A method, device, and equipment for monitoring the performance of a tower of a power transmission line, and a storage medium

Abstract

This invention discloses a method for monitoring the performance of transmission line towers, comprising the steps of: acquiring a first feature image corresponding to a target tower via satellite equipment; if a first deformation feature is confirmed to exist in the first feature image, acquiring a second feature image corresponding to the target tower via UAV equipment; acquiring a second deformation feature in the second feature image corresponding to the first deformation feature; and monitoring the performance of the target tower based on the first and second deformation features and using a tower deformation monitoring model. This invention also discloses corresponding devices, equipment, and storage media. Implementing this invention can reduce the installation and maintenance costs of tower performance monitoring methods and improve the accuracy of the final performance monitoring results.

Description

Technical Field

[0001] This invention relates to the field of tower performance testing, and in particular to a method, apparatus, equipment, and storage medium for monitoring the performance of transmission line towers. Background Technology

[0002] With the continuous development of power systems and the acceleration of urbanization, the safety and stability of transmission lines are receiving increasing attention. As an important component of transmission lines, the performance monitoring of power transmission towers plays a crucial role in ensuring power supply and reducing accident risks.

[0003] In existing technologies, the performance of transmission line towers is typically monitored through manual inspections or the installation of fixed monitoring equipment. For manual inspections, professional monitoring personnel need to periodically monitor the towers along fixed routes; however, this method is prone to data errors due to human factors, making it difficult to guarantee the accuracy of the monitoring results. While installing fixed monitoring equipment can provide continuous data, the high installation and maintenance costs mean that large-scale performance monitoring of transmission line towers requires substantial financial resources.

[0004] Therefore, there is an urgent need for a method and device for monitoring the performance of transmission line towers, in order to reduce the installation and maintenance costs of traditional tower performance monitoring methods and improve the accuracy of the final results of tower performance monitoring. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method, device, equipment and storage medium for monitoring the performance of transmission line towers, which can reduce the installation and maintenance costs of tower performance monitoring equipment and improve the accuracy of monitoring results.

[0006] As a first aspect of the present invention, a method for monitoring the performance of transmission line towers is provided, comprising:

[0007] The first feature image corresponding to the target tower is obtained through satellite equipment;

[0008] If the first deformation feature is confirmed to exist in the first feature image, the second feature image corresponding to the target tower is obtained through the drone equipment;

[0009] Obtain the second deformation feature in the second feature image that corresponds to the first deformation feature;

[0010] Based on the tower deformation monitoring model, the performance of the target tower is monitored through the first deformation feature and the second deformation feature.

[0011] Optionally, acquiring the first feature image corresponding to the target tower via satellite equipment specifically includes: acquiring the target feature image corresponding to the target tower, and acquiring the sharpness value corresponding to the target feature image; acquiring the number of multiple deformation features to be confirmed in the target feature image; determining whether the sharpness value is greater than or equal to a preset sharpness value, and determining whether the number of multiple deformation features to be confirmed is greater than a preset number of features; if the sharpness value is greater than or equal to the preset sharpness value, and the number of multiple deformation features to be confirmed is greater than or equal to the preset number of features, the target feature image is used as the first feature image.

[0012] Optionally, confirming the existence of a first deformation feature in the first feature image specifically includes: acquiring multiple first deformation features to be confirmed in the first feature image, the multiple first deformation features to be confirmed including a first target deformation feature to be confirmed, the first target deformation feature to be confirmed being any one of the multiple first deformation features to be confirmed; the multiple first deformation features to be confirmed including tilt angle features, lateral displacement features, and large crack features; determining whether the similarity value between the first target deformation feature to be confirmed and a preset deformation feature is greater than a preset similarity value; if the similarity value between the first target deformation feature to be confirmed and the preset deformation feature is greater than the preset similarity value, then confirming the existence of a first deformation feature in the first feature image.

[0013] Optionally, obtaining the second deformation feature corresponding to the first deformation feature in the second feature image specifically includes: obtaining multiple second deformation features to be confirmed in the first feature image, the multiple second deformation features to be confirmed including a second target deformation feature to be confirmed, the second target deformation feature to be confirmed being any one of the multiple second deformation features to be confirmed; the multiple second deformation features to be confirmed include the loosening degree feature of the tower connection component, the corrosion degree feature of the tower surface, and the settlement feature of the tower support foundation; calculating the correlation coefficient between the second deformation feature to be confirmed and the first deformation feature to be confirmed; determining whether the correlation coefficient is greater than a preset correlation coefficient; if the correlation coefficient is greater than the preset correlation coefficient, then confirming the second deformation feature to be confirmed as the second deformation feature.

