Overhead cable positioning and coiling length identification method and device
By identifying sampling points on the overhead cable, obtaining vibration signal spectra, and performing dynamic filtering and kernel function convolution optimization, the problem of accurate identification of overhead cable positioning and coiling length was solved, improving management efficiency and accuracy.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-28
- Publication Date
- 2026-03-24
AI Technical Summary
In existing technologies, fault location and maintenance of overhead cables rely on on-site inspection, which is complex, costly, and inefficient, and makes it difficult to accurately identify and manage faults in complex noise environments.
By determining multiple sampling points of the overhead cable, vibration signal spectra are obtained. ANOVA is used to dynamically filter non-periodic interference, and kernel function convolution optimization is combined to identify abnormal signal sequences, thereby realizing the location and coil length identification of the overhead cable.
It enhances the anti-interference capability in complex noise environments, enables precise positioning and coil length identification of overhead cables, improves the level of digital and intelligent management and operation and maintenance efficiency, and supports the accurate identification of overhead cable asset ledgers.
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Figure CN121720428A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the fields of data processing, infrastructure, and communication testing technology, and in particular to a method and apparatus for locating and identifying the coil length of an overhead cable. Background Technology
[0002] Among related technologies, optical fiber networks are passive infrastructure, making them difficult to monitor and provide early warnings. Fault location and maintenance mainly rely on on-site inspections, which involve complex procedures, high manpower requirements, and long construction cycles. For aerial cables, natural disasters such as lightning strikes and flash floods, animal bites or bird pecking, wind and rain interference, various human factors, system failures, and other unknown causes are more likely to lead to performance degradation and interruptions, making their management more complex, asset records unclear, and overhead cable handling costly and inefficient.
[0003] Therefore, adapting to complex noise environments, accurately identifying the location and coiling length of overhead cables, enhancing anti-interference capabilities, improving the digital and intelligent management and control level of overhead cable resources, improving operation and maintenance efficiency, supporting the accurate identification of overhead cable asset ledgers, and improving the accuracy of overhead cable identification have become important research directions. Summary of the Invention
[0004] This application aims to at least partially address one of the technical problems in the related art. The technical solution disclosed herein is as follows: The first aspect of this application proposes a method for locating and identifying the coil length of an overhead cable, including: Multiple sampling points of the overhead cable were determined, and the vibration signal spectrum of each sampling point was obtained. The dimensions of the vibration signal spectrum include fiber distance, sampling time, and signal strength. Multiple anomalous signals in the vibration signal spectrum are identified, and the vibration signal spectrum is dynamically filtered based on the variance of the time interval between adjacent anomalous signals to obtain the target signal sequence. Spatial distribution extraction and kernel function convolution optimization are performed on the target signal sequence to determine the initial estimated position of the overhead cable; The overhead cable is located based on the initial estimated position, and the cable length is identified based on the target signal sequence.
[0005] A second aspect of this application provides a device for locating and identifying the coil length of an overhead cable, comprising: The first acquisition module is used to determine multiple sampling points of the overhead cable and acquire the vibration signal spectrum of each sampling point. The dimensions of the vibration signal spectrum include fiber distance, acquisition time and signal strength. The second acquisition module is used to identify multiple abnormal signals in the vibration signal spectrum, and dynamically filter the vibration signal spectrum according to the variance of the time interval between adjacent abnormal signals to obtain the target signal sequence. The determination module is used to extract the spatial distribution of the target signal sequence and optimize the kernel function convolution to determine the initial estimated position of the overhead cable; The processing module is used to locate the overhead cable based on the initial estimated position and identify the coil length of the overhead cable based on the target signal sequence.
[0006] A third aspect of this application provides an electronic device, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the instructions to implement the method for locating and identifying the coil length of an overhead cable as provided in the first aspect of this application.
[0007] The fourth aspect of this application provides a computer-readable storage medium that, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform the overhead cable positioning and coil length identification method provided in the first aspect of this application.
