Optical cable sag detection method, device, equipment and medium

Abstract

The present invention discloses a method, device, equipment and medium for detecting sag of optical cables. The method comprises: obtaining environmental data information and tower data information of the sag of optical cables to be detected between towers; determining the theoretical sag position coordinates and theoretical ground distance of the sag of optical cables to be detected according to the tower data information; determining the final sag position coordinates and final ground distance of the sag of optical cables to be detected according to the environmental data information, the theoretical sag position coordinates, the theoretical ground distance and a preset sag detection model. The final sag position coordinates and final ground distance of the sag of optical cables to be detected are determined by combining the theoretical sag position coordinates and the theoretical ground distance with a neural network model. Accurate detection and real-time detection of the sag position coordinates and ground distance of optical cables are achieved, detection efficiency is improved, the accuracy of sag calculation is effectively improved, and the safe and stable operation of power communication is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission lines, and in particular to an optical cable sag detection method, device, equipment and medium. Background Art

[0002] With the rapid development of smart grids, the importance of power communication optical cable operation and maintenance management has become increasingly prominent. All-dielectric self-supporting optical cables are widely used in the backbone communication network of power systems because they are not easily struck by lightning, do not contain metal materials, have good electromagnetic shielding effects, and are easy to install. However, due to its all-dielectric material characteristics and the fact that most of the lines are distributed in suburban wilderness, they are susceptible to objective natural factors such as bad weather and hillside geographical conditions, which can easily cause safety hazards such as excessive cable sag and insufficient distance to the ground, directly affecting the stable operation of power communications. Therefore, online monitoring of sag of all-dielectric self-supporting optical cables is of great significance.

[0003] Currently, the traditional inspection method for power communication lines is manual inspection, but the monitoring efficiency is too low and real-time monitoring is impossible. The use of laser radar for sag monitoring is relatively costly and is not suitable for large-scale promotion across the entire network. Summary of the invention

[0004] The present invention provides an optical cable sag detection method, device, equipment and medium to achieve accurate detection of optical cable sag.

[0005] According to a first aspect of the present invention, there is provided a method for detecting sag of an optical cable, comprising:

[0006] Obtain environmental data information of the optical cable sag to be detected between towers and tower data information;

[0007] Determine the theoretical sag position coordinates and theoretical ground distance of the optical cable sag to be detected according to the tower data information;

[0008] According to the environmental data information, the theoretical sag position coordinates, the theoretical distance to the ground and a preset sag detection model, the final sag position coordinates and the final distance to the ground of the optical cable sag to be detected are determined.

[0009] According to a second aspect of the present invention, there is provided an optical cable sag detection device, comprising:

[0010] An information acquisition module is used to obtain environmental data information of the optical cable sag to be detected between the poles and towers and tower data information;

[0011] A first determination module is used to determine the theoretical sag position coordinates and theoretical ground distance of the optical cable sag to be detected according to the tower data information;

[0012] The second determination module is used to determine the final sag position coordinates and the final ground distance of the optical cable sag to be detected according to the environmental data information, the theoretical sag position coordinates, the theoretical ground distance and a preset sag detection model.

[0013] According to a third aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the optical cable sag detection method described in any embodiment of the present invention.

[0017] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the optical cable sag detection method described in any embodiment of the present invention when executed.

[0018] The technical solution of the embodiment of the present invention obtains environmental data information and tower data information of the optical cable sag to be detected between towers; determines the theoretical sag position coordinates and theoretical ground distance of the optical cable sag to be detected according to the tower data information; determines the final sag position coordinates and final ground distance of the optical cable sag to be detected according to the environmental data information, theoretical sag position coordinates, theoretical ground distance and a preset sag detection model. The final sag position coordinates and final ground distance of the optical cable sag to be detected are determined by combining the theoretical sag position coordinates and theoretical ground distance with the neural network model. Accurate detection and real-time detection of the optical cable sag position coordinates and ground distance are achieved, which improves detection efficiency, effectively improves the accuracy of sag calculation, and ensures the safe and stable operation of power communication.