[0014] Optionally, before monitoring the performance of the target tower using the first and second deformation features based on the tower deformation monitoring model, the method further includes: constructing the tower deformation monitoring model: acquiring historical deformation feature data, which includes data on the loosening degree of tower connection components, data on the corrosion degree of the tower surface, and settlement data of the tower support foundation; and constructing the tower deformation monitoring model based on the historical deformation feature data.

[0015] Optionally, the target tower is monitored for performance using the first deformation feature and the second deformation feature, specifically including: constructing a first deformation feature vector matrix corresponding to the first deformation feature, and constructing a second deformation feature vector matrix corresponding to the second deformation feature; and performing feature fusion on the first deformation feature vector matrix and the second deformation feature vector matrix according to the correspondence between each first deformation feature and each second deformation feature.

[0016] Construct a comprehensive deformation feature matrix after feature fusion; use the comprehensive deformation feature matrix and the following formula to calculate the performance index value corresponding to the target tower, so as to monitor performance based on the performance index value:

[0017]

[0018] Where P is the performance index value. V is the comprehensive deformable feature matrix after feature fusion, where α, β, and γ are all weight coefficients. 1i Let V be the first deformation eigenvector matrix, where i is the number of first deformation features. 2j Let r be the second deformation eigenvector matrix, j be the number of second deformation features, and r be the number of features. ij The correlation coefficient is used to represent the degree of association between the first deformation feature and the corresponding second deformation feature.

[0019] Optionally, after calculating the performance index value corresponding to the target tower by integrating the deformation feature matrix and performing performance monitoring based on the performance index value, the method further includes: obtaining the performance index range corresponding to the performance index value and determining whether the performance index range is a preset low performance range; if the performance index range is a preset low performance range, confirming the performance improvement plan corresponding to the target tower based on the performance monitoring operation, and sending the performance improvement plan to the user terminal to facilitate performance optimization operation for the target tower.

[0020] As a second aspect of the present invention, a tower performance monitoring device for transmission lines is also provided. The device includes an acquisition module and a processing module, wherein...

[0021] The acquisition module is used to acquire a first feature image corresponding to the target tower through satellite equipment; if it is confirmed that there is a first deformation feature in the first feature image, it acquires a second feature image corresponding to the target tower through UAV equipment; and acquires a second deformation feature in the second feature image that corresponds to the first deformation feature.

[0022] The processing module is used to monitor the performance of the target tower by using the first deformation feature and the second deformation feature according to the tower deformation monitoring model.

[0023] As a third aspect of the present invention, an electronic device is also provided, including a processor, a memory, a user interface, and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any of the above.

[0024] As a fourth aspect of the invention, a computer-readable storage medium is also provided, which stores a computer program that is executed by a processor according to any of the above methods.

[0025] Implementing the embodiments of the present invention has the following beneficial effects:

[0026] This invention provides a method, apparatus, device, and storage medium for monitoring the performance of transmission line towers. A first feature image corresponding to the target tower is acquired via satellite equipment, and a first deformation feature is identified within this image. A second feature image corresponding to the target tower is acquired via UAV equipment, and a second deformation feature corresponding to the first deformation feature is obtained from this second feature image. Based on a tower deformation monitoring model, the performance of the target tower is monitored using both the first and second deformation features. This combination of satellite and UAV equipment achieves efficient and low-cost monitoring. Satellite equipment provides broad area coverage and identifies potential problems, while UAVs can acquire detailed deformation data, making the monitoring results more accurate and reliable.

[0027] In this embodiment, the correlation coefficient between the second deformation feature to be confirmed and the first deformation feature to be confirmed is calculated, and it is determined whether the correlation coefficient is greater than a preset correlation coefficient. Only when the correlation coefficient is greater than the preset correlation coefficient is the second deformation feature to be confirmed confirmed. This makes the second deformation feature obtained in the end not only more accurate, but also better reflect the actual state and change trend of the tower.