[0008] In this embodiment, non-periodic interference is dynamically filtered through variance analysis, which can enhance anti-interference capability and adapt to complex noise environments. Based on kernel function convolution to enhance signal spatial characteristics, spatial focusing of the signal can be achieved, suppressing the diffusion effect of long-term signals. This application can accurately identify the location and coiling length of overhead cables, improve the level of digital and intelligent management of overhead cable resources, improve operation and maintenance efficiency, and support the accurate identification of overhead cable asset ledgers.
[0009] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description
[0010] Figure 1 This is a flowchart of a method for locating and identifying the coil length of an overhead cable according to an embodiment of this application; Figure 2 This is a schematic diagram of a method for locating and identifying the coil length of an overhead cable according to an embodiment of this application; Figure 3 This is a schematic diagram of abnormal signal data calibration according to an embodiment of this application; Figure 4 This is a schematic diagram of a sliding window traversal signal according to an embodiment of this application; Figure 5 This is a schematic diagram of a method for locating and identifying the coil length of an overhead cable according to an embodiment of this application; Figure 6This is a schematic diagram of a method for locating and identifying the coil length of an overhead cable according to an embodiment of this application; Figure 7 This is a structural block diagram of an overhead cable positioning and coiling length identification device according to an embodiment of this application; Figure 8 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation
[0011] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.
[0012] The following describes, with reference to the accompanying drawings, a method and apparatus for locating and identifying the coil length of overhead cables according to embodiments of this application.
[0013] Figure 1 This is a flowchart of a method for locating and identifying the coil length of an overhead cable according to an embodiment of this application, as follows: Figure 1 As shown, the method includes the following steps: S101, determine multiple sampling points of the overhead cable and obtain the vibration signal spectrum of each sampling point.
[0014] Optical cables include overhead cables and underground cables. In this embodiment, only the positioning and coiling length identification of overhead cables are performed. Overhead cables are optical cables that are suspended in sections above the ground via poles.
[0015] In some implementations, data in a preset database can be queried to distinguish the overhead cables in the optical cables to be tested and to determine multiple sampling points of the overhead cables.
[0016] In some implementations, to improve data accuracy, after obtaining the vibration signal spectrum of each sampling point using the tapping method, the vibration characteristics of overhead and underground cables can be further identified and differentiated to filter the vibration signals of underground cables. For example, the vibration signal spectrum can be input into a preset neural network or recognition model to filter the vibration signals of underground cables. It should be noted that for underground cables, the spatial location of vibration sensing and the non-vibration location (which can be understood as background noise) have two distinct characteristics: 1. The vibration intervals are relatively uniformly distributed; 2. The noise and signal at non-vibration locations are very small, making the distinction between vibration and non-vibration significant. On the other hand, for overhead cables, the vibration transmission force is greater, and there are also two distinct characteristics: 1. The vibration intervals are irregularly distributed, and the irregular intervals cover a large area; 2. Due to wind, rain, and various natural environmental influences, the background noise at non-vibration locations is also irregular and high, resulting in insufficient distinction between the boundaries of vibration and non-vibration intervals.
[0017] The impact test is a non-destructive testing method that evaluates the physical properties of materials or structures by analyzing the vibration signals generated by controlled impacts.
[0018] In some implementations, a sampling point can be understood as any point along the optical cable where it is struck. A cable-striking device can be used to obtain the vibration signal spectrum of each sampling point using the striking method.
[0019] In this embodiment of the disclosure, the working method of the cable tapping device is that the phase-sensitive optical time domain reflectometer (phase OTDR device) is connected to the equipment room (ODF) or optical distribution box. According to the tapping method, the tapping hammer or other passive tapping device taps from multiple points simultaneously. Sensors are deployed in multiple spatial locations to collect time-series signals, obtain signal strength, and combine the fiber optic distance and the acquisition time to obtain the vibration signal spectrum of each sampling point.
[0020] The dimensions of the vibration signal spectrum include fiber optic distance, acquisition time, and signal strength. Optionally, the horizontal axis of the vibration signal spectrum represents fiber optic distance, the vertical axis represents time (acquisition time), and the color depth of each point represents a different amplitude, indicating signal strength.
