Communication line fault determination method and system

Abstract

The invention discloses a communication line fault determination method and system, and relates to the technical field of communication line fault detection, and the method comprises the steps: S1, deploying multiple types of Internet of Things sensors at key nodes of a communication line, and collecting physical state data and electrical parameter data in real time; by deploying multiple types of Internet of Things sensors, synchronously collecting physical states and electrical parameter data, and combining a vibration effective impact value formula to quantify the actual influence of vibration on a line, all-directional monitoring of the line operation state is achieved, data and a GIS map are dynamically associated, the fault positioning range is narrowed to a specific line section or equipment, and the fault positioning accuracy is improved. The fault positioning precision is improved; according to the method, the abnormal degree is judged by adopting an abnormal deviation degree formula, the data change trend is comprehensively considered, misjudgment and missed judgment conditions are reduced, fault information is visually displayed through a GIS map, the surrounding geographical environment and a line topological structure are integrated, comprehensive reference is provided for fault processing, and the efficiency and accuracy of fault determination are integrally improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication line fault detection, in particular to a communication line fault determination method and system. BACKGROUND

[0002] The communication line refers to the physical medium for transmitting electrical signals or optical signals, including overhead lines, cables, optical cables, etc., and is the core infrastructure supporting the realization of information interaction of modern communication networks (such as telephone, Internet, mobile communication), which covers from city backbone network to remote branch line, and penetrates through various environments such as ground, underground and high altitude.

[0003] The fault types of the communication line are various, mainly including physical damage type (such as line breakage caused by external force, joint loosening), electrical abnormality type (such as short circuit, current and voltage mutation caused by overload), environmental influence type (such as insulation layer aging caused by high temperature, line fatigue damage caused by vibration) and natural loss type (such as material degradation after long-term use), and the quick determination of the communication line fault is the key to guarantee the continuity of the communication network and reduce the loss of fault downtime, which can avoid large-area communication interruption caused by fault expansion, and provide accurate basis for dispatching of repair resources.

[0004] However, the prior art has obvious deficiencies in communication line fault determination, and the prior art in determining the communication line fault has single data collection dimension, mainly relying on single type sensor or manual inspection, which is difficult to fully reflect the physical state and electrical parameters of the line, resulting in incomplete fault cause analysis, in addition, lacking deep integration with geographic information, low fault positioning accuracy, simple abnormality determination method, not considering data change trend and comprehensive influence, easy to appear misjudgment or omission, therefore, it is of great significance to develop a communication line fault determination method and system. SUMMARY

[0005] The purpose of the present application is to make up for the deficiencies of the prior art, and provide a communication line fault determination method and system, which can collect physical state data and electrical parameter data of the communication line at the same time by deploying multiple types of Internet of Things sensors, covering multiple key aspects of line operation, providing rich and comprehensive data support for fault detection, improving the accuracy of fault detection, dynamically associating real-time data with GIS map, and quickly and accurately determining the fault location in combination with the spatial positioning and visualization capability of GIS when data is abnormal.

[0006] The present application provides the following technical solutions to solve the above technical problems: a communication line fault determination method, comprising the following steps: S1, deploying multiple types of Internet of Things sensors at key nodes of the communication line, and collecting physical state data and electrical parameter data in real time; S2, pre-processing and cleaning the collected data; S3, dynamically associating the processed data with the GIS map to establish an associated database; S4, real-time detection of abnormal data, and triggering fault analysis if the data is abnormal; S5, locating the fault based on the associated data and the line topology structure and integrating the surrounding geographic information; S6, visualizing and displaying the fault-related information on the GIS map.

[0007] Further, in step S1, the multiple types of Internet of Things sensors include temperature sensors, vibration sensors, current sensors, and voltage sensors, which are respectively deployed at cable joints, overhead line towers, and line branch points. The temperature sensor collects temperature data of the cable joint and the line skin, the vibration sensor collects vibration amplitude and frequency data of the overhead line, the current sensor and the voltage sensor respectively collect real-time current and voltage values of the line, and the sensor uploads the collected data to the data processing center in real time through the LoRa wireless transmission module. When calculating the effective impact value of the vibration signal, the formula is used: wherein is the effective impact value of the vibration, is the vibration amplitude collected for the time, is the corresponding vibration frequency, is the number of collections, which is used to quantify the actual impact of the vibration on the line.