[0019] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 is a flow chart of a method for detecting optical cable sag according to Embodiment 1 of the present invention;

[0022] Figure 2 is a flow chart of a method for detecting optical cable sag according to Embodiment 2 of the present invention;

[0023] Figure 3 is a schematic diagram of sag in a method for detecting sag of an optical cable provided in Embodiment 2 of the present invention;

[0024] Figure 4 is a structural schematic diagram of an optical cable sag detection device provided according to Embodiment 3 of the present invention;

[0025] Figure 5 It is a schematic diagram of the structure of an electronic device implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] Embodiment 1

[0029] Figure 1 A flowchart of a method for detecting sag of an optical cable is provided for the first embodiment of the present invention. This embodiment can be applied to the automatic real-time detection of sag of a self-supporting optical cable. The method can be performed by an optical cable sag detection device. The optical cable sag detection device can be implemented in the form of hardware and / or software. The optical cable sag detection device can be configured in an electronic device. Figure 1As shown, the method includes:

[0030] S110, obtaining environmental data information of the optical cable sag to be detected between the poles and towers and pole tower data information.

[0031] In this embodiment, the pole tower can be understood as a pole tower used to install optical cables, such as a telephone pole. The sag of the optical cable to be detected can be understood as an optical cable with sag that needs to be detected. The sag refers to the bending shape formed by the weight of the optical cable. The environmental data information can be understood as the environmental information at the location of the optical cable sag. The pole tower data information can be understood as the static data between the two pole towers where the optical cable to be detected is installed.

[0032] Specifically, the processor can obtain nonlinear factors such as the temperature, humidity and ambient wind speed of the location where the optical cable to be tested is located measured by the corresponding sensor as environmental data information, as well as static data such as the pole tower spacing, the hanging point altitudes of the near and far pole towers, etc., combined with the optical cable hanging inclination data at the pole tower hanging point provided by the pole tower inclination sensor as the pole tower data information.

[0033] S120. Determine the theoretical sag position coordinates and theoretical ground distance of the optical cable sag to be detected according to the tower data information.

[0034] In this embodiment, the theoretical sag position coordinates can be understood as the coordinates of the sag position determined theoretically. The theoretical distance to the ground can be understood as the distance of the sag from the ground determined theoretically.

[0035] Specifically, the processor can determine the theoretical sag position coordinates and theoretical ground distance of the optical cable sag to be detected based on the physical force mechanism of the ADSS optical cable and the hanging point inclination angle according to the tower data information.

[0036] S130, determining the final sag position coordinates and the final ground distance of the optical cable sag to be detected according to the environmental data information, the theoretical sag position coordinates, the theoretical ground distance and the preset sag detection model.

[0037] In this embodiment, the preset sag detection model can be understood as a model for determining the deviation of the theoretical value caused in the current environment.

[0038] Specifically, the processor can input environmental data information into a preset sag detection model to determine the error caused under the environmental data, and then correct the theoretical sag position coordinates and theoretical distance to the ground according to the error situation to determine the final sag position coordinates and final distance to the ground of the optical cable sag to be detected.

[0039] The technical solution of the embodiment of the present invention obtains environmental data information and tower data information of the optical cable sag to be detected between towers; determines the theoretical sag position coordinates and theoretical ground distance of the optical cable sag to be detected according to the tower data information; determines the final sag position coordinates and final ground distance of the optical cable sag to be detected according to the environmental data information, theoretical sag position coordinates, theoretical ground distance and a preset sag detection model. The final sag position coordinates and final ground distance of the optical cable sag to be detected are determined by combining the theoretical sag position coordinates and theoretical ground distance with the neural network model. Accurate detection and real-time detection of the optical cable sag position coordinates and ground distance are achieved, which improves detection efficiency, effectively improves the accuracy of sag calculation, and ensures the safe and stable operation of power communication.

