Electric power grid construction project quality monitoring unmanned aerial vehicle and monitoring method

Abstract

The invention relates to an unmanned aerial vehicle for monitoring the quality of a power grid construction project and a monitoring method. The unmanned aerial vehicle comprises flight equipment, a ground console, load equipment, communication equipment and a data processing system, the flight equipment is a basic part of the unmanned aerial vehicle and comprises a fuselage of the unmanned aerial vehicle, power equipment and flight control equipment; the communication equipment comprises a navigation unit and a communication unit, the navigation unit is used for ensuring that the unmanned aerial vehicle can be accurately navigated and positioned, and the communication unit is used for data transmission between the flight equipment and the ground console; parameters processed by the analysis unit are optimized and adjusted according to environmental conditions of information collected by the load equipment, so that an inspection result is more practical under special conditions, and misjudgment is avoided; by collecting the environment wind speed, the flight speed of the flight equipment is adjusted in a self-adaptive mode, and bumping is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid construction project supervision, and in particular to a drone and a monitoring method for power grid construction project quality monitoring. Background Art

[0002] In order to avoid quality problems during the construction of power grid projects, they need to be inspected regularly. Traditional manual inspections have many problems, such as low safety, difficult management, and low efficiency. With the popularization of drone technology, drone inspection technology is gradually being adopted for power grid construction projects. Drone inspection technology utilizes wireless communication technology, aerial remote sensing and mapping technology, GPS navigation and positioning technology, and automatic control technology to effectively solve the problem of power grid line inspection. During the construction and acceptance of power grids, drones can cover all construction links in an all-round way, improve supervision efficiency, and enhance coordination between front and rear departments.

[0003] Chinese patent application CN108447075A proposes a drone monitoring system and a monitoring method thereof, the drone monitoring method comprising: step 1, acquiring an image in a scanning area; step 2, forming a stereoscopic vision through a first camera and a second camera to determine whether there is a suspicious target in the image; step 3, if so, calculating the standard position of the suspicious target, and tracking and photographing the suspicious target. The present invention sets up multiple servo pan-tilt platforms in the area to be detected, and sets up a first camera and a second camera on both side end faces of each servo pan-tilt platform, respectively, and uses two cameras to form binocular vision to discover drones and detect the location information of drones. After discovering the drone, the drone is tracked and monitored according to the location information of the drone through binocular vision, so as to realize the discovery, identification and disposal of low-altitude, slow and small targets.

[0004] However, due to the recent popularity of drone technology, it is difficult to have professional operators at power grid construction sites. Therefore, during the inspection process, the flight of drones may be bumpy and unstable, affecting the collection of inspection information and subsequent judgment. Moreover, since power grid projects are generally located in remote areas, communication signals may be unstable, affecting data transmission between flight equipment and ground control consoles. If this type of data interference occurs, data gaps will occur, affecting the results of the inspection. Summary of the invention

[0005] The purpose of the present invention is to provide a power grid construction project quality monitoring drone and monitoring method to overcome the shortcomings of the prior art. The inspection results of the present invention are more in line with reality, avoid misjudgment, and can adaptively adjust the flight speed of the flying equipment to reduce turbulence.

[0006] A power grid construction project quality monitoring drone proposed in the present invention is characterized by comprising flight equipment, a ground control console, payload equipment, communication equipment and a data processing system;

[0007] The flight equipment is the basic part of the UAV, and the flight equipment includes the fuselage, power equipment and flight control equipment of the UAV;

[0008] The communication device includes a navigation unit and a communication unit, wherein the navigation unit is used to ensure that the UAV can accurately navigate and locate, and the communication unit is used for data transmission between the flight device and the ground control console;

[0009] The payload equipment includes a high-resolution camera, a thermal imaging camera, a laser radar, and an infrared sensor;

[0010] The ground control console is used to operate flight equipment;

[0011] The data processing system includes a data acquisition module, a data processing module, a security module and a data storage module;

[0012] The acquisition module is used to obtain the operating parameters of the flight equipment and the data information collected by the payload equipment;

[0013] The data processing module includes an analysis unit, which is used to analyze the data information collected by the payload equipment, specifically:

[0014] Obtain the structural integrity analysis value Q of the power grid construction project;

[0015] Obtain a thermal imaging recognition value W of the power grid construction project;

[0016] Obtain the laser radar accuracy score E of the power grid construction project;

[0017] Obtain the infrared vegetation index R of the power grid construction project;

[0018] According to the formula T = (Q*β1) + (W*β2) + (E*β3) + (R*β4), the comprehensive inspection evaluation value T of the power grid construction project is calculated and obtained, where β1, β2, β3 and β4 are preset weight coefficients;

[0019] A threshold value of the comprehensive inspection evaluation value is set in advance. If the comprehensive inspection evaluation value T is greater than the threshold value of the comprehensive inspection evaluation value, it is judged that there is a potential threat to the power grid construction project. Otherwise, it is judged that the power grid construction project is normal.

