Overhead cable fault prediction system

Abstract

The embodiment of the application discloses an overhead cable fault prediction system, which comprises: a parameter calculation module configured to acquire first overhead cable images and second overhead cable images collected at different times, identify cable bending degrees in the first overhead cable images and the second overhead cable images, and obtain first bending parameters and second bending parameters; a scale determination module configured to determine a bending change scale of the cable based on the first bending parameters and the second bending parameters; and a fault result prediction module configured to obtain cable fault prediction parameters based on the bending change scale and time data of the different times, and predict cable faults based on the cable fault prediction parameters. The scheme solves the problem that most schemes in the prior art can only perform fault monitoring but cannot achieve efficient fault prediction, and provides a feasible scheme to realize prediction of cable faults.

Description

Technical Field

[0001] This application relates to the field of cable monitoring technology, and in particular to an overhead cable fault prediction system. Background Technology

[0002] With the widespread use of cables, their applications are becoming increasingly broad. One common example is the use of overhead cables. Overhead cables, also known as overhead insulated cables, are overhead conductors with insulation layers and protective sheaths. They are a special type of cable manufactured using a process similar to that of cross-linked cables, representing a new power transmission method between overhead conductors and underground cables.

[0003] In related technologies, when monitoring cable faults, data is usually collected by setting up sensors and compared with a set comparison threshold. Once the comparison threshold is reached, an alarm is triggered. This method can only monitor faults and cannot effectively predict faults. Summary of the Invention

[0004] This invention provides an overhead cable fault prediction system, which solves the problem that most existing solutions can only perform fault monitoring but cannot achieve efficient fault prediction. It provides a feasible solution to predict cable faults.

[0005] In a first aspect, embodiments of the present invention provide an overhead cable fault prediction system, comprising:

[0006] The parameter calculation module is configured to acquire the first overhead cable image and the second overhead cable image collected in time-division, identify the cable bending degree in the first overhead cable image and the second overhead cable image, and obtain the first bending parameter and the second bending parameter.

[0007] The scale determination module is configured to determine the bending change scale of the cable based on the first bending parameter and the second bending parameter;

[0008] The fault result prediction module is configured to obtain cable fault prediction parameters based on the bending change scale and the time-division time data, and to predict cable faults based on the cable fault prediction parameters.

[0009] Furthermore, the parameter calculation module is configured as follows:

[0010] The cables in the first overhead cable image and the second overhead cable image are identified to obtain the first cable curve and the second cable curve. The first bending parameter of the first cable curve and the second bending parameter of the second cable curve are determined.

[0011] The scale determination module is configured as follows:

[0012] The ratio of the second bending parameter to the first bending parameter is determined as the bending change scale.

[0013] Furthermore, the fault result prediction module is configured as follows:

[0014] Obtain the time data of the time division and determine the time intervals before and after the time data;

[0015] Based on the time interval, a preset fault prediction weight is determined, and cable fault prediction parameters are calculated based on the fault prediction weight and the bending change scale.

[0016] Furthermore, the fault result prediction module is configured as follows:

[0017] The preset parameter range into which the cable fault falls is determined based on the cable fault prediction parameters;

[0018] The pre-set cable fault prediction information corresponding to the preset parameter range is determined as the cable fault prediction result.

[0019] Secondly, embodiments of the present invention also provide a method for predicting faults in overhead cables, comprising:

[0020] The first and second overhead cable images are acquired in a time-division manner. The cable bending degree in the first and second overhead cable images is identified to obtain the first bending parameter and the second bending parameter.

[0021] The bending change scale of the cable is determined based on the first bending parameter and the second bending parameter;

[0022] Cable fault prediction parameters are obtained based on the bending change scale and the time-division time data, and cable faults are predicted based on the cable fault prediction parameters.

[0023] Furthermore, the step of identifying the cable bend degree in the first overhead cable image and the second overhead cable image to obtain the first bend parameter and the second bend parameter includes:

[0024] The cables in the first overhead cable image and the second overhead cable image are identified to obtain the first cable curve and the second cable curve. The first bending parameter of the first cable curve and the second bending parameter of the second cable curve are determined.

[0025] Determining the bending change scale of the cable based on the first bending parameter and the second bending parameter includes:

[0026] The ratio of the second bending parameter to the first bending parameter is determined as the bending change scale.