[0028] In this embodiment, a first deformation feature vector matrix corresponding to the first deformation feature is constructed, and a second deformation feature vector matrix corresponding to the second deformation feature is constructed. Based on the correspondence between each first deformation feature and each second deformation feature, feature fusion is performed on the first deformation feature vector matrix and the second deformation feature vector matrix to construct a comprehensive deformation feature matrix after feature fusion. The performance index value corresponding to the target tower is calculated through the comprehensive deformation feature matrix, thereby realizing a comprehensive evaluation of the tower performance. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies, the drawings used in the description of the embodiments of the present invention or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a schematic diagram of the main flow of an embodiment of a method for monitoring the performance of transmission line towers provided by the present invention;

[0031] Figure 2 This is a schematic diagram of a module of an embodiment of a transmission line tower performance monitoring device provided by the present invention;

[0032] Figure 3 This is a schematic diagram of the structure of an embodiment of an electronic device provided by the present invention.

[0033] Explanation of reference numerals in the attached figures: 21. Acquisition module; 22. Processing module; 301. Processor; 302. Communication bus; 303. User interface; 304. Network interface; 305. Memory. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0035] Please refer to Figure 1 This document illustrates a flowchart of an embodiment of a method for monitoring the performance of transmission line towers provided by the present invention. In this embodiment, the method includes at least the following steps:

[0036] Step S101: Obtain the first feature image corresponding to the target tower through satellite equipment.

[0037] Specifically, when a user monitors the performance of a target tower through a target terminal, satellite equipment is used to remotely acquire images of the target tower along a preset route to obtain the first feature image corresponding to the target tower. This process aims to obtain preliminary deformation information of the tower and capture tower feature images over a large area through satellite equipment, providing a basis for judging the overall status of the tower. Here, the target tower refers to any transmission line tower within the capture range of the satellite equipment. During the monitoring process, the specific target tower can be selected as needed, usually based on the priority or focus set in the monitoring plan. The status, location, and surrounding environment of each target tower may affect its structural safety and function, so independent evaluation is required. This embodiment does not limit the selection of target towers.

[0038] In one possible implementation, step S101 further includes: acquiring a target feature image corresponding to the target tower, and acquiring a sharpness value corresponding to the target feature image; acquiring the number of multiple deformation features to be confirmed in the target feature image; determining whether the sharpness value is greater than or equal to a preset sharpness value, and determining whether the number of multiple deformation features to be confirmed is greater than a preset number of features; if the sharpness value is greater than or equal to the preset sharpness value, and the number of multiple deformation features to be confirmed is greater than or equal to the preset number of features, the target feature image is used as the first feature image.

[0039] Specifically, the target tower is photographed multiple times using satellite equipment to obtain multiple feature images corresponding to the target tower. Each feature image is then filtered, and the target feature image is any feature image among the multiple feature images. Image processing is performed on the target feature image to calculate its sharpness value. Multiple deformation features to be confirmed are identified within the target feature image, and their numbers are counted. The obtained sharpness value is then checked against a preset sharpness value, set based on previous image quality standards to ensure the acquired target feature image is sufficiently sharp for subsequent analysis. Simultaneously, the number of deformation features to be confirmed is checked against a preset number of features to confirm sufficient features for subsequent deformation analysis. If the sharpness value is greater than or equal to the preset sharpness value, and the number of deformation features to be confirmed is greater than or equal to the preset number of features, the acquired target feature image is used as the first feature image. In this case, the target feature image is considered valid and used for deformation monitoring in subsequent steps. If the sharpness value is less than the preset sharpness value, or the number of deformation features to be confirmed is less than the preset number of features, multiple feature images are traversed, and the above steps are repeated until a suitable target feature image is obtained.

[0040] Step S102: If it is confirmed that the first deformation feature exists in the first feature image, the second feature image corresponding to the target tower is obtained through the UAV equipment.

[0041] Specifically, it is determined whether each deformation feature to be confirmed in step S101 is a first deformation feature. If any deformation feature to be confirmed is confirmed as a first deformation feature, then the first deformation feature is confirmed to exist in the first feature image. The second feature image corresponding to the target tower is obtained through the UAV device. The UAV device also obtains multiple feature images corresponding to the target tower and performs the same traversal process in step S101 until a suitable second feature image is obtained.

[0042] In one possible implementation, step S102 further includes: acquiring a plurality of first deformation features to be confirmed in the first feature image, wherein the plurality of first deformation features to be confirmed include a first target deformation feature to be confirmed, wherein the first target deformation feature to be confirmed is any one of the plurality of first deformation features to be confirmed; the plurality of first deformation features to be confirmed include tilt angle features, lateral displacement features, and large crack features; determining whether the similarity value between the first target deformation feature to be confirmed and a preset deformation feature is greater than a preset similarity value; if the similarity value between the first target deformation feature to be confirmed and the preset deformation feature is greater than the preset similarity value, then it is confirmed that a first deformation feature exists in the first feature image.

