Defect detection and defect identification method for anti-falling guide rail of power transmission line

Abstract

The invention discloses a defect detection and identification method for an anti-falling guide rail of a power transmission line, and relates to the technical field of power transmission, and the method comprises the following steps: carrying out the preliminary identification of the defect of the guide rail through the data collected by an unmanned plane; according to the preliminary detection result of the unmanned aerial vehicle, a key monitoring area is determined, and a corresponding sensor module is installed for continuous time sequence data acquisition; dynamically evaluating the health state of the guide rail according to the time sequence change monitoring data; according to the health state assessment result, the system can automatically mark high-risk areas on the guide rail, and workers need to maintain and repair the high-risk areas in time. The sensors are arranged in key monitoring areas, real-time monitoring of the state of the guide rail is achieved, the change condition of defects can be continuously tracked, expansion and deterioration of the defects can be captured in time, potential safety hazards are prevented from being further aggravated, and analysis of time sequence data and evaluation of the defect evolution trend are further carried out.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission, and in particular to a method for detecting and identifying defects in a transmission line anti-fall guide rail. Background Art

[0002] With the rapid development of power transmission systems, the safety and stability of transmission lines have become extremely important, especially for high-altitude transmission lines and their ancillary equipment, such as safety devices such as anti-fall rails. In daily operation, these rails are exposed to the external environment for a long time and are affected by natural factors such as wind, rain, lightning, ice and snow. They are prone to structural defects such as bending, breaking, dislocation and loose bolts. If these defects are not discovered and handled in time, they may lead to safety hazards of transmission lines and even cause large-scale power outages, resulting in significant economic losses and safety risks.

[0003] In the existing technology, the combination of drones and sensors can realize all-weather and all-angle monitoring of transmission line ancillary facilities, effectively improving the efficiency and accuracy of detection. However, the existing technology still has deficiencies in defect identification and dynamic monitoring, especially the lack of early warning and dynamic trend analysis of rail defects, and cannot timely and effectively evaluate the health status of the rails. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the problems existing in the above-mentioned existing method for detecting and identifying defects in a transmission line anti-fall guide rail, the present invention is proposed.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] A method for detecting and identifying defects in a transmission line anti-falling guide rail, the method comprising the following steps:

[0008] Step 1: The drone conducts a comprehensive inspection of the anti-fall rails on the transmission line, focusing on high-altitude areas and complex curved areas that are difficult to reach or view manually. The data collected by the drone is used to preliminarily identify the defects of the rails, mainly including bending, fracture, loose bolts, and misalignment;

[0009] Step 2: Based on the preliminary inspection results of the drone, determine the key monitoring areas and install corresponding sensor modules for continuous time series data collection, mainly to monitor whether the defects on the guide rails are expanding or worsening;

[0010] Step 3: Based on the data from time-series monitoring, the health status of the guide rail will be dynamically evaluated;

[0011] Step 4: Based on the health status assessment results, the system will automatically mark the high-risk areas on the rails. For these high-risk areas, staff should perform maintenance and repairs in a timely manner.

[0012] As a preferred solution of the method for detecting and identifying defects in a transmission line anti-falling guide rail of the present invention, in which: in the process of step 1, the preliminary identification is specifically: using the image processing system of the drone to identify and mark possible defects, each defect is assigned an initial state value, then the defective data set D = {D 1 ,D 2 ,D 3 ,...,D i}.

[0013] As a preferred solution of the method for detecting and identifying defects in a transmission line anti-falling guide rail according to the present invention, the method further includes: a data review and optimization mechanism, which is: after the guide rail defect detection and maintenance work is completed, the system will review and summarize all the detection data to evaluate the accuracy of the entire process;

[0014] The error evaluation model is used to calculate the accuracy of the detection process, and its expression is as follows:

[0015]

[0016] Among them, R i is the actual state value of the guide rail after repair, D i is the detected state value, is the total number of monitoring points, and the error E exceeds the set threshold, indicating that there is a large deviation in the detection process, reminding the staff to optimize the detection route or monitoring parameters.