[0008] Further, in step S2, the pre-processing and cleaning include: eliminating abnormal values that exceed the reasonable range due to sensor failure and transmission interference, using linear interpolation method to complete the missing data in a short time, converting different formats of sensor data into standardized data in JSON format, and forming a unified data basis.

[0009] Further, in step S3, the dynamic association of the data with the GIS map is specifically: calling the GIS map interface, binding the pre-processed data with the corresponding line position on the GIS map according to the latitude and longitude information of the sensor installation position, establishing an associated database of "sensor ID-line position coordinates-real-time monitoring data", and performing real-time mapping of the data and the spatial position of the line.

[0010] Further, in step S4, the abnormal data detection is specifically: comparing the collected data with the preset threshold, and using the abnormal deviation degree formula to determine the degree of abnormality, wherein is the current collected data, is the preset threshold, is a very small constant (to avoid a denominator of 0), And when the condition is met for 3 times in succession, it is determined that the data is abnormal and a fault analysis is triggered, and the time of the abnormality and the initial data characteristics are recorded.

[0011] Further, in step S5, the fault positioning is specifically: for the abnormal position, the GIS map associated data and the line topology structure information are called, the line topology structure information includes the connection mode of the node and the adjacent node, the line type, the fault range is narrowed through a self-defined spatial decay algorithm, and the formula is , wherein is the actual fault influence radius, is the theoretical maximum influence radius, is a line decay coefficient (related to the line material), is the deviation of the abnormal data from the historical average, and the specific fault section and equipment are determined through .

[0012] Further, in step S5, the integrated surrounding geographic information includes the terrain (such as mountains, plains) around the fault point, the surrounding building distribution and the traffic road information, and in step S6, the visual display includes marking the fault position on the GIS map with a red flashing point, and displaying the abnormal data type (such as temperature being too high, current being interrupted), the real-time monitoring data curve (the change trend in the past 10 minutes), the surrounding geographic environment picture and the line topology structure diagram in the pop-up information window.

[0013] Further, in step S1, when the sensor is deployed, the collection frequency of each sensor also needs to be preset, and the collection frequency is set to be collected once every 10-15 seconds according to the characteristics of the line and the monitoring demand, and in step S4, after the fault analysis process is triggered, the system also tracks and records the change trend of the abnormal data in real time.

[0014] The application also provides a communication line fault determination system for executing the communication line fault determination method according to any one of the above, and the system comprises a sensor module, a data transmission module, a data processing module, an abnormality detection module and a GIS visualization module. The sensor module is composed of multiple types of Internet of Things sensors, and is used for collecting physical state data and electrical parameter data of the communication line. The data transmission module is used for uploading the data collected by the sensor to the data processing module in real time. The data processing module is used for pre-processing and cleaning the original data, and dynamically associating the processed data with the GIS map to establish an associated database. The abnormality detection module is used for detecting whether the data is abnormal in real time, and triggering a fault analysis when the data is abnormal. The GIS visualization module is used for locating faults based on associated data and line topology structure, integrating surrounding geographic information, and visualizing fault-related information on a GIS map.

[0015] Compared with the prior art, the communication line fault determination method and system have the following beneficial effects: The present application realizes all-around monitoring of the line operation state by deploying multiple types of Internet of Things sensors, synchronously collecting physical state and electrical parameter data, quantifying the actual influence of vibration on the line by a vibration effective impact value formula, dynamically correlating the data with a GIS map, and accurately calculating the fault influence radius by a self-defined spatial decay algorithm, thereby reducing the fault positioning range to a specific line section or equipment and improving the fault positioning accuracy; the abnormal deviation degree formula is used to determine the abnormality degree, and the data change trend is comprehensively considered, thereby reducing the misjudgment and omission; the GIS map is used to visually display the fault information, and the surrounding geographic environment and line topology structure are integrated, thereby providing comprehensive reference for fault processing and improving the efficiency and accuracy of fault determination.