[0043] Specifically, the steps for confirming the number of first deformation features in the first feature image are as follows: For each first deformation feature to be confirmed, a similarity calculation is performed between it and a corresponding preset deformation feature. The preset deformation feature is a pre-saved standard feature template. For any first deformation feature to be confirmed, i.e., the target first deformation feature to be confirmed, the feature similarity value between the target first deformation feature to be confirmed and the preset deformation feature is calculated. Then, by comparing whether the feature similarity value is greater than the preset similarity value, the target first deformation feature to be confirmed with a value greater than the preset similarity value is included as the first deformation feature in the first feature image, and the target first deformation feature to be confirmed with a value less than the preset similarity value is not included as the first deformation feature in the first feature image. Target first deformation features less than the preset similarity value are removed. First deformation features include, but are not limited to: tilt angle features, lateral displacement features, large crack features, etc. Since the first deformation feature is extracted from the first feature image obtained by satellite equipment, it can only roughly reflect the current performance of the tower and cannot accurately assess the stability of the tower. Therefore, it is necessary to further obtain specific feature data related to the first deformation feature through UAVs to provide a more accurate and detailed deformation analysis.

[0044] Step S103: Obtain the second deformation feature in the second feature image that corresponds to the first deformation feature.

[0045] Specifically, the second deformation feature is the specific deformation feature in the second feature image that corresponds to the first deformation feature. The second feature image is acquired by drone equipment, possessing higher resolution and detail, and can capture minute deformations and states of the tower. For example, when the first deformation feature is a tilt angle feature, the second deformation feature can be a specific tilt measurement of the tower base, such as the offset angle of the base relative to the vertical line; when the first deformation feature is a lateral displacement feature, the second deformation feature can be the degree of looseness of the tower connection components, such as the actual horizontal displacement of a connection point; when the first deformation feature is a large crack feature, the second deformation feature can be a measurement of the crack width and depth, specifically recording the actual size and development state of the crack; the specific second deformation feature... The acquisition method is as follows: Multiple second deformation features to be confirmed are acquired from the first feature image. These multiple second deformation features include a second target deformation feature to be confirmed, which is any one of the multiple second deformation features to be confirmed. The multiple second deformation features to be confirmed include, but are not limited to: the specific tilt of the tower base, the looseness of the tower connection components, the width and depth of cracks, the degree of corrosion on the tower surface, and the settlement characteristics of the tower support foundation. The correlation coefficient between the second deformation feature to be confirmed and the first deformation feature to be confirmed is calculated. It is determined whether the correlation coefficient is greater than a preset correlation coefficient. If the correlation coefficient is greater than the preset correlation coefficient, the second deformation feature to be confirmed is confirmed as a second deformation feature.

[0046] Step S104: Based on the tower deformation monitoring model, the performance of the target tower is monitored through the first deformation feature and the second deformation feature.

[0047] Specifically, before the test, a tower deformation monitoring model needs to be constructed. The construction process is as follows: Historical deformation characteristic data is acquired, including data on the loosening degree of tower connection components, the corrosion degree of the tower surface, and the settlement data of the tower support foundation. Based on this historical deformation characteristic data, a tower deformation monitoring model is constructed. Then, the first and second deformation characteristics are input into the constructed tower deformation monitoring model, allowing the model to monitor the performance of the target tower using these characteristics. The performance monitoring operation involves calculating the performance index values ​​of the target tower using a tower deformation monitoring model. The calculation process is as follows: First, a first deformation feature vector matrix is ​​constructed corresponding to the first deformation feature, and a second deformation feature vector matrix is ​​constructed corresponding to the second deformation feature; based on the correspondence between each first and second deformation feature, feature fusion is performed on the first and second deformation feature vector matrices; a comprehensive deformation feature matrix is ​​constructed after feature fusion; using the comprehensive deformation feature matrix, the performance index values ​​corresponding to the target tower are calculated according to the following formula, and performance monitoring is performed based on these performance index values:

[0048]

[0049] Where P is the performance index value. V is the comprehensive deformable feature matrix after feature fusion, where α, β, and γ are all weight coefficients. 1i Let V be the first deformation eigenvector matrix, where i is the number of first deformation features. 2j Let r be the second deformation eigenvector matrix, j be the number of second deformation features, and r be the number of features. ij The correlation coefficient is used to represent the degree of association between the first deformation feature and the corresponding second deformation feature.

