path information, and each electrical equipment has a magnetic field radiation value. Then the magnetic field radiation value of each electrical equipment is identified. If the magnetic field radiation value of the electrical equipment is greater than the radiation threshold, an avoidance instruction is generated. Based on the avoidance instruction and the targeted UAV path information, path correction information is generated on the basis of the targeted UAV path information, that is, the original path correction information is corrected. The flight route of the targeted UAV is corrected, and the UAV is flown around specific points. After the targeted UAV starts flying, during the flight of the targeted UAV, several magnetic field sensors installed on the targeted UAV will continuously collect real-time magnetic field sensing information and real-time wind field information, identify and analyze the magnetic field radiation information and wind field information generated during the actual flight, and then judge whether the real-time magnetic field radiation information and wind field information will pose a flight threat to the inspection UAV during the actual flight. First, the real-time magnetic field sensing information is identified to generate the first path calibration information, and then the real-time wind field information is identified to generate the second path calibration information. Based on the first path calibration information and the second path calibration information, an intervention adjustment instruction is generated to perform real-time path adjustment on the targeted UAV performing flight inspection to prevent the targeted UAV from entering high magnetic field radiation areas and strong wind areas, reduce interference with the UAV's flight, and ensure the stability of power inspection.

As shown in FIG2 , as a preferred embodiment of the present invention, the identifying of the magnetic field information of the electrical equipment and generating the avoidance instruction specifically includes:

Step S301: importing magnetic field information of several electrical devices;

Step S302: determining whether the magnetic field information of a number of electrical devices is greater than a magnetic field strength threshold;

Step S303: If the magnetic field information of a number of electrical devices is greater than the magnetic field strength threshold;

Step S304: marking and storing obstacle avoidance positioning information of electrical equipment whose magnetic field information is greater than a magnetic field strength threshold;

Step S305: generating a first obstacle avoidance compensation value based on the obstacle avoidance positioning information;

When this embodiment is applied, the magnetic field information of the electrical equipment in the path information can be pre-calculated through the calibrated electrical parameters of the electrical equipment. The calibrated electrical parameters of the electrical equipment are public information. First, the magnetic field information of several electrical equipment is imported, and a magnetic field strength threshold is preset. When the drone flies in a range greater than the magnetic field strength, it will be disturbed. It is determined whether the magnetic field information of several electrical equipment is greater than the magnetic field strength threshold. If the magnetic field information of several electrical equipment is greater than the magnetic field strength threshold, the obstacle avoidance positioning information of the electrical equipment whose magnetic field information is greater than the magnetic field strength threshold is marked and stored. Based on the obstacle avoidance positioning information, a first obstacle avoidance compensation value is generated. The obstacle avoidance compensation value can be used for targeted drone bypass control.

As shown in FIG. 3 , as a preferred embodiment of the present invention, the generating of the path correction information based on the avoidance instruction and the target UAV path information specifically includes:

Step S401: importing obstacle avoidance positioning information;

Step S402: Identify the time node of the obstacle avoidance positioning information in the UAV path information;

Step S403: generating an obstacle avoidance advance time based on the first obstacle avoidance compensation value;

Step S404: Generate an obstacle avoidance path based on the target UAV path information and the obstacle avoidance advance time.

When this embodiment is applied, the obstacle avoidance positioning information is first imported, the time node of the obstacle avoidance positioning information in the UAV path information is identified, and the obstacle avoidance advance time is generated based on the first obstacle avoidance compensation value, that is, based on the time node of the obstacle avoidance positioning information in the UAV path information, obstacle avoidance is performed in advance, and then based on the targeted UAV path information and the obstacle avoidance advance time, the high-intensity magnetic field area is bypassed to generate an obstacle avoidance path, and the targeted UAV flies according to the obstacle avoidance path when conducting inspections.

As shown in FIG. 4 , as a preferred embodiment of the present invention, the identifying of real-time magnetic field sensing information and generating first flight path calibration information specifically includes:

Step S601: Acquire real-time magnetic field sensing information;

Step S602: determining whether the real-time magnetic field sensing information is greater than a magnetic field intensity threshold;

Step S603: If the real-time magnetic field sensing information is greater than the magnetic field intensity threshold;

Step S604: generating a second obstacle avoidance compensation value;

Step S605: Calculate and generate a first flight route adjustment strategy based on the second obstacle avoidance compensation value;

It should be understood that during the flight, real-time magnetic field sensing information is continuously obtained to determine whether the real-time magnetic field sensing information is greater than the magnetic field strength threshold. If the real-time magnetic field sensing information is greater than the magnetic field strength threshold, there is an area with an excessively strong magnetic field, and a second obstacle avoidance compensation value is generated in a timely manner. The second obstacle avoidance compensation value may include adjustments to flight parameters, such as heading angle, speed, etc. Based on the second obstacle avoidance compensation value, a first flight route adjustment strategy is calculated and generated. The first flight route adjustment strategy includes calculating the heading angle or position offset that needs to be increased or decreased based on the magnetic field sensing information so that the drone can fly in the desired direction and trajectory.

