A strain clamp detection method and device, computer equipment and storage medium
By dividing the target area in the tension clamp inspection, utilizing line operation benchmark data and UAV imagery, and combining it with the line monitoring model, the problems of low inspection efficiency and safety risks were solved, achieving efficient and safe tension clamp inspection.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- GUANGZHOU ZHONGTIAN ENG TESTING SERVICE CO LTD
- Filing Date
- 2023-06-01
- Publication Date
- 2026-06-23
AI Technical Summary
In existing technologies, the testing efficiency of tension clamps is low and there are safety risks, mainly because it requires manual climbing of high-voltage power line towers for testing.
By dividing the target area to be detected, obtaining baseline data and abnormal data of the line operation, using drones to take pictures, and inputting them into the line monitoring model for detection, manual intervention is reduced.
It improves the convenience and efficiency of testing, reduces the safety risks for staff, and enhances the accuracy and reliability of testing.
Smart Images
Figure CN116482115B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the technical field of engineering testing, and in particular to a method, apparatus, computer equipment, and storage medium for testing tension clamps. Background Technology
[0002] Tension clamps are commonly used equipment in power transmission lines. They are used to crimp high-voltage lines on overhead power transmission lines. If the crimping process of the tension clamps on high-voltage lines is improper or the crimping quality is substandard, it can easily lead to damage and breakage of the steel core inside the line, or even line breakage accidents. Therefore, tension clamps need to be inspected regularly.
[0003] Currently, the most common method for inspecting tension clamps is to use equipment with X-ray capabilities. X-ray equipment can detect whether there are defects such as poor crimping quality inside the tension clamp.
[0004] The existing technical solutions described above have the following drawbacks:
[0005] When using X-ray equipment for inspection, it is common practice for personnel to carry the corresponding X-ray equipment and climb up the high-voltage tower for inspection. However, the frequent climbing of high-voltage towers by personnel to inspect tension clamps affects the efficiency of the inspection and increases the safety risks during the inspection. Summary of the Invention
[0006] To improve the convenience of tension clamp testing, this application provides a tension clamp testing method, apparatus, computer equipment, and storage medium.
[0007] The above-mentioned objective of this application is achieved through the following technical solution:
[0008] A method for testing tension clamps, the method comprising:
[0009] Obtain the target area to be detected, and obtain the tension clamps to be detected and the corresponding detection positions of each tension clamp from the target area to be detected;
[0010] Based on the tension clamp to be tested, obtain the corresponding line operation reference data, and perform line testing on the target area to be tested based on the line operation reference data to obtain line operation data.
[0011] Obtain abnormal operation data of the line operation data, trigger remote sensing monitoring messages based on the abnormal operation data, and obtain line image data;
[0012] The line image data and corresponding operational anomaly data are input into a preset line monitoring model for detection, and a tension clamp detection message is triggered based on the detection results.
[0013] By adopting the above technical solution, when inspecting tension clamps, different target areas can be divided for regional inspection, which is beneficial for analyzing the actual condition of the tension clamps. By acquiring the line operation benchmark data corresponding to each tension clamp to be inspected, the tension clamps in the target area can be inspected quickly and accurately. Based on the detected line operation data, abnormal operation data can be filtered out and the remote sensing monitoring message can be triggered to control the drone to take pictures of the tension clamps corresponding to the abnormal operation data. The line image data and abnormal operation data are then input into the line monitoring model for inspection, which can filter out the operational anomalies that may be caused by tension clamp failure. This effectively reduces the frequency of workers climbing high-voltage towers to inspect tension clamps using X-ray equipment, thereby improving the convenience and efficiency of inspection and reducing the safety risks for workers.
[0014] In a preferred embodiment, this application can be further configured as follows: obtaining corresponding line operation reference data based on the tension clamp to be tested, and performing line testing on the target area to be tested based on the line operation reference data to obtain line operation data, specifically includes:
[0015] From each of the line operation reference data, there is the line operation type, and the corresponding type operation reference data for each of the line operation types;
[0016] Obtain the corresponding real-time operation data of the line according to the line operation type, and calculate the operation data fluctuation value according to the real-time operation data of the line and the corresponding operation benchmark data of the type.