[0016] Other advantages, objects, and features of the present application will be in part apparent and in part pointed out below in the specification, and in part will be observed by persons skilled in the art upon examination of the following specification, or can be learned by practice of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description only show some embodiments of the present application, and other drawings can be obtained by persons skilled in the art without creative labor on the basis of these drawings.

[0018] Fig. 1 A flowchart of a communication line fault determination method; Fig. 2 A structural schematic diagram of a communication line fault determination system. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the present application more clear, the technical solutions in the present application will be described clearly and completely in the following with reference to the drawings in the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative labor are within the scope of protection of the present application. The following embodiments are used to illustrate the present application, but cannot be used to limit the scope of the present application.

[0020] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in the present specification and the features of the different embodiments or examples can be combined and combined by those skilled in the art without contradiction.

[0021] The application is described below in conjunction with Figs. 1-2 The embodiments of the present application are described.

[0022] In order to make the purpose, technical scheme and advantages of the present application clearer, the technical scheme of the present application is described in detail below in conjunction with specific embodiments. The communication line fault determination method provided by the present application realizes accurate positioning and efficient processing of communication line faults through multi-dimensional data acquisition, intelligent analysis and deep integration of geographic information systems (GIS), and is suitable for real-time monitoring scenarios of various overhead lines, cable lines, optical cable lines and other communication infrastructure.

[0023] The communication line fault determination method described in the present application is based on the cooperative operation of Internet of Things sensing technology and geographic information systems, and builds a full-process closed-loop system of "data acquisition-processing-association-analysis-positioning-display". The overall steps include: S1, deploying multiple types of Internet of Things sensors at key nodes of the communication line to collect physical state data and electrical parameter data in real time; S2, preprocessing and cleaning the collected data; S3, dynamically associating the processed data with GIS maps to establish an association database; S4, real-time detection of whether the data is abnormal, and triggering fault analysis if the data is abnormal; S5, positioning the fault based on the associated data and the line topology structure and integrating the surrounding geographic information; S6, visualizing and displaying the fault-related information on the GIS map.

[0024] Through the above steps, by deploying multiple types of Internet of Things sensors, physical state and electrical parameter data are synchronously collected, the actual impact of vibration on the line is quantified by combining the vibration effective impact value formula, the operation state of the line is comprehensively monitored, the data is dynamically associated with the GIS map, the fault influence radius is accurately calculated by means of a self-defined spatial decay algorithm, and the fault positioning range is reduced to a specific line section or equipment, thereby improving the fault positioning accuracy.

[0025] S1, deploying multiple types of Internet of Things sensors at key nodes of the communication line to collect physical state data and electrical parameter data in real time; In this phase, we deploy a variety of Internet of Things sensors at key nodes of the communication line to achieve real-time and comprehensive monitoring of the physical state and electrical parameters of the line, providing multi-dimensional data support for subsequent fault analysis.

[0026] Sensor types and deployment locations, the multiple types of Internet of Things sensors used by the present application include temperature sensors, vibration sensors, current sensors, and voltage sensors, and the specific deployment strategy is as follows: Temperature sensor: mainly installed at cable joints, overhead line surface, and line branch points. Cable joints are areas with concentrated line resistance, and overheating faults are prone to occur due to poor contact. Real-time monitoring of temperature can promptly detect problems such as insulation aging and joint loosening. The temperature of the line surface can reflect the influence of environmental temperature (such as high temperature in summer and low temperature in winter) on line operation, avoiding performance degradation of the insulation layer due to abnormal temperature.

[0027] Vibration sensor: deployed at the top of the overhead line tower, the wire suspension point, and the line crossing section. Overhead lines are subject to periodic vibration due to wind and airflow for a long time. Excessive vibration can cause wire fatigue, tower bolt loosening, and even wire breakage. Therefore, vibration data is needed to assess the risk of mechanical damage to the line.

