System for monitoring power facility employing mobile office vehicle with drone

Abstract

The invention relates to a transmission tower management system using a drone. A plurality of transmission towers (100) located within a set area, each connected by a power line; a drone (200) that travels to the transmission towers (100) via a mobile office vehicle (10) and flies around the transmission towers (100). A transmission tower management system using a drone, comprising: a control device (300) that monitors the transmission tower (100) and the power lines of the transmission tower (100) by collecting a 3D point cloud of the transmission tower (100) using a LiDAR sensor and measuring the inclination of the power lines of the transmission tower (100) using the 3D point cloud information of the drone (200); wherein the method of monitoring the transmission tower (100) and the power lines of the transmission tower (100) by the control device (300) comprises: a first step of calculating the inclination of the power lines of the transmission tower (100) by measuring the shape of the corresponding power lines using the 3D point cloud of the LiDAR sensor with respect to the power lines of the transmission tower (100) from the drone (200); a second step of collecting and registering multiple inclination information for the corresponding power lines during a first set period in the first step; and a third step of calculating the average value of the inclination information of the second step. In the first step above, a fourth step of collecting and registering multiple slope information for the corresponding wire during a second setting period; in the fourth step above, the second setting period is longer than the first setting period by a setting value, and in the fourth step above, a fifth step of leveling the corresponding slope information during the second setting period into 1-5 steps to set a fuzzy value for each step; in the fifth step above, the fuzzy value is fuzzy value 0 for the first step, fuzzy value 0.5 for the second step, fuzzy value 1 for the third step, and fuzzy value 1 for the fourth step.5; the above 5th step is set to a fuzzy value of 2, and after the above 5th step, the step to which the average value of the above 3rd step belongs is identified among the above 1-5 steps, and the fuzzy value of the corresponding step is set; the above 6th step, after the above 6th step, a weight of 1 is assigned if the average value of the above 3rd step is within the 1st setting standard range, and a weight of 2 is assigned if it is within the 2nd setting threshold range; in the above 7th step, the 2nd setting range is higher than the 1st setting range by a set value, and the corresponding average value is corrected by multiplying the fuzzy value of the above 6th step and the weight of the above 7th step by the average value of the above 2nd step; the above 9th step compares the average value of the above 8th step with the setting standard average value; and in the above 9th step, the reference average value is the average value of the corresponding slope information during the second setting period of the above 4th step, and in the above 9th step, if the average value of the above 8th step is less than or equal to the reference average value, the slope calculation of the above 1st step is determined to be in a normal state, and if it exceeds, it is determined to be in an abnormal state; characterized by including.

Description

Technology Field

[0001] The contents disclosed in this specification relate to a mobile office vehicle, a method for managing transmission towers in various locations using a drone mounted on the vehicle, and a system. Background Technology

[0002] Unless otherwise indicated in this specification, the contents described in this section are not prior art for the claims of this application, and are not to be recognized as prior art simply because they are included in this section.

[0003] Recent mobile office vehicles are vehicles converted into mobile office spaces and can be equipped with drones. They are used in disaster sites, accident sites, and power facility sites that are difficult for rescuers or managers to access, collecting video information and monitoring and managing the site through control devices.

[0004] The above control device is linked with the drone and collects video information of disaster sites, accident sites, and power facilities from the drone, thereby identifying the situation of disasters, accidents, and power facilities (e.g., distribution lines).

[0005] For example, in the case of power facilities, the fault condition is identified by checking the inclination, sagging, and breakage of the distribution lines.

[0007] The present invention relates to such a system, and among them, to a method for monitoring transmission towers installed in various locations.

[0008] In particular, regarding the management of power lines on transmission towers, since the wires connecting the towers are generally long and tend to bend or sag, this invention provides a method to measure their inclination and re-check whether they are in a normal state, thereby enabling comprehensive monitoring including the presence of fire.

[0009] For reference, the prior art with this background is as follows. Prior art literature

[0010] Reference 1 Domestic Registration No. 10-2671710

[0011] Reference 2 Domestic Registration No. 10-2478413

[0012] Reference 3 Domestic Publication No. 10-2025-0115799

[0013] Reference 4 U.S. Publication No. 2024-0230901 The problem to be solved

[0014] The disclosed content aims to provide a transmission tower management system using a drone, which can monitor and manage transmission towers and power lines in various locations by utilizing a mobile office vehicle, a drone mounted on the vehicle, and a control device.

[0015] In particular, it involves monitoring and managing the power lines of transmission towers; since the wires connecting the towers are generally long and tend to bend or sag, the inclination of the wires is accurately measured to suit the site conditions.

[0016] In addition, the normality of the measurement is re-checked, enabling comprehensive fire monitoring.

[0017] To elaborate, the overall shape of a power line used for slope measurement can change multiple times due to pressure from wind, air pressure, the density of obstructions such as dust and particles, and other pressures. Therefore, an accurate definition (specification of the power line's external shape) must be established beforehand.

[0018] In addition, the embodiment performs a re-inspection of the inclination measurement of these wires to accurately verify whether the inclination measurement is normal, thereby enabling comprehensive fire monitoring. means of solving the problem

[0019] A transmission tower management system using a drone according to an embodiment is,

[0020] Multiple transmission towers located within a set area, each connected by a power line;

[0021] A drone that travels to the transmission tower via a mobile office vehicle and flies around the transmission tower; and

[0022] The above drone is,

[0023] A 3D point cloud is collected for the above transmission tower using a LiDAR sensor, and

[0024] A transmission tower management system using a drone, comprising: a control device that monitors the transmission tower and the power lines of the transmission tower by measuring the inclination of the power lines of the transmission tower using 3D point cloud information of the drone;

[0025] The method for monitoring the transmission tower and the power lines of the transmission tower of the above-mentioned control device is,

[0026] A first step of calculating the inclination by measuring the shape of the power line of the transmission tower using the 3D point cloud of the lidar sensor with respect to the power line of the transmission tower in the above drone;

[0027] In the first step above, a second step of collecting and registering multiple inclination information for the corresponding wire during a first setting period;

[0028] A third step for calculating the average value of the slope information of the second step above;

[0029] In the above first step, a fourth step of collecting and registering multiple slope information for the corresponding wire during the second setting period;