[0050] In one possible implementation, step S104 further includes: obtaining the performance index range corresponding to the performance index value, and determining whether the performance index range is a preset low performance range; if the performance index range is a preset low performance range, confirming the performance improvement plan corresponding to the target tower according to the performance monitoring operation, and sending the performance improvement plan to the user terminal so as to perform performance optimization operation on the target tower.

[0051] Specifically, by analyzing the first and second deformation characteristics, the performance index value of the target tower is calculated. This index value is a comprehensive assessment of the tower's condition, including quantitative results of structural strength, stability, and safety. Based on the obtained performance index value, its corresponding performance index range is determined. This range describes the tower's normal state and potential risk level by setting a certain range, typically including different state categories such as high, medium, and low. In this embodiment, the state categories of the performance index range can be further subdivided, resulting in more than three performance index ranges. The number of performance index ranges can be set according to actual needs, and this invention does not limit the number of performance index ranges. Next, it is determined whether the performance index range is a preset low performance range. The preset low performance range is set based on historical data and standard specifications. When the performance index value falls within this range, it indicates that the target tower may have safety hazards or a risk of performance degradation. If the performance index range is the preset low performance range, the specific reasons for the performance degradation are further analyzed, and based on the obtained second deformation characteristics, i.e., specific terrain deformation characteristics, the corresponding performance improvement plan for the target tower is confirmed. Performance improvement plans include, but are not limited to, specific measures such as reinforcing the structure, replacing damaged parts, and conducting regular maintenance. The confirmed performance improvement plans will then be sent to the user terminal to ensure that relevant personnel receive improvement suggestions in a timely manner so that they can take rapid action to optimize the performance of the target tower and ensure its safe and stable operation.

[0052] This invention employs the aforementioned method to acquire a first feature image corresponding to the target tower via satellite equipment, confirming the presence of a first deformation feature within the first feature image. Then, it acquires a second feature image corresponding to the target tower via UAV equipment, obtaining a second deformation feature within the second feature image that corresponds to the first deformation feature. Based on the tower deformation monitoring model, the performance of the target tower is monitored using both the first and second deformation features. This combination of satellite and UAV equipment achieves efficient and low-cost monitoring. Satellite equipment provides broad area coverage and identifies potential problems, while UAVs can acquire detailed deformation data, making the monitoring results more accurate and reliable.

[0053] Please refer to Figure 2 This diagram illustrates a module schematic of a transmission line tower performance monitoring device according to an embodiment of the present invention. The device includes an acquisition module 21 and a processing module 22, wherein...

[0054] The acquisition module 21 is used to acquire a first feature image corresponding to the target tower through satellite equipment; if it is confirmed that there is a first deformation feature in the first feature image, it acquires a second feature image corresponding to the target tower through UAV equipment; and acquires a second deformation feature in the second feature image that corresponds to the first deformation feature.

[0055] The processing module 22 is used to monitor the performance of the target tower by means of the first deformation feature and the second deformation feature according to the tower deformation monitoring model.

[0056] In one possible implementation, the acquisition module 21 is used to acquire a first feature image corresponding to the target tower via satellite equipment, specifically including: acquiring a target feature image corresponding to the target tower, and acquiring a sharpness value corresponding to the target feature image; acquiring the number of multiple deformation features to be confirmed in the target feature image; determining whether the sharpness value is greater than or equal to a preset sharpness value, and determining whether the number of multiple deformation features to be confirmed is greater than a preset number of features; if the sharpness value is greater than or equal to the preset sharpness value, and the number of multiple deformation features to be confirmed is greater than or equal to the preset number of features, the target feature image is used as the first feature image.

[0057] In one possible implementation, the acquisition module 21 is used to confirm the existence of a first deformation feature in the first feature image, specifically including: acquiring a plurality of first deformation features to be confirmed in the first feature image, the plurality of first deformation features to be confirmed including a first target deformation feature to be confirmed, the first target deformation feature to be confirmed being any one of the plurality of first deformation features to be confirmed; the plurality of first deformation features to be confirmed including tilt angle features, lateral displacement features and large crack features; determining whether the similarity value between the first target deformation feature to be confirmed and a preset deformation feature is greater than a preset similarity value; if the similarity value between the first target deformation feature to be confirmed and the preset deformation feature is greater than the preset similarity value, then confirming the existence of a first deformation feature in the first feature image.