[0020] The safety module is used to determine whether there is a potential threat to the flight equipment during inspection and to perform safe operations when encountering obstacles and failures;

[0021] The data storage module is used to store information collected by the acquisition module and the data processing module.

[0022] Preferably, the specific method of obtaining the structural integrity analysis value Q may be:

[0023] Obtain standard image information of the power grid construction project in advance, process the high-quality images and video data captured by a high-resolution camera into image photos of the same size as the standard image information at one frame per second, compare the standard image information with the image photos, and obtain the number of pixels of defects in the image photos and the ratio of the defect area to the total area;

[0024] The defect detection value Q1 of the power grid construction project is obtained based on the ratio of the defect area to the defect number;

[0025] Then, according to Q=(Q1*γ1)+(Q2*γ2)+(Q3*γ3), the defect detection value Q1 of the power grid construction project is calculated and obtained, wherein γ1, γ2 and γ3 are preset weight coefficients.

[0026] Preferably, the infrared vegetation index R may be obtained by:

[0027] According to the formula The infrared vegetation index R is obtained, wherein R1 is the reflectivity of the near-infrared band, and R2 is the reflectivity of the red band.

[0028] Preferably, the data processing module further includes an optimization unit, and the specific working mode of the optimization unit is as follows:

[0029] Obtain the environmental parameters in the current environment, including light intensity L, temperature value t and visibility V;

[0030] Pre-set the threshold ranges of environmental parameters, including the light intensity threshold range, temperature threshold range and visibility threshold range;

[0031] Compare the environmental parameters in the current environment with the threshold range of the environmental parameters one by one. If a certain environmental parameter is within the threshold range of the corresponding environmental parameter, the infrared vegetation index R, the laser radar accuracy score value E, the thermal imaging recognition value W and the structural integrity analysis value Q obtained are used and transmitted to the analysis unit for calculation. Otherwise, it is determined that adjustments need to be made according to the environmental parameters.

[0032] According to the environmental parameters, data processing parameters are obtained, including an image exposure compensation value U1, a thermal imaging color scale range value U2, a laser radar point cloud resolution value U3, and an infrared image gain value U4;

[0033] According to the formula Qnew =Q(U1*k1),W new =W(U2*k2),E new =E(U3*k3) and R new =R(U4*k4), respectively calculate and obtain the adjusted infrared vegetation index R new , LiDAR accuracy score E new , thermal imaging recognition value W new and structural integrity analysis value Q new ; Where k1, k2, k3 and k4 are preset adjustment coefficients;

[0034] The adjusted infrared vegetation index R new , LiDAR accuracy score E new , thermal imaging recognition value W new and structural integrity analysis value Q new The data are transmitted to the analysis unit to calculate a new comprehensive inspection evaluation value.

[0035] Preferably, the specific method of obtaining the data processing parameters is as follows:

[0036] According to the formula The image exposure compensation value U1 is obtained by calculation, where and is the preset compensation coefficient;

[0037] According to the formula The thermal imaging color code range value U2 is obtained by calculation, where and is the preset compensation coefficient, t0 is the reference temperature;

[0038] According to the formula The laser radar point cloud resolution value U3 is obtained by calculation, where e is the base of the natural logarithm and g is the maximum value of the infrared image gain;

[0039] According to the formula U4 = min (V, P max ), calculate and obtain the infrared image gain value U4, where P max is the maximum resolution of the LiDAR point cloud.

[0040] Preferably, the safety module includes a path optimization unit, which is used to optimize the operating parameters of the flight equipment during the inspection process to avoid possible turbulence and instability, specifically:

[0041] Obtain the flight speed H of the flying device at the current time point;

[0042] Set the speed vector of the flight device at the current time point to H1, and the ambient wind speed vector to H2.