[0030] In the above fourth step, the above second setting period is,

[0031] It is longer than the first setting period above by a setting value, and

[0032] In the above 4th step, the 5th step involves leveling the corresponding slope information during the 2nd setting period into 1-5 steps and setting a fuzzy value for each step;

[0033] In the above 5th step, the fuzzy value is,

[0034] The above first step is set to a fuzzy value of 0, the above second step to a fuzzy value of 0.5, the above third step to a fuzzy value of 1, the above fourth step to a fuzzy value of 1.5, and the above fifth step to a fuzzy value of 2, and

[0035] Step 6, after Step 5 above, identifying the step to which the average value of Step 3 belongs among Steps 1-5 above, and setting the fuzzy value of the corresponding step;

[0036] After the above 6th step, a 7th step of assigning weight 1 if the average value of the above 3rd step is within the 1st set standard range and weight 2 if it is within the 2nd set threshold range;

[0037] In the above 7th step, the above 2nd setting range is,

[0038] It is higher than the above-mentioned first setting range by a set value, and

[0039] Step 8, which corrects the average value of Step 2 by multiplying the fuzzy value of Step 6 and the weight of Step 7;

[0040] Step 9, which compares the average value of Step 8 above with the set standard average value; and

[0041] In the above 9th step, the above reference average value is,

[0042] It is the average value of the corresponding slope information during the second setting period of the above fourth step, and

[0043] The method is characterized by including a 10th step in which, in the 9th step above, the slope calculation of the 1st step is determined to be in a normal state if the average value of the 8th step is less than or equal to the reference average value, and in an abnormal state if it exceeds the reference average value.

[0044] And, the above fourth step is,

[0045] Step 4-1, for the corresponding slope information during the second setting period above, extracting a plurality of K points using the K-means technique and setting them as center points;

[0046] Step 4-2 for calculating the difference in distance between the center point of Step 4-1 and the remaining points;

[0047] Step 4-3, which performs clustering by assigning the remaining points to the nearest center point based on the distance from Step 4-2 above;

[0048] Step 4-4, which updates the point corresponding to the center in the cluster of Step 4-3 above as the center point;

[0049] Step 4-5, which repeatedly performs steps 4-2 through 4-4 until the center point of step 4-4 does not change;

[0050] Step 4-6, which obtains the corresponding slope information during the second setting period using the center point of Step 4-5 as a valid value; and

[0051] It includes a 4-7 step of leveling the slope information of the 4-6 steps to the 1-5 steps and setting fuzzy values ​​for each step.

[0052] After the above 10th step,

[0053] If the slope calculation state of the above-mentioned first stage is a normal state, the slope at the current time is compared with the set reference slope, and if, as a result of the comparison, the slope at the current time differs from the reference slope by more than the set value, a fire monitoring request is performed to the drone,

[0054] The above drone is,

[0055] In response to a fire monitoring request from the above control device, fire monitoring video information is acquired using a video monitoring device embedded in the above transmission tower, and

[0056] The above control device is,

[0057] It is characterized by performing fire monitoring on the transmission tower using fire monitoring video information from the above drone. Effects of the invention

[0058] According to embodiments, a transmission tower management system using a drone is provided, which can monitor and manage transmission towers and power lines in various locations by using a mobile office vehicle, a drone mounted on the vehicle, and a control device.

[0059] In particular, it involves monitoring and managing the power lines of transmission towers; since the wires connecting the towers are generally long and tend to bend or sag, the inclination of the wires is accurately measured to suit the site conditions.

[0060] In addition, the normality of the measurement is re-checked, enabling comprehensive fire monitoring.

[0061] In other words, if there is an abnormality in the inclination of the relevant power line, additional video surveillance of the transmission tower and power line can be performed to identify whether a fire has occurred in advance.

[0062] To elaborate, since the overall shape of a power line used for slope measurement can change multiple times due to pressure from wind, air pressure, the density of obstructions such as dust or particles, the external shape of the power line must be accurately defined beforehand.

[0063] In addition, the inclination measurements of these wires are re-inspected to accurately verify their normality, thereby enabling comprehensive fire monitoring. Brief explanation of the drawing

[0064] FIG. 1 is a conceptual diagram of a transmission tower management system using a drone according to a first embodiment. FIG. 2 is a drawing illustrating the system of FIG. 1 in its entirety. Figure 3 is a configuration diagram of a control device applied to the system of Figure 1. FIG. 4 is a diagram illustrating the operation of the system of FIG. 1. Specific details for implementing the invention

[0065] FIG. 1 is a diagram conceptually illustrating a transmission tower management system using a drone according to a first embodiment.

[0066] As illustrated in FIG. 1, the system according to the first embodiment first performs monitoring of transmission towers (100) in various locations using a mobile office vehicle (10), a power facility (100), a drone (200) mounted on the vehicle (10), and a control device (300).

[0067] For reference, the above transmission tower (100) is located in a place where it is difficult for a manager to access, and it is difficult to monitor and manage due to the installation location, the actual location of the object, etc., including the pole and power line according to the first embodiment.

[0068] As described above, the system according to the first embodiment is a method for monitoring and managing a transmission tower and its power lines, which accurately measures the inclination of the power lines according to the field conditions and, through this, accurately identifies whether there is an abnormality in the transmission, distribution, etc. lines.

[0069] In particular, since the power lines connecting transmission towers are generally long and tend to bend or sag, the inclination of the lines is accurately measured to suit the site conditions.

[0070] In addition, the normality of the measurement is re-checked, enabling comprehensive fire monitoring.

[0071] In other words, if there is an abnormality in the inclination of the relevant power line, additional video surveillance of the transmission tower and power line can be performed to identify whether a fire has occurred in advance.

[0072] To elaborate, since the overall shape of a power line used for slope measurement can change multiple times due to pressure from wind, air pressure, the density of obstructing materials such as dust or particles, an accurate definition must be established beforehand.

[0073] Depending on these various factors, the overall shape of the power line and the resulting changes in slope can vary, and it is necessary to understand the overall shape and slope characteristics of the power line in accordance with such changes.

[0074] The first embodiment takes into account these changes and the resulting deviations, and to this end, extracts valid data from the shape of the wire and the slope that may appear in various ways in a single wire and identifies the characteristics.

[0075] Through this, the measurement error can be reduced and the accuracy of the analysis increased.