[0021] S102, identify multiple abnormal signals in the vibration signal spectrum, and dynamically filter the vibration signal spectrum according to the variance of the time interval between adjacent abnormal signals to obtain the target signal sequence.
[0022] In this embodiment of the application, in order to improve data accuracy, it is necessary to filter out interference signals caused by non-uniform tapping or environmental noise.
[0023] In some implementations, the difference in signal intensity in the vibration signal spectrum is calculated, and multiple abnormal signals in the vibration signal spectrum are determined based on the magnitude of the difference.
[0024] In some implementations, the time interval variance of adjacent abnormal signals is calculated according to a preset sliding window, the non-periodic interference signal is determined according to the variance magnitude, and the non-periodic interference signal is dynamically filtered to obtain the target signal sequence.
[0025] S103, Spatial distribution extraction and kernel function convolution optimization are performed on the target signal sequence to determine the initial estimated position of the overhead cable.
[0026] In some implementations, since the distinction between the vibration zone and the non-vibration zone is not clear enough, additional algorithm enhancement is required. In this embodiment of the present disclosure, the spatial distribution of the target signal sequence can be extracted according to a preset distribution function to obtain the instantaneous signal intensity of the sensor at each sampling point. Then, kernel function convolution optimization is performed according to a preset kernel function to enhance the spatial aggregation characteristics of the signal and locate the initial estimated position of the overhead cable.
[0027] S104, locate the overhead cable based on the initial estimated position, and identify the coil length of the overhead cable based on the target signal sequence.
[0028] In some implementations, the overhead cable is positioned based on an initial estimated location to determine the precise striking coordinates on the pole.
[0029] In some implementations, the number of consecutive points in the target signal sequence is obtained, and a preset conversion function is used to calculate the length of the overhead cable coiled on the pole at that location.
[0030] In some implementations, after obtaining the location coordinates and coil length of the overhead cable, it can be combined with map information from a Geographic Information System (GIS) and uploaded to the resource management system to support the collection of information (GIS points, distances, coil lengths, etc.) from various dimensions of the overhead cable asset ledger.
[0031] In this embodiment, non-periodic interference is dynamically filtered through variance analysis, which can enhance anti-interference capability and adapt to complex noise environments. Based on kernel function convolution to enhance signal spatial characteristics, spatial focusing of the signal can be achieved, suppressing the diffusion effect of long-term signals. This application can accurately identify the location and coiling length of overhead cables, improve the level of digital and intelligent management of overhead cable resources, improve operation and maintenance efficiency, and support the accurate identification of overhead cable asset ledgers.
[0032] Figure 2 This is a flowchart of a method for locating and identifying the coil length of an overhead cable according to an embodiment of this application, as follows: Figure 2 As shown, the method includes the following steps: S201, determine multiple sampling points of the overhead cable and obtain the vibration signal spectrum of each sampling point. The dimensions of the vibration signal spectrum include fiber distance, sampling time and signal strength.
[0033] For a description of step S201, please refer to the relevant content in the above embodiments, which will not be repeated here.
[0034] S202, obtain the signal strength difference value for each sampling point.
[0035] In this embodiment of the disclosure, abnormal vibration events with sudden changes in signal strength are identified based on the signal strength difference value.
[0036] Since the vibration signal is collected continuously, for example, 2000 times per second, in some implementations, the difference in signal strength is calculated point by point at time to facilitate subsequent detection of signal abrupt changes based on the difference in signal strength.
[0037] The frequency index can be set, but this application embodiment does not impose any restrictions on it.
[0038] S203, based on the signal intensity difference value, filters multiple abnormal signals in the vibration signal spectrum.
[0039] In some implementations, the global standard deviation of the signal strength corresponding to the sampling point is obtained, and a differential threshold is determined based on the global standard deviation. For each sampling point, a continuous abnormal interval where the signal strength differential value is greater than the differential threshold is determined, and multiple abnormal signals are obtained.