[0043] Obtain the angle θ between the current flight direction of the flight device and the ambient wind speed;

[0044] According to the formula H3=(H1+H2) / cos(θ), the adjusted flight equipment speed value H3 is calculated and obtained;

[0045] The adjusted flight equipment speed value H3 is transmitted to the ground control console, and then the flight parameters of the flight equipment are optimized.

[0046] Preferably, the safety module further includes a fault monitoring unit, which is used to monitor in real time whether there is any abnormality in the flight equipment during inspection. The specific working method is as follows:

[0047] Obtaining a deviation percentage value M1 between the battery voltage of the flight equipment and the rated voltage;

[0048] Obtaining a battery power detection value M2 of the flight equipment;

[0049] Obtaining a battery temperature detection value M3 of the flight equipment;

[0050] Obtaining a battery current detection value M4 of the flight equipment;

[0051] According to the formula B=(M1*μ1)+(M2*μ2)+(M3*μ3)+(M4*μ4), the battery abnormality value B of the flight equipment is calculated and obtained;

[0052] A battery abnormality threshold is set in advance. If the battery abnormality value B is less than the battery abnormality threshold, it indicates that the flight device is abnormal. Otherwise, it indicates that the flight device is operating normally.

[0053] Preferably, the safety module further includes an adjustment unit, which is used to further adjust the flight parameters when an abnormality occurs in the flight equipment:

[0054] According to the formula The adjustment value ΔS of the flight device is obtained by calculation, wherein G1 is the target height required for the flight device to land safely, G is the current height of the flight device, H1 is the target speed required for the flight device to land safely, and q is the exponent of the speed adjustment; it should be noted that, in this embodiment, q is 0.983;

[0055] According to the formula ΔG=G*(1-ΔS), ΔV=H*(1-ΔS), a new flight height ΔG and a new flight speed ΔV of the flying device are calculated and obtained;

[0056] According to the formula The pitch angle ΔC of the flight device after adjustment is calculated, where C is the initial pitch angle of the flight device. is the preset pitch angle variation coefficient; it should be noted that, in this embodiment The value of is 1.023;

[0057] The adjusted ΔG, ΔV and ΔC are transmitted to the ground control console to control the flight equipment.

[0058] Preferably, the data storage module includes a block storage unit, and the block storage unit is used to store the data of the load device separately by minute;

[0059] And according to the importance and urgency of the data, the thermal imaging or infrared image data of the payload equipment is stored preferentially.

[0060] A monitoring method for a power grid construction project quality monitoring drone according to the present invention comprises the following steps:

[0061] Step 1: Obtaining the operating parameters of the flight equipment and the data information collected by the payload equipment;

[0062] Step 2: Evaluate the inspection results of the power grid construction project based on the collected data information;

[0063] Step 3: Determine whether the flight equipment has potential threats during inspection and perform safe operations when encountering obstacles and failures:

[0064] The process of step two includes: obtaining the structural integrity analysis value of the power grid construction project; obtaining the thermal imaging recognition value of the power grid construction project; obtaining the lidar accuracy score value of the power grid construction project; obtaining the infrared vegetation index of the power grid construction project; calculating and obtaining the comprehensive inspection assessment value of the power grid construction project; setting a threshold value of the comprehensive inspection assessment value in advance, if the comprehensive inspection assessment value T is greater than the threshold value of the comprehensive inspection assessment value, then it is judged that there is a potential threat to the power grid construction project, otherwise, it is judged that the power grid construction project is normal.

[0065] The beneficial effects of the present invention are as follows: by optimizing and adjusting the parameters processed by the analysis unit according to the environmental conditions of the information collected by the payload equipment, the inspection results are more in line with reality in special circumstances, thus avoiding misjudgment; by collecting the environmental wind speed, the flight speed of the flight equipment is adaptively adjusted to reduce the occurrence of turbulence. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a flow chart of the monitoring drone control method of the present invention.

[0067] Figure 2 It is a flow chart of the monitoring method of the unmanned aerial vehicle of the present invention. DETAILED DESCRIPTION

[0068] The specific implementation modes of the present invention are further described in detail below in conjunction with the accompanying drawings and embodiments.

[0069] Example 1. Reference Figure 1 , the present invention proposes a power grid construction project quality monitoring drone, including flight equipment, ground control console, payload equipment, communication equipment and data processing system;

[0070] The flight equipment is the basic part of the UAV, and the flight equipment includes the fuselage, power equipment and flight control equipment of the UAV;

[0071] The communication device includes a navigation unit and a communication unit. The navigation unit is used to ensure that the UAV can accurately navigate and locate, and the communication unit is used for data transmission between the flight device and the ground control console. It should be noted that in this embodiment, the navigation unit includes GPS and various sensors to achieve the positioning function, and the data transmission of the communication unit includes real-time video, images and data of the load equipment.