[0017] The line operation data is generated based on the operation data fluctuation value corresponding to each of the tension clamps to be tested.
[0018] By adopting the above technical solution, since the line's operating data will fluctuate abnormally when or before a fault occurs, by obtaining the operating data fluctuation value corresponding to each line's operating type, the line operating data generated based on the operating data fluctuation value can be directly analyzed and detected, thereby improving the accuracy and efficiency of detection.
[0019] In a preferred embodiment, this application can be further configured as follows: obtaining abnormal operation data of the line operation data, triggering a remote sensing monitoring message based on the abnormal operation data, and obtaining line image data, specifically includes:
[0020] The cable tension data is obtained from the abnormal operation data, and the position to be detected corresponding to the cable tension data exceeding the preset value is obtained as the position to be inspected;
[0021] The remote sensing monitoring message is triggered based on the location to be inspected.
[0022] By adopting the above technical solution, since the steel core inside the line may be damaged or broken when the tension clamp fails, the tension of the cable on the tension clamp may increase. Therefore, by obtaining cable tension data from the abnormal operation data, and then filtering out the locations where the cable tension data exceeds the preset value from the locations to be inspected, and triggering remote sensing monitoring messages based on the locations to be inspected, it is possible to further accurately identify tension clamps that may be abnormal, which helps to improve the efficiency of manual inspection of tension clamps.
[0023] In a preferred embodiment, this application can be further configured as follows: before inputting the line image data and corresponding operational anomaly data into a preset line monitoring model for detection, and triggering a tension clamp detection message based on the detection result, the method for training the line monitoring model includes:
[0024] Obtain the model data of the tension clamp, and obtain historical abnormal data of the clamp based on the model data of the tension clamp;
[0025] The abnormal cable images and abnormal fluctuation values of the operating data are obtained from the historical abnormal data of the cable clamps. The line monitoring model is then trained based on the abnormal cable images and abnormal fluctuation values of the operating data.
[0026] By adopting the above technical solution, the corresponding historical anomaly data is obtained based on the tension clamp model data. The resulting line monitoring model can perform targeted detection based on different models of tension clamps. Therefore, in actual testing, it can detect different models of tension clamps, thereby improving the reliability of the detection.
[0027] In a preferred embodiment, this application can be further configured as follows: inputting the line image data and corresponding operational anomaly data into a preset line monitoring model for detection, and triggering a tension clamp detection message based on the detection result, specifically includes:
[0028] Obtain the model number of the clamp to be tested from the abnormal operation data, input the model number of the clamp to be tested into the line monitoring model, and obtain the corresponding clamp detection sub-model.
[0029] The line image data and the operational anomaly data are input into the corresponding clamp detection sub-model for detection. Based on the line image data and operational anomaly data of the clamp model to be detected, it is determined whether the tension clamp detection message is triggered.
[0030] By adopting the above technical solution, the corresponding wire clamp detection sub-model can be obtained based on the model of the wire clamp to be tested, and targeted testing can be carried out based on the model of the tension clamp, thereby making the test results more accurate and improving the testing efficiency.
[0031] The second objective of this invention is achieved through the following technical solution:
[0032] A tension clamp testing device, the tension clamp testing device comprising:
[0033] The target acquisition module is used to acquire the target area to be detected, and to acquire the tension clamp to be detected and the corresponding detection position of each tension clamp to be detected from the target area to be detected.
[0034] The data acquisition module is used to acquire the corresponding line operation reference data based on the tension clamp to be tested, and to perform line testing on the target area to be tested based on the line operation reference data to obtain line operation data.
[0035] The image acquisition module is used to acquire abnormal operation data of the line operation data, trigger remote sensing monitoring messages based on the abnormal operation data, and obtain line image data.
[0036] The data detection module is used to input the line image data and the corresponding operational anomaly data into a preset line monitoring model for detection, and trigger a tension clamp detection message based on the detection results.