As shown in FIG. 5 , as a preferred embodiment of the present invention, the identifying of real-time wind field information and generating second path calibration information specifically includes:

Step S701: obtaining real-time wind field information;

Step S702: extracting real-time wind force information from real-time wind field information;

Step S703: determining whether the real-time wind force information is greater than the wind force threshold;

Step S704: If the real-time wind force information is greater than the wind force threshold;

Step S705: generating a third obstacle avoidance compensation value;

Step S706: Calculate and generate a second flight path adjustment strategy based on the third obstacle avoidance compensation value;

When this embodiment is applied, while continuously acquiring real-time magnetic field sensing information, real-time wind field information is continuously acquired, real-time wind force information in the real-time wind field information is extracted, and it is determined whether the real-time wind force information is greater than the wind force threshold. If the real-time wind force information is greater than the wind force threshold, there is an area with excessive wind force, and a third obstacle avoidance compensation value is generated in time. The third obstacle avoidance compensation value may also include adjustment of flight parameters, such as heading angle, speed, etc. Based on the third obstacle avoidance compensation value, a second flight route adjustment strategy is calculated and generated. The second flight route adjustment strategy includes calculating the thrust that needs to be increased or decreased based on the wind force so that the UAV can fly at the desired speed and trajectory.

As shown in FIG6 , as another preferred embodiment of the present invention, on the other hand, a UAV power inspection risk detection system comprises:

The acquisition module 100 is used to obtain the target UAV path information;

An extraction module 200 is used to extract the magnetic field information of the electrical equipment from the target UAV path information;

A first identification module 300, used to identify magnetic field information of electrical equipment;

A first generating module 400, configured to generate a circumvention instruction;

The second generating module 500 is used to generate path correction information based on the avoidance instruction and the target UAV path information;

The first acquisition module 600 is used to collect real-time magnetic field sensing information during the flight of the targeted UAV;

The second collection module 700 is used to collect real-time wind field information during the flight of the targeted UAV;

The second identification module 800 is used to identify real-time magnetic field sensing information;

The third generating module 900 is used to generate first path calibration information;

The third identification module 1000 is used to identify real-time wind field information;

The fourth generating module 1100 is used to generate second path calibration information;

The fifth generating module 1200 is used to generate an intervention adjustment instruction.

When this embodiment is applied, the acquisition module 100 is used to acquire the path information of the targeted UAV, the extraction module 200 extracts the magnetic field information of the electrical equipment in the path information of the targeted UAV, the first identification module 300 identifies the magnetic field information of the electrical equipment, the first generation module 400 generates an avoidance instruction, the second generation module 500 generates path correction information based on the avoidance instruction and the path information of the targeted UAV, the first acquisition module 600 collects real-time magnetic field sensing information during the flight of the targeted UAV, the second acquisition module 700 collects real-time wind field information during the flight of the targeted UAV, the second identification module 800 identifies real-time magnetic field sensing information, the third generation module 900 generates first path calibration information, the third identification module 1000 identifies real-time wind field information, the fourth generation module 1100 generates second path calibration information, and the fifth generation module 1200 generates intervention adjustment instructions.

As shown in FIG. 7 , as another preferred embodiment of the present invention, the first identification module 300 specifically includes:

The import unit 301 is used to import magnetic field information of a plurality of electrical devices;

The first judging unit 302 is used to judge whether the magnetic field information of a plurality of electrical devices is greater than a magnetic field strength threshold;

A marking storage unit 303 is used to mark and store obstacle avoidance positioning information of the electrical devices whose magnetic field information is greater than the magnetic field strength threshold if the magnetic field information of the electrical devices is greater than the magnetic field strength threshold;

The first generating unit 304 is configured to generate a first obstacle avoidance compensation value.

When this embodiment is applied, the import unit 301 imports magnetic field information of several electrical devices, the first judgment unit 302 judges whether the magnetic field information of several electrical devices is greater than the magnetic field strength threshold, if the magnetic field information of several electrical devices is greater than the magnetic field strength threshold, the marking storage unit 303 marks and stores the obstacle avoidance positioning information of the electrical devices whose magnetic field information is greater than the magnetic field strength threshold, and the first generation unit 304 generates a first obstacle avoidance compensation value.