[0040] Embodiment 2

[0041] Figure 2 This is a flow chart of a method for detecting optical cable sag provided in the second embodiment of the present invention. This embodiment is a further refinement of the above embodiment. Figure 2 As shown, the method includes:

[0042] S210, obtaining environmental data information of the optical cable sag to be detected between the poles and towers and pole tower data information.

[0043] S220, extracting the pole tower span, the near-end pole tower cable hanging inclination angle relative to the sag point of the optical cable sag to be detected, the near-end pole tower hanging point altitude relative to the sag point, and the far-end pole tower hanging point altitude relative to the sag point from the pole tower data information.

[0044] In this embodiment, the pole tower spacing can be understood as the distance between the two pole towers on which the optical cable to be detected is hung. The proximal pole tower can be understood as the pole tower that is closer to the sag point among the two pole towers on which the optical cable to be detected is hung. The sag point can be understood as the lowest point of the sag of the optical cable to be detected. The proximal pole tower can be understood as the pole tower that is farther from the sag point among the two pole towers on which the optical cable to be detected is hung. The proximal pole tower optical cable hanging inclination angle can be understood as the drooping angle of the optical cable to be detected at the pole tower. The proximal pole tower hanging point altitude can be understood as the altitude corresponding to the position where the proximal pole tower hangs the optical cable to be detected. The distal pole tower hanging point altitude can be understood as the altitude corresponding to the position where the distal pole tower hangs the optical cable to be detected.

[0045] For example, in order to facilitate understanding of the various data included in the tower data information, a specific example is used for display. Figure 3 A schematic diagram of sag in a method for detecting sag of an optical cable provided in Embodiment 2 of the present invention is shown in FIG. Figure 3As shown, pole 1 and pole 2 are pole towers for erecting optical cables to be tested, the distance between pole 1 and pole 2 is the span, the lowest point of the optical cable is the sag point, pole 1 is the proximal pole tower relative to the sag point of the sag of the optical cable to be tested, the position of the optical cable to be tested on pole 1 is the proximal pole tower hanging point relative to the sag point, the altitude of hanging point 1 is the altitude of the proximal pole tower hanging point, and the inclination angle 1 is the inclination angle of the optical cable hanging at the proximal pole tower. Pole 2 is the distal pole tower relative to the sag point of the sag of the optical cable to be tested, the position of the optical cable to be tested on pole 2 is the distal pole tower hanging point relative to the sag point, the altitude of hanging point 2 is the altitude of the distal pole tower hanging point, and the inclination angle 2 is the distal pole tower cable hanging inclination.

[0046] S230, determine the horizontal distance between the sag point of the optical cable to be detected and the near-end tower and the theoretical distance to the ground by the following formula:

[0047]

[0048]

[0049] Among them, d is the horizontal distance between the sag point and the near-end tower, h is the theoretical distance to the ground, L is the tower spacing, α is the near-end tower cable hanging inclination, h1 is the near-end tower hanging point altitude, and h2 is the far-end tower hanging point altitude.

[0050] S240. Determine the coordinates of the theoretical sag position according to the horizontal distance and the theoretical distance to the ground.

[0051] Specifically, the processor can use the horizontal distance as the horizontal coordinate, or determine the horizontal coordinate by combining the coordinates of the far and near end tower hanging points with the horizontal distance, and use the theoretical distance to the ground as the vertical coordinate to obtain the theoretical sag position coordinates.

[0052] S250: Inputting environmental data information into a preset sag detection model.

[0053] Specifically, the processor may input environmental data information into a preset sag detection model to determine the influence of nonlinear factors such as temperature, humidity and environmental wind speed at the location where the optical cable sag to be detected is located on the theoretical value.

[0054] S260: Determine error information by using a preset sag detection model.

[0055] In this embodiment, the error information may be understood as information used to correct the theoretical sag position coordinates and the theoretical distance to the ground.

[0056] Specifically, the processor can obtain the result of the preset sag detection model output, that is, the error information, and reflect the influence of nonlinear factors such as temperature, humidity and ambient wind speed at the location of the optical cable sag to be detected on the theoretical value through the error information.