[0028] Current sensor and voltage sensor: in series or parallel with the main section and important branch points of the line to collect real-time current and voltage values, respectively. Current anomalies (such as sudden increase, sudden drop, and interruption) are usually related to short circuits, overloads, and line breaks. Voltage fluctuations (such as persistent low and high-frequency oscillation) may reflect insufficient power supply stability or line insulation failure. The combination of the two can comprehensively evaluate the electrical performance of the line.

[0029] The collection frequency of the sensor needs to be dynamically adjusted according to the importance of the line and the monitoring requirements. For key sections such as urban trunk lines and hub room incoming lines, the collection frequency is set to once every 10 seconds to ensure the timeliness of fault response. For non-critical sections such as remote area branch lines and temporary lines, the collection frequency can be reduced to once every 15 seconds to balance the monitoring accuracy and device energy consumption.

[0030] The sensor uploads the collected data to the data processing center in real time through the LoRa wireless transmission module. The reason for choosing LoRa technology is its low power consumption and long distance, which is suitable for signal transmission in the wild and supports multi-node concurrent communication, avoiding delays caused by data congestion.

[0031] In this stage, in order to accurately assess the actual impact of vibration on the line, instead of simply relying on a single indicator of vibration amplitude or frequency, we introduce the effective impact value formula of vibration, which combines vibration amplitude and frequency to calculate: wherein, is the effective impact value of vibration, is the vibration amplitude (unit: mm) collected for the first time, is the corresponding frequency (unit: Hz), is the number of collections (by default, 4 sets of data within 1 minute, i.e., 4 consecutive data collected every 15 seconds).

[0032] In this stage, the formula can standardize the vibration amplitude at different frequencies, for example: low-frequency high-amplitude vibration (such as 5Hz, 10mm) and high-frequency low-amplitude vibration (such as 50Hz, 2mm) have different effects on the line, the value can quantify this difference, and in practical applications, when , the wear rate of the connection between the conductor and the tower increases significantly, so this value can be set as the vibration abnormality warning threshold for subsequent abnormality detection.

[0033] S2, pre-process and clean the collected data; in this stage, the originally collected data may have noise, missing or inconsistent formats, etc., and need to be pre-processed to ensure data quality and provide a reliable basis for subsequent data analysis and correlation.

[0034] Due to sensor failure (such as temperature sensor drift), transmission interference (such as electromagnetic signal interference), or extreme weather (such as signal interruption caused by heavy rain), some data may be outside the reasonable range, and for different types of sensors, a physical threshold is pre-set; data that exceeds the threshold is marked, and if it is an abnormal value for 5 consecutive collections, it is determined as a sensor failure, triggering a device maintenance alarm, and automatically switching to a backup sensor.

[0035] During wireless transmission, data may be missing due to signal interruption, for short-term missing, linear interpolation method is used to complete, to ensure data continuity, in this stage, for long-term missing data, do not complete, but marked as "data interruption", and combined with line topology information to determine whether it is line break or sensor offline, to avoid introducing errors by completing data.

[0036] The output format of different types of sensors is different, and needs to be converted to standardized JSON format for cross-platform transmission and database storage, in this stage, standardization processing also includes data format verification, unit unification, etc., to ensure that the field matches correctly when associated with GIS map in the future.

[0037] S3, dynamically associate the processed data with the GIS map, and establish a correlation database; In this phase, by binding the processed data with the GIS map, the spatial expression of the line state is realized, providing a geographic coordinate reference for fault location, and at the same time, a correlation database is constructed to realize efficient management and query of data.

[0038] The data processing center calls the API interface of the GIS platform to accurately associate the latitude and longitude information of the sensor with the line layer on the map. The specific process is as follows: When the sensor is deployed, its installation position is recorded by GPS positioning; a "communication line thematic layer" is created in the GIS system, which contains line direction, tower position, cable depth, equipment model and other attribute information; through the "sensor ID-coordinate-line ID" association table, real-time data is bound to the corresponding line segment, realizing real-time mapping of data and spatial position.