[0040] In this embodiment of the disclosure, the differential threshold is N times the global standard deviation as an example. If the signal strength differential value exceeds N times the global standard deviation, it is marked as an abnormal sampling point.
[0041] Optionally, N is a variable value greater than 0, and the corresponding engineering optimization value can be statistically determined through a large number of data samples.
[0042] Abnormal sampling points are merged into intervals. Specifically, adjacent abnormal sampling points are merged along the time dimension to identify abnormal signal segments with consecutive abnormal intervals exceeding a preset minimum duration.
[0043] In this embodiment of the disclosure, through mutation detection and interval merging, the output abnormal signal is a set of time intervals of abnormal vibration events.
[0044] S204, extract the center time of the abnormal signal and calculate the time interval between adjacent abnormal signals.
[0045] In the overhead cable environment, abnormal vibration events are abundant due to various natural environmental factors such as wind blowing grass, rain, bird pecking, and squirrel gnawing. It is necessary to eliminate all interference signals and filter out interference signals caused by non-uniform impact or environmental noise.
[0046] First, time difference calculation is performed. In this embodiment, the center time of all abnormal signals is extracted, and the time interval between adjacent signals is calculated. Abnormal signal data calibration is as follows: Figure 3 As shown, the vertical axis represents amplitude (i.e., signal strength), and the horizontal axis represents time.
[0047] S205, within a preset time sliding window, calculate the time interval variance of each abnormal signal based on the time interval of adjacent abnormal signals.
[0048] Further variance comparisons were performed. For example... Figure 4 As shown in the embodiment of this application, a preset time sliding window is used to traverse all signals within the sliding window, and the variance of the time interval between adjacent abnormal signals is calculated, where the vertical axis represents amplitude (i.e., signal strength) and the horizontal axis represents time. This yields the variance of the time interval between different signals within each sliding window.
[0049] S206. Determine the interference signal within the time sliding window based on the time interval variance, and dynamically filter the interference signal to obtain the target signal sequence.
[0050] In this embodiment of the disclosure, a variance threshold is obtained, and in response to the time interval variance of the abnormal signal being greater than the variance threshold, the abnormal signal is determined to be a non-periodic interference signal.
[0051] In some implementations, the global average variance can be obtained based on the time interval variance, and the product of a preset coefficient and the global average variance can be used as a variance threshold. For example, the preset coefficient can be 50% or other values. In other implementations, the variance threshold can also be other variation values statistically derived from a large number of data samples, and this application does not limit this.
[0052] In some implementations, if the variance of the time interval within the sliding window exceeds the variance threshold, it is determined to be a non-periodic interference signal and the corresponding time is removed to obtain the target signal sequence of effective knocking after interference removal.
[0053] In overhead cable environments, abnormal vibration events caused by various natural environmental factors such as wind, rain, bird pecking, and squirrel gnawing have a significantly different distribution over time compared to impact events. In the impact method, the impact events have relatively constant amplitude and frequency, while other interference signals are random and have significant differences in amplitude and frequency compared to impact events. Therefore, using time difference and variance comparison is an effective means of removing interference signals from overhead cables.
[0054] S207, Spatial distribution extraction and kernel function convolution optimization are performed on the target signal sequence to determine the initial estimated position of the overhead cable.
[0055] S208, locate the overhead cable based on the initial estimated position, and identify the coil length of the overhead cable based on the target signal sequence.
[0056] For a description of steps S207 to S208, please refer to the relevant content in the above embodiments, which will not be repeated here.
[0057] This application obtains the signal intensity difference value of each sampling point, filters multiple abnormal signals in the vibration signal spectrum based on the signal intensity difference value, extracts the center time of the abnormal signals, calculates the time interval of adjacent abnormal signals, and calculates the time interval variance of each abnormal signal within a preset time sliding window based on the time interval of adjacent abnormal signals. Based on the time interval variance, it determines the non-periodic interference signals within the time sliding window and dynamically filters the non-periodic interference signals to obtain the target signal sequence. This avoids the problem of vibration signals being greatly affected by environmental interference and the problem of inaccurate positioning caused by the signal dispersion of the overhead cable itself. It truly realizes efficient non-contact resource data collection and management by striking the pole of the overhead cable to achieve accurate positioning and accurate output of the coil length of the overhead cable.