[0057] S270, correcting the theoretical sag position coordinates and the theoretical distance to the ground based on the error information, and obtaining the final sag position coordinates and the final distance to the ground of the optical cable sag to be detected.

[0058] Specifically, the processor can correct the theoretical sag position coordinates and the theoretical ground distance based on the error information, such as adding the error value used to correct the theoretical sag position coordinates in the error information to the theoretical sag position coordinates, and adding the error value used to correct the theoretical ground distance in the error information to the theoretical ground distance. The final sag position coordinates and the final ground distance of the optical cable sag to be detected are obtained.

[0059] The technical solution of the embodiment of the present invention determines the theoretical sag position coordinates and the theoretical distance to the ground by combining the tower data information with the set formula, determines the error information corresponding to the current environmental data information by the preset sag detection model, corrects the theoretical value by the error information, and determines the final sag position coordinates and the final distance to the ground of the optical cable sag to be detected. Accurate detection and real-time detection of the optical cable sag position coordinates and the distance to the ground are achieved, which improves the detection efficiency, effectively improves the accuracy of sag calculation, and ensures the safe and stable operation of power communication.

[0060] As a first optional embodiment of the second embodiment, based on the above embodiment, the step of constructing the preset sag detection model further includes:

[0061] a1. Obtain the laser point cloud dataset of the optical cable sag to be detected, the environmental measurement dataset and the tower measurement dataset on the same day as the laser point cloud dataset.

[0062] In this embodiment, the laser point cloud data set can be understood as including a lot of point cloud data of the optical cable sag to be detected collected by the laser radar. The environmental measurement data set can be understood as a set of environmental measurement data corresponding to the time of each laser point cloud data in the laser point cloud data set. The tower measurement data set can be understood as a set of tower measurement data corresponding to the time of each laser point cloud data in the laser point cloud data set.

[0063] Among them, the laser point cloud data in the laser point cloud data set include: the ground altitude of the optical cable sag to be detected, the optical cable sag value, the altitude of the sag point, the distance of the sag point from the ground, the position coordinates of the sag point and the position coordinates of the tower hanging point.

[0064] Specifically, laser point cloud data for one or more days can be collected by lidar at a certain collection interval, such as once every 5 minutes, to form a laser point cloud data set. Environmental measurement data and tower measurement data of the sag of the optical cable to be detected can be measured by corresponding sensors at the same time to form an environmental measurement data set and a tower measurement data set.

[0065] b1. According to the tower measurement data set, determine the theoretical sag position coordinate set and theoretical ground distance set of the optical cable sag to be detected.

[0066] Specifically, the processor can substitute the parameters corresponding to the tower measurement data in the tower measurement data set into the formula provided in step S230, determine the theoretical sag position coordinates and theoretical distance to the ground corresponding to each tower measurement data, and then obtain the theoretical sag position coordinate set and theoretical distance to the ground set of the optical cable sag to be detected.

[0067] c1. Determine the preset sag detection model based on the theoretical sag position coordinate set, theoretical ground distance set, laser point cloud data set and environmental measurement data set.

[0068] Specifically, the processor can determine the actual value set of the sag of the optical cable to be detected through the laser point cloud data set, and train the pre-built sag detection model through the actual value set, theoretical sag position coordinate set, theoretical ground distance set and environmental measurement data set to obtain a preset sag detection model.

[0069] Further, based on the above embodiment, the step of determining the preset sag detection model according to the theoretical sag position coordinate set, the theoretical ground distance set, the laser point cloud data set and the environmental measurement data set can be optimized as follows:

[0070] c11. Determine the actual sag position coordinate set and the actual ground distance set based on the laser point cloud data set.

[0071] In this embodiment, the actual sag position coordinate set can be understood as a coordinate set for reflecting the actual position of the sag point of the optical cable sag to be detected. The actual ground distance set can be understood as a set for reflecting the actual ground distance of the sag point of the optical cable sag to be detected.

[0072] Specifically, the processor can calculate the actual sag position coordinates and actual ground distance corresponding to each laser point cloud data according to the data included in each laser point cloud data set in the laser point cloud data set, and form an actual sag position coordinate set and an actual ground distance set.

