Power cable external damage active early warning method and system

Abstract

This invention provides a proactive early warning method and system for preventing external damage to power cables, relating to the field of power cable risk control technology. The method includes: acquiring data from multiple power cable monitoring points; acquiring the classification rules corresponding to each parameter type in the acquired data; wherein the classification rules for each parameter type are selected based on geographical location; after determining the early warning level analysis result based on the acquired data and the classification rules, generating corresponding early warning information based on the early warning level analysis result. This invention solves the problem of early warning delay or insufficient sensitivity caused by the inability to distinguish parameter characteristics using traditional single fixed thresholds through multi-parameter collaborative analysis and dynamic threshold adaptation. Simultaneously, by matching different risk levels through a graded early warning mechanism, it improves the real-time performance and accuracy of early warning for preventing external damage to power cables, ensuring that sudden risks can be quickly identified and trigger targeted responses.

Description

Technical Field

[0001] This invention relates to the field of power cable risk control technology, and in particular to a proactive early warning method and system for preventing external damage to power cables. Background Technology

[0002] Damage to power cables from external forces is a frequent occurrence. Traditional warning methods have many limitations: their warning effect is poor, their effective range is short, and once a problem occurs, the fault location must be manually located. To address the over-reliance on manual labor in traditional warning methods, a smart early warning system for power cables has been proposed. This system uses wireless communication technology to collect cable environmental data and analyzes this data using big data analytics to issue fault warnings based on set thresholds. While this solves the problem of over-reliance on manual labor, it still presents the challenge of delayed response to external damage warnings for power cables. Summary of the Invention

[0003] This invention provides a method and system for proactive early warning of external damage to power cables, in order to solve the problem of untimely early warning response to external damage to power cables.

[0004] In a first aspect, embodiments of the present invention provide an active early warning method for preventing external damage to power cables, characterized in that it includes: Acquire data from multiple power cable monitoring points; Obtain the level classification rules corresponding to each parameter type in the collected data information; After determining the warning level analysis result based on the collected data information and the level classification rules, a warning prompt message of the corresponding level is generated based on the warning level analysis result; wherein, the parameter types in the collected data information include temperature, humidity, altitude above the ground, geographical location and magnetic field strength; the level classification rules corresponding to each parameter type are selected according to the geographical location.

[0005] In one possible implementation, generating warning information corresponding to the warning level based on the warning level analysis results includes: When the warning level analysis results include warning information determined based on the altitude above the ground, determine whether warning information determined based on the magnetic field strength is also included. When the warning information is determined based on the magnetic field strength, a warning message of the corresponding level is generated based on the warning level analysis results corresponding to the ground altitude; otherwise, the step of generating a warning message of the corresponding level based on the warning level analysis results corresponding to the ground altitude is not executed.

[0006] In one possible implementation, determining the early warning level analysis result based on the collected data information and the level classification rules includes: Determine the maximum and minimum values ​​for each level in the classification rules; The collected data for each parameter type are compared with the maximum and minimum values ​​for the corresponding levels; If the data value exceeds the maximum value or falls below the minimum value, a warning message of the corresponding level will be generated.

[0007] In one possible implementation, the grading rule is determined based on the slope of data change; Before determining the early warning level analysis result based on the collected data information and the level classification rules, the method further includes: Plot the data change curves for each parameter type using a preset time unit; Calculate the slope of the curves showing the changes in each of the data; Accordingly, the collected data for each parameter type is compared with the maximum and minimum values ​​for each corresponding level, including: The slope calculation results are compared with the maximum and minimum values ​​of each corresponding level.

[0008] In one possible implementation, after generating the corresponding warning message based on the warning level analysis result, the method further includes: Determine if identical warning messages exist; If present, calculate the time window for the current warning and historical warnings; If the time window exceeds the set duration, a new warning will be generated; otherwise, the original warning will be updated.

[0009] In one possible implementation, the time window is calculated based on a sliding time window algorithm, as shown in the formula: .