[0058] Figure 5 This is a flowchart of a method for locating and identifying the coil length of an overhead cable according to an embodiment of this application, as follows: Figure 5 As shown, the method includes the following steps: S501, determine multiple sampling points of the overhead cable and obtain the vibration signal spectrum of each sampling point. The dimensions of the vibration signal spectrum include fiber distance, sampling time and signal strength.
[0059] S502, identify multiple abnormal signals in the vibration signal spectrum, and dynamically filter the vibration signal spectrum according to the variance of the time interval between adjacent abnormal signals to obtain the target signal sequence.
[0060] For a description of steps S501 to S502, please refer to the relevant content in the above embodiments, which will not be repeated here.
[0061] S503, based on the one-dimensional spatial vibration intensity distribution function, extracts the instantaneous signal intensity corresponding to each target signal sequence.
[0062] Because the vibration of overhead cables is transmitted quickly and has a large amplitude, compared with buried cables, the vibration segment of the entire optical cable caused by a single impact point is long and the vibration amplitude is uneven. Using a single maximum point vibration signal as the impact point is not suitable for overhead cable scenarios. In order to extract the precise spatial location of each impact from the long-term signal, the target signal sequence needs to be optimized.
[0063] First, spatial distribution extraction is performed on each target signal sequence. Specifically, for each effective impact moment, the one-dimensional spatial vibration intensity distribution function corresponding to different sampling points at each moment is extracted, which is the instantaneous signal intensity at each sensor position.
[0064] S504 performs convolution operations on the instantaneous signal intensity according to the preset Gaussian kernel function to obtain the convolution result.
[0065] Furthermore, kernel function convolution optimization is performed on the instantaneous signal intensity. Specifically, a suitable Gaussian kernel function is selected to perform convolution operation on the one-dimensional spatial distribution function to enhance the spatial aggregation characteristics of the signal.
[0066] In this embodiment of the disclosure, the Gaussian kernel function can be a preset Gaussian kernel function, or a Gaussian kernel function with different parameters can be used for different signal modes. This part belongs to fine-tuning.
[0067] S505 locates the sampling point corresponding to the maximum value of the convolution result, and uses it as the initial estimated position.
[0068] In some implementations, the sampling point location corresponding to the maximum value of the localization convolution result is used as the initial estimated location for a single tap. Optionally, the sampling point location can be obtained based on the fiber optic distance.
[0069] Optionally, the overhead cable can be located subsequently based on the average of the preliminary estimated locations from multiple taps.
[0070] S506, locate the overhead cable based on the initial estimated position, and identify the coil length of the overhead cable based on the target signal sequence.
[0071] For a description of step S506, please refer to the relevant content in the above embodiments, which will not be repeated here.
[0072] This application uses variance analysis to dynamically filter non-periodic interference to adapt to the complex noise environment of overhead cables and enhances the spatial characteristics of signals based on kernel function convolution to suppress the diffusion effect of long-term signals, thereby achieving spatial focusing and precise positioning, and enabling accurate identification of the positioning and coiling length of overhead cables.
[0073] Figure 6 This is a flowchart of a method for locating and identifying the coil length of an overhead cable according to an embodiment of this application, as follows: Figure 6 As shown, the method includes the following steps: S601, determine multiple sampling points of the overhead cable and obtain the vibration signal spectrum of each sampling point. The dimensions of the vibration signal spectrum include fiber distance, sampling time and signal strength.
[0074] S602, identify multiple abnormal signals in the vibration signal spectrum, and dynamically filter the vibration signal spectrum according to the variance of the time interval between adjacent abnormal signals to obtain the target signal sequence.