[0073] c12. Determine the correction amount information set of the sag of the optical cable to be detected based on the actual sag position coordinate set, the actual distance to the ground set, the theoretical sag position coordinate set and the theoretical distance to the ground set.

[0074] In this embodiment, the correction amount information set can be understood as a collection of correction amount information.

[0075] Specifically, the processor can subtract each actual sag position coordinate in the actual sag position coordinate set from the corresponding theoretical sag position coordinate in the theoretical sag position coordinate set to obtain a correction value set corresponding to the sag position coordinate set, subtract each actual ground distance in the actual distance to the ground set from the corresponding theoretical ground distance in the theoretical distance to the ground set to obtain a correction value set corresponding to the ground distance, pair the correction value set corresponding to the sag position coordinate set with the correction value set corresponding to the sag position coordinate set, and obtain a correction value information set for the sag of the optical cable to be detected.

[0076] c13. Determine a preset sag detection model based on the correction information set, the environmental measurement data set, the theoretical sag position coordinate set and the theoretical ground distance set.

[0077] Among them, based on the above embodiment, the step of determining the preset sag detection model according to the correction amount information set, the environmental measurement data set, the theoretical sag position coordinate set and the theoretical ground distance set can be optimized as follows:

[0078] The input quantity is determined according to the environmental measurement data set, the theoretical sag position coordinate set and the theoretical ground distance set, the corresponding correction quantity information in the correction quantity information set is used as the output quantity, and the input quantity and the output quantity are divided into a training set and a test set according to a set ratio; the preset neural network model is trained based on the training set and the test set, and the weights and thresholds of the preset neural network model are adjusted using the gradient descent method and the back propagation method; until the output error value of the preset neural network model output is within the set error range, the preset sag detection model is obtained.

[0079] Specifically, the processor can construct a BP neural network model, and use the environmental measurement data in the environmental measurement data set as the input quantities of the input layer in the BP neural network model. For example, the nonlinear factors such as temperature, humidity, and ambient wind speed in the environmental measurement data, and the inclination angle at the tower hanging point, the altitude value of the theoretical sag point to the ground, and the horizontal distance between the theoretical sag point and the near-end tower are used as input quantities, then the number of nodes in the input layer is 8, and the corresponding correction information in the correction information set is used as the output quantity of the output layer, then the number of nodes in the output layer is 2. The number of nodes in the hidden layer is obtained by the following empirical formula:

[0080]

[0081] Among them, y is the number of hidden layer nodes, i is the number of input layer nodes, n is the number of output layer nodes, and a is a constant between 1 and 10.

[0082] The processor can divide its data set into training set and test set according to the set ratio of 8:2, and input it into the BP neural network. The output of the hidden layer is as follows:

[0083] H y =g(∑Wny X n -a y )

[0084] Among them, H y is the output of the hidden layer, is the sigmoid activation function, W ny is the weight of the hidden layer, X n is the input of the hidden layer, a y is the threshold of the hidden layer.

[0085] The output of the output layer looks like this:

[0086] O i =g(∑W yi H y -a i )

[0087] Among them, O i is the output of the output layer, i=1,2,…,W yi is the weight of the output layer, H y is the input of the output layer, a i is the threshold of the output layer.

[0088] Furthermore, when the predicted output differs from the expected output, the network is trained with the minimum sum of squared errors as the objective function, and the weights and thresholds of each layer are continuously adjusted using the gradient descent method and back propagation until the output error value of the preset neural network model is within the set error range. At this time, the final sag position coordinates and the final distance to the ground are obtained by adding the calculated correction value to the theoretical value, so that the error between the actual value and the actual value is within an acceptable range, thereby constructing a preset sag detection model.

[0089] Embodiment 3

[0090] Figure 4 This is a structural schematic diagram of an optical cable sag detection device provided in Embodiment 3 of the present invention.