[0072] The payload equipment includes a high-resolution camera, a thermal imaging camera, a laser radar and an infrared sensor; it should be noted that, in this embodiment, the high-resolution camera is used to capture high-quality images and video data, the thermal imaging camera is used to monitor hot spots and identify overheated components or potential faults, the laser radar is used for high-precision three-dimensional terrain mapping and power grid line modeling, and the infrared sensor is used for vegetation analysis and fault monitoring;

[0073] The ground control console is used to operate the flight equipment; it should be noted that, in this embodiment, it specifically includes planning the flight and receiving and analyzing the data;

[0074] The data processing system includes a data acquisition module, a data processing module, a security module and a data storage module;

[0075] The acquisition module is used to obtain the operating parameters of the flight equipment and the data information collected by the payload equipment; it should be noted that, in this embodiment, the operating parameters of the flight equipment include battery status: battery voltage: monitor the current voltage level of the battery; battery power: the percentage of remaining battery power; battery temperature: ensure that the battery operates within a safe temperature range; power management: current consumption: the current consumption of the drone during flight; power efficiency: the efficiency of the power system in converting electrical energy into usable energy for the drone; flight parameters: flight altitude: the current flight altitude of the drone; flight speed: the flight speed of the drone; flight direction: the flight direction or heading of the drone; system health: motor status: the operating status of the drone motor, including speed and temperature; it should also be noted that the above parameters are obtained through various sensors installed on the flight equipment;

[0076] The data processing module includes an analysis unit, which is used to analyze the data information collected by the payload equipment, specifically:

[0077] The structural integrity analysis value Q of the power grid construction project is obtained; it should be noted that, in this embodiment, the structural integrity analysis value Q is used to evaluate defects in the image data;

[0078] A thermal imaging recognition value W of the power grid construction project is obtained; it should be noted that, in this embodiment, the value of the thermal imaging recognition value W may be, a temperature threshold is set in advance, the area of ​​the power grid construction project image whose temperature exceeds the temperature threshold is determined, and the ratio of the area exceeding the temperature threshold to the total area is marked as the thermal imaging recognition value W;

[0079] Obtaining a laser radar accuracy score value E of the power grid construction project; it should be noted that, in this embodiment, the value of the laser radar accuracy score value E can be obtained by calculating the resolution and error of the point cloud data, such as the root mean square error;

[0080] Obtain the infrared vegetation index R of the power grid construction project;

[0081] According to the formula T=(Q*β1)+(W*β2)+(E*β3)+(R*β4), the comprehensive inspection evaluation value T of the power grid construction project is calculated, wherein β1, β2, β3 and β4 are preset weight coefficients; it should be noted that, in this embodiment, the values ​​of β1, β2, β3 and β4 can be 0.183, 0.298, 0.169 and 0.35;

[0082] A threshold value of the comprehensive inspection evaluation value is set in advance. If the comprehensive inspection evaluation value T is greater than the threshold value of the comprehensive inspection evaluation value, it is judged that there is a potential threat to the power grid construction project. Otherwise, it is judged that the power grid construction project is normal. It should be noted that by comprehensively calculating and evaluating the data information collected by the load equipment, the inspection results of the power grid construction project can be comprehensively evaluated, and the subsequent corresponding measures can be taken to assist in processing.

[0083] The specific method for obtaining the structural integrity analysis value Q may be:

[0084] Obtain standard image information of the power grid construction project in advance, process the high-quality images and video data captured by a high-resolution camera into image photos of the same size as the standard image information at one frame per second, compare the standard image information with the image photos, and obtain the number of pixels of defects in the image photos and the ratio of the defect area to the total area;

[0085] The defect detection value Q1 of the power grid construction project is obtained based on the ratio of the defect area to the defect number;

[0086] Then, according to Q=(Q1*γ1)+(Q2*γ2)+(Q3*γ3), the defect detection value Q1 of the power grid construction project is calculated, where γ1, γ2 and γ3 are preset weight coefficients. It should be noted that in this embodiment, the values ​​of γ1, γ2 and γ3 can be 0.283, 0.298 and 0.419, Q2 is the number of defects in the image, and Q3 is the average size of the defects in the image.