[0076] To extract such valid data, the K-MEANS technique can be utilized to apply a method of finding the average value of multiple shape (slope) measurements at the same point or on a single wire at that point.

[0077] Rather than focusing on this aspect, the present invention re-examines the normality of the measurement in such cases, particularly since the power lines connecting transmission towers are generally long and tend to bend or sag, and thereby performs fire monitoring comprehensively.

[0078] For reference, this is also possible using measurement methods other than the aforementioned one.

[0079] The system according to the first embodiment first includes a mobile office vehicle (10), a transmission tower (100), a drone (200), and a control device (300) for this purpose.

[0080] The above mobile office vehicle (10) is a mobile office converted into a vehicle, and various types of vehicles are possible using conventional technology.

[0081] The above mobile office vehicle (10) is a drone station for the above drone (200). This is an automatic flight method in which take-off and landing are performed while stationary and diagnostic flight follows the mobile office vehicle (10). When diagnosing a route where vehicle entry is difficult, automatic flight is enabled by manual operation or by specifying the coordinates of the route.

[0082] Configure a backup battery sufficient to perform diagnostic tasks continuously for at least 5 hours a day and a fast charging system powered by an electric vehicle.

[0083] In addition, at least one spare drone is always carried on the vehicle to handle unexpected situations, such as a breakdown of the flying drone.

[0084] The above transmission tower (100) is one or more power facilities located within a set area and equipped with a pole and a power line.

[0085] The drone (200) is mounted on the mobile office vehicle (10) and moves toward the transmission tower (100), and flies around the transmission tower (100) by ascending and descending using the mobile office vehicle (10) as a drone station. According to the first embodiment, the drone (200) collects a 3D point cloud with a LiDAR sensor for the transmission tower to obtain external shape information such as utility poles and power lines.

[0086] In the above drone (200), the lidar sensor is a high-precision lidar, and can acquire the external shape of a utility pole, power line, communication line, etc. as a 3D point cloud and quantify structural information such as tilt, bending, sagging of the power line, and height of the communication line.

[0087] The above control device (300) is mounted on the mobile office vehicle (10) and monitors and manages the transmission tower (100) and its power lines using the external information of the drone (200). Specific details will be explained with reference to FIG. 4.

[0088] The above control device (300) must be capable of processing the 3D point cloud and image data in real-time or in batches, and must be able to learn the facility standards presented by the facility management agency and automatically determine the risk level (e.g., good, warning, dangerous) for each facility.

[0089] It must be possible to predict the risk level of facilities in advance by accumulating and managing the database collected through repetitive diagnostic tasks, and notify the facility management agency of this.

[0090] Additionally, among the databases, it must be possible to transmit databases of facilities that exceed the standards required by the facility management agency or have a high risk level using a 5G or LTE communication network.

[0091] The above control device (300) must be able to quantitatively diagnose all of the following items by analyzing collected data such as a lidar sensor, optical camera (RGB), thermal imaging camera, ultrasound, RTK, etc.

[0092] It is built as an expandable system capable of additional analysis at the request of the facility management agency.

[0093] * Inclination deviation of the utility pole (angle calculation based on XZ vector), bending, cracks

[0094] * Wire sag (difference in Z-axis distance between the lowest point and the pole), heat generation conditions at connection points, etc.

[0095] Cracks, damage, and overheating conditions in major facilities such as insulators and switchgear

[0096] * Whether shared communication lines installed on KEPCO poles fail to meet installation standards, such as ground height

[0097] Analysis of mutual separation distances between power facilities and other facilities and determination of substandard standards

[0098] According to the first embodiment, the method of monitoring the inclination of the transmission tower (100), that is, the power line, of the control device (300) is as follows.

[0099] First, the overall system flowchart is as follows.

[0100] ① Position correction by prior RTK drone flight or UAV RTK loading in conjunction with the control device (300)

[0101] ② The UAV automatically flies along a set path in conjunction with the control device (300) (i.e., the vehicle).

[0102] ③ Collect equipment external shape information using a LiDAR sensor (or, + optical (RGB) camera).

[0103] ④ The in-vehicle control unit (300, AI diagnostic device) analyzes data in real-time or batch mode.

[0104] ⑤ Automatic determination of quantitative indicators of diagnostic equipment, such as tilt, bending, cracks, and sagging

[0105] ⑥ Hazardous locations are classified and reported to and recorded in the facility management agency's maintenance system.

[0106] For reference, the system according to the first embodiment may additionally include the following configuration.

[0107] The above system may relate to an on-device AI control system equipped with an Unmanned Aerial Vehicle (UAV), a detachable precision sensor module, and in-vehicle AI technology, based on an electric vehicle converted into a mobile office.

[0108] This is equipped with a lidar sensor, an optical camera (RGB) image sensor, a detachable RTK GNSS module, a thermal imaging camera, an ultrasonic diagnostic device, etc., to quantitatively measure structural conditions such as tilt, bending, cracks, wire sagging, height of communication lines, heat generation, and partial discharge of utility poles and power facilities.

[0109] Risk levels are automatically determined through an on-device AI analyzer installed in mobile office vehicles, and this information can be linked with power facility management systems (such as NDIS) to enable continuous and quantitative facility diagnosis.

[0110] The above detachable RTK GNSS module (a detachable type when necessary) collects quantitative data of facilities by utilizing only an RTK correction drone or through parallel diagnosis with LiDAR to perform high-precision coordinate correction of existing power facilities before diagnostic flight.

[0111] The above-mentioned detachable ultrasonic receiver (detachable as needed) detects abnormal sounds such as partial discharge (PD), corona, arc, and mechanical friction occurring in power equipment during flight.

[0112] It performs 'reception of ultrasound via microphone or sensor → FFT-based signal processing → transmission to the vehicle's on-device control system → AI analysis and accumulated DB management'.

[0113] Figure 2 is a diagram illustrating the system of Figure 1 in its entirety.

[0114] Referring to FIG. 2, the system of FIG. 1 largely comprises transmission towers (100) installed at multiple different locations, and for monitoring them, includes a mobile office vehicle (10), a drone (200) mounted on the vehicle, and a control device (300).

[0115] Additionally, it includes a management information processing device (400) located at a remote location that collects monitoring information from each system, notifies the manager, and manages it. It also includes a manager terminal (500) for this purpose.