[0091] like Figure 4 As shown, the device comprises:

[0092] The information acquisition module 41 is used to acquire environmental data information of the optical cable sag to be detected between the towers and tower data information;

[0093] A first determination module 42 is used to determine the theoretical sag position coordinates and theoretical ground distance of the optical cable sag to be detected according to the tower data information;

[0094] The second determination module 43 is used to determine the final sag position coordinates and the final ground distance of the optical cable sag to be detected according to the environmental data information, the theoretical sag position coordinates, the theoretical ground distance and a preset sag detection model.

[0095] The technical solution of the embodiment of the present invention obtains environmental data information and tower data information of the optical cable sag to be detected between towers; determines the theoretical sag position coordinates and theoretical ground distance of the optical cable sag to be detected according to the tower data information; determines the final sag position coordinates and final ground distance of the optical cable sag to be detected according to the environmental data information, theoretical sag position coordinates, theoretical ground distance and a preset sag detection model. The final sag position coordinates and final ground distance of the optical cable sag to be detected are determined by combining the theoretical sag position coordinates and theoretical ground distance with the neural network model. Accurate detection and real-time detection of the optical cable sag position coordinates and ground distance are achieved, which improves detection efficiency, effectively improves the accuracy of sag calculation, and ensures the safe and stable operation of power communication.

[0096] Further, the first determining module 42 is specifically configured to:

[0097] Extract the pole tower span, the near-end pole tower cable hanging inclination angle relative to the sag point of the optical cable sag to be detected, the near-end pole tower hanging point altitude relative to the sag point, and the far-end pole tower hanging point altitude relative to the sag point from the pole tower data information;

[0098] The horizontal distance between the sag point of the optical cable to be detected and the near-end tower and the theoretical distance to the ground are determined by the following formula:

[0099]

[0100]

[0101] Among them, d is the horizontal distance between the sag point and the near-end tower, h is the theoretical distance to the ground, L is the tower spacing, α is the near-end tower cable hanging inclination, h1 is the near-end tower hanging point altitude, and h2 is the far-end tower hanging point altitude;

[0102] The theoretical sag position coordinates are determined according to the horizontal distance and the theoretical distance to the ground.

[0103] Further, the second determining module 43 is specifically configured to:

[0104] Inputting the environmental data information into a preset sag detection model;

[0105] Determining error information through the preset sag detection model;

[0106] The theoretical sag position coordinates and the theoretical distance to the ground are corrected based on the error information to obtain final sag position coordinates and final distance to the ground of the optical cable sag to be detected.

[0107] Optionally, the device also includes: a model building module.

[0108] The model building module includes:

[0109] A data set acquisition unit, used to acquire a laser point cloud data set of the optical cable sag to be detected, an environmental measurement data set and a tower measurement data set on the same day as the laser point cloud data set;

[0110] A first determining unit is used to determine a theoretical sag position coordinate set and a theoretical ground distance set of the optical cable sag to be detected according to the tower measurement data set;

[0111] The second determining unit is used to determine a preset sag detection model according to the theoretical sag position coordinate set, the theoretical ground distance set, the laser point cloud data set and the environmental measurement data set.

[0112] Among them, the laser point cloud data in the laser point cloud data set includes: the ground altitude of the optical cable sag to be detected, the optical cable sag value, the altitude of the sag point, the distance of the sag point from the ground, the position coordinates of the sag point and the tower hanging point position coordinates.

[0113] Further, the second determining unit includes:

[0114] A first determination subunit is used to determine an actual sag position coordinate set and an actual ground distance set according to the laser point cloud data set;

[0115] A second determining subunit is used to determine a correction amount information set of the optical cable sag to be detected according to the actual sag position coordinate set, the actual distance to the ground set, the theoretical sag position coordinate set and the theoretical distance to the ground set;

[0116] The third determination subunit is used to determine a preset sag detection model according to the correction amount information set, the environmental measurement data set, the theoretical sag position coordinate set and the theoretical ground distance set.