[0027] Furthermore, obtaining cable fault prediction parameters based on the bending change scale and the time-division time data includes:

[0028] Obtain the time data of the time division and determine the time intervals before and after the time data;

[0029] Based on the time interval, a preset fault prediction weight is determined, and cable fault prediction parameters are calculated based on the fault prediction weight and the bending change scale.

[0030] Furthermore, the prediction of cable faults based on the cable fault prediction parameters includes:

[0031] The preset parameter range into which the cable fault falls is determined based on the cable fault prediction parameters;

[0032] The pre-set cable fault prediction information corresponding to the preset parameter range is determined as the cable fault prediction result.

[0033] Thirdly, embodiments of the present invention also provide an overhead cable fault prediction device, the device comprising:

[0034] One or more processors;

[0035] Storage device for storing one or more programs.

[0036] When the one or more programs are executed by the one or more processors, the one or more processors implement the overhead cable fault prediction method described in the embodiments of the present invention.

[0037] Fourthly, embodiments of the present invention also provide a storage medium for storing computer-executable instructions, which, when executed by a computer processor, are used to execute the overhead cable fault prediction method described in the embodiments of the present invention.

[0038] In this embodiment of the invention, the overhead cable fault prediction system includes: a parameter calculation module configured to acquire a first overhead cable image and a second overhead cable image collected at different times, identify the cable bending degree in the first overhead cable image and the second overhead cable image, and obtain a first bending parameter and a second bending parameter; a scale determination module configured to determine the bending change scale of the cable based on the first bending parameter and the second bending parameter; and a fault result prediction module configured to obtain cable fault prediction parameters based on the bending change scale and the time-divided time data, and predict cable faults based on the cable fault prediction parameters. This solution solves the problem that most existing solutions can only perform fault monitoring but cannot achieve efficient fault prediction, and provides a feasible solution for predicting cable faults. Attached Figure Description

[0039] Figure 1 A module structure block diagram of an overhead cable fault prediction system provided in an embodiment of the present invention;

[0040] Figure 2 A schematic diagram for calculating the bending parameters of the cable;

[0041] Figure 3 A flowchart of an overhead cable fault prediction method provided in an embodiment of the present invention;

[0042] Figure 4 This is a schematic diagram of the structure of an overhead cable fault prediction device provided in an embodiment of the present invention. Detailed Implementation

[0043] The embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and not intended to limit the scope of the invention. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the embodiments of the present invention, and not all structures.

[0044] Figure 1 A module structure block diagram of an overhead cable fault prediction system provided in an embodiment of the present invention is shown below. Figure 1 As shown, the system specifically includes:

[0045] The parameter calculation module 101 is configured to acquire the first overhead cable image and the second overhead cable image collected in time-division, identify the cable bending degree in the first overhead cable image and the second overhead cable image, and obtain the first bending parameter and the second bending parameter.

[0046] The scale determination module 102 is configured to determine the bending change scale of the cable based on the first bending parameter and the second bending parameter;

[0047] The fault result prediction module 103 is configured to obtain cable fault prediction parameters based on the bending change scale and the time-division time data, and to predict cable faults based on the cable fault prediction parameters.

[0048] The application scenario of this technical solution is to determine the degree of bending change in a cable by collecting cable images at different time points in order to predict cable faults. Based on the above application scenario, it can be understood that the implementing entity of this technical solution can be an overhead cable fault prediction system, or a smart device that integrates an overhead cable fault prediction system, such as a mobile phone, tablet computer, or laptop computer.

[0049] In one embodiment, the time-division acquisition can be based on acquiring cable images of the overhead cable at different time points. The overhead cable can be understood as an overhead conductor with an insulation layer and a protective sheath; it is a special type of cable manufactured using a process similar to that of cross-linked cables, representing a new power transmission method between overhead conductors and underground cables. Overhead cables are all single-core and, according to their structure, can be classified into hard aluminum wire structure, hard-drawn copper wire structure, aluminum alloy wire structure, steel core or aluminum alloy core supported structure, and self-supporting three-core composite structure (the core can be hard aluminum or hard copper wire), etc.