[0075] S603 performs spatial distribution extraction and kernel function convolution optimization on the target signal sequence to determine the initial estimated position of the overhead cable.
[0076] For a description of steps S601 to S603, please refer to the relevant content in the above embodiments, which will not be repeated here.
[0077] S604, obtain multiple sets of initial estimated positions.
[0078] In this embodiment of the disclosure, multiple taps are performed, and steps S601 to S603 are executed for each tap to obtain a set of initial estimated positions, and finally multiple sets of initial estimated positions are obtained.
[0079] S605, the overhead cable is located based on the average of multiple initial estimated positions to obtain the location result of the overhead cable.
[0080] In some implementations, the average of multiple initial estimated locations is used to perform location fusion, which serves as the final accurate positioning result.
[0081] S606, obtain the number of consecutive sampling points in the target signal sequence.
[0082] In some implementations, the number of consecutive sampling points in the target signal sequence is obtained to facilitate subsequent calculation of consecutive spatial location points.
[0083] S607, calculate the length based on the number of points to obtain the coiled length of the overhead cable.
[0084] In this embodiment of the disclosure, after eliminating the continuous points or segments of interference through the above steps, the data of a continuous segment of points is finally left. For each accurate positioning result of the continuous segment, the number of continuous points is calculated and converted into length data, which is the length of the overhead cable coiled on the pole at that location. Thus, the accurate striking position coordinates and coiling length on the pole are obtained.
[0085] This disclosure locates the overhead cable based on the average of multiple initial estimated locations, thereby obtaining the location result of the overhead cable. This can further improve data accuracy, enhance anti-interference capability, improve operation and maintenance efficiency, and increase the accuracy of identifying overhead cables.
[0086] Figure 7 This is a structural block diagram of an overhead cable positioning and coiling length identification device according to an embodiment of this disclosure, as shown below. Figure 7 As shown, the overhead cable positioning and coiling length identification device 700 includes: The first acquisition module 710 is used to determine multiple sampling points of the overhead cable and acquire the vibration signal spectrum of each sampling point. The dimensions of the vibration signal spectrum include fiber optic distance, acquisition time and signal strength. The second acquisition module 720 is used to determine multiple abnormal signals in the vibration signal spectrum, and dynamically filter the vibration signal spectrum according to the variance of the time interval between adjacent abnormal signals to obtain the target signal sequence. The determination module 730 is used to extract the spatial distribution of the target signal sequence and optimize the kernel function convolution to determine the initial estimated position of the overhead cable; The processing module 740 is used to locate the overhead cable based on the initial estimated position and identify the coil length of the overhead cable based on the target signal sequence.
[0087] In some embodiments, the second acquisition module 720 is further configured to: Obtain the signal strength difference value for each sampling point; Based on the signal intensity difference value, multiple abnormal signals in the vibration signal spectrum are filtered.
[0088] In some embodiments, the second acquisition module 720 is further configured to: Obtain the global standard deviation of the signal intensity corresponding to the sampling point, and determine the differential threshold based on the global standard deviation; For each sampling point, a continuous abnormal interval where the signal strength difference value is greater than the difference threshold is determined, and multiple abnormal signals are obtained.
[0089] In some embodiments, the second acquisition module 720 is further configured to: Extract the center time of the abnormal signal and calculate the time interval between adjacent abnormal signals; Within a preset time sliding window, the time interval variance of each abnormal signal is calculated based on the time interval between adjacent abnormal signals. The interference signal within the time sliding window is determined based on the time interval variance, and the interference signal is dynamically filtered to obtain the target signal sequence.
[0090] In some embodiments, the second acquisition module 720 is further configured to: Obtain the variance threshold; If the variance of the time interval responding to an abnormal signal is greater than the variance threshold, the abnormal signal is determined to be an interference signal.
[0091] In some implementations, the determining module 730 is further configured to: Based on the one-dimensional spatial vibration intensity distribution function, the instantaneous signal intensity corresponding to each target signal sequence is extracted; Based on the preset Gaussian kernel function, a convolution operation is performed on the instantaneous signal intensity to obtain the convolution result; The location of the sampling point corresponding to the maximum value of the convolution result is used as the initial estimated location.