[0087] The infrared vegetation index R may be obtained by:

[0088] According to the formula The infrared vegetation index R is obtained, wherein R1 is the reflectivity of the near-infrared band, and R2 is the reflectivity of the red band.

[0089] Embodiment 2. Based on the above embodiment 1, an optimization unit is further included, and the specific working mode of the optimization unit is as follows: It should be noted that since the inspection method mainly relies on the information collected by the payload device, the quality of images and videos may be affected in environments with many visual obstacles or poor lighting conditions, making visual-based analysis difficult. Moreover, if the performance of the drone or sensor equipment used is insufficient, the required accuracy and resolution may not be achieved, affecting the accuracy of the analysis results. Existing image recognition and data analysis software may not be able to identify all types of defects or anomalies, especially in complex or non-standard situations. This technical solution optimizes and adjusts the parameters processed by the analysis unit according to the environmental conditions of the information collected by the payload device, so that in special cases, the inspection results are more in line with reality and avoid misjudgment.

[0090] Obtain the environmental parameters in the current environment, including light intensity L, temperature value t and visibility V;

[0091] Pre-set the threshold ranges of environmental parameters, including the light intensity threshold range, temperature threshold range and visibility threshold range;

[0092] The environmental parameters in the current environment are compared with the threshold range of the environmental parameters one by one. If a certain environmental parameter is within the threshold range of the corresponding environmental parameter, the obtained infrared vegetation index R, lidar accuracy score value E, thermal imaging recognition value W and structural integrity analysis value Q are used and transmitted to the analysis unit for calculation. Otherwise, it is judged that adjustment needs to be made according to the environmental parameters. It should be noted that the threshold range of the environmental parameters is the normal working environment of the payload equipment. If the current environmental parameters are not the normal working environment, the data collected by the payload equipment needs to be adjusted accordingly. It should also be noted that the structural integrity analysis value Q is obtained by judging the collected images, so it is related to the light intensity L, temperature value t and visibility V. When the light intensity L, temperature value t and visibility V are not within the preset range, they need to be adjusted.

[0093] According to the environmental parameters, data processing parameters are obtained, including an image exposure compensation value U1, a thermal imaging color scale range value U2, a laser radar point cloud resolution value U3, and an infrared image gain value U4;

[0094] According to the formula Q new =Q(U1*k1),W new =W(U2*k2),E new =E(U3*k3) and R new =R(U4*k4), respectively calculate and obtain the adjusted infrared vegetation index R new , LiDAR accuracy score E new , thermal imaging recognition value W new and structural integrity analysis value Q new ; Wherein k1, k2, k3 and k4 are preset adjustment coefficients; It should be noted that, in this embodiment, the values ​​of k1, k2, k3 and k4 can be 1.023, 2.039, 1.293 and 0.798;

[0095] The adjusted infrared vegetation index R new , LiDAR accuracy score E new , thermal imaging recognition value W new and structural integrity analysis value Q new The data are transmitted to the analysis unit to calculate a new comprehensive inspection evaluation value.

[0096] The specific method of obtaining the data processing parameters is as follows:

[0097] According to the formula The image exposure compensation value U1 is obtained by calculation.

[0098] middle and is a preset compensation coefficient; it should be noted that, in this embodiment, and The values ​​of can be 0.293 and 1.203;

[0099] According to the formula The thermal imaging color code range value U2 is obtained by calculation, where and is a preset compensation coefficient, and t0 is a reference temperature; it should be noted that, in this embodiment, and The values ​​of can be 0.733 and 1.015;

[0100] According to the formula The laser radar point cloud resolution value U3 is obtained by calculation, where e is the base of the natural logarithm and g is the maximum value of the infrared image gain;

[0101] According to the formula U4 = min (V, P max ), calculate and obtain the infrared image gain value U4, where P max is the maximum resolution of the LiDAR point cloud.

[0102] Embodiment 3. Based on the above embodiments 1 and 2, a safety module is further included, which is used to determine whether there is a potential threat when the flight equipment is inspected, and to perform safe operations when encountering obstacles and failures;

[0103] The safety module includes a path optimization unit, which is used to optimize the operating parameters of the flight equipment during the inspection process to avoid possible turbulence and instability, specifically:

[0104] Obtain the flight speed H of the flying device at the current time point;

[0105] Set the speed vector of the flight device at the current time point to H1, and the ambient wind speed vector to H2.