[0116] Each device is connected via a private network or a leased network, and can use existing wired and wireless communication networks.

[0117] The above mobile office vehicle (10) is a mobile office converted into a vehicle, as described in FIG. 1, and various types of vehicles are possible with conventional technology.

[0118] The above transmission tower (100) is one or more power facilities located within a set area and equipped with a pole and a power line.

[0119] The drone (200) is mounted on the mobile office vehicle (10) and moves toward the transmission tower (100), and flies around the transmission tower (100) by ascending and descending using the mobile office vehicle (10) as a drone station. According to the first embodiment, the drone (200) collects a 3D point cloud with a LiDAR sensor for the transmission tower to obtain external shape information such as utility poles and power lines.

[0120] The above control device (300) is mounted on the mobile office vehicle (10) and monitors the transmission tower (100) using the external information of the drone (200). According to the first embodiment, the inclination of the power line is measured to determine whether there is an abnormality.

[0121] FIG. 3 is a configuration diagram of a control device (300) applied to the system of FIG. 1.

[0122] Referring to FIG. 3, the control device (300) mainly includes an interface unit (301), a signal processing unit (302), a storage unit (303), a key signal input unit (304), a display unit (305), and a control unit (306).

[0123] The above interface unit (301) is connected to the above drone (200), the above management information processing device (400), etc., to transmit and receive various information.

[0124] The above signal processing unit (302) processes and converts the information according to a determined signal format in order to transmit and receive information of the interface unit (301), and this includes signal processing according to the transmission (communication) signal format.

[0125] The above storage unit (303) stores the 3D point cloud of the drone (200) and the shape information of the wires, etc., that come out through the above signal processing unit (302).

[0126] The key signal input unit (304) receives setting information for monitoring power facility_transmission towers according to the first embodiment based on user key operation. For example, the reference slope value of the corresponding power line, K-MEANS algorithm, etc.

[0127] The above display unit (305) displays the results of the inclination measurement of the wire, etc., under the control of the above control unit (306).

[0128] The above control unit (306) controls each of the above units and, through this, performs monitoring operations of the transmission tower and the power line according to the first embodiment, and the specific details are as shown in FIG. 4.

[0129] Figure 4 is a diagram illustrating the operation of the system of Figure 1.

[0130] Referring to FIG. 4, the system of FIG. 1 first relates to a method of monitoring the transmission tower (100) of the control device (300).

[0131] As shown in Fig. 1, the basic concept is that when the control device (300) collects 3D point cloud information from multiple different points on the drone (200) using a lidar sensor, it receives this information.

[0132] That is, 1-n3D point cloud information is collected for each 1-n measurement point.

[0133] Next, in this first-n3D point cloud, K actual point signals are extracted from mutually corresponding positions, i.e., points, and fixed as center points to set initial values.

[0134] The distance between the center points set in this way and the remaining points (locations or their signals) is calculated, and the remaining parts are assigned to the nearest center point for clustering.

[0135] The center point is updated for each cluster using the part (point or spot) corresponding to the center of the above-mentioned processed cluster as the center point.

[0136] Therefore, the process is repeated until the above-mentioned updated center point no longer changes, and the center point is obtained as a valid value.

[0137] These valid values ​​(i.e., average values) are calculated to generate metadata for each location.

[0138] The slope of the corresponding wire is calculated using the above metadata, and since the method for calculating the slope is conventional technology, a detailed explanation thereof is omitted here.

[0139] Specifically, the control device (300) receives first-n3D point cloud information for each first-n measurement point for the power line of the power facility (100) from the drone (200).

[0140] From the input 1-n3D point cloud information above, mutually corresponding point locations are found, and K points are extracted for each based on the K-MEANS technique and set as center points.

[0141] For example, mutually corresponding point positions correspond when the 13D point cloud information is 1, 3, 5, 8, 9 (e.g., 1 is a position), and when the 23D point cloud information is 1, 4, 5, 10, 11, 1 and 1, 3 and 4, 5 and 5, 8 and 10, and 9 and 11 correspond.

[0142] When multiple different measurement points are as close as possible, better results can be obtained, but the shape of the 3D point cloud should be generally similar enough to be obtained even with factors such as wind pressure and air pressure.

[0143] Calculate the distance between the center point set above and the remaining points.

[0144] Cluster processing is performed by assigning the remaining points to the nearest center point based on the distance calculated above.

[0145] The point corresponding to the center in the above-processed cluster is updated as the center point.

[0146] Each of the aforementioned steps is performed repeatedly until the updated center point no longer changes.

[0147] The average point is extracted using the center point performed above as a valid value.

[0148] The slope of the above wire is calculated using the above average point.

[0149] Meanwhile, another embodiment can calculate valid values ​​for each interval between each point and generate metadata of the point signal for each interval between each point.

[0150] This finds valid values ​​between intervals through linear regression-based trend line analysis.

[0151] This is calculated through linear regression-based trend line analysis according to the equation below, and additionally generates metadata for point signals for each segment.

[0152] [Equation 1] y i = x i T β + ε i (i = 1, ... , n),

[0153] (here, y i is the valid value for each interval between each point, x i T is the predicted point signal for each interval between each point (T is the transpose), and β is x i T Coefficient, ε i is an error variable)

[0154] Therefore, the slope of the aforementioned front is calculated using each of the aforementioned effective values, namely the aforementioned average point and the aforementioned average interval.

[0155] Through this, the shape and slope of the power line may change due to factors such as wind pressure, air pressure, or pressure caused by obstructing factors like dust or particles, but a certain degree of accuracy can be achieved using the aforementioned average value (i.e., the average point or a combination of the average point and the average section).

[0156] In particular, in such cases, since the power lines connecting the transmission towers are generally long and tend to bend or sag, this embodiment re-checks the normality of the measurements and performs fire monitoring comprehensively through this process.

[0157] For reference, this is also possible using measurement methods other than the aforementioned one.

[0158] To this end, the control device (300) measures the shape of the wire of the transmission tower (100) using a 3D point cloud of the lidar sensor and calculates the inclination (Step 1).

[0159] In the first step above, multiple slope information for the relevant wire is collected and registered during the first setting period, for example, the last 1 month, 6 months, or the last 1 year (second step).

[0160] Calculate the average value of the slope information from the above 2nd step (3rd step).