[0037] By adopting the above technical solution, when inspecting tension clamps, different target areas can be divided for regional inspection, which is beneficial for analyzing the actual condition of the tension clamps. By acquiring the line operation benchmark data corresponding to each tension clamp to be inspected, the tension clamps in the target area can be inspected quickly and accurately. Based on the detected line operation data, abnormal operation data can be filtered out and the remote sensing monitoring message can be triggered to control the drone to take pictures of the tension clamps corresponding to the abnormal operation data. The line image data and abnormal operation data are then input into the line monitoring model for inspection, which can filter out the operational anomalies that may be caused by tension clamp failure. This effectively reduces the frequency of workers climbing high-voltage towers to inspect tension clamps using X-ray equipment, thereby improving the convenience and efficiency of inspection and reducing the safety risks for workers.
[0038] The above-mentioned objective three of this application is achieved through the following technical solution:
[0039] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described tension clamp detection method.
[0040] The fourth objective of this application is achieved through the following technical solution:
[0041] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described tension clamp detection method.
[0042] In summary, this application includes at least one of the following beneficial technical effects:
[0043] 1. Trigger tension clamp detection message.
[0044] By adopting the above technical solution, when inspecting tension clamps, different target areas can be divided for regional inspection, which is beneficial for analyzing the actual situation of the tension clamps. By acquiring the line operation benchmark data corresponding to each tension clamp to be inspected, the tension clamps in the target area can be inspected quickly and accurately. Based on the detected line operation data, abnormal operation data can be filtered out and the remote sensing monitoring message can be triggered to control the drone to take pictures of the tension clamps corresponding to the abnormal operation data. The line image data and abnormal operation data are then input into the line monitoring model for inspection, which can filter out the operational anomalies that may be caused by tension clamp failure. This effectively reduces the frequency of workers climbing high-voltage towers to inspect tension clamps using X-ray equipment, thereby improving the convenience and efficiency of inspection and reducing the safety risks for workers.
[0045] 2. Since the operating data of a line will fluctuate abnormally when or before a fault occurs, by obtaining the operating data fluctuation value corresponding to each type of line operation, the line operation data generated based on the operating data fluctuation value can be directly analyzed and detected, thereby improving the accuracy and efficiency of detection.
[0046] 3. Based on the model data of tension clamps, corresponding historical anomaly data is obtained, and the resulting line monitoring model can perform targeted detection for different models of tension clamps. This improves the reliability of the detection during actual testing. Attached Figure Description
[0047] Figure 1 This is a flowchart of a tension clamp detection method in one embodiment of this application;
[0048] Figure 2 This is a flowchart illustrating the implementation of step S20 in the tension clamp detection process of one embodiment of this application.
[0049] Figure 3 This is a flowchart illustrating the implementation of step S30 in the tension clamp detection process of one embodiment of this application.
[0050] Figure 4 This is another implementation flowchart of tension clamp detection in one embodiment of this application;
[0051] Figure 5 This is a flowchart illustrating the implementation of step S40 in the tension clamp detection process of one embodiment of this application.
[0052] Figure 6 This is a schematic block diagram of a tension clamp detection device according to an embodiment of this application;
[0053] Figure 7 This is a schematic diagram of a device according to one embodiment of this application. Detailed Implementation
[0054] The present application will be further described in detail below with reference to the accompanying drawings.
[0055] In one embodiment, such as Figure 1 As shown, this application discloses a method for testing tension clamps, which specifically includes the following steps:
[0056] S10: Obtain the target area to be tested, and obtain the tension clamps to be tested and the corresponding test positions of each tension clamp from the target area to be tested.
[0057] In this embodiment, the target area to be tested refers to the area where the tension clamps that need to be uniformly tested are located. The test location refers to the specific location of each tension clamp to be tested within the target area to be tested.
[0058] Specifically, in high-voltage transmission lines, due to differences in electricity consumption and external environment in different areas, the operating conditions of high-voltage lines also vary. Therefore, it is necessary to acquire electricity demand data for each region, divide the first target area based on the electricity demand data, further acquire external environment data for each first target area, divide the first target area into a second target area based on the differences in external environment data, and use the second target area as the target area to be tested. This ensures that the transmission cables in each target area to be tested have the same electricity consumption attributes, improving the uniformity and accuracy of subsequent testing of tension clamps.
[0059] Furthermore, after dividing each target area to be tested, the location of each tension clamp to be tested and the location of each tension clamp to be tested in each target area to be tested are obtained as the test location.
[0060] S20: Obtain the corresponding line operation reference data based on the tension clamp to be tested, and perform line testing on the target area to be tested based on the line operation reference data to obtain line operation data.