[0010] Secondly, embodiments of the present invention provide an active early warning system for preventing external damage to power cables, comprising: a data acquisition terminal, a front-end device, and a back-end server; Among them, the data acquisition terminal is set up at multiple power cable monitoring points and is configured to collect data information from power cable monitoring in real time; The front-end device is configured to call the API service to package the collected data information from each power cable monitoring point and send it to the back-end server; The backend server is configured to store the level classification rules corresponding to each parameter type, perform calculations and judgments based on the collected data information and the level classification rules, determine the warning level analysis results and generate a level warning message, and use a message middleware to complete the distribution of the level warning message. The front-end device is also configured to maintain a long connection with the back-end server, receive level warning messages distributed by the message middleware in real time, and generate warning prompt information of the corresponding level based on the warning messages.

[0011] In one possible implementation, the grading rules corresponding to each parameter type are stored in the configuration file of the backend server in JSON format, so as to achieve data comparison through real-time parsing.

[0012] In one possible implementation, the backend server uses a multi-threaded mechanism combined with Redis caching technology to process data, and receives packaged data of the collected data information sent by the frontend device through the Restful interface.

[0013] In one possible implementation, the front-end device establishes a long connection with the back-end server via WebSocket technology to receive alert messages in real time.

[0014] In this embodiment of the invention, multi-parameter data from multiple power cable monitoring points are acquired in real time. Combined with dynamically categorized rules designed independently for different parameters, the actual values ​​of each parameter are compared differentially with the maximum and minimum values ​​of their corresponding levels to generate tiered early warning information. Furthermore, the tiered rules for the same parameter are adjusted to adapt to different geographical locations. Through multi-parameter collaborative analysis and dynamic threshold adaptation, the problem of early warning delays or insufficient sensitivity caused by the inability to distinguish parameter characteristics in traditional single fixed thresholds is solved. Simultaneously, the tiered early warning mechanism matches different risk levels, improving the real-time performance and accuracy of power cable damage prevention early warnings, ensuring that sudden risks can be quickly identified and trigger targeted responses. Attached Figure Description

[0015] Figure 1 This is an application scenario diagram of the active early warning method for preventing external damage to power cables provided in an embodiment of the present invention; Figure 2 This is a flowchart illustrating the implementation of an active early warning method for preventing external damage to power cables according to an embodiment of the present invention. Figure 3 This is a schematic diagram of the structure of an active early warning system for preventing external damage to power cables provided in an embodiment of the present invention. Detailed Implementation

[0016] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0017] Figure 1 This is an application scenario diagram of the active early warning method for preventing external damage to power cables provided in an embodiment of the present invention. For example... Figure 1 As shown, the active early warning system for preventing external damage to power cables includes sensors, terminals, and servers.

[0018] In practical implementation, sensors, terminals, and servers communicate via a network. Sensors are placed at different power cable monitoring points to achieve multi-point data collection, thereby improving the accuracy and reliability of monitoring. The sensors transmit key data such as ambient temperature, humidity, altitude above ground, geographical location, and magnetic field strength to the terminals and servers in real time via the network. The terminals and servers are responsible for efficiently analyzing this collected data and providing timely proactive warnings for the power cables to prevent external damage and potential faults. This intelligent monitoring system not only monitors the status of power cables in real time but also predicts potential problems through data analysis, thus providing strong support for the stable operation of the power system.

[0019] In actual deployment, there can be different deployment methods.

[0020] In one possible implementation, the active early warning system for external damage to power cables includes: sensors, terminals, and a server. Optionally, the sensors communicate with the server through the terminal; the sensors and server do not establish direct communication. The terminal is responsible for transmitting the sensor-collected data to the server, where it analyzes and processes the data to generate an early warning message. The server then feeds back the early warning message to the terminal, which provides an early warning alert, facilitating maintenance by staff. Alternatively, the sensors communicate with the server through the terminal; the sensors and server do not establish direct communication. The terminal analyzes and processes the sensor-collected data to generate an early warning message, and simultaneously sends the early warning message and corresponding collected data to the server for storage, facilitating later early warning traceability and in-depth analysis of the early warning scheme.