[0106] Obtain the angle θ between the current flight direction of the flight device and the ambient wind speed;

[0107] According to the formula H3=(H1+H2) / cos(θ), the adjusted flight equipment speed value H3 is calculated and obtained;

[0108] The adjusted flight equipment speed value H3 is transmitted to the ground control console, and then the flight parameters of the flight equipment are optimized. It should be noted that since the operation technology of drones is not popular, when encountering strong winds, turbulence is inevitable, which affects the collection of data. This technical solution can collect the ambient wind speed and adaptively adjust the flight speed of the flight equipment to reduce the generation of turbulence.

[0109] The safety module also includes a fault monitoring unit, which is used to monitor in real time whether there is any abnormality in the flight equipment during inspection. The specific working method is as follows:

[0110] Obtaining a deviation percentage value M1 between the battery voltage of the flight equipment and the rated voltage;

[0111] Obtaining a battery power detection value M2 of the flight equipment;

[0112] Obtaining a battery temperature detection value M3 of the flight equipment;

[0113] Obtaining a battery current detection value M4 of the flight equipment;

[0114] According to the formula B=(M1*μ1)+(M2*μ2)+(M3*μ3)+(M4*μ4), the battery abnormality value B of the flight equipment is calculated and obtained;

[0115] A battery abnormality threshold is set in advance. If the battery abnormality value B is less than the battery abnormality threshold, it indicates that the flight device is abnormal. Otherwise, it indicates that the flight device is operating normally.

[0116] As an optional embodiment: the safety module further includes an adjustment unit, and the adjustment unit is used to further adjust the flight parameters when the flight equipment is abnormal:

[0117] According to the formula The adjustment value ΔS of the flight device is obtained by calculation, wherein G1 is the target height required for the flight device to land safely, G is the current height of the flight device, H1 is the target speed required for the flight device to land safely, and q is the exponent of the speed adjustment; it should be noted that, in this embodiment, q is 0.983;

[0118] According to the formula ΔG=G*(1-ΔS), ΔV=H*(1-ΔS), a new flight height ΔG and a new flight speed ΔV of the flying device are calculated and obtained;

[0119] According to the formula The pitch angle ΔC of the flight device after adjustment is calculated, where C is the initial pitch angle of the flight device. is the preset pitch angle variation coefficient; it should be noted that, in this embodiment The value of is 1.023.

[0120] Embodiment 4. Based on the above embodiments 1, 2 and 3, a data storage module is further included, the data storage module includes a block storage unit, and the block storage unit is used to store the data of the load device separately by minute; it should be noted that even if an interruption occurs during the data transmission process, data loss can be reduced;

[0121] And according to the importance and urgency of the data, the thermal imaging or infrared image data of the payload equipment is stored preferentially. It should be noted that the block storage unit is also used to temporarily store the data that has not been successfully transmitted and resend it when the communication conditions improve.

[0122] like Figure 2 As shown, the present invention also proposes a monitoring method for a power grid construction project quality monitoring drone, comprising the following steps:

[0123] Step 1: Obtaining the operating parameters of the flight equipment and the data information collected by the payload equipment;

[0124] Step 2: Evaluate the inspection results of the power grid construction project based on the collected data information;

[0125] Step 3: Determine whether the flight equipment has potential threats during inspection and perform safe operations when encountering obstacles and failures:

[0126] The process of step two includes: obtaining the structural integrity analysis value of the power grid construction project; obtaining the thermal imaging recognition value of the power grid construction project; obtaining the lidar accuracy score value of the power grid construction project; obtaining the infrared vegetation index of the power grid construction project; calculating and obtaining the comprehensive inspection assessment value of the power grid construction project; setting a threshold value of the comprehensive inspection assessment value in advance, if the comprehensive inspection assessment value T is greater than the threshold value of the comprehensive inspection assessment value, then it is judged that there is a potential threat to the power grid construction project, otherwise, it is judged that the power grid construction project is normal.

[0127] The working principle of the present invention is as follows: by optimizing and adjusting the parameters processed by the analysis unit according to the environmental conditions of the information collected by the payload equipment, the inspection results are more in line with reality under special circumstances to avoid misjudgment; by collecting the environmental wind speed, the flight speed of the flight equipment is adaptively adjusted to reduce the occurrence of turbulence.