[0050] The first overhead cable image can be an image of the overhead cable acquired at a first time point, and the second overhead cable image can be an image of the overhead cable acquired at a second time point. The first time point is prior to the second time point. The cable bending degree can be the degree of curvature of the overhead cable. The first bending parameter can be the curvature of the cable in the first overhead cable image, and the second bending parameter can be the curvature of the cable in the second overhead cable image. The curvature can be understood as the rate of rotation of the tangent direction angle about the arc length at a certain point on a curve, defined by differentiation, indicating the degree to which the curve deviates from a straight line. It is a numerical value used to indicate the degree of curvature of the curve at a certain point. It can be understood that the greater the curvature, the greater the degree of curvature of the curve.

[0051] In one embodiment, the parameter calculation module 101 acquires aerial cable images captured by a camera device mounted on an overhead cable rack at preset time points. These aerial cable images include a first aerial cable image captured at a first time point and a second aerial cable image captured at a second time point after a preset time period. It is understood that cable images meeting preset conditions are used as the first and second aerial cable images; these preset conditions may include the ability to clearly determine the degree of bending of the aerial cable from the images. The parameter calculation module 101 identifies the degree of bending of the cable in the first and second aerial cable images and calculates a first bending parameter and a second bending parameter.

[0052] In one possible embodiment, the parameter calculation module 101 is configured as follows:

[0053] The cables in the first overhead cable image and the second overhead cable image are identified to obtain the first cable curve and the second cable curve. The first bending parameter of the first cable curve and the second bending parameter of the second cable curve are determined.

[0054] The first cable curve may be a curve with the same shape as the cable in the first overhead cable image. The second cable curve may be a curve with the same shape as the cable in the second overhead cable image.

[0055] In one embodiment, the parameter determination module 101 identifies the cable shape in the first overhead cable image and the second overhead cable image, and determines the first cable curve and the second cable curve. It then calculates a first bending parameter for the first cable curve and determines a second bending parameter for the second cable curve.

[0056] Reference Figure 2 The first bending parameter and the second bending parameter can be calculated in the following ways:

[0057] Take an arc segment on the cable starting from point M, with a length of Δs and a corresponding tangent angle of Δα. It can be understood that point M is the peak point of the aerial cable.

[0058] The mean curvature on arc segment Δs is defined as:

[0059] The curvature at point M is:

[0060] In one embodiment, the bending change scale can be understood as the degree of change in the bending of the overhead cable. The method for determining the bending change scale of the cable based on the first bending parameter and the second bending parameter can be to calculate the difference between the second bending parameter and the first bending parameter. It is understood that since a greater degree of bending corresponds to a greater curvature, i.e., a larger bending parameter, it can be determined that a larger difference indicates a greater degree of bending change in the cable.

[0061] In one embodiment, the scale determination module 102 is used to determine the degree of bending change of the cable based on the first bending parameter and the second bending parameter. Specifically, it calculates the difference between the second bending parameter and the first bending parameter, and determines the degree of bending change of the cable based on a preset difference range. For example, the second bending parameter is 2.2, the first bending parameter is 2, and the difference is 0.2. By querying the preset difference range, it can be determined that 0.2 corresponds to the first difference range, which corresponds to a slight change.

[0062] In one possible embodiment, the scale determination module 102 is configured as follows:

[0063] The ratio of the second bending parameter to the first bending parameter is determined as the bending change scale.

[0064] The scale determination module 102 determines the degree of bending change by calculating the ratio of the second bending parameter to the first bending parameter. It is understood that a greater degree of bending corresponds to a greater curvature, i.e., a larger bending parameter. Therefore, a larger ratio indicates a greater degree of bending change in the cable. For example, cable a has a second bending parameter of 2.2 and a first bending parameter of 2, with a ratio of 1.1; cable b has a second bending parameter of 2.4 and a first bending parameter of 2, with a ratio of 1.2. It can be determined that the degree of bending change in cable b is greater than that in cable a.

[0065] In one embodiment, the time data may be the time difference between the time points corresponding to the acquisition of the first aerial cable image and the second aerial cable image. The cable fault prediction parameter may be a predicted value for the occurrence of a cable fault. It is understood that the larger the predicted value, the greater the probability of a cable fault occurring.

[0066] In one embodiment, the fault prediction module 103 is used to determine cable fault prediction parameters based on the degree of bending change of the cable and the time difference corresponding to the degree of change, and to predict cable faults based on the cable fault prediction parameters. For example, if the degree of bending change of the cable is slight, and the corresponding time data is 2 hours, the cable fault prediction parameter is determined to be 1, and the predicted probability of cable fault occurrence is 5%. If the degree of bending change of the cable is significant, and the corresponding time data is 2 hours, the cable fault prediction parameter is determined to be 2, and the predicted probability of cable fault occurrence is 25%.