[0092] In some embodiments, the processing module 740 is further configured to: Obtain multiple sets of initial estimated positions; The location of the overhead cable is determined by averaging the values of multiple initial estimated locations.
[0093] In some embodiments, the processing module 740 is further configured to: Obtain the number of consecutive sampling points in the target signal sequence; The length of the overhead cable is calculated by converting the points to the length.
[0094] like Figure 7 As shown in this embodiment, the overhead cable positioning and coiling length identification device 700 can also be combined with the terminal device application APP and the cable tapping control platform on the cloud server (cloud) to report the positioning results and coiling length, thereby supporting accurate ledger identification and fault point identification of the entire overhead cable, and associating it with GIS coordinates, thus realizing a complete intelligent digital resource management system for overhead cables. It can support one-click navigation for faults. At the same time, it also provides accurate routing data support for real-time monitoring of the Direct-Attached Storage (DAS) of the distributed open system of overhead cables, ultimately enabling the management and perception of all overhead cables and all optical cable network resources, reducing the risk of future service degradation or interruption, and saving resources.
[0095] In this embodiment, non-periodic interference is dynamically filtered through variance analysis, which can enhance anti-interference capability and adapt to complex noise environments. Based on kernel function convolution to enhance signal spatial characteristics, spatial focusing of the signal can be achieved, suppressing the diffusion effect of long-term signals. This application can accurately identify the location and coiling length of overhead cables, improve the level of digital and intelligent management of overhead cable resources, improve operation and maintenance efficiency, and support the accurate identification of overhead cable asset ledgers.
[0096] Figure 8 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure.
[0097] like Figure 8 As shown, the electronic device 800 includes: The memory 801 and processor 802 are connected by a bus 803 that connects different components (including the memory 801 and the processor 802). The memory 801 stores a computer program. When the processor 802 executes the program, it implements the method for locating and identifying the coil length of the overhead cable according to the present disclosure.
[0098] Bus 803 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.
[0099] Electronic device 800 typically includes a variety of electronic device readable media. These media can be any available media that can be accessed by electronic device 800, including volatile and non-volatile media, removable and non-removable media.
[0100] Memory 801 may also include computer system readable media in the form of volatile memory, such as random access memory (RAM) 804 and / or cache memory 805. Electronic device 800 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 806 may be used to read and write non-removable, non-volatile magnetic media (… Figure 8 Not shown; usually referred to as a "hard drive"). Although Figure 8 As not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 803 via one or more data media interfaces. Memory 801 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this disclosure.
[0101] A program / utility 808 having a set (at least one) of program modules 807 may be stored, for example, in memory 801. Such program modules 807 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 807 typically perform the functions and / or methods described in the embodiments of this disclosure.
[0102] Electronic device 800 can also communicate with one or more external devices 809 (e.g., keyboard, pointing device, display 811, etc.), and with one or more devices that enable a user to interact with the electronic device 800, and / or with any device that enables the electronic device 800 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed through input / output (I / O) interface 812. Furthermore, electronic device 800 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 813. Figure 8 As shown, network adapter 813 communicates with other modules of electronic device 800 via bus 803. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with electronic device 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.
[0103] The processor 802 executes various functional applications and data processing by running programs stored in the memory 801.
[0104] It should be noted that the implementation process and technical principles of the electronic device in this embodiment are explained in the foregoing description of the method for locating and recoil length identification of overhead cables in this disclosure embodiment, and will not be repeated here.
[0105] To implement the above embodiments, this disclosure also proposes a computer-readable storage medium.
[0106] When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is able to perform the aforementioned method for locating and identifying the coil length of the overhead cable. Optionally, the computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, or optical data storage device, etc.
[0107] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.
[0108] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.