[0021] In this deployment method, sensors, terminals, and servers work together. The terminals are responsible for data transmission or preliminary analysis, while the servers centrally process the data and generate early warning messages, supporting large-scale data processing and historical tracing in a wide range of monitoring scenarios.

[0022] In another possible implementation, the active early warning system for external damage to power cables includes sensors and terminals. The sensors communicate with the terminals. The terminals are responsible for analyzing and processing the data collected by the sensors and providing early warnings, facilitating maintenance by staff based on these warnings.

[0023] In actual implementation, the sensor and the terminal interact directly. The terminal independently completes data processing and early warning prompts. Through localized real-time analysis (such as the terminal directly calculating the slope of temperature change), communication layer delay is eliminated. It is suitable for small-scale high-frequency monitoring scenarios (such as densely cabled areas in substations), improving response speed and deployment flexibility.

[0024] The above implementation methods address the conflict between efficiency and accuracy in power cable damage prevention and early warning systems under different scenarios through "centralized complex analysis" and "rapid edge response," respectively. In practical applications, deployment options can be selected based on actual needs.

[0025] The early warning analysis process remains the same across different scenarios. The early warning analysis process of this invention will now be described in detail with reference to the accompanying drawings.

[0026] See Figure 2 This is a flowchart illustrating the implementation of an active early warning method for preventing external damage to power cables according to an embodiment of the present invention, comprising the following steps: S201, acquire data from multiple power cable monitoring points; the acquired data includes various parameter types.

[0027] The execution entity in the various embodiments of this application can be a server, processor, microprocessor, or other device with data processing capabilities. In actual implementation, the specific implementation method of the execution entity can be selected according to actual needs. Figure 1 As shown, the executing entity is either a terminal or a server.

[0028] In the discussion of this embodiment, no special restrictions are placed on this; any device with data processing capabilities is acceptable.

[0029] Taking the safety early warning scenario of power cables as an example, this scenario generally requires real-time monitoring of various data of the cable. Sensors are set up at vulnerable locations to monitor the real-time data of the cable operation and the environmental data, such as the cable's ambient temperature, humidity, and height above the ground.

[0030] In one possible implementation, the parameter types in the data information include temperature, humidity, altitude above the ground, geographical location, and magnetic field strength. The combination of parameter types in the data information varies across different embodiments.

[0031] Among these measures, monitoring the temperature of the cable surface and its surrounding environment is crucial. Abnormal temperature rises may indicate overload operation, insulation aging, or threats from external heat sources (such as welding operations or fire hazards). For example, a sudden rise in cable joint temperature may be due to localized overheating caused by poor contact, while abnormal ambient temperature may be caused by high-temperature exhaust from construction machinery.

[0032] Inspecting the humidity level of the cable's environment is crucial, as high humidity can lead to decreased insulation performance, corrosion of metal components, or water accumulation. For example, a sudden increase in humidity in underground cable trenches may be due to pipe rupture or heavy rain, and prolonged high humidity environments can accelerate the aging of the cable sheath.

[0033] The vertical distance between the cable and the ground is measured. A sudden drop in height above the ground may be caused by objects exceeding the height limit snagging on it, strong winds, ground subsidence, illegal excavation, or heavy objects running over it.

[0034] Cable coordinates are tracked using GPS or BeiDou positioning, and cable displacement or deformation is analyzed using GIS maps. Furthermore, the environments in which power cables are located differ across regions, resulting in variations in normal ranges for temperature, humidity, altitude above ground, and magnetic field strength. Therefore, different classification rules are determined based on geographical location to adapt to differentiated safety standards in different regions, avoiding false alarms or missed alarms caused by uniform thresholds, and improving the flexibility and regional adaptability of early warning rules.

[0035] Accordingly, in one possible implementation, when the parameter type in the data information includes geographical location, the hierarchical classification rules corresponding to each parameter type in the collected data information are obtained, including: Select the corresponding level classification rules for each parameter type based on geographical location.

[0036] Monitoring changes in the magnetic field around the cable is crucial. Abnormal magnetic fields may be caused by current leakage, electricity theft, or strong electromagnetic interference. For example, illegal cable splicing can lead to localized magnetic field distortion, while large mechanical electromagnetic equipment may interfere with the normal operation of the cable.