[0067] In one possible embodiment, the fault result prediction module 103 is configured as follows:

[0068] Obtain the time data of the time division and determine the time intervals before and after the time data;

[0069] Based on the time interval, a preset fault prediction weight is determined, and cable fault prediction parameters are calculated based on the fault prediction weight and the bending change scale.

[0070] The time interval can be understood as the time difference between capturing the first aerial cable image and capturing the second aerial cable image at corresponding time points. The fault prediction weight can be the weight of the cable fault corresponding to the time difference. Specifically, the smaller the time difference, the higher the corresponding weight. It can be understood that a smaller time difference indicates a shorter time for bending changes, and a higher probability of cable fault occurrence. The cable fault prediction parameter calculated based on the fault prediction weight and the bending change scale can be determined by multiplying the fault prediction weight and the bending change scale.

[0071] In one embodiment, the fault result prediction module 103 is used to acquire first time point data and second time point data corresponding to the captured images of the first and second aerial cables. The difference between the first and second time point data is determined as the time interval. A fault prediction weight corresponding to the time interval is determined; for example, when the time interval is 0-2 hours, the corresponding fault prediction weight is 0.4; when the time interval is 2-4 hours, the corresponding fault prediction weight is 0.3; when the time interval is 4-6 hours, the corresponding fault prediction weight is 0.2; and when the time interval is more than 6 hours, the corresponding fault prediction weight is 0.1.

[0072] The product of the fault prediction weight and the bending change scale is determined as the cable fault prediction parameter. For example, the ratio of the second bending parameter to the first bending parameter of the cable is 1.5, the time interval between acquiring the first and second aerial cable images is 5 hours, corresponding to a fault prediction weight of 0.2. Therefore, the cable fault prediction parameter is determined to be 0.3.

[0073] As described above, the process involves acquiring the time-division data, determining the time intervals between the data points, determining preset fault prediction weights based on these time intervals, and calculating cable fault prediction parameters based on these weights and the bending change scale. By jointly determining the cable fault prediction parameters using time intervals and fault prediction weights, the system predicts fault occurrence not only based on the degree of bending but also on the bending change rate, thus achieving effective cable fault prediction. Furthermore, determining the cable fault prediction parameters based on multi-dimensional information makes the calculation results more reasonable.

[0074] In one possible embodiment, the fault result prediction module 103 is configured as follows:

[0075] The preset parameter range into which the cable fault falls is determined based on the cable fault prediction parameters;

[0076] The pre-set cable fault prediction information corresponding to the preset parameter range is determined as the cable fault prediction result.

[0077] The preset parameter range can be a cable fault prediction parameter range pre-defined by technicians. The cable fault prediction information can be the probability of a cable fault occurring. It is understood that the larger the value of the cable fault prediction parameter, the higher the probability of a cable fault occurring.

[0078] In one embodiment, the technician pre-sets a prediction parameter range. For example, when the cable fault prediction parameter is 0.2-0.5, it corresponds to the first range; when it is 0.5-1, it corresponds to the second range; when it is 1-1.5, it corresponds to the third range; and when it is 1.5-2.0, it corresponds to the fourth range. Correspondingly, the cable fault prediction information for the first range indicates a 15% probability of cable fault occurrence, the second range indicates a 30% probability, the third range indicates a 50% probability, and the first range indicates an 80% probability. It is understood that when the cable fault prediction parameter is less than 0.2, the probability of cable fault occurrence is 0%, and when it is greater than 2, the probability is 100%.

[0079] As described above, the cable fault prediction parameters determine the preset parameter range into which the fault falls, and the pre-set cable fault prediction information corresponding to the preset parameter range is determined as the cable fault prediction result. Based on the parameter range corresponding to the cable fault prediction parameters, the probability of cable fault occurrence is determined. This allows for more accurate prediction of cable faults.

[0080] The fault result prediction module 103 is used to determine the prediction parameter range corresponding to the cable fault prediction parameters, and to determine the cable fault prediction information based on the prediction parameter range.