[0128] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technical staff in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A drone for monitoring the quality of power grid construction projects, characterized in that: It includes flight equipment, ground control console, payload equipment, communication equipment and data processing system; the flight equipment includes the fuselage, power equipment and flight control equipment of the UAV; the communication equipment includes a navigation unit and a communication unit, the navigation unit is used to ensure that the UAV can accurately navigate and locate, and the communication unit is used for data transmission between the flight equipment and the ground control console; the payload equipment includes a high-resolution camera, a thermal imaging camera, a laser radar and an infrared sensor; the ground control console is used to operate the flight equipment; the data processing system includes a data acquisition module, a data processing module, a security module and a data storage module; the acquisition module is used to obtain the operating parameters of the flight equipment and the data information collected by the payload equipment; the data processing module includes an analysis unit, and the analysis unit is used to analyze the data information collected by the payload equipment, specifically: Obtain the structural integrity analysis value Q of the power grid construction project; Obtain a thermal imaging recognition value W of the power grid construction project; Obtain the laser radar accuracy score E of the power grid construction project; Obtain the infrared vegetation index R of the power grid construction project; According to the formula T = (Q*β1) + (W*β2) + (E*β3) + (R*β4), the comprehensive inspection evaluation value T of the power grid construction project is calculated and obtained, where β1, β2, β3 and β4 are preset weight coefficients; A threshold value of the comprehensive inspection evaluation value is set in advance. If the comprehensive inspection evaluation value T is greater than the threshold value of the comprehensive inspection evaluation value, it is judged that there is a potential risk in the power grid construction project. Otherwise, it is judged that the power grid construction project is normal. The safety module is used to determine whether there are potential risks in the flight equipment during inspection and to perform safe operations when encountering obstacles and failures; The data storage module is used to store information collected by the acquisition module and the data processing module.

2. The power grid construction project quality monitoring drone according to claim 1, characterized in that: The specific method for obtaining the structural integrity analysis value Q may be: Obtain standard image information of the power grid construction project in advance, process the high-quality images and video data captured by a high-resolution camera into image photos of the same size as the standard image information at one frame per second, compare the standard image information with the image photos, and obtain the number of pixels of defects in the image photos and the ratio of the defect area to the total area; The defect detection value Q1 of the power grid construction project is obtained based on the ratio of the defect area to the defect number; Then, according to Q=(Q1*γ1)+(Q2*γ2)+(Q3*γ3), the structural integrity analysis value Q of the power grid construction project is calculated and obtained, where γ1, γ2 and γ3 are preset weight coefficients, Q2 is the number of defects in the image, and Q3 is the average size of the defects in the image.

3. The power grid construction project quality monitoring drone according to claim 2, characterized in that: The infrared vegetation index R may be obtained by: According to the formula The infrared vegetation index R is obtained, wherein R1 is the reflectivity of the near-infrared band, and R2 is the reflectivity of the red band.

4. The power grid construction project quality monitoring drone according to claim 1, characterized in that: The data processing module also includes an optimization unit, and the specific working method of the optimization unit is as follows: Obtain the environmental parameters in the current environment, including light intensity L, temperature value t and visibility V; Pre-set the threshold ranges of environmental parameters, including the light intensity threshold range, temperature threshold range and visibility threshold range; Compare the environmental parameters in the current environment with the threshold range of the environmental parameters one by one. If a certain environmental parameter is within the threshold range of the corresponding environmental parameter, the infrared vegetation index R, the laser radar accuracy score value E, the thermal imaging recognition value W and the structural integrity analysis value Q obtained are used and transmitted to the analysis unit for calculation. Otherwise, it is determined that adjustments need to be made according to the environmental parameters. According to the environmental parameters, data processing parameters are obtained, including an image exposure compensation value U1, a thermal imaging color scale range value U2, a laser radar point cloud resolution value U3, and an infrared image gain value U4; According to the formula Q new =Q(U1*k1),W new =W(U2*k2),E new =E(U3*k3) and R new =R(U4*k4), respectively calculate and obtain the adjusted infrared vegetation index R new , LiDAR accuracy score E new , thermal imaging recognition value W new and structural integrity analysis value Q new ; Where k1, k2, k3 and k4 are preset adjustment coefficients; The adjusted infrared vegetation index R new , LiDAR accuracy score E new , thermal imaging recognition value W new and structural integrity analysis value Q new The data are transmitted to the analysis unit to calculate a new comprehensive inspection evaluation value.