[0061] In this embodiment, the line operation reference data refers to the operation data of each power transmission cable under normal power transmission within the area to be tested. The line operation data refers to the operation data of each power transmission cable under actual power transmission within the area to be tested.
[0062] Specifically, the data dimensions that need to be measured during power transmission within the area to be tested are pre-determined. Based on these data dimensions, the corresponding operational data during normal power transmission is obtained to form the line operation baseline data. Furthermore, during operation of the power transmission cables within the target area to be tested, various data of the power transmission cables are obtained based on these data dimensions, and line operation data is generated from this data.
[0063] S30: Obtain abnormal operation data of the line operation data, trigger remote sensing monitoring messages based on the abnormal operation data, and obtain line image data.
[0064] Specifically, when detecting line operation data, if it is found that the line operation data deviates significantly from the line operation baseline data, it is marked as abnormal operation data.
[0065] Furthermore, after acquiring the abnormal operation data, the specific power transmission cable involved in generating the abnormal operation data is obtained, and the specific tension clamp is located, thereby triggering the remote sensing monitoring message and controlling the UAV remote sensing equipment to take pictures of the power transmission cable and tension clamp at that location to obtain line image data.
[0066] S40: Input the line image data and corresponding operational anomaly data into the preset line monitoring model for detection, and trigger the tension clamp detection message based on the detection results.
[0067] Specifically, a line detection model is pre-trained to detect whether the tension clamps in the target area are abnormal. The line image data and the corresponding operational anomaly data are input into the line monitoring model. The operational anomaly data and the image features at the tension clamp are used to determine whether there is a possibility of a quality problem with the tension clamp. If it is determined that the operational anomaly data is highly likely to be due to a quality problem with the tension clamp, a tension clamp detection message is triggered, and the corresponding personnel are notified to bring the corresponding equipment to further inspect and confirm the specific tension clamp.
[0068] In this embodiment, when inspecting tension clamps, different target areas can be divided for regional inspection, which facilitates the analysis of the actual condition of the tension clamps. By acquiring the line operation benchmark data corresponding to each tension clamp to be inspected, the tension clamps in the target area can be inspected quickly and accurately. Abnormal operation data can be filtered out based on the detected line operation data, and the remote sensing monitoring message can be triggered to control the drone to take pictures of the tension clamps corresponding to the abnormal operation data. The line image data and abnormal operation data are then input into the line monitoring model for inspection, thereby filtering out operational anomalies that may be caused by tension clamp failures. This effectively reduces the frequency of workers climbing high-voltage towers to inspect tension clamps using X-ray equipment, thus improving the convenience and efficiency of inspection and reducing the safety risks for workers.
[0069] In one embodiment, such as Figure 2 As shown, in step S20, the corresponding line operation reference data is obtained based on the tension clamp to be tested, and line testing is performed on the target area to be tested based on the line operation reference data to obtain line operation data, specifically including:
[0070] S21: From the line operation baseline data for each line, the line operation type, and the corresponding type operation baseline data for each line operation type.
[0071] Specifically, the data dimensions divided in step S20 are used as the operation type of the line, and the corresponding operation benchmark data are obtained according to the operation type of the line as the operation benchmark data of the type.
[0072] S22: Obtain the corresponding real-time operation data of the line according to the line operation type, and calculate the operation data fluctuation value based on the real-time operation data of the line and the corresponding type of operation benchmark data.
[0073] Specifically, during the actual operation of the power transmission cable, the corresponding operating data is obtained according to the type of operation of the line, and used as real-time operating data.
[0074] Furthermore, the real-time operating data corresponding to each line operation type is compared with the type operation benchmark data, and the comparison results are used to form the operating data fluctuation value.
[0075] S23: Generate line operation data based on the operation data fluctuation value corresponding to each tension clamp to be tested.
[0076] Specifically, after obtaining the operation data fluctuation value of each transmission line, the tension clamp to be tested associated with that transmission line is obtained as the associated tension clamp, and then the operation data fluctuation value of the transmission line is used as the line operation data of the associated tension clamp, thereby obtaining the line operation data corresponding to each tension clamp to be tested.