Claims
1. A method for locating and identifying the coiled length of an overhead cable, characterized in that, include: Multiple sampling points of the overhead cable are determined, and a vibration signal spectrum is obtained for each sampling point. The dimensions of the vibration signal spectrum include fiber distance, sampling time, and signal strength. Multiple abnormal signals in the vibration signal spectrum are identified, and the vibration signal spectrum is dynamically filtered according to the variance of the time interval between adjacent abnormal signals to obtain the target signal sequence. Spatial distribution extraction and kernel function convolution optimization are performed on the target signal sequence to determine the initial estimated position of the overhead cable; The overhead cable is located based on the initial estimated position, and the coil length of the overhead cable is identified based on the target signal sequence.
2. The method according to claim 1, characterized in that, The determination of multiple abnormal signals in the vibration signal spectrum includes: Obtain the signal strength difference value for each of the sampling points; Based on the signal intensity difference value, multiple abnormal signals in the vibration signal spectrum are filtered out.
3. The method according to claim 2, characterized in that, The step of filtering multiple abnormal signals in the vibration signal spectrum based on the signal intensity difference value includes: Obtain the global standard deviation of the signal intensity corresponding to the sampling point, and determine the differential threshold based on the global standard deviation; For each sampling point, a continuous abnormal interval where the signal strength difference value is greater than the difference threshold is determined, and multiple abnormal signals are obtained.
4. The method according to any one of claims 1-3, characterized in that, The step of dynamically filtering the vibration signal spectrum based on the variance of the time interval between adjacent abnormal signals to obtain the target signal sequence includes: Extract the center time of the abnormal signal and calculate the time interval between adjacent abnormal signals; Within a preset time sliding window, the variance of the time interval for each abnormal signal is calculated based on the time interval between adjacent abnormal signals. The interference signal within the time sliding window is determined based on the time interval variance, and the interference signal is dynamically filtered to obtain the target signal sequence.
5. The method according to claim 4, characterized in that, Determining the interference signal within the time sliding window based on the time interval variance includes: Obtain the variance threshold; In response to the abnormal signal having a time interval variance greater than the variance threshold, the abnormal signal is determined to be the interference signal.
6. The method according to any one of claims 1-3, characterized in that, The step of extracting the spatial distribution of the target signal sequence and performing kernel function convolution optimization to determine the initial estimated position of the overhead cable includes: Based on the one-dimensional spatial vibration intensity distribution function, the instantaneous signal intensity corresponding to each target signal sequence is extracted; The instantaneous signal intensity is convolved according to a preset Gaussian kernel function to obtain the convolution result; The location of the sampling point corresponding to the maximum value of the convolution result is used as the initial estimated location.
7. The method according to any one of claims 1-3, characterized in that, The step of locating the overhead cable based on the initial estimated location includes: Obtain multiple sets of the initial estimated positions; The overhead cable is located based on the average of multiple sets of initial estimated positions to obtain the location result of the overhead cable.
8. The method according to any one of claims 1-3, characterized in that, The step of identifying the coil length of the overhead cable based on the target signal sequence includes: Obtain the number of consecutive sampling points in the target signal sequence; The length of the overhead cable is obtained by converting the points into lengths.
9. A device for positioning and recoil length identification of overhead cables, characterized in that, include: The first acquisition module is used to determine multiple sampling points of the overhead cable and acquire the vibration signal spectrum of each sampling point. The dimensions of the vibration signal spectrum include fiber distance, acquisition time and signal strength. The second acquisition module is used to determine multiple abnormal signals in the vibration signal spectrum, and dynamically filter the vibration signal spectrum according to the variance of the time interval between adjacent abnormal signals to obtain the target signal sequence. The determination module is used to extract the spatial distribution of the target signal sequence and optimize the kernel function convolution to determine the initial estimated position of the overhead cable; The processing module is used to locate the overhead cable based on the initial estimated position and to identify the coil length of the overhead cable based on the target signal sequence.
10. An electronic device, characterized in that, include: processor; Memory for storing the executable instructions of the processor; The processor is configured to execute the instructions to implement the method as described in any one of claims 1-8.