[0058] In one possible implementation, the acquisition module 21 is used to acquire a second deformation feature in the second feature image corresponding to the first deformation feature, specifically including: acquiring a plurality of second deformation features to be confirmed in the first feature image, the plurality of second deformation features to be confirmed including a second target deformation feature to be confirmed, the second target deformation feature to be confirmed being any one of the plurality of second deformation features to be confirmed; the plurality of second deformation features to be confirmed including the loosening degree feature of the tower connection component, the corrosion degree feature of the tower surface, and the settlement feature of the tower support foundation; calculating the correlation coefficient between the second deformation feature to be confirmed and the first deformation feature to be confirmed; determining whether the correlation coefficient is greater than a preset correlation coefficient; if the correlation coefficient is greater than the preset correlation coefficient, then confirming the second deformation feature to be confirmed as the second deformation feature.

[0059] In one possible implementation, before the processing module 22 performs performance monitoring on the target tower based on the tower deformation monitoring model using the first deformation feature and the second deformation feature, the method further includes: constructing the tower deformation monitoring model by: acquiring historical deformation feature data, which includes data on the loosening degree of tower connection components, data on the corrosion degree of the tower surface, and settlement data of the tower support foundation; and constructing the tower deformation monitoring model based on the historical deformation feature data.

[0060] In one possible implementation, the processing module 22 is used to monitor the performance of the target tower using a first deformation feature and a second deformation feature. Specifically, this includes: constructing a first deformation feature vector matrix corresponding to the first deformation feature, and constructing a second deformation feature vector matrix corresponding to the second deformation feature; performing feature fusion on the first deformation feature vector matrix and the second deformation feature vector matrix according to the correspondence between each first deformation feature and each second deformation feature; constructing a comprehensive deformation feature matrix after feature fusion; and calculating the performance index value corresponding to the target tower using the comprehensive deformation feature matrix and according to the following formula, so as to monitor performance based on the performance index value:

[0061]

[0062] Where P is the performance index value. V is the comprehensive deformable feature matrix after feature fusion, where α, β, and γ are all weight coefficients. 1i Let V be the first deformation eigenvector matrix, where i is the number of first deformation features. 2j Let r be the second deformation eigenvector matrix, j be the number of second deformation features, and r be the number of features. ij The correlation coefficient is used to represent the degree of association between the first deformation feature and the corresponding second deformation feature.

[0063] In one possible implementation, the processing module 22 is used to calculate the performance index value corresponding to the target tower by comprehensively analyzing the deformation feature matrix and according to the following formula, and to perform performance monitoring based on the performance index value. The method further includes: obtaining the performance index range corresponding to the performance index value, and determining whether the performance index range is a preset low performance range; if the performance index range is a preset low performance range, confirming the performance improvement plan corresponding to the target tower according to the performance monitoring operation, and sending the performance improvement plan to the user terminal to facilitate performance optimization operation for the target tower.

[0064] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0065] The present invention also provides an electronic device. (See reference...) Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The electronic device may include: at least one processor 301, at least one communication bus 302, a user interface 303, at least one network interface 304, and a memory 305.

[0066] The communication bus 302 is used to enable communication between these components.

[0067] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0068] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0069] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling data stored in the memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0070] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. (Refer to...) Figure 3 The memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for monitoring the performance of transmission line towers.

[0071] exist Figure 3In the illustrated electronic device, the user interface 303 is primarily used to provide an input interface for the user and acquire user input data; while the processor 301 can be used to call the transmission line tower performance monitoring application stored in the memory 305. When executed by one or more processors 301, the electronic device performs one or more of the methods described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0072] The present invention also provides a computer-readable storage medium storing instructions. When executed by one or more processors, these instructions cause an electronic device to perform one or more of the methods described in the above embodiments.

[0073] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0074] In the various embodiments provided by this invention, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0075] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0076] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0077] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0078] Implementing the embodiments of the present invention has the following beneficial effects:

[0079] This invention provides a method, apparatus, device, and storage medium for monitoring the performance of transmission line towers. A first feature image corresponding to the target tower is acquired via satellite equipment, and a first deformation feature is identified within this image. A second feature image corresponding to the target tower is acquired via UAV equipment, and a second deformation feature corresponding to the first deformation feature is obtained from this second feature image. Based on a tower deformation monitoring model, the performance of the target tower is monitored using both the first and second deformation features. This combination of satellite and UAV equipment achieves efficient and low-cost monitoring. Satellite equipment provides broad area coverage and identifies potential problems, while UAVs can acquire detailed deformation data, making the monitoring results more accurate and reliable.