[0161] In the first step above, multiple slope information for the corresponding wire during the second setting period is collected and registered (step 4).

[0162] In the above fourth step, the second setting period is longer than the first setting period by a set value, and this corresponds to a long period, for example, more than one year or about five years.

[0163] In the above 4th step, the corresponding slope information during the above 2 setting period is leveled into 1-5 steps, and a fuzzy value is set for each step (5th step).

[0164] The above 1-5 steps are leveled based on the multiple slope information, where step 1 corresponds to the smallest value and category, and step 5 corresponds to the highest value and category.

[0165] These steps may be reduced as in steps 1-3 or increased as in steps 1-10, depending on the amount and importance of the information.

[0166] For reference, the above steps 1-5 correspond to the average value among them.

[0167] In the above 5th step, the fuzzy value is set as fuzzy value 0 for the 1st step, fuzzy value 0.5 for the 2nd step, fuzzy value 1 for the 3rd step, fuzzy value 1.5 for the 4th step, and fuzzy value 2 for the 5th step.

[0168] For reference, Fuzzy Inference is a decision-making method based on fuzzy logic that is used to process uncertain information or ambiguous data. It is a method that performs more flexible reasoning by utilizing continuous values ​​(values ​​between 0 and 1) instead of traditional binary logic (0 or 1).

[0169] After the above 5th step, the step to which the average value of the above 3rd step belongs is identified among the above 1-5 steps, and the fuzzy value of the corresponding step is set (6th step).

[0170] After the above Step 6, if the average value of the above Step 3 is within the first set standard range, a weight of 1 is assigned, and if it is within the second set threshold range, a weight of 2 is assigned (Step 7).

[0171] In the above 7th step, the above 1st setting standard range is a reference value of the corresponding average value, and is a boundary value that can be considered an abnormal state if the value exceeds that range, and a normal state if it is less than that value.

[0172] On the other hand, the second setting range is higher than the first setting range by a set value, and if it exceeds that value, it is a boundary value that can be seen as a specific abnormal state exceeding the threshold value.

[0173] The fuzzy value of the 6th step and the weight of the 7th step are multiplied by the average value of the 2nd step to correct the average value (8th step).

[0174] Compare the average value of the above 8th step with the set standard average value (9th step).

[0175] In the above 9th step, the reference average value is the average value of the corresponding slope information during the 2nd setting period of the above 4th step.

[0176] In the above 9th step, if the average value of the above 8th step is less than or equal to the reference average value, the slope calculation of the above 1st step is determined to be in a normal state, and if it exceeds it, it is determined to be in an abnormal state (10-11th step).

[0177] The system according to the second embodiment additionally performs video monitoring of the power facility in the case where there is an abnormality in the inclination of the power line in the first embodiment, thereby identifying in advance whether a fire has occurred.

[0178] This is to check for any fire hazards caused by an abnormality in the inclination of the power line.

[0179] The system according to the second embodiment is equipped with a video surveillance device on the drone (200), and when the control device (300) detects an abnormality in the inclination of the wire, i.e., a fire risk, it identifies the surrounding situation through the video surveillance device.

[0180] If there is an abnormality in the slope of the wire, the slope of the wire is calculated according to the first embodiment, and the slope is compared with a set reference value. If, as a result of the comparison, the two values ​​differ by more than the set value, it can be determined to be an abnormal state.

[0181] The above video monitoring device is in a standby state before recognizing an abnormal inclination of the wire, and after such recognition, it starts operating under the control of the control device (300).

[0182] The above video monitoring device sets the video display method according to the fire monitoring location for rapid and accurate monitoring, and informs the control device (300) of the corresponding video information.

[0183] Specific details will be described later.

[0184] In this case, the corresponding video is compressed and transmitted to the control device (300).

[0185] Meanwhile, another embodiment uses a mobile app to allow the manager to bypass a separate communication process near a specific drone (200) and directly collect fire monitoring video information, thereby enabling the manager to inspect it quickly and easily.

[0186] That is, when the manager goes near a specific drone (200), the manager automatically collects the corresponding surveillance information about the drone (200) using the mobile app on the mobile terminal he / she owns.

[0187] To do this, perform the following actions.

[0188] First, the above drone (200) sets identification information and usage location information as beacon signals for each of the multiple different drones, and registers the beacon signals for the identification information and usage location information of the corresponding drone (200) being used (Step 1).

[0189] The above-mentioned drone can be used by designating a specific location for each transmission tower, that is, for each transmission tower located in multiple different places.

[0190] Each drone sets a beacon signal for each of these usage locations and includes respective identification information.

[0191] Each of the above information is performed according to the administrator settings.

[0192] In the first step above, a beacon signal is collected from a mobile app that is within a set distance of the drone (200) (second step).

[0193] The beacon signal of the second stage and the beacon signal of the first stage are compared (third stage).

[0194] In the third step above, if the beacon signal of the second step and the beacon signal of the first step are the same, the acquired fire monitoring video information is automatically transmitted to the mobile app (fourth step).

[0195] In such cases, the mobile app performs the following actions.

[0196] First, the above mobile app is installed on the mobile terminal of the manager, and the identification information and usage location information for each drone are set as beacon signals, and the beacon signals for the identification information and usage location information of the drone (200) in use are registered (Step 1).

[0197] In the first step above, the mobile app is started at a location close to the drone (200) within the set distance (second step).

[0198] After the above second step, the registered beacon signal is transmitted (third step).

[0199] After the above third step, the fire monitoring video information obtained above is collected from the drone (200) (fourth step).

[0200] Meanwhile, another embodiment determines the image display method differently depending on the fire detection location and the number thereof, as described above.

[0201] For example, regarding fire detection locations, if a utility pole or its wire is positioned vertically within the monitoring area of ​​a power facility, such as a transmission tower, it is divided horizontally; if it is positioned horizontally, it is divided vertically; and if there are multiple locations in each case, the number of divisions is determined according to the corresponding number.

[0202] The video compression format is created by setting a first frame rate for high quality for the object and a second frame rate for low quality for the background, respectively, for each of the above video display methods.

[0203] The first frame rate is higher than the normal frame rate (based on a single display) by a first setting value, and the second frame rate is lower than the normal frame rate by a second setting value.

[0204] When objects (smoke, flames, etc.) and backgrounds are extracted from the video information of the relevant video surveillance device, high-quality and low-quality processing is performed on those objects and backgrounds using the aforementioned video compression format to perform video compression.