[0077] In one embodiment, such as Figure 3 As shown, in step S30, abnormal operation data of the line operation data is acquired, and a remote sensing monitoring message is triggered based on the abnormal operation data to obtain line image data. Specifically, this includes:
[0078] S31: Obtain cable tension data from abnormal operation data, and identify the locations to be inspected when the cable tension data exceeds a preset value.
[0079] In this embodiment, the cable tension data refers to the tension data formed between the power transmission cable connected to each tension clamp and the tension clamp.
[0080] Specifically, a corresponding tension acquisition device is pre-installed at each tension clamp. During the operation of the power transmission cable, the corresponding cable tension data is acquired in real time. If abnormal operation data is found, it indicates that there is a quality problem with the tension clamp. When the tension clamp has a quality problem, it will damage and break the steel core of the line. Therefore, the clamping force of the tension clamp on the power transmission cable decreases, resulting in an increase in the cable tension data. Therefore, the position corresponding to the cable tension data exceeding the preset value is designated as the inspection position.
[0081] S32: Trigger remote sensing monitoring messages based on the location to be inspected.
[0082] Specifically, the remote sensing monitoring message is triggered based on the location to be inspected, so as to control the unmanned remote sensing equipment to plan the inspection route according to the location to be inspected and to take pictures of the tension clamp at the location to be inspected.
[0083] In one embodiment, such as Figure 4 As shown, before step S40, the method for training the line monitoring model includes:
[0084] S401: Obtain tension clamp model data and retrieve historical abnormal data of the clamp based on the tension clamp model data.
[0085] Specifically, tension clamp model data is compiled based on the models of tension clamps used in the past. Further, based on the tension clamp model data, historical anomaly data caused by tension clamp malfunctions within a certain period are obtained and categorized according to the type of line operation.
[0086] S402: Obtain cable anomaly images and abnormal fluctuation values of operating data from the historical anomaly data of the cable clamps, and train the line monitoring model based on the cable anomaly images and abnormal fluctuation values of operating data.
[0087] Specifically, the corresponding cable anomaly image and the corresponding abnormal fluctuation value of the operating data are extracted from the historical anomaly data of the tension clamp. The cable anomaly image and the abnormal fluctuation value of the operating data are trained according to the tension clamp model data to obtain the sub-model corresponding to each tension clamp model data, and the sub-models are combined to form the line monitoring model.
[0088] In one embodiment, such as Figure 5 As shown, in step S40, the line image data and corresponding operational anomaly data are input into a preset line monitoring model for detection. Based on the detection results, a tension clamp detection message is triggered, specifically including:
[0089] S41: Obtain the model number of the clamp to be tested from the abnormal operation data, input the model number of the clamp to be tested into the line monitoring model, and obtain the corresponding clamp detection sub-model.
[0090] Specifically, the model number of the tension clamp associated with the abnormal operation data is obtained as the model number of the clamp to be tested. This model number is then input into the line detection model to match the corresponding sub-module, which is used as the clamp detection sub-model.
[0091] S42: Input the line image data and operation abnormality data into the corresponding clamp detection sub-model for detection, and determine whether to trigger the tension clamp detection message based on the line image data and operation abnormality data of the clamp model to be detected.
[0092] Specifically, the line image data is first input into the clamp detection sub-model to obtain the probability value of whether the tension clamp corresponding to the clamp model to be detected has failed. That is, the similarity between the feature value of the line image data and the feature value of the cable abnormal image is calculated, and the calculated similarity is used as the probability value. If the probability value is greater than the preset value, the corresponding abnormal operation data is input into the clamp detection sub-model to further determine the probability of the tension clamp having an abnormality, and then determine whether to trigger the tension clamp detection message.
[0093] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.
[0094] In one embodiment, a tension clamp detection device is provided, which corresponds one-to-one with the tension clamp detection method described in the above embodiments. For example... Figure 6 As shown, the tension clamp detection device includes a target acquisition module, a data acquisition module, an image acquisition module, and a data detection module. Detailed descriptions of each functional module are as follows:
[0095] The target acquisition module is used to acquire the target area to be detected, and to acquire the tension clamps to be detected and the corresponding detection positions of each tension clamp from the target area to be detected.