As shown in FIG. 8 , as another preferred embodiment of the present invention, the second identification module 800 specifically includes:

A first acquisition unit 801 is used to acquire real-time magnetic field sensing information;

The second judging unit 802 is used to judge whether the real-time magnetic field sensing information is greater than the magnetic field intensity threshold;

The second generating unit 803 is used to generate a second obstacle avoidance compensation value if the real-time magnetic field sensing information is greater than the magnetic field strength threshold;

A first calculation and generation unit 804 is used to calculate and generate a first flight path adjustment strategy based on the second obstacle avoidance compensation value;

When this embodiment is applied, the first acquisition unit 801 acquires real-time magnetic field sensing information, the second judgment unit 802 judges whether the real-time magnetic field sensing information is greater than the magnetic field strength threshold, if the real-time magnetic field sensing information is greater than the magnetic field strength threshold, the second generation unit 803 generates a second obstacle avoidance compensation value, based on the second obstacle avoidance compensation value, the first calculation generation unit 804 calculates and generates a first flight path adjustment strategy.

As shown in FIG. 9 , as another preferred embodiment of the present invention, the third identification module 1000 specifically includes:

The second acquisition unit 1001 is used to acquire real-time wind field information;

An extraction unit 1002 is used to extract real-time wind force information from the real-time wind field information;

The third judgment unit 1003 is used to judge whether the real-time wind force information is greater than the wind force threshold;

The third generating unit 1004 is used to generate a third obstacle avoidance compensation value if the real-time wind information is greater than the wind threshold;

A second calculation and generation unit 1005 is used to calculate and generate a second flight path adjustment strategy based on a third obstacle avoidance compensation value;

When this embodiment is applied, the second acquisition unit 1001 acquires real-time wind field information, the extraction unit 1002 extracts real-time wind force information from the real-time wind field information, and the third judgment unit 1003 judges whether the real-time wind force information is greater than the wind force threshold. If the real-time wind force information is greater than the wind force threshold, the third generation unit 1004 generates a third obstacle avoidance compensation value. Based on the third obstacle avoidance compensation value, the second calculation generation unit 1005 calculates and generates a second flight route adjustment strategy.

In the above embodiment of the present invention, a method for detecting the risk of power inspection of a UAV is provided, and a system for detecting the risk of power inspection of a UAV is provided. Before the targeted UAV takes off for inspection, path information is preset, and the targeted UAV will fly according to the path information. During the navigation of the targeted UAV along the path information, there are a variety of electrical equipment and lines, and the electrical equipment and lines will generate a certain magnetic field, which will cause certain interference to the UAV. A number of magnetic field sensors and wind field sensors are installed on the targeted UAV for real-time monitoring of the surrounding magnetic field strength. After the path information of the targeted UAV is obtained, the magnetic field information of the electrical equipment in the path information of the targeted UAV is extracted. There may be a variety of electrical equipment in the path information, and each electrical equipment has a magnetic field radiation value. Then, the magnetic field radiation value of each electrical equipment is identified. If the magnetic field radiation value of the electrical equipment is greater than the radiation threshold, an avoidance instruction is generated. Based on the avoidance instruction and the path information of the targeted UAV, path correction information is generated on the basis of the path information of the targeted UAV, that is, the flight route of the original targeted UAV is corrected, and the flight bypasses a specific point. After the targeted UAV starts to fly, during the flight of the targeted UAV, Several magnetic field sensors set on the targeted UAV will continuously collect real-time magnetic field sensing information and real-time wind field information, identify and analyze the magnetic field radiation information and wind field information generated during the actual flight process, and then determine whether the real-time magnetic field radiation information and wind field information will pose a flight threat to the inspection UAV during the actual flight process. First, the real-time magnetic field sensing information is identified to generate the first path calibration information, and then the real-time wind field information is identified to generate the second path calibration information. Based on the first path calibration information and the first path calibration information, an intervention adjustment instruction is generated to adjust the path of the targeted UAV performing flight inspection in real time to prevent the targeted UAV from entering the high magnetic field radiation area and the strong wind area, reduce the flight interference to the UAV, and ensure the stability of the power inspection. The method and system can realize the composite risk avoidance judgment during the UAV power inspection process. First, the magnetic field information of the electrical equipment in the UAV path information is pre-judged, and then in the actual flight inspection process, the real-time magnetic field sensing information and the real-time wind field information are synchronously collected and identified. After the risk is found, the intervention adjustment instruction is generated in time, the corresponding compensation value is calculated and generated, the flight trajectory is adjusted, and the stability of the power inspection is ensured.

In order to load the above-mentioned method and system and enable it to run smoothly, the system, in addition to the various modules mentioned above, may also include more or fewer components than described above, or a combination of certain components, or different components, for example, it may include input and output devices, network access devices, buses, processors and memories, etc.

The processor may be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the system, and various interfaces and lines are used to connect various parts.