[0081] In one embodiment, optionally, a corresponding fault warning method is determined based on the cable fault prediction information. For example, when the probability of a cable fault occurring is less than or equal to 50%, a cable fault warning is sent via pop-up window or SMS. The cable fault warning information includes data such as cable location information, the time interval before and after the cable bending change, cable fault prediction parameters, and cable fault prediction information. When the probability of a cable fault occurring is greater than 50%, a cable fault warning is sent via light, ringing, or voice announcement.

[0082] As can be seen from the above, different early warning methods can be adopted for different cable fault probabilities. Fault warnings can be issued based on the urgency of the fault, and the warning information includes cable-related data, enabling maintenance personnel to more clearly determine the current status of the cable.

[0083] As described above, the overhead cable fault prediction system includes: a parameter calculation module configured to acquire a first overhead cable image and a second overhead cable image collected at different times, identify the cable bending degree in the first overhead cable image and the second overhead cable image, and obtain a first bending parameter and a second bending parameter; a scale determination module configured to determine the bending change scale of the cable based on the first bending parameter and the second bending parameter; and a fault result prediction module configured to obtain cable fault prediction parameters based on the bending change scale and the time-divided time data, and predict cable faults based on the cable fault prediction parameters. This solution solves the problem that most existing solutions can only perform fault monitoring but cannot achieve efficient fault prediction, and provides a feasible solution for predicting cable faults.

[0084] Figure 3 A flowchart of an overhead cable fault prediction method provided in an embodiment of the present invention is shown below. Figure 3 As shown, the method specifically includes the following steps:

[0085] S301. Acquire the first overhead cable image and the second overhead cable image collected in time-division, identify the cable bending degree in the first overhead cable image and the second overhead cable image, and obtain the first bending parameter and the second bending parameter;

[0086] S302. Determine the bending change scale of the cable based on the first bending parameter and the second bending parameter;

[0087] S303. Based on the bending change scale and the time-division time data, obtain cable fault prediction parameters, and predict cable faults based on the cable fault prediction parameters.

[0088] As described above, the overhead cable fault prediction system includes: a parameter calculation module configured to acquire a first overhead cable image and a second overhead cable image collected at different times, identify the cable bending degree in the first overhead cable image and the second overhead cable image, and obtain a first bending parameter and a second bending parameter; a scale determination module configured to determine the bending change scale of the cable based on the first bending parameter and the second bending parameter; and a fault result prediction module configured to obtain cable fault prediction parameters based on the bending change scale and the time-divided time data, and predict cable faults based on the cable fault prediction parameters. This solution solves the problem that most existing solutions can only perform fault monitoring but cannot achieve efficient fault prediction, and provides a feasible solution for predicting cable faults.

[0089] In one possible embodiment, identifying the cable bend degree in the first overhead cable image and the second overhead cable image to obtain a first bend parameter and a second bend parameter includes:

[0090] The cables in the first overhead cable image and the second overhead cable image are identified to obtain the first cable curve and the second cable curve. The first bending parameter of the first cable curve and the second bending parameter of the second cable curve are determined.

[0091] Determining the bending change scale of the cable based on the first bending parameter and the second bending parameter includes:

[0092] The ratio of the second bending parameter to the first bending parameter is determined as the bending change scale.

[0093] In one possible embodiment, obtaining cable fault prediction parameters based on the bending change scale and the time-division time data includes:

[0094] Obtain the time data of the time division and determine the time intervals before and after the time data;

[0095] Based on the time interval, a preset fault prediction weight is determined, and cable fault prediction parameters are calculated based on the fault prediction weight and the bending change scale.

[0096] As described above, the cable fault prediction parameters determine the preset parameter range into which the fault falls, and the pre-set cable fault prediction information corresponding to the preset parameter range is determined as the cable fault prediction result. Based on the parameter range corresponding to the cable fault prediction parameters, the probability of cable fault occurrence is determined. This allows for more accurate prediction of cable faults.

[0097] In one possible embodiment, the prediction of cable faults based on the cable fault prediction parameters includes:

[0098] The preset parameter range into which the cable fault falls is determined based on the cable fault prediction parameters;

[0099] The pre-set cable fault prediction information corresponding to the preset parameter range is determined as the cable fault prediction result.

[0100] As can be seen from the above, different early warning methods can be adopted for different cable fault probabilities. Fault warnings can be issued based on the urgency of the fault, and the warning information includes cable-related data, enabling maintenance personnel to more clearly determine the current status of the cable.