[0080] In this embodiment, the correlation coefficient between the second deformation feature to be confirmed and the first deformation feature to be confirmed is calculated, and it is determined whether the correlation coefficient is greater than a preset correlation coefficient. Only when the correlation coefficient is greater than the preset correlation coefficient is the second deformation feature to be confirmed confirmed. This makes the second deformation feature obtained in the end not only more accurate, but also better reflect the actual state and change trend of the tower.

[0081] In this embodiment, a first deformation feature vector matrix corresponding to the first deformation feature is constructed, and a second deformation feature vector matrix corresponding to the second deformation feature is constructed. Based on the correspondence between each first deformation feature and each second deformation feature, feature fusion is performed on the first deformation feature vector matrix and the second deformation feature vector matrix to construct a comprehensive deformation feature matrix after feature fusion. The performance index value corresponding to the target tower is calculated through the comprehensive deformation feature matrix, thereby realizing a comprehensive evaluation of the tower performance.

[0082] The above description is merely a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. Therefore, any equivalent variations made in accordance with the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for monitoring the performance of transmission line towers, characterized in that, The method includes: The first feature image corresponding to the target tower is obtained through satellite equipment; If it is confirmed that the first deformation feature exists in the first feature image, the second feature image corresponding to the target tower is obtained through the drone equipment; Obtain the second deformation feature in the second feature image that corresponds to the first deformation feature; Based on the first deformation characteristics and the second deformation characteristics, the performance of the target tower is monitored using the tower deformation monitoring model; Specifically, acquiring the first feature image corresponding to the target tower via satellite equipment includes: Obtain the target feature image corresponding to the target tower, and obtain the sharpness value corresponding to the target feature image; Obtain the number of multiple deformation features to be confirmed in the target feature image; Determine whether the sharpness value is greater than or equal to a preset sharpness value, and determine whether the number of the plurality of deformed features to be confirmed is greater than a preset number of features; If the sharpness value is greater than or equal to the preset sharpness value, and the number of multiple deformation features to be confirmed is greater than or equal to the preset number of features, the target feature image is used as the first feature image. Specifically, confirming the presence of a first deformation feature in the first feature image includes: Multiple first deformation features to be confirmed are obtained from the first feature image. The multiple first deformation features to be confirmed include a first target deformation feature to be confirmed. The first target deformation feature to be confirmed is any one of the multiple first deformation features to be confirmed. The multiple first deformation features to be confirmed include tilt angle features, lateral displacement features, and large crack features. Determine whether the similarity value between the first target deformation feature to be confirmed and the preset deformation feature is greater than the preset similarity value; If the similarity value between the first target deformation feature to be confirmed and the preset deformation feature is greater than the preset similarity value, then it is confirmed that the first deformation feature exists in the first feature image. Specifically, obtaining the second deformation feature in the second feature image corresponding to the first deformation feature includes: Multiple second deformation features to be confirmed are obtained from the first feature image. The multiple second deformation features to be confirmed include a second target deformation feature to be confirmed. The second target deformation feature to be confirmed is any one of the multiple second deformation features to be confirmed. The multiple second deformation features to be confirmed include the loosening degree of the tower connection components, the corrosion degree of the tower surface, and the settlement of the tower support foundation. Calculate the correlation coefficient between the second deformation feature to be confirmed and the first deformation feature to be confirmed; Determine whether the correlation coefficient is greater than a preset correlation coefficient; If the correlation coefficient is greater than the preset correlation coefficient, then the second deformation feature to be confirmed is confirmed as the second deformation feature; Specifically, the performance monitoring of the target tower using the first deformation feature and the second deformation feature includes: Construct a first deformation feature vector matrix corresponding to the first deformation feature, and construct a second deformation feature vector matrix corresponding to the second deformation feature; Based on the correspondence between each of the first deformation features and each of the second deformation features, feature fusion is performed on the first deformation feature vector matrix and the second deformation feature vector matrix; Construct the comprehensive deformation feature matrix after feature fusion; The performance index value corresponding to the target tower is calculated using the comprehensive deformation feature matrix and the following formula, and the performance monitoring operation is performed based on the performance index value: in, The performance index value is... The composite deformation feature matrix after feature fusion. All are weighting coefficients. The first deformation eigenvector matrix, The number of the first deformation features. This is the second deformation eigenvector matrix. The number of the second deformation features. The correlation coefficient is used to represent the degree of association between the first deformation feature and the corresponding second deformation feature.