[0017] As a preferred solution of the method for detecting and identifying defects in a transmission line anti-falling guide rail according to the present invention, in the step 2, monitoring whether the defects on the guide rail become larger specifically includes:

[0018] S201: Install the sensor module in the area where the defect is found during the preliminary inspection and record the real-time data collected as S i (t), where i represents different monitoring points, t represents time, and the change in defect ΔD=S at the i-th monitoring point within time t is calculated i (t)-D i ;

[0019] S202: By analyzing the time series data S of the monitoring point i(t) is derived to obtain the defect expansion rate, and then the defect expansion rate is integrated to calculate the cumulative defect expansion amount ΔD of the guide rail. total (t).

[0020] As a preferred solution of the method for detecting and identifying defects in a transmission line anti-falling guide rail according to the present invention, wherein: the ΔD total The larger the (t) value, the more serious the defect expansion and the more dangerous the guide rail condition; the smaller the value, the healthier the guide rail is.

[0021] As a preferred solution of the method for detecting and identifying defects in a transmission line anti-falling guide rail according to the present invention, the calculation method of the rate of defect expansion is:

[0022]

[0023] Cumulative defect extension ΔD total (t) is calculated as:

[0024]

[0025] Among them, w i represents the weight coefficient, α represents the attenuation coefficient, e -αt Used to reflect change over time, with newer data being more important for evaluating results.

[0026] As a preferred solution of the method for detecting and identifying defects in a transmission line anti-fall guide rail described in the present invention, during the step three, the data of all monitoring points are summarized by a computer, and a health score or health grade of the guide rail is given in combination with trend analysis.

[0027] The above-mentioned system for detecting and identifying defects in a transmission line anti-falling guide rail includes the following functional modules:

[0028] The system includes: a drone inspection module, which is responsible for the automated execution of inspection tasks by drones and the collection of high-definition images and videos of the anti-fall guide rails;

[0029] The defect preliminary identification module performs preliminary analysis on the collected images and data to identify common rail defects, such as bending, breakage, loose bolts, and misalignment.

[0030] The key monitoring area identification module combines the defect identification results to screen out potential risk areas on the rail, and the system automatically generates a priority list for subsequent sensor installation;

[0031] Sensor module, used to continuously monitor key areas for a long time and collect time series data to track defect changes;

[0032] The time series data processing and storage module receives real-time monitoring data from sensors, stores and analyzes the data, and tracks the evolution trend of defects;

[0033] and a dynamic defect assessment module, which dynamically assesses the health status of the guide rail based on time series data to determine whether it is safe and requires immediate repair.

[0034] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned method for detecting and identifying defects in a transmission line anti-fall guide rail when executing the computer program.

[0035] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for detecting and identifying defects in a transmission line anti-fall guide rail.

[0036] Beneficial effects of the present invention: The present invention arranges sensors in key monitoring areas to achieve real-time monitoring of the guide rail status, which can continuously track changes in defects, timely capture the expansion and deterioration of defects, and avoid further aggravation of safety hazards. Furthermore, through the analysis of time series data and the evaluation of defect evolution trends, the health status of the guide rail can be dynamically evaluated, and high-risk areas can be automatically marked to provide timely warnings and decision-making support for operation and maintenance personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0038] Figure 1 The present invention provides a schematic diagram of the overall process of a method for detecting and identifying defects in a transmission line anti-fall guide rail. DETAILED DESCRIPTION

[0039] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0040] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0041] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0042] Reference Figure 1 , as an embodiment of the present invention, provides a method for detecting and identifying defects in a transmission line anti-falling guide rail, the identification method comprising the following steps:

[0043] Step 1: The drone conducts a comprehensive inspection of the anti-fall rails on the transmission line, focusing on high-altitude areas and complex curved areas that are difficult to reach or view manually. The data collected by the drone is used to preliminarily identify the defects of the rails, mainly including bending, fracture, loose bolts, and misalignment;

[0044] Specifically: The image processing system of the drone is used to identify and mark possible defects. Each defect is assigned an initial state value. The defect data set D = {D 1 ,D 2 ,D 3 ,...,D i}.