[0096] The data acquisition module is used to acquire the corresponding line operation reference data based on the tension clamp to be tested, and to perform line testing on the target area to be tested based on the line operation reference data to obtain line operation data.
[0097] The image acquisition module is used to acquire abnormal operation data of the line operation data, trigger remote sensing monitoring messages based on the abnormal operation data, and obtain line image data.
[0098] The data detection module is used to input line image data and corresponding operational anomaly data into a preset line monitoring model for detection, and trigger tension clamp detection messages based on the detection results.
[0099] Optionally, the data acquisition module includes:
[0100] The benchmark data acquisition submodule is used to obtain the line operation type from the benchmark data of each line operation, as well as the category operation benchmark data corresponding to each line operation type.
[0101] The fluctuation value acquisition submodule is used to obtain the corresponding real-time operation data of the line according to the line operation type, and calculate the operation data fluctuation value based on the real-time operation data of the line and the corresponding type of operation benchmark data.
[0102] The data generation submodule is used to generate line operation data based on the operation data fluctuation value corresponding to each tension clamp to be tested.
[0103] Optionally, the image acquisition module includes:
[0104] The inspection location acquisition submodule is used to obtain cable tension data from abnormal operation data, and to obtain the locations to be inspected when the cable tension data exceeds a preset value.
[0105] The image acquisition submodule is used to trigger remote sensing monitoring messages based on the location to be inspected.
[0106] Optionally, the tension clamp testing device also includes:
[0107] The historical data acquisition module is used to acquire tension clamp model data and retrieve historical abnormal data of the clamps based on the tension clamp model data.
[0108] The model training module is used to obtain cable anomaly images and abnormal fluctuation values of operating data from the historical anomaly data of the cable clamps, and to train the line monitoring model based on the cable anomaly images and abnormal fluctuation values of operating data.
[0109] Optionally, the data detection module includes:
[0110] The model filtering submodule is used to obtain the model of the clamp to be tested from the abnormal operation data, input the model of the clamp to be tested into the line monitoring model, and obtain the corresponding clamp detection sub-model.
[0111] The data judgment submodule is used to input line image data and operational anomaly data into the corresponding clamp detection sub-model for detection, and to determine whether to trigger the tension clamp detection message based on the line image data and operational anomaly data of the clamp model to be detected.
[0112] Specific limitations regarding the tension clamp testing device can be found in the limitations of the tension clamp testing method described above, and will not be repeated here. Each module in the aforementioned tension clamp testing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.
[0113] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 7 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for detecting tension clamps.
[0114] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:
[0115] Obtain the target area to be inspected, and from the target area to be inspected, obtain the tension clamps to be inspected and the corresponding inspection positions of each tension clamp to be inspected;
[0116] Based on the tension clamp to be tested, obtain the corresponding line operation reference data, and perform line testing on the target area to be tested based on the line operation reference data to obtain the line operation data.
[0117] Obtain abnormal operation data of the line operation data, trigger remote sensing monitoring messages based on the abnormal operation data, and obtain line image data;
[0118] The line image data and corresponding operational anomaly data are input into the preset line monitoring model for detection, and the tension clamp detection message is triggered based on the detection results.
[0119] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:
[0120] Obtain the target area to be inspected, and from the target area to be inspected, obtain the tension clamps to be inspected and the corresponding inspection positions of each tension clamp to be inspected;
[0121] Based on the tension clamp to be tested, obtain the corresponding line operation reference data, and perform line testing on the target area to be tested based on the line operation reference data to obtain the line operation data.
[0122] Obtain abnormal operation data of the line operation data, trigger remote sensing monitoring messages based on the abnormal operation data, and obtain line image data;
[0123] The line image data and corresponding operational anomaly data are input into the preset line monitoring model for detection, and the tension clamp detection message is triggered based on the detection results.
[0124] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.
[0125] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.
[0126] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.