[0205] The above object creates a first frame rate for high quality by increasing the frame rate above the corresponding normal frame rate and performs high-quality processing. A first frame rate for high quality is created for each object according to the video display method.

[0206] For example, horizontal and vertical divisions have a higher first frame rate than a single display, and specific values ​​are determined differently for each object, such as the size, shape, resolution, and screen frequency of the object.

[0207] The background or environment creates a second frame rate for low quality by lowering the frame rate below the corresponding normal frame rate. A second frame rate for low quality is created for each video display method, and masked by low quality processing.

[0208] For example, horizontal and vertical divisions have a second frame rate lower than a single display, and specific values ​​are determined differently for each background object, such as the size, shape, resolution, and screen frequency of the background.

[0209] Specifically, the system according to the second embodiment has the control device (300) compare the slope of the eighth stage with the set reference slope, and if the result of the comparison shows that the slope of the eighth stage is different from the reference slope by more than the set value, it performs a fire monitoring request to the drone (200).

[0210] The above drone (200) acquires fire monitoring video information for the transmission tower (100) using a video monitoring device in response to a fire monitoring request from the control device (300).

[0211] The control device (300) monitors whether a fire is detected in the transmission tower (100) using fire monitoring video information from the drone (200).

[0212] In such cases, the above video surveillance device performs the following operations.

[0213] First, a different image display method is set for each monitoring area of ​​the transmission tower (100), and a different image compression format is set and registered for each image display method.

[0214] In the first step above, the image display method includes one or more of single display, horizontal split, vertical split, and PIP.

[0215] The above video compression format determines a first frame rate for high quality and a second frame rate for low quality for each of the above video display methods.

[0216] The first frame rate is higher than the set normal frame rate by a first set value, and the second frame rate is lower than the normal frame rate by a second set value.

[0217] After the above first step, the characteristics of the fire-related object to be monitored are defined, and the object information and characteristic information of the object are determined and registered.

[0218] The image display method of the video surveillance device is determined and set in correspondence with the object of the second stage above.

[0219] After the third step above, the control device (300) receives a request for input of video information of the video surveillance device.

[0220] In the above 4th step, the control device (300) compares the slope of the 8th step with the set reference slope, and if the slope of the 8th step differs from the reference slope by more than the set value, it performs the input request.

[0221] After the above 4th step, video information is collected from the video surveillance device.

[0222] The object is extracted from the image information of the above 5th step and feature information is tracked.

[0223] The object information and feature information of the above 6th step are compared with the object information and feature information of the above 2nd step to recognize fire-related objects.

[0224] In the above 7th step, the corresponding object and background are classified.

[0225] In the above 8th step, an image compression format corresponding to the image display method of the above 3rd step is extracted from the registration information of the above 2nd step.

[0226] Using the video compression format of the 9th step above, the object is processed in high quality and the background in low quality.

[0227] For reference, the above video compression format is identical to the compression method described above.

[0228] That is, the above video compression format is created by setting a first frame rate for high quality for the object and a second frame rate for low quality for the background, respectively, for each of the above video display methods.

[0229] The first frame rate is higher than the normal frame rate (based on a single display) by a first setting value, and the second frame rate is lower than the normal frame rate by a second setting value.

[0230] When objects (smoke, flames, etc.) and backgrounds are extracted from the video information of the relevant video surveillance device, high-quality and low-quality processing is performed on those objects and backgrounds using the aforementioned video compression format to perform video compression.

[0231] The above object creates a first frame rate for high quality by increasing the frame rate above the corresponding normal frame rate and performs high-quality processing. A first frame rate for high quality is created for each object according to the video display method.

[0232] For example, horizontal and vertical divisions have a higher first frame rate than a single display, and specific values ​​are determined differently for each object, such as the size, shape, resolution, and screen frequency of the object.

[0233] The background or environment creates a second frame rate for low quality by lowering the frame rate below the corresponding normal frame rate. A second frame rate for low quality is created for each video display method, and masked by low quality processing.

[0234] For example, horizontal and vertical divisions have a second frame rate lower than a single display, and specific values ​​are determined differently for each background object, such as the size, shape, resolution, and screen frequency of the background.

[0235] Compress the background of the above 10th step.

[0236] After the above 11th step, the corresponding video is transmitted to the control device (300).

[0237] Meanwhile, another embodiment can improve the compression function by using first and second frame rates for high quality and low quality for the above-mentioned video compression format, finding sections where a lot of noise appears in the video information, and increasing the compression rate in those sections.

[0238] In this embodiment, the video monitoring device sets a noise interval greater than a set value for the signal strength variation range in the video information for each of the first frame rates for high definition, for each of the video compression formats and video display methods.

[0239] The above noise section is processed at a 1-1 frame rate that is lower than the above 1 frame rate by a set value, and the remaining section is processed at the above 1 frame rate.

[0240] And, for each of the above-mentioned video display methods, a noise interval greater than a set value is set for the second frame rate for low quality, in which the range of change in signal strength in the video information is set.

[0241] The above noise section is processed at a 2-1 frame rate that is lower than the above 2 frame rate by a set value, and the remaining section is processed at the above 2 frame rate.

[0242] Therefore, when an object and background are extracted from the aforementioned image information and processed into high or low quality, the range of signal strength change in each section of the object and background is detected.

[0243] The range where the detected signal strength change is greater than the set value is detected as a noise range.

[0244] The detected noise section and the remaining section are processed into high quality and low quality using the above video compression format.

[0245] That is, for the object, the noise section is processed at a 1-1 frame rate that is lower than the 1st frame rate by a set value, and the remaining section is processed at the 1st frame rate, thereby processing it in high quality.

[0246] Regarding the background, the noise section is processed at a 2-1 frame rate that is lower than the set value of the 2nd frame rate, and the remaining section is processed at the 2nd frame rate, thereby processing it as low quality.

[0247] This can be applied equally to low frequencies for background compression.

[0248] For reference, low frequency for background compression is used to process the background at low quality and then compress it again using low frequency.

[0249] To this end, the video surveillance device sets and registers a first low frequency for background compression for each of the video display methods.

[0250] For the above first low frequency, the noise section is set to a first' low frequency that is lower than the first low frequency by a set value, and the remaining section is set to the first low frequency.