[0045] Step 2: Based on the preliminary detection results of the drone, determine the key monitoring areas (such as high-altitude dangerous areas, bending positions, etc.), and install corresponding sensor modules for continuous time series data collection, mainly to monitor whether the defects on the guide rail are expanding or worsening;

[0046] Specifically: S201: Install the sensor module in the area where the defect is found during the preliminary inspection to record the real-time collected data as S i (t), where i represents different monitoring points, t represents time, and the change in defect ΔD=S at the i-th monitoring point within time t is calculated i (t)-D i ;

[0047] S202: Evaluate the defect expansion rate of each monitoring point to determine the damage speed of the guide rail by analyzing the time series data S of the monitoring point. i (t) is derived to obtain the defect expansion rate, which is calculated as follows:

[0048]

[0049] Then the defect expansion rate is integrated to calculate the cumulative defect expansion of the guide rail ΔD total (t), calculated as:

[0050]

[0051] Among them, w i represents the weight coefficient, α represents the attenuation coefficient, e -αt Used to reflect changes over time, with newer data being more important for evaluating results

[0052] Here ΔD total The larger the (t) value, the more serious the defect expansion and the more dangerous the rail condition; the smaller the value, the healthier the rail is.

[0053] Step 3: Based on the data from time-series monitoring, the health status of the guide rail will be dynamically evaluated;

[0054] That is, the data from all monitoring points are summarized by computer, and combined with trend analysis, a health score or health grade of the guide rail is given. For example, if most of the monitoring points show that defects are gradually worsening, the system will judge that the guide rail is in poor health and may issue an alarm to prompt maintenance. For example, if most of the monitoring points show that defects are gradually worsening, the system will judge that the guide rail is in poor health and may issue an alarm to prompt maintenance.

[0055] Step 4: Based on the health status assessment results, mark the high-risk areas on the guide rails. Workers should perform maintenance and repairs on these high-risk areas in a timely manner.

[0056] In addition, the method also includes: a data review and optimization mechanism, which is: after the guide rail defect detection and maintenance work is completed, the system will review and summarize all the detection data to evaluate the accuracy of the entire process;

[0057] The error evaluation model is used to calculate the accuracy of the detection process, and its expression is as follows:

[0058]

[0059] Among them, R i is the actual state value of the guide rail after repair, D i is the detected state value, is the total number of monitoring points, and the error E exceeds the set threshold, indicating that there is a large deviation in the detection process, reminding the staff to optimize the detection route or monitoring parameters.

[0060] The above-mentioned identification system for the power transmission line anti-falling rail defect detection and defect identification method comprises: a drone inspection module, which is responsible for the drone to automatically perform inspection tasks and collect high-definition images and videos of the anti-falling rail;

[0061] The defect preliminary identification module performs preliminary analysis on the collected images and data to identify common rail defects, such as bending, breakage, loose bolts, and misalignment.

[0062] The key monitoring area identification module combines the defect identification results to screen out potential risk areas on the rail, and the system automatically generates a priority list for subsequent sensor installation;

[0063] Sensor module, used to continuously monitor key areas for a long time and collect time series data to track defect changes;

[0064] The time series data processing and storage module receives real-time monitoring data from sensors, stores and analyzes the data, and tracks the evolution trend of defects;

[0065] And the defect dynamic assessment module, based on the time series data, dynamically assesses the health status of the guide rail to determine whether it is safe and needs immediate maintenance

[0066] This embodiment also provides a computer device, which is suitable for a method for detecting and identifying defects in a transmission line anti-falling guide rail, and includes: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a method for detecting and identifying defects in a transmission line anti-falling guide rail as proposed in the above embodiment.

[0067] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0068] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, a method for detecting and identifying defects in a transmission line anti-fall guide rail is implemented as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a disk or an optical disk.