[0117] The third determining subunit is specifically used for:

[0118] Determine an input quantity according to the environmental measurement data set, the theoretical sag position coordinate set and the theoretical ground distance set, use the corresponding correction quantity information in the correction quantity information set as output quantity, and divide the input quantity and the output quantity into a training set and a test set according to a set ratio;

[0119] Training a preset neural network model based on the training set and the test set, and adjusting the weights and thresholds of the preset neural network model using a gradient descent method and a back propagation method;

[0120] Until the output error value of the preset neural network model output is within the set error range, the preset sag detection model is obtained.

[0121] The optical cable sag detection device provided in the embodiment of the present invention can execute the optical cable sag detection method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0122] Embodiment 4

[0123] Figure 5 A schematic diagram of an electronic device 50 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0124] like Figure 5 As shown, the electronic device 50 includes at least one processor 51, and a memory connected to the at least one processor 51 in communication, such as a read-only memory (ROM) 52, a random access memory (RAM) 53, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 51 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 52 or the computer program loaded from the storage unit 58 to the random access memory (RAM) 53. In the RAM 53, various programs and data required for the operation of the electronic device 50 can also be stored. The processor 51, the ROM 52, and the RAM 53 are connected to each other via a bus 54. An input / output (I / O) interface 55 is also connected to the bus 54.

[0125] A number of components in the electronic device 50 are connected to the I / O interface 55, including: an input unit 56, such as a keyboard, a mouse, etc.; an output unit 57, such as various types of displays, speakers, etc.; a storage unit 58, such as a disk, an optical disk, etc.; and a communication unit 59, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 59 allows the electronic device 50 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0126] The processor 51 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 51 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 51 executes the various methods and processes described above, such as an optical cable sag detection method.

[0127] In some embodiments, the optical cable sag detection method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 58. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 50 via the ROM 52 and / or the communication unit 59. When the computer program is loaded into the RAM 53 and executed by the processor 51, one or more steps of the optical cable sag detection method described above can be performed. Alternatively, in other embodiments, the processor 51 can be configured to perform the optical cable sag detection method in any other appropriate manner (e.g., by means of firmware).

[0128] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0129] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0130] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0131] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0132] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0133] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0134] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0135] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting sag of an optical cable, characterized in that: include: Obtain environmental data information of the optical cable sag to be detected between towers and tower data information; Determine the theoretical sag position coordinates and theoretical ground distance of the optical cable sag to be detected according to the tower data information; Determine the final sag position coordinates and the final ground distance of the optical cable sag to be detected according to the environmental data information, the theoretical sag position coordinates, the theoretical ground distance and a preset sag detection model; The step of constructing the preset sag detection model includes: Acquire a laser point cloud dataset of the optical cable sag to be detected, an environmental measurement dataset and a tower measurement dataset on the same day as the laser point cloud dataset; Determine a theoretical sag position coordinate set and a theoretical ground distance set of the optical cable sag to be detected according to the tower measurement data set; Determine a preset sag detection model according to the theoretical sag position coordinate set, the theoretical ground distance set, the laser point cloud data set and the environmental measurement data set; Wherein, determining a preset sag detection model according to the theoretical sag position coordinate set, the theoretical ground distance set, the laser point cloud data and the environmental measurement data set includes: Determine an actual sag position coordinate set and an actual ground distance set according to the laser point cloud data set; Determine a correction amount information set of the optical cable sag to be detected according to the actual sag position coordinate set, the actual distance to the ground set, the theoretical sag position coordinate set and the theoretical distance to the ground set; Determine a preset sag detection model according to the correction information set, the environmental measurement data set, the theoretical sag position coordinate set and the theoretical ground distance set; Wherein, determining a preset sag detection model according to the correction amount information set, the environmental measurement data set, the theoretical sag position coordinate set and the theoretical ground distance set includes: Determine an input quantity according to the environmental measurement data set, the theoretical sag position coordinate set and the theoretical ground distance set, use the corresponding correction quantity information in the correction quantity information set as output quantity, and divide the input quantity and the output quantity into a training set and a test set according to a set ratio; Training a preset neural network model based on the training set and the test set, and adjusting the weights and thresholds of the preset neural network model using a gradient descent method and a back propagation method; Until the output error value of the preset neural network model output is within the set error range, the preset sag detection model is obtained.