The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims (9)

1. A UAV power inspection risk detection method, characterized in that the method comprises: Obtain the flight path information of the targeted UAV; Extract the magnetic field information of electrical equipment from the flight path information of the targeted UAV; Identify the magnetic field information of electrical equipment and generate avoidance instructions; Generate path correction information based on avoidance instructions and target drone path information; During the flight of the targeted drone, real-time magnetic field sensing information and real-time wind field information are collected; Identify real-time magnetic field sensing information and generate first path calibration information; Identify real-time wind field information and generate second path calibration information; An intervention adjustment instruction is generated based on the first path calibration information and the second path calibration information. 2. The UAV power inspection risk detection method according to claim 1 is characterized in that the identifying of the magnetic field information of the electrical equipment and generating the avoidance instruction specifically comprises: Import magnetic field information of several electrical devices; Determine whether the magnetic field information of several electrical devices is greater than a magnetic field strength threshold; If the magnetic field information of several electrical devices is greater than the magnetic field strength threshold; Mark and store obstacle avoidance positioning information of electrical equipment whose magnetic field information is greater than the magnetic field strength threshold; Based on the obstacle avoidance positioning information, a first obstacle avoidance compensation value is generated. 3. The UAV power inspection risk detection method according to claim 2 is characterized in that the generating of the path correction information based on the avoidance instruction and the target UAV path information specifically comprises: Import obstacle avoidance positioning information; Identify the time nodes of obstacle avoidance positioning information in the UAV path information; generating an obstacle avoidance advance time based on the first obstacle avoidance compensation value; An obstacle avoidance path is generated based on the target UAV path information and obstacle avoidance advance time. 4. The UAV power inspection risk detection method according to claim 1 is characterized in that the identifying real-time magnetic field sensing information and generating the first path calibration information specifically comprises: Get real-time magnetic field sensing information; Determine whether the real-time magnetic field sensing information is greater than the magnetic field strength threshold; If the real-time magnetic field sensing information is greater than the magnetic field strength threshold; generating a second obstacle avoidance compensation value; Based on the second obstacle avoidance compensation value, a first flight path adjustment strategy is calculated and generated. 5. The UAV power inspection risk detection method according to claim 1 is characterized in that the identifying real-time wind field information and generating the second path calibration information specifically comprises: Get real-time wind field information; Extracting real-time wind information from real-time wind field information; Determine whether the real-time wind speed information is greater than the wind speed threshold; If the real-time wind speed information is greater than the wind speed threshold; generating a third obstacle avoidance compensation value; Based on the third obstacle avoidance compensation value, a second flight path adjustment strategy is calculated and generated. 6. A UAV power inspection risk detection system, characterized in that the system includes: An acquisition module is used to obtain the path information of the targeted UAV; An extraction module is used to extract the magnetic field information of electrical equipment from the flight path information of the targeted UAV; A first identification module, used to identify the magnetic field information of the electrical equipment; A first generating module, used to generate an evasion instruction; The second generation module is used to generate path correction information based on the avoidance instruction and the target UAV path information; The first acquisition module is used to collect real-time magnetic field sensing information during the flight of the targeted UAV; The second acquisition module is used to collect real-time wind field information during the flight of the targeted UAV; A second identification module is used to identify real-time magnetic field sensing information; A third generating module, used to generate first path calibration information; A third identification module is used to identify real-time wind field information; A fourth generating module, used to generate second path calibration information; The fifth generating module is used to generate an intervention adjustment instruction. 7. The UAV power inspection risk detection system according to claim 6, characterized in that the first identification module specifically comprises: An import unit, used to import magnetic field information of several electrical devices; A first judging unit, used to judge whether the magnetic field information of a plurality of electrical devices is greater than a magnetic field strength threshold; A marking storage unit is used to mark and store obstacle avoidance positioning information of the electrical devices whose magnetic field information is greater than the magnetic field strength threshold if the magnetic field information of the electrical devices is greater than the magnetic field strength threshold; The first generating unit is used to generate a first obstacle avoidance compensation value. 8. The UAV power inspection risk detection system according to claim 6, characterized in that the second identification module specifically includes: A first acquisition unit, used to acquire real-time magnetic field sensing information; A second judgment unit is used to judge whether the real-time magnetic field sensing information is greater than a magnetic field intensity threshold; A second generating unit, configured to generate a second obstacle avoidance compensation value if the real-time magnetic field sensing information is greater than a magnetic field strength threshold; The first calculation and generation unit is used to calculate and generate a first flight path adjustment strategy based on the second obstacle avoidance compensation value. 9. The UAV power inspection risk detection system according to claim 6, characterized in that the third identification module specifically comprises: A second acquisition unit is used to acquire real-time wind field information; An extraction unit, used for extracting real-time wind force information from real-time wind field information; A third judgment unit is used to judge whether the real-time wind force information is greater than a wind force threshold; A third generating unit, configured to generate a third obstacle avoidance compensation value if the real-time wind force information is greater than the wind force threshold; The second calculation and generation unit is used to calculate and generate a second flight path adjustment strategy based on the third obstacle avoidance compensation value.