[0037] In the specific implementation process, optionally, integrated sensors can be installed at each power cable monitoring point to achieve centralized acquisition of various parameter types. Alternatively, corresponding sensors can be installed for different parameter types.

[0038] S202, Obtain the level classification rules corresponding to each parameter type in the collected data information.

[0039] The classification rules aim to reflect different warning levels, making it easier for staff to intuitively determine the urgency of the warning and take appropriate power cable maintenance measures.

[0040] The methods for classifying warning levels differ depending on the implementation method.

[0041] Optionally, the warning levels can be further refined into yellow, orange, and red. The urgency levels, from lowest to highest, are yellow (general risk), orange (significant risk), and red (major risk). Taking the ground height parameter of a certain power cable as an example, assuming its normal ground height is 6-8m, and it frequently falls below 8m without posing a danger, a power cable ground height below 6m presents a minor safety hazard, below 5.5m there is a greater likelihood of problems, and a sudden decrease in ground height will inevitably lead to danger. Therefore, the threshold data for the ground height warning level of this power cable can be understood as: yellow warning (6-7m), orange warning (5.5-6m), and red warning (5m).

[0042] Optionally, the warning levels can be further subdivided into Level 1, Level 2, and Level 3. The urgency levels, from lowest to highest, are Level 1 (general risk), Level 2 (relatively high risk), and Level 3 (major risk).

[0043] Optionally, the warning levels can be further refined into general, relatively large, and serious.

[0044] Among different warning level classification methods, the tiered response mechanism can be adapted to different risk scenarios, thereby improving the targeting of warnings.

[0045] Due to the different parameter types, it is necessary to customize the classification rules for different parameters based on their physical characteristics, historical data statistics, and actual application scenarios in order to achieve risk quantification and graded response.

[0046] S203, after determining the warning level analysis results based on the collected data information and the level classification rules, generate the corresponding warning prompt information based on the warning level analysis results.

[0047] In different application scenarios, the executing entity for the early warning level analysis results is determined based on the collected data and the level classification rules: either a terminal or a server. When the active early warning system for external damage to power cables includes sensors, terminals, and servers, it is preferable to perform the early warning level analysis through the server, leveraging the server's superior processing and computing capabilities to improve the efficiency of the analysis. When the system includes both sensors and terminals, the early warning level analysis is performed through the terminals. The terminals perform targeted early warning level analysis based on the collected data within their jurisdiction, enabling localized early warning processing and avoiding potential delays caused by poor network quality when communicating with the server.

[0048] In this embodiment, multi-parameter data from multiple power cable monitoring points are acquired in real time. Combined with dynamically categorized rules designed independently for different parameters, the actual values ​​of each parameter are compared differentially with the maximum and minimum values ​​of their corresponding levels to generate tiered early warning information. Furthermore, the tiered rules for the same parameter are adjusted to suit different geographical locations. Through multi-parameter collaborative analysis and dynamic threshold adaptation, the problem of early warning delays or insufficient sensitivity caused by the inability to distinguish parameter characteristics in traditional single fixed thresholds is solved. Simultaneously, the tiered early warning mechanism matches different risk levels, improving the real-time performance and accuracy of power cable damage prevention early warnings, ensuring that sudden risks can be quickly identified and trigger targeted responses.

[0049] The foregoing embodiments described that the combination of parameter types in the data information differs in different embodiments. Depending on the combination method, the specific process of generating corresponding warning information based on the warning level analysis results varies.

[0050] In one possible implementation, when the parameter types in the data information include Earth altitude and magnetic field strength, a corresponding level of warning information is generated based on the warning level analysis results, including: When the early warning level analysis results include early warning information determined based on the altitude above the ground, determine whether early warning information determined based on the magnetic field strength should also be included. When the warning information is determined based on the magnetic field strength, a warning message of the corresponding level is generated based on the warning level analysis results corresponding to the ground altitude; otherwise, the step of generating a warning message of the corresponding level based on the warning level analysis results corresponding to the ground altitude is not executed.