2. The method according to claim 1, characterized in that, Before performing performance monitoring of the target tower based on the first deformation characteristic and the second deformation characteristic using the tower deformation monitoring model, the method further includes constructing the tower deformation monitoring model: Acquire historical deformation characteristic data, which includes data on the loosening degree of tower connection components, data on the corrosion degree of tower surface, and settlement data of tower support foundation; The tower deformation monitoring model is constructed based on the historical deformation characteristic data.

3. The method according to claim 2, characterized in that, In the performance monitoring of the target tower using the first deformation feature and the second deformation feature, after performing the performance monitoring operation based on the performance index value, the method further includes: Obtain the performance index range corresponding to the performance index value, and determine whether the performance index range is a preset low performance range; If the performance index range is within the preset low performance range, the performance improvement plan corresponding to the target tower is confirmed according to the performance monitoring operation, and the performance improvement plan is sent to the user terminal to facilitate performance optimization operation for the target tower.

4. A tower performance monitoring device for transmission lines, characterized in that, The device includes an acquisition module and a processing module, wherein, The acquisition module is used to acquire a first feature image corresponding to the target tower through satellite equipment; if it is confirmed that a first deformation feature exists in the first feature image, it acquires a second feature image corresponding to the target tower through UAV equipment; and acquires a second deformation feature in the second feature image that corresponds to the first deformation feature. The processing module is used to monitor the performance of the target tower based on the first deformation feature and the second deformation feature, and through the tower deformation monitoring model. Specifically, the acquisition module acquires a first feature image corresponding to the target tower via satellite equipment, including: Obtain the target feature image corresponding to the target tower, and obtain the sharpness value corresponding to the target feature image; Obtain the number of multiple deformation features to be confirmed in the target feature image; Determine whether the sharpness value is greater than or equal to a preset sharpness value, and determine whether the number of the plurality of deformed features to be confirmed is greater than a preset number of features; If the sharpness value is greater than or equal to the preset sharpness value, and the number of multiple deformation features to be confirmed is greater than or equal to the preset number of features, the target feature image is used as the first feature image. Specifically, in the acquisition module, the presence of a first deformation feature in the first feature image is confirmed, including: Multiple first deformation features to be confirmed are obtained from the first feature image. The multiple first deformation features to be confirmed include a first target deformation feature to be confirmed. The first target deformation feature to be confirmed is any one of the multiple first deformation features to be confirmed. The multiple first deformation features to be confirmed include tilt angle features, lateral displacement features, and large crack features. Determine whether the similarity value between the first target deformation feature to be confirmed and the preset deformation feature is greater than the preset similarity value; If the similarity value between the first target deformation feature to be confirmed and the preset deformation feature is greater than the preset similarity value, then it is confirmed that the first deformation feature exists in the first feature image. Specifically, the acquisition module acquires the second deformation feature in the second feature image that corresponds to the first deformation feature, including: Multiple second deformation features to be confirmed are obtained from the first feature image. The multiple second deformation features to be confirmed include a second target deformation feature to be confirmed. The second target deformation feature to be confirmed is any one of the multiple second deformation features to be confirmed. The multiple second deformation features to be confirmed include the loosening degree of the tower connection components, the corrosion degree of the tower surface, and the settlement of the tower support foundation. Calculate the correlation coefficient between the second deformation feature to be confirmed and the first deformation feature to be confirmed; Determine whether the correlation coefficient is greater than a preset correlation coefficient; If the correlation coefficient is greater than the preset correlation coefficient, then the second deformation feature to be confirmed is confirmed as the second deformation feature; Specifically, in the processing module, the performance monitoring of the target tower is performed using the first deformation feature and the second deformation feature, including: Construct a first deformation feature vector matrix corresponding to the first deformation feature, and construct a second deformation feature vector matrix corresponding to the second deformation feature; Based on the correspondence between each of the first deformation features and each of the second deformation features, feature fusion is performed on the first deformation feature vector matrix and the second deformation feature vector matrix; Construct the comprehensive deformation feature matrix after feature fusion; The performance index value corresponding to the target tower is calculated using the comprehensive deformation feature matrix and the following formula, and the performance monitoring operation is performed based on the performance index value: in, The performance index value is... The composite deformation feature matrix after feature fusion. All are weighting coefficients. The first deformation eigenvector matrix, The number of the first deformation features. This is the second deformation eigenvector matrix. The number of the second deformation features. The correlation coefficient is used to represent the degree of association between the first deformation feature and the corresponding second deformation feature.

5. An electronic device, characterized in that, The device includes a processor, a communication bus, a user interface, a network interface, and a memory. The memory is used to store instructions. The user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1 to 3.

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