[0039] In the construction phase of the correlation database, the correlation database adopts a mixed architecture of MySQL and PostGIS, taking into account the storage needs of structured data and spatial data: MySQL stores sensor basic information (model, installation time, maintenance record), real-time monitoring data (temperature, current, vibration value) and abnormal record; PostGIS stores geographic spatial data (line topology, tower coordinates, surrounding terrain vector data), supporting spatial query.

[0040] In this phase, the database synchronizes with the sensor data at regular intervals (every 5 minutes) to ensure that the information on the GIS map is updated in real time. At the same time, the database supports historical data backtracking (such as querying the temperature change trend of a certain line in the past 7 days), providing data support for line aging analysis.

[0041] S4, real-time detection of data anomalies, if data is abnormal, trigger fault analysis; In this phase, by setting dynamic threshold and trend analysis, the data anomaly is accurately identified, avoiding false positives or false negatives caused by single threshold, and ensuring that fault analysis is triggered only when it is really needed.

[0042] In this phase, the abnormal deviation degree formula is used to quantify the degree of data anomaly, rather than simply relying on the binary judgment of "whether the data exceeds the threshold": where, is the current collected data, is the preset threshold, is a very small constant (to avoid division by zero), in this phase, and satisfy for 3 times in a row, it is determined that the data is abnormal.

[0043] After triggering the fault analysis process, the system automatically records the time of abnormal occurrence, initial characteristics, and real-time tracking of data trends, such as: whether the temperature continues to rise (to determine whether it is a malignant fault); whether the vibration value increases (to determine whether it is an electrical fault caused by mechanical damage); whether the adjacent sensors appear abnormal at the same time (to determine whether it is a regional fault).

[0044] In this phase, trend tracking data will be an important basis for fault cause analysis, for example: if the temperature continues to rise and the current drops sharply, it may be a short circuit fault; if only the temperature is abnormal and the current is normal, it may be a poor contact joint.

[0045] S5, based on the correlation data and line topology structure to locate the fault and integrate the surrounding geographic information; In this phase, combined with the data in the correlation database, line topology structure and spatial algorithm, the fault range is accurately narrowed down, and the surrounding geographic information is integrated to provide comprehensive reference for the formulation of repair scheme.

[0046] Calculation of fault influence radius, in this phase, for the abnormal position, the GIS map associated data and line topology structure information (such as the connection mode of the node and the adjacent node, line type, material) are retrieved, and the fault influence range is determined through self-defined spatial decay algorithm: Wherein, is the actual fault influence radius, is the theoretical maximum influence radius, is the line attenuation coefficient, is the deviation of abnormal data from the historical average.

[0047] Integration of surrounding geographic information, the system automatically retrieves the geographic information around the fault point, including terrain information: such as mountainous area (high difficulty of repair, need to carry climbing equipment), plain (easy for vehicles to enter, can use large-scale repair machinery), river (need to consider the repair scheme of cross-river line); building distribution: such as there are residential areas near the fault point, need to coordinate the power-off time in advance; if there is a high-voltage substation nearby, safety isolation measures need to be developed; traffic road information: whether there is a trunk road directly (such as the nearest highway is 1 km), whether it needs to be detoured (such as the construction section is closed).

[0048] In this phase, these information are obtained from third-party platforms (such as Baidu Map, Tianditu) through GIS interface, and stored in the correlation database after fusion with line topology data, so as to facilitate the repair personnel to quickly master the on-site environment.

[0049] S6, visualizing the fault related information on GIS map; In this phase, the fault information is intuitively presented through visualization technology, reducing the information understanding cost, improving the fault handling efficiency, and realizing "one map" command for repair.

[0050] Visual display, mark the fault location with a red flashing dot on the GIS map, and pop up an information window after clicking the marker. The content includes abnormal data type, real-time monitoring data curve, surrounding geographical environment picture, and line topology structure diagram.