[0069] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for detecting and identifying defects in a transmission line anti-falling guide rail, characterized in that: The method comprises the following steps: Step 1: The drone conducts a comprehensive inspection of the anti-fall rails on the transmission line, focusing on high-altitude areas and complex curved areas that are difficult to reach or view manually. The data collected by the drone is used to preliminarily identify the defects of the rails, mainly including bending, fracture, loose bolts, and misalignment; Step 2: Based on the preliminary inspection results of the drone, determine the key monitoring areas and install corresponding sensor modules for continuous time series data collection, mainly to monitor whether the defects on the guide rails are expanding or worsening; Step 3: Based on the data from time-series monitoring, the health status of the guide rail will be dynamically evaluated; Step 4: Based on the health status assessment results, the system will automatically mark the high-risk areas on the rails. For these high-risk areas, staff should perform maintenance and repairs in a timely manner.

2. A method for detecting and identifying defects in a transmission line anti-falling guide rail according to claim 1, characterized in that: In the process of step 1, the preliminary identification is specifically as follows: using the image processing system of the drone, possible defects are identified and marked, and each defect is assigned an initial state value. The defect data set D = {D1, D2, D3, ..., D i }.

3. A method for detecting and identifying defects in a transmission line anti-falling guide rail according to claim 2, characterized in that: The method also includes: a data review and optimization mechanism, which is: after the guide rail defect detection and maintenance work is completed, the system will review and summarize all the detection data and evaluate the accuracy of the entire process; The error evaluation model is used to calculate the accuracy of the detection process, and its expression is as follows: Among them, R i is the actual state value of the guide rail after repair, D i is the detected state value, is the total number of monitoring points, and the error E exceeds the set threshold, indicating that there is a large deviation in the detection process, reminding the staff to optimize the detection route or monitoring parameters.

4. A method for detecting and identifying defects in a transmission line anti-falling guide rail according to claim 3, characterized in that: In the step 2, monitoring whether the defect on the guide rail becomes larger specifically includes: S201: Install the sensor module in the area where the defect is found during the preliminary inspection and record the real-time data collected as S i (t), where i represents different monitoring points, t represents time, and the change in defect ΔD=S at the i-th monitoring point within time t is calculated i (t)-D i ; S202: By analyzing the time series data S of the monitoring point i (t) is derived to obtain the defect expansion rate, and then the defect expansion rate is integrated to calculate the cumulative defect expansion amount ΔD of the guide rail. total (t).

5. A method for detecting and identifying defects in a transmission line anti-falling guide rail according to claim 4, characterized in that: The ΔD total The larger the (t) value, the more serious the defect expansion and the more dangerous the guide rail condition; the smaller the value, the healthier the guide rail is.

6. A method for detecting and identifying defects in a transmission line anti-falling guide rail according to claim 5, characterized in that: The defect expansion rate is calculated as follows: Cumulative defect extension ΔD total (t) is calculated as: Among them, w i represents the weight coefficient, α represents the attenuation coefficient, e -αt Used to reflect change over time, with newer data being more important for evaluating results.

7. A method for detecting and identifying defects in a transmission line anti-falling guide rail according to claim 6, characterized in that: During step three, the data from all monitoring points are summarized by computer, and combined with trend analysis, a health score or health grade of the guide rail is given.

8. The identification system of the power transmission line anti-falling guide rail defect detection and defect identification method according to claim 7 is characterized in that: The system includes: a drone inspection module, which is responsible for the automated execution of inspection tasks by drones and the collection of high-definition images and videos of the anti-fall guide rails; The defect preliminary identification module performs preliminary analysis on the collected images and data to identify common rail defects, such as bending, breakage, loose bolts, and misalignment. The key monitoring area identification module combines the defect identification results to screen out potential risk areas on the rail, and the system automatically generates a priority list for subsequent sensor installation; Sensor module, used to continuously monitor key areas for a long time and collect time series data to track defect changes; The time series data processing and storage module receives real-time monitoring data from sensors, stores and analyzes the data, and tracks the evolution trend of defects; and a dynamic defect assessment module, which dynamically assesses the health status of the guide rail based on time series data to determine whether it is safe and requires immediate repair.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a method for detecting and identifying defects in a transmission line anti-fall guide rail as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for detecting and identifying defects in a transmission line anti-fall guide rail as described in any one of claims 1 to 7 are implemented.

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