[0101] Figure 4This is a schematic diagram of the structure of an overhead cable fault prediction device provided in an embodiment of the present invention, as shown below. Figure 4 As shown, the device includes a processor 401, a memory 402, an input device 403, and an output device 404; the number of processors 401 in the device can be one or more. Figure 4 Taking a processor 401 as an example; the processor 401, memory 402, input device 403, and output device 404 in the device can be connected via a bus or other means. Figure 4 Taking a bus connection as an example, the memory 402, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the overhead cable fault prediction method in this embodiment of the invention. The processor 401 executes various functional applications and data processing of the device by running the software programs, instructions, and modules stored in the memory 402, thereby realizing the aforementioned overhead cable fault prediction method. The input device 403 can be used to receive input digital or character information and generate key signal inputs related to user settings and function control of the device. The output device 404 may include a display screen or other display device.

[0102] This invention also provides a storage medium containing computer-executable instructions. When executed by a computer processor, the computer-executable instructions are used to perform an overhead cable fault prediction method. The method includes: acquiring a first overhead cable image and a second overhead cable image collected in a time-division manner; identifying the cable bending degree in the first overhead cable image and the second overhead cable image to obtain a first bending parameter and a second bending parameter; determining the bending change scale of the cable based on the first bending parameter and the second bending parameter; obtaining cable fault prediction parameters based on the bending change scale and the time-division time data; and predicting cable faults based on the cable fault prediction parameters.

[0103] It is worth noting that in the embodiments of the above-mentioned overhead cable fault prediction system device, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the protection scope of the embodiments of the present invention.

[0104] Note that the above are merely preferred embodiments and the technical principles applied in this invention. Those skilled in the art will understand that the embodiments of this invention are not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the protection scope of this invention. Therefore, although the embodiments of this invention have been described in detail above, the embodiments of this invention are not limited to the above embodiments. More other equivalent embodiments may be included without departing from the concept of the embodiments of this invention, and the scope of the embodiments of this invention is determined by the scope of the appended claims.

Claims

1. An overhead cable fault prediction system, characterized in that, include: The parameter calculation module is configured to acquire the first overhead cable image and the second overhead cable image collected in a time-division manner, identify the cable in the first overhead cable image and the second overhead cable image to obtain the first cable curve and the second cable curve, determine the first bending parameter of the first cable curve, and determine the second bending parameter of the second cable curve. The scale determination module is configured to determine the ratio of the second bending parameter to the first bending parameter as the bending change scale; The fault result prediction module is configured to acquire the time data of the time division, determine the time interval before and after the time data, determine the preset fault prediction weight according to the time interval before and after, calculate the cable fault prediction parameters based on the fault prediction weight and the bending change scale, and predict the cable fault based on the cable fault prediction parameters.

2. The overhead cable fault prediction system according to claim 1, characterized in that, The fault result prediction module is configured as follows: The preset parameter range into which the cable fault falls is determined based on the cable fault prediction parameters; The pre-set cable fault prediction information corresponding to the preset parameter range is determined as the cable fault prediction result.

3. A method for predicting faults in overhead cables, characterized in that, include: The system acquires a first overhead cable image and a second overhead cable image collected in a time-division manner. It identifies the cables in the first overhead cable image and the second overhead cable image to obtain a first cable curve and a second cable curve. It then determines a first bending parameter of the first cable curve and a second bending parameter of the second cable curve. The ratio of the second bending parameter to the first bending parameter is determined as the bending change scale; The time data is acquired, the time intervals before and after the acquisition are determined, a preset fault prediction weight is determined based on the time intervals, cable fault prediction parameters are calculated based on the fault prediction weights and the bending change scale, and cable faults are predicted based on the cable fault prediction parameters.

4. The overhead cable fault prediction method according to claim 3, characterized in that, The prediction of cable faults based on the cable fault prediction parameters includes: The preset parameter range into which the cable fault falls is determined based on the cable fault prediction parameters; The pre-set cable fault prediction information corresponding to the preset parameter range is determined as the cable fault prediction result.

5. An overhead cable fault prediction device, the device comprising: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the overhead cable fault prediction method as described in any one of claims 3-4.

6. A storage medium storing computer-executable instructions, which, when executed by a computer processor, are used to perform the overhead cable fault prediction method as described in any one of claims 3-4.

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