[0051] Multi-terminal synchronous display, visual information supports synchronous display on computer, mobile phone APP, and monitoring large screen, meeting different scene needs. Monitoring large screen: for dispatch center to monitor the whole network status in real time and quickly locate the distribution of multiple fault points. Mobile phone APP: for repair personnel to view on site and plan the optimal route combined with navigation function (such as avoiding congested road sections). Computer: for technical personnel to analyze data in depth and generate fault reports.

[0052] In this stage, multi-terminal synchronization ensures that information transmission has no delay. For example, after the dispatch center finds a fault, the mobile phone APP of the repair team will immediately receive a push containing the fault location, abnormal details, and surrounding information, greatly shortening the response time.

[0053] The present application solves the pain points in traditional communication line fault detection through multi-technology fusion and process optimization: Through the cooperative collection of multiple types of sensors such as temperature, vibration, current, and voltage, the physical state and electrical performance of the line are covered, avoiding the limitations of single parameter monitoring. For example, only monitoring temperature may miss mechanical failures caused by vibration, and only monitoring current may ignore overheating hazards at joints. Multi-dimensional data combination can comprehensively capture fault signs. Combined with GIS maps and spatial decay algorithms, fault positioning error is shortened, reducing the on-site troubleshooting time of repair personnel. The abnormal deviation formula and the continuous 3-time judgment mechanism effectively filter out transient interference data. GIS maps integrate line topology and surrounding geographical information to provide "one-stop" reference for repair. The multi-terminal synchronous GIS visualization interface realizes real-time information sharing among the dispatch center, repair team, and technical personnel.

[0054] The present application also provides a communication line fault determination system for executing a communication line fault determination method according to any one of the above, comprising: a sensor module, a data transmission module, a data processing module, an anomaly detection module, and a GIS visualization module, which correspond one-to-one to the above method. The function design of each module serves the efficient execution of the method steps: The sensor module corresponds to S1, ensuring accurate collection of multiple types of data; The data transmission module supports data upload from S1 to S2, and uses LoRa technology to ensure stability; The data processing module realizes data cleaning and GIS association from S2 to S3, and constructs an associated database; The anomaly detection module performs abnormality judgment in S4, reducing false positives through algorithms; The GIS visualization module completes the fault location and display of S5-S6, and integrates geographic information and topological structure.

[0055] The cooperative operation of the modules of the system ensures efficient operation of the whole process closed loop from data collection to fault display, and is suitable for communication line fault detection scenes in the fields of electric power, communication, radio and television and the like.

[0056] The communication line fault determination method and system provided by the application provide a complete, efficient and accurate solution for communication line fault determination through technical innovation, realize all-round monitoring of line operation state by deploying multiple types of Internet of Things sensors to synchronously collect physical state and electrical parameter data, combining vibration effective impact value formula to quantize the actual influence of vibration on the line, dynamically correlating data and GIS map, and accurately calculating fault influence radius by means of a self-defined spatial decay algorithm, reducing the fault location range to a specific line section or equipment and improving fault location precision; the abnormal deviation degree formula is used to determine the abnormal degree, and the data change trend is comprehensively considered to reduce misjudgment and missed judgment; the GIS map is used to visually display fault information, integrate surrounding geographic environment and line topological structure, and provide comprehensive reference for fault processing, thereby improving the efficiency and accuracy of fault determination.

[0057] The above is only a preferred embodiment of the application, and does not limit the application in any form. Although the application has been disclosed as above, it is not intended to limit the application. Any person skilled in the art can make some changes or modifications to the above disclosed technical content to obtain equivalent embodiments with equivalent changes, without departing from the scope of the technical solution of the application. Any simplification, modification, equivalent change and modification of the above embodiments made according to the technical essence of the application, without departing from the technical solution of the application, still belong to the scope of the technical solution of the application.

Claims

1. A method of communication line fault determination, characterized by, The method comprises the following steps: S1, deploying multiple types of Internet of Things sensors at key nodes of the communication line to collect physical state data and electrical parameter data in real time; S2, preprocessing and cleaning the collected data; S3, dynamically associating the processed data with a GIS map to establish an associated database; S4, real-time detection of data anomalies, triggering fault analysis if data anomalies exist; S5, locating faults based on associated data and line topology structure and integrating surrounding geographic information; S6, visualizing and displaying fault-related information on the GIS map.