[0251] Therefore, when compressing a low-resolution background using low frequency, the noise sections of the background and the remaining sections are compressed using the aforementioned video compression format.

[0252] That is, regarding the background, the noise section is compressed by processing the first' low frequency, which is lower than the first low frequency by a set value, and the remaining section is compressed by processing the first low frequency.

[0253] The above settings are configured differently according to the type of video surveillance device (SDI camera, HDMI, etc.) and the type of surveillance location, allowing the background to be compressed to low frequency for each section. The settings are set relatively higher in the case of an SDI type or a surveillance location that is a high-crime area.

[0254] Meanwhile, another embodiment involves equipping an edge camera with an SoC chip to perform image compression, VPN, and artificial intelligence data processing directly on the camera. By performing these tasks directly on the camera and transmitting only the results to a server or central processing unit, data communication bandwidth can be reduced.

[0255] To this end, the present embodiment directly configures a VPN server of a Virtual Private Network (VPN) for communication access to a video surveillance device and configures a VPN client capable of connecting to the VPN server.

[0256] The above VPN client is a control device (300), a management information processing device (400), etc.

[0257] The above VPN server authenticates a connection request to a VPN client, such as a control device (300) or a management information processing device (400), for example.

[0258] In the above VPN server, the operation of configuring the VPN server in the video surveillance device includes generating a VPN server certificate.

[0259] It performs the function of generating a VPN client profile for the VPN client based on the above server certificate and processes its connection.

[0260] In order to retrieve video from the VPN camera from the above VPN client, the VPN camera receives and stores a VPN client profile generated by a client, such as a control device (300) or a management information processing device (400). After setting the client-specific OVPN file issued by the above client to the camera, the client can connect to the VPN.

[0261] Video compression functions according to each of the above embodiments can be applied to the above server certificate or the above VPN client profile.

[0262] For example, the above VPN client profile defines and registers a data compression format for each video display method according to the type of video surveillance device, and performs data compression accordingly before transmission.

[0263] The above-mentioned video surveillance device type includes one or more of an IP camera, an SDI camera, and an HDMI, and the above-mentioned video display method includes one or more of a single display, horizontal split, vertical split, and PIP.

[0264] In addition, there are scaling, resizing, translation, rotation, flipping, perspective transform, noise, lighting condition, and background and style changes.

[0265] The above data compression format is made different for each of the above image display methods.

[0266] For example, in the case of horizontal splitting—that is, for video surveillance device types suitable for horizontal splitting—if the outline of the file or its data is used as the reference value (i.e., a single display), compression to an intermediate resolution, such as noise filtering and sampling, can be performed. This varies depending on the type, size, and number of files. If there are many file types, sampling is performed proportionally.

[0267] In the case of PIP, compared to the method corresponding to horizontal splitting, sampling can be performed more clearly; for example, the number of samples can be reduced while repetitions can be performed more frequently.

[0268] In the case of a scene where rotation and perspective transformation are performed in addition to PIP, sampling is further reduced to slightly increase sharpness.

[0269] For such sampling, the data compression format is created by setting a first-1 frame rate for high quality for data such as text within the file and a second-1 frame rate for low quality for margins (background) within the file, respectively, for each image display method.

[0270] The above data compression format determines a first-1 frame rate for high quality and a second-1 frame rate for low quality for each of the above video display methods.

[0271] The above 1-1 frame rate is higher than the normal frame rate by the 1-1 setting value, and the above 2-1 frame rate is lower than the normal frame rate by the 2-1 setting value.

[0272] For reference, the above 1-1 frame rate for high quality and low quality is determined to be distinct from the 1 frame rate of the above-described embodiment, and the above 1-1 and 2-1 setting values ​​are also the same.

[0273] Meanwhile, in each of the aforementioned embodiments, the overall shape of the wire for measuring the inclination may change multiple times due to pressure caused by wind, air pressure, density of dust or particles, etc.

[0274] To this end, another embodiment measures the density of disabled persons and provides this along with the inclination information of the corresponding wire, or corrects it by multiplying it by the inclination value, thereby allowing an administrator to determine the accuracy of the wire inclination and the resulting abnormalities.

[0275] When measuring the density of disabled persons with a LiDAR sensor in the above drone (200), the following operations can be performed.

[0276] First, when calculating the density of persons with disabilities using the above-mentioned LiDAR sensor, a reference person with a disability, the reference density of the reference person with a disability, the reference flight time for the signal of the corresponding LiDAR sensor, and the reference number of pulses are determined and registered.

[0277] For example, information on persons with disabilities takes into account factors such as dust and particulate matter within power facilities, pressure, air pressure, and wind pressure, and the types of standard persons with disabilities and standard densities can be set differently to suit the location.

[0278] In the above reference disabled person, the reference density is a reference value in the embodiment, and the density is estimated by multiplying the laser flight time and the number of pulses for the laser signal of the lidar sensor of the disabled person located at each location, and a correction value is obtained by multiplying that by the reference density. That is, the density of obstacles is estimated.

[0279] The flight time, number of pulses, or density may be set differently for each type of person with a standard disability or a person with a disability located at each location, and the type of obstacle may be identified in such cases.

[0280] In this state, a laser is irradiated using the corresponding LiDAR sensor.

[0281] The laser reflected through the above-mentioned laser is received.

[0282] Therefore, the difference between the above-mentioned investigation time and the light reception time is calculated to determine the flight time.

[0283] Next, the number of pulses of the received laser is counted.

[0284] Calculate the difference between the above-registered standard flight time, standard number of pulses, the above-calculated flight time, and number of pulses.

[0285] Therefore, the obstacle density of the corresponding obstacle is calculated by multiplying the above-registered standard density by the correction value.

[0286] The above correction value is calculated as the difference in the calculated flight time × the difference in the number of pulses.

[0287] Through this process, the density of persons with disabilities located at the given location is calculated, and this value is used to estimate the density of persons with disabilities, which is then provided along with slope information. For reference, the slope value can also be corrected by multiplying it by this value.

[0288] Meanwhile, as mentioned above, the identification of types of persons with disabilities is as follows.

[0289] First, when a reflected laser is received, the frequency deviation value of the received laser is detected and compared with the frequency deviation value set for each type of disabled person.