Claims
1. A method for testing tension clamps, characterized in that, The tension clamp testing method includes: Obtain the target area to be detected, and obtain the tension clamps to be detected and the corresponding detection positions of each tension clamp from the target area to be detected; Based on the tension clamp to be tested, obtain the corresponding line operation reference data, and perform line testing on the target area to be tested based on the line operation reference data to obtain line operation data. Obtain operational anomaly data from the line operation data, trigger remote sensing monitoring messages based on the operational anomaly data, and obtain line image data, specifically including: The cable tension data is obtained from the abnormal operation data, and the position to be detected corresponding to the cable tension data exceeding the preset value is obtained as the position to be inspected; The remote sensing monitoring message is triggered based on the location to be inspected; Obtain the model data of the tension clamp, and obtain historical abnormal data of the clamp based on the model data of the tension clamp; The abnormal cable images and abnormal fluctuation values of the operating data are obtained from the historical abnormal data of the clamps. The line monitoring model is trained based on the abnormal cable images and abnormal fluctuation values of the operating data. The line image data and corresponding operational anomaly data are input into a preset line monitoring model for detection. Based on the detection results, a tension clamp detection message is triggered, specifically including: Obtain the model number of the clamp to be tested from the abnormal operation data, input the model number of the clamp to be tested into the line monitoring model, and obtain the corresponding clamp detection sub-model. The line image data and the operational anomaly data are input into the corresponding clamp detection sub-model for detection. Based on the line image data and operational anomaly data of the clamp model to be detected, it is determined whether the tension clamp detection message is triggered.
2. The method for detecting tension clamps according to claim 1, characterized in that, The step of obtaining corresponding line operation reference data based on the tension clamp to be tested, and performing line testing on the target area to be tested based on the line operation reference data to obtain line operation data, specifically includes: From each of the line operation reference data, there is the line operation type, and the corresponding type operation reference data for each of the line operation types; Obtain the corresponding real-time operation data of the line according to the line operation type, and calculate the operation data fluctuation value according to the real-time operation data of the line and the corresponding operation benchmark data of the type. The line operation data is generated based on the operation data fluctuation value corresponding to each of the tension clamps to be tested.
3. A tension clamp testing device, characterized in that, The tension clamp detection device includes: The target acquisition module is used to acquire the target area to be detected, and to acquire the tension clamp to be detected and the corresponding detection position of each tension clamp to be detected from the target area to be detected. The data acquisition module is used to acquire the corresponding line operation reference data based on the tension clamp to be tested, and to perform line testing on the target area to be tested based on the line operation reference data to obtain line operation data. An image acquisition module is used to acquire abnormal operation data of the line operation data, trigger remote sensing monitoring messages based on the abnormal operation data, and obtain line image data. The image acquisition module includes: The inspection location acquisition submodule is used to acquire cable tension data from the abnormal operation data, and to acquire the location to be inspected corresponding to the cable tension data exceeding a preset value, as the inspection location; The image acquisition submodule is used to trigger the remote sensing monitoring message based on the location to be inspected; The historical data acquisition module is used to acquire tension clamp model data and retrieve historical abnormal data of the clamps based on the tension clamp model data. The model training module is used to obtain cable anomaly images and abnormal fluctuation values of operational data from the historical anomaly data of the cable clamps, and to train the line monitoring model based on the cable anomaly images and abnormal fluctuation values of operational data. The data detection module is used to input the line image data and corresponding operational anomaly data into a preset line monitoring model for detection, and trigger a tension clamp detection message based on the detection results. The data detection module includes: The model filtering submodule is used to obtain the model of the clamp to be tested from the abnormal operation data, input the model of the clamp to be tested into the line monitoring model, and obtain the corresponding clamp detection sub-model. The data judgment submodule is used to input line image data and operational anomaly data into the corresponding clamp detection sub-model for detection, and to determine whether to trigger the tension clamp detection message based on the line image data and operational anomaly data of the clamp model to be detected.
4. The tension clamp testing device according to claim 3, characterized in that, The data acquisition module includes: The benchmark data acquisition submodule is used to obtain the line operation type from each of the line operation benchmark data, and the type operation benchmark data corresponding to each of the line operation types; The fluctuation value acquisition submodule is used to acquire the corresponding real-time operation data of the line according to the line operation type, and calculate the operation data fluctuation value according to the real-time operation data of the line and the corresponding operation benchmark data of the type. The data generation submodule is used to generate the line operation data based on the operation data fluctuation value corresponding to each of the tension clamps to be tested.
5. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the tension clamp detection method as described in any one of claims 1 to 2.
6. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the tension clamp detection method as described in any one of claims 1 to 2.