2. A method of communication line fault determination according to claim 1, characterised in that, In step S1, the various types of IoT sensors include temperature sensors, vibration sensors, current sensors, and voltage sensors, which are deployed at cable joints, overhead line towers, and line branch points, respectively. The temperature sensors collect temperature data from the cable joints and line sheaths; the vibration sensors collect vibration amplitude and frequency data from the overhead lines; and the current and voltage sensors collect real-time current and voltage values ​​from the lines, respectively. The sensors upload the collected data to the data processing center in real time via a LoRa wireless transmission module. When calculating the effective impact value of the vibration signal, the following formula is used: ,in The effective impact value of vibration. For the first The vibration amplitude of the second sample. For the corresponding vibration frequency, The number of data collections is used to quantify the actual impact of vibration on the line.

3. A method of communication line fault determination according to claim 1, wherein, In step S2, the preprocessing and cleaning include: eliminating abnormal values that exceed the reasonable range due to sensor failure and transmission interference, using linear interpolation method to complete the missing data in a short time, converting different formats of sensor data into standardized JSON format data to form a unified data basis.

4. The method of claim 1, wherein, In step S3, the dynamic association of data and GIS map is specifically: calling a GIS map interface, binding the preprocessed data with the corresponding line location on the GIS map according to the latitude and longitude information of the sensor installation location, establishing an associated database of "sensor ID-line location coordinates-real-time monitoring data", and performing real-time mapping of data and line spatial location.

5. The method of claim 1, wherein, In step S4, the abnormal data detection specifically comprises: comparing the collected data with a preset threshold, and adopting an abnormal deviation degree formula determining the abnormal degree, wherein is the current collected data, is the preset threshold, is a very small constant, and when the condition is met for three times in succession, it is determined that the data is abnormal and a fault analysis is triggered, and the abnormal occurrence time and initial data characteristics are recorded.

6. The method of claim 1, wherein, In step S5, the fault location is specifically: for the abnormal position, call the GIS map associated data and line topology structure information, the line topology structure information includes the connection mode of the node and the adjacent node, the line type, the fault range is narrowed through the self-defined space attenuation algorithm, the formula is Wherein is the actual fault influence radius, is the theoretical maximum influence radius, is the line attenuation coefficient, is the deviation of abnormal data and historical average, and the specific fault section and equipment are determined through .

7. The method of claim 1, wherein the step of determining the fault comprises the step of: In step S5, the integrated surrounding geographic information includes the terrain, surrounding building distribution, and traffic road information around the fault point, and in step S6, the visualized display includes marking the fault location with a red flashing point on the GIS map, and displaying the abnormal data type, real-time monitoring data curve, surrounding geographic environment picture, and line topology structure diagram in the information window. ​ 8. The method of claim 1, wherein, In step S1, when deploying the sensors, the collection frequency of each sensor also needs to be preset, and the collection frequency is set to collect once every 10-15 seconds according to the characteristics of the line and monitoring requirements, and in step S4, after triggering the fault analysis process, the change trend of abnormal data is recorded in real time.

9. A communication line fault determining system according to claim 1, for carrying out a communication line fault determining method according to any one of claims 1 to 8, characterized in that, The system comprises: a sensor module, a data transmission module, a data processing module, an anomaly detection module, and a GIS visualization module; The sensor module is composed of multiple types of Internet of Things sensors for collecting physical state data and electrical parameter data of the communication line; The data transmission module is used to upload the data collected by the sensors to the data processing module in real time; The data processing module is used to preprocess and clean the raw data, and dynamically associate the processed data with a GIS map to establish an associated database; The anomaly detection module is used to detect whether the data is abnormal in real time, and trigger fault analysis if the data is abnormal; The GIS visualization module is used to locate faults based on associated data and line topology structure, integrate surrounding geographic information, and visualize and display fault-related information on the GIS map.

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