[0051] In this regard, considering that vehicles or drones exceeding the height limit may snag on power cables, ground subsidence caused by excavation equipment, strong winds, and natural subsidence can all lead to abnormal changes in the height above the ground, the accuracy of the early warning is improved by combining the changes in magnetic field strength with the early warning information determined based on the height above the ground.

[0052] Specifically, the magnetic field strength changes significantly when vehicles pass under power cables, drones fly near power cables, or excavating equipment digs. If the warning information includes data determined by altitude above the ground but not by magnetic field strength, it is determined that there are no vehicles or drones around the power cables, and the change in altitude above the ground is due to strong winds or natural subsidence; therefore, no corresponding level of warning information is generated.

[0053] When both warning information based on ground altitude and magnetic field strength are included, it indicates that there are vehicles or drones around the power cable, and the cause of the change in ground altitude is collision or excavation. Based on the ground altitude, a warning message of the corresponding level is generated so that staff can carry out maintenance tasks as soon as possible.

[0054] In this embodiment, when only the altitude above the ground is abnormal while the magnetic field is normal, the system automatically filters out invalid warnings. If the magnetic field strength is abnormal and accompanied by changes in altitude above the ground, the altitude indicates human-caused damage, and a timely warning is issued. Through a multi-parameter correlation analysis mechanism, a warning is only generated when the magnetic field strength and altitude above the ground simultaneously trigger anomalies, significantly improving the accuracy of identifying external force damage risks.

[0055] In one possible implementation, the early warning level analysis results are determined based on the collected data and the level classification rules, including: Determine the maximum and minimum values ​​for each level in the grading rules; The collected data for each parameter type are compared with the maximum and minimum values ​​for the corresponding levels; If the data value exceeds the maximum value or falls below the minimum value, a warning message of the corresponding level will be generated.

[0056] In this embodiment, the direct comparison between real-time data and preset thresholds enables millisecond-level response, ensuring timely early warning. Clear numerical boundaries reduce the need for manual intervention and improve the degree of automation.

[0057] In one possible implementation, the grading rules are determined based on the slope of data change; Before determining the early warning level analysis results based on the collected data and level classification rules, the following steps are also included: Plot the data change curves for each parameter type using a preset time unit; Calculate the slope of the curves showing the changes in each data point; Accordingly, the collected data for each parameter type is compared with the maximum and minimum values ​​for each corresponding level, including: The slope calculation results are compared with the maximum and minimum values ​​of each corresponding level.

[0058] In the specific implementation process, the collected data information within time T is plotted as a curve. The horizontal axis of the curve is time, and the vertical axis is the unit of collected data information. The trend of the curve within this time period is calculated, and the slope is used as the unit of measurement. When the slope exceeds the maximum or minimum value, it indicates that the data information (such as temperature) is changing too fast or too slow, which may indicate a fault or safety hazard, and thus a warning message is generated.

[0059] In this embodiment, by calculating the slope of the data change curve and using the slope as a basis for early warning judgment, sudden risks (such as sudden temperature rises or sudden altitude drops) can be captured, potential risks can be predicted in advance, and the sensitivity and accuracy of early warning can be improved. At the same time, slope analysis can identify gradual faults such as insulation aging, making up for the blind spot of static thresholds in detecting trend problems.

[0060] In one possible implementation, after generating the corresponding warning message based on the warning level analysis results, the method further includes: Determine if identical warning messages exist; If present, calculate the time window for the current warning and historical warnings; If the time window exceeds the set duration, a new warning will be generated; otherwise, the original warning will be updated.

[0061] The calculation of the time window is implemented using a time window algorithm, also known as a sliding time window algorithm. This is a rate limiting algorithm that allows a fixed number of requests to enter within a fixed time window. In this application, the specific application is to determine whether two identical warning messages exist within a given time window.

[0062] In this embodiment, a sliding time window algorithm is used to merge duplicate warnings, avoid redundant warnings, thereby reducing interference from invalid warnings and improving operation and maintenance efficiency.

[0063] In one possible implementation, the time window is calculated based on a sliding time window algorithm, with the following formula: .