[0290] For example, the above-mentioned types of persons with disabilities are classified into three stages: small, medium, and large, and the above-mentioned frequency deviation values ​​are set to a relatively low frequency deviation value in the case of small and a relatively high frequency deviation value in the case of large. Specific values ​​may be determined based on the size and weight of the person with disabilities, whether they move, their speed, etc.

[0291] Based on the above comparison result, if there is an identical frequency deviation value between the frequency deviation value of the received laser and the frequency deviation value set above, it is identified as the type of obstacle for the disabled person.

[0292] Meanwhile, based on the detection information of the above-mentioned lidar sensor, it is possible to detect whether there is a risk to the disabled person.

[0293] The risk to the disabled person is identified by emitting a laser from the lidar sensor and determining if the number of pulses of the received reflected laser falls within a set number of pulses. The set number of pulses can be determined in correspondence with the disabled person's area, height, size, etc., and one or more of these can be applied depending on the type.

[0294] Specifically, the above-mentioned lidar sensor is controlled to irradiate a laser, and the reflected laser is received.

[0295] The number of pulses of the received laser is counted and compared with the number of pulses set corresponding to the size of the disabled person, etc.

[0296] Based on the above comparison result, if the number of pulses corresponds to the number of pulses set above, the disabled person is determined to be at risk.

[0297] Meanwhile, if the number of pulses mentioned above corresponds to the number of pulses set above—that is, if it is confirmed that the disabled person is at risk—the frequency deviation value of the corresponding laser is detected and compared with the frequency deviation value set for each type of risk.

[0298] Based on the above comparison results, if there is an identical frequency shift value among the frequency shift value of the corresponding laser and the set frequency shift value, it is identified as the risk type.

[0299] The risk types of the aforementioned disabled persons can be classified into three levels: weak, medium, and strong. In this case, a relatively high frequency deviation value will be assigned to the strong group, while lower frequency deviation values ​​will be assigned to the medium and weak groups in that order. Specific values ​​may be determined based on weight, size, mobility, speed, etc. Explanation of the symbols

[0300] 10 : Mobile office vehicle 100 : Transmission tower 200 : Drone 300 : Control unit

Claims

Claim 1 A plurality of transmission towers (100) located within a set area, each connected by a wire; a drone (200) that travels to the transmission towers (100) via a mobile office vehicle (10) and flies around the transmission towers (100); A transmission tower management system using a drone, comprising: a control device (300) that monitors the transmission tower (100) and the power lines of the transmission tower (100) by collecting a 3D point cloud of the transmission tower (100) using a LiDAR sensor and measuring the inclination of the power lines of the transmission tower (100) using the 3D point cloud information of the drone (200); wherein the control device (300) comprises: a first step of measuring the shape of the power lines of the transmission tower (100) using the 3D point cloud of the LiDAR sensor to calculate the inclination of the power lines of the transmission tower (100) from the drone (200); a second step of collecting and registering multiple inclination information for the power lines during a first set period in the first step; a third step of calculating the average value of the inclination information in the second step; and a third step of collecting and registering multiple inclination information for the power lines during a second set period in the first step. Step 4; in Step 4, the second setting period is longer than the first setting period by a set value, and in Step 4, the corresponding slope information during the second setting period is leveled to Steps 1-5 to set a fuzzy value for each step; in Step 5, the fuzzy value is 0 for Step 1, 0.5 for Step 2, 1 for Step 3, and 1 for Step 4.5; the above 5th step is set to a fuzzy value of 2, and after the above 5th step, the step to which the average value of the above 3rd step belongs is identified among the above 1-5 steps, and the fuzzy value of the corresponding step is set; the above 6th step, after the above 6th step, a weight of 1 is assigned if the average value of the above 3rd step is within a first set standard range, and a weight of 2 is assigned if it is within a second set threshold range; in the above 7th step, the above 2 set threshold range is higher than the above 1 set standard range by a set value, and the above 8th step corrects the corresponding average value by multiplying the fuzzy value of the above 6th step and the weight of the above 7th step by the average value of the above 2nd step; the above 9th step compares the average value of the above 8th step with the set standard average value; A transmission tower management system using a drone, comprising: in the above 9th step, the reference average value is the average value of the corresponding slope information during the second setting period of the above 4th step; and in the above 9th step, determining the slope calculation of the above 1st step as normal state if the average value of the above 8th step is less than or equal to the reference average value, and determining it as abnormal state if it exceeds the above reference average value. Claim 2 In claim 1, the fourth step comprises: a 4-1 step of extracting a plurality of K points using the K-means technique for the corresponding slope information during the second setting period and setting them as center points; a 4-2 step of calculating the difference in distance between the center point of the 4-1 step and the remaining points; a 4-3 step of clustering the remaining points by assigning them to the closest center point based on the distance of the 4-2 step; a 4-4 step of updating the point corresponding to the center in the cluster of the 4-3 step as the center point; a 4-5 step of repeatedly performing steps 4-2 through 4-4 until the center point of the 4-4 step does not change; and a 4-6 step of obtaining the corresponding slope information during the second setting period using the center point of the 4-5 step as a valid value. A transmission tower management system using a drone, comprising: a 4-7 step of leveling the slope information of the 4-6 steps to the 1-5 steps and setting fuzzy values ​​for each step. Claim 3 In claim 2, after the 10th step, if the slope calculation state of the 1st step is in a normal state, the current slope and the set reference slope are compared, and if, as a result of the comparison, the current slope differs from the reference slope by more than the set value, a fire monitoring request is performed to the drone (200); the drone (200) acquires fire monitoring video information using a video monitoring device embedded in the transmission tower (100) in accordance with the fire monitoring request of the control device (300); the control device (300) performs fire monitoring on the transmission tower (100) using the fire monitoring video information of the drone (200); the drone (200) sets identification information and usage location information as beacon signals for each of a plurality of different drones, and registers beacon signals for the identification information and usage location information of the corresponding drone (200) being used; in the 1st step, a set mobile that is close within a set distance to the corresponding drone (200). A transmission tower management system using a drone, comprising: a second step of collecting a beacon signal from an app; a third step of comparing the beacon signal of the second step with the beacon signal of the first step; and a fourth step of transmitting the acquired fire monitoring video information to the mobile app if, in the third step, the beacon signal of the second step and the beacon signal of the first step are identical.

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