5. The power grid construction project quality monitoring drone according to claim 4, characterized in that: The specific method of obtaining the data processing parameters is as follows: According to the formula The image exposure compensation value U1 is obtained by calculation, where and is the preset compensation coefficient; According to the formula The thermal imaging color code range value U2 is obtained by calculation, where and is the preset compensation coefficient, t0 is the reference temperature; According to the formula The laser radar point cloud resolution value U3 is obtained by calculation, where e is the base of the natural logarithm and g is the maximum value of the infrared image gain; According to the formula U4 = min (V, P max ), calculate and obtain the infrared image gain value U4, where P max is the maximum resolution of the LiDAR point cloud.

6. The power grid construction project quality monitoring drone according to claim 1, characterized in that: The safety module includes a path optimization unit, which is used to optimize the operating parameters of the flight equipment during the inspection process to avoid possible turbulence and instability, specifically: Obtain the flight speed H of the flying device at the current time point; Set the speed vector of the flight device at the current time point to H1, and the ambient wind speed vector to H2. Obtain the angle θ between the current flight direction of the flight device and the ambient wind speed; According to the formula H3=(H1+H2) / cos(θ), the adjusted flight equipment speed value H3 is calculated and obtained; The adjusted flight equipment speed value H3 is transmitted to the ground control console, and then the flight parameters of the flight equipment are optimized.

7. The power grid construction project quality monitoring drone according to claim 6, characterized in that: The safety module also includes a fault monitoring unit, which is used to monitor in real time whether the flight equipment has any abnormality during inspection. The specific working method is as follows: Obtaining a deviation percentage value M1 between the battery voltage and the rated voltage of the flight equipment; Obtaining a battery power detection value M2 of the flight equipment; Obtaining a battery temperature detection value M3 of the flight equipment; Obtaining a battery current detection value M4 of the flight equipment; According to the formula B=(M1*μ1)+(M2*μ2)+(M3*μ3)+(M4*μ4), the battery abnormality value B of the flight equipment is calculated and obtained; A battery abnormality threshold is set in advance. If the battery abnormality value B is less than the battery abnormality threshold, it indicates that the flight device is abnormal. Otherwise, it indicates that the flight device is operating normally.

8. The power grid construction project quality monitoring drone according to claim 7, characterized in that: The safety module further includes an adjustment unit, which is used to further adjust the flight parameters when an abnormality occurs in the flight equipment: According to the formula The adjustment value ΔS of the flight device is obtained by calculation, wherein G1 is the target height required for the flight device to land safely, G is the current height of the flight device, H1 is the target speed required for the flight device to land safely, and q is the exponent of the speed adjustment; it should be noted that, in this embodiment, q is 0.983; According to the formula ΔG=G*(1-ΔS), ΔV=H*(1-ΔS), a new flight height ΔG and a new flight speed ΔV of the flying device are calculated and obtained; The pitch angle ΔC after adjustment of the flight device is calculated according to the formula ΔC=C+(ΔS*θ), where C is the initial pitch angle of the flight device, and θ is the preset pitch angle change coefficient; The adjusted ΔG, ΔV and ΔC are transmitted to the ground control console to control the flight equipment.

9. The power grid construction project quality monitoring drone according to claim 1, characterized in that: The data storage module includes a block storage unit, which is used to store the data of the payload equipment separately by minutes; and according to the importance and urgency of the data, the thermal imaging or infrared image data of the payload equipment is stored preferentially.

10. The method for monitoring the quality of power grid construction projects by using a drone according to claim 1, characterized in that: The following steps are involved: Step 1: Obtaining the operating parameters of the flight equipment and the data information collected by the payload equipment; Step 2: Evaluate the inspection results of the power grid construction project based on the collected data information; Step 3: Determine whether the flight equipment has potential threats during inspection and perform safe operations when encountering obstacles and failures: The process of step two includes: obtaining the structural integrity analysis value of the power grid construction project; obtaining the thermal imaging recognition value of the power grid construction project; obtaining the lidar accuracy score value of the power grid construction project; obtaining the infrared vegetation index of the power grid construction project; calculating and obtaining the comprehensive inspection assessment value of the power grid construction project; setting a threshold value of the comprehensive inspection assessment value in advance, if the comprehensive inspection assessment value T is greater than the threshold value of the comprehensive inspection assessment value, then it is judged that the power grid construction project has potential risks, otherwise, it is judged that the power grid construction project is normal.

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