[0064] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0065] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0066] Figure 3 A schematic diagram of the active early warning system for preventing external damage to power cables provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below: like Figure 3 As shown, the active early warning system for preventing external damage to power cables includes: a data acquisition terminal 301, a front-end device 302, and a back-end server 303.

[0067] The data acquisition terminal 301 is installed at multiple power cable monitoring points and is configured to collect real-time data information from power cable monitoring; the collected data information includes various parameter types.

[0068] The front-end device 302 is configured to call the API service to package the collected data information from each power cable monitoring point and send it to the back-end server 303.

[0069] The backend server 303 is configured to store the level classification rules corresponding to each parameter type. Based on the collected data and the level classification rules, it performs calculations and judgments to determine the warning level analysis results and generate a level warning message. The message middleware is then used to distribute the level warning message. The parameter types in the collected data include temperature, humidity, altitude above the ground, geographical location, and magnetic field strength; the level classification rules corresponding to each parameter type are selected based on the geographical location.

[0070] The front-end device 302 is also configured to maintain a long connection with the back-end server, receive level warning messages distributed by the message middleware in real time, and generate warning prompts of the corresponding level based on the warning messages.

[0071] exist Figure 3In the active early warning system for external damage to power cables shown, the steps mentioned in the aforementioned embodiments, such as determining the early warning level analysis results based on collected data information and level classification rules, and determining the level classification rules based on the slope of data changes, are executed by the backend server, reducing the computational pressure on the front-end equipment and improving the early warning response efficiency.

[0072] In this embodiment of the invention, multi-parameter data from multiple power cable monitoring points are acquired in real time. Combined with dynamically categorized rules designed independently for different parameters, the actual values ​​of each parameter are compared differentially with the maximum and minimum values ​​of their corresponding levels to generate tiered early warning information. Furthermore, the tiered rules for the same parameter are adjusted to adapt to different geographical locations. Through multi-parameter collaborative analysis and dynamic threshold adaptation, the problem of early warning delays or insufficient sensitivity caused by the inability to distinguish parameter characteristics in traditional single fixed thresholds is solved. Simultaneously, the tiered early warning mechanism matches different risk levels, improving the real-time performance and accuracy of power cable damage prevention early warnings, ensuring that sudden risks can be quickly identified and trigger targeted responses.

[0073] In one possible implementation, the grading rules corresponding to each parameter type are stored in a configuration file on the backend server in JSON format, so that data comparison can be achieved through real-time parsing.

[0074] In this embodiment, the threshold data is stored in JSON format. Due to JSON's ease of writing, high readability, and fast computer parsing and generation speed, the efficiency of building the threshold data configuration file in the early stages is significantly improved. In this application, the data comparison efficiency is greatly enhanced when dealing with a large number of sensors. Furthermore, it enables calculations without data storage, offering a particularly significant efficiency advantage compared to traditional methods.

[0075] In one possible implementation, the backend server uses a multi-threaded mechanism combined with Redis caching technology to process data, and receives packaged data of the collected data information sent by the frontend device through the Restful interface.

[0076] In this embodiment, after receiving data via the RESTful interface, the backend server processes the data using a multi-threaded mechanism combined with Redis caching technology. The processed data is marked as historical data and stored in the database, ensuring the synchronous updating of real-time data values. By combining the RESTful interface, multi-threaded processing, and Redis technology, the efficiency of data computation is improved.

[0077] In one possible implementation, the front-end device establishes a long-lived connection with the back-end server via WebSocket technology to receive alert messages in real time.

[0078] In this embodiment, the front-end interface, acting as a client, maintains a long-term connection with the back-end server using WebSocket technology, receiving alert messages distributed by the message middleware in real time. This transforms the interface from passive to proactive, enabling alert notifications. WebSocket simplifies data exchange between the client and server, allowing the server to proactively push data to the client. Combined with the message middleware, this enables real-time transmission of back-end data to the front-end, ultimately achieving proactive alert functionality.

[0079] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0080] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for proactive early warning of external damage to power cables, characterized in that, include: Acquire data from multiple power cable monitoring points; Obtain the grading rules corresponding