2. The method according to claim 1, characterized in that Determining the theoretical sag position coordinates and theoretical ground distance of the optical cable sag to be detected according to the tower data information includes: Extract the pole tower span, the near-end pole tower cable hanging inclination angle relative to the sag point of the optical cable sag to be detected, the near-end pole tower hanging point altitude relative to the sag point, and the far-end pole tower hanging point altitude relative to the sag point from the pole tower data information; The horizontal distance between the sag point of the optical cable to be detected and the near-end tower and the theoretical distance to the ground are determined by the following formula: Among them, d is the horizontal distance between the sag point and the near-end tower, h is the theoretical distance to the ground, L is the tower spacing, and α is the near-end tower cable hanging inclination angle. is the elevation of the near-end tower hanging point, The altitude of the remote tower hanging point; The theoretical sag position coordinates are determined according to the horizontal distance and the theoretical distance to the ground.

3. The method according to claim 1, characterized in that Determining the final sag position coordinates and the final ground distance of the optical cable sag to be detected according to the environmental data information, the theoretical sag position coordinates, the theoretical ground distance and the preset sag detection model includes: Inputting the environmental data information into a preset sag detection model; Determining error information through the preset sag detection model; The theoretical sag position coordinates and the theoretical distance to the ground are corrected based on the error information to obtain final sag position coordinates and final distance to the ground of the optical cable sag to be detected.

4. The method according to claim 1, characterized in that The laser point cloud data in the laser point cloud data set include: the ground altitude of the optical cable sag to be detected, the optical cable sag value, the altitude of the sag point, the distance of the sag point from the ground, the position coordinates of the sag point and the tower hanging point position coordinates.

5. An optical cable sag detection device, characterized in that: include: An information acquisition module is used to obtain environmental data information of the optical cable sag to be detected between the poles and towers and tower data information; A first determination module is used to determine the theoretical sag position coordinates and theoretical ground distance of the optical cable sag to be detected according to the tower data information; A second determination module is used to determine the final sag position coordinates and the final ground distance of the optical cable sag to be detected according to the environmental data information, the theoretical sag position coordinates, the theoretical ground distance and a preset sag detection model; Wherein, the device further comprises: a model building module; The model building module includes: A data set acquisition unit, used to acquire a laser point cloud data set of the optical cable sag to be detected, an environmental measurement data set and a tower measurement data set on the same day as the laser point cloud data set; A first determining unit is used to determine a theoretical sag position coordinate set and a theoretical ground distance set of the optical cable sag to be detected according to the tower measurement data set; A second determining unit is used to determine a preset sag detection model according to the theoretical sag position coordinate set, the theoretical ground distance set, the laser point cloud data set and the environmental measurement data set; Wherein, the second determining unit includes: A first determination subunit is used to determine an actual sag position coordinate set and an actual ground distance set according to the laser point cloud data set; A second determining subunit is used to determine a correction amount information set of the optical cable sag to be detected according to the actual sag position coordinate set, the actual distance to the ground set, the theoretical sag position coordinate set and the theoretical distance to the ground set; A third determination subunit is used to determine a preset sag detection model according to the correction amount information set, the environmental measurement data set, the theoretical sag position coordinate set and the theoretical ground distance set; Wherein, the third determining subunit is specifically used for: The input quantity is determined according to the environmental measurement data set, the theoretical sag position coordinate set and the theoretical ground distance set, and the corresponding correction quantity information in the correction quantity information set is used as the output quantity, and the input quantity and the output quantity are divided into a training set and a test set according to a set ratio; the preset neural network model is trained based on the training set and the test set, and the weights and thresholds of the preset neural network model are adjusted using the gradient descent method and the back propagation method; until the output error value output by the preset neural network model is within the set error range, the preset sag detection model is obtained.

6. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the optical cable sag detection method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the optical cable sag detection method according to any one of claims 1 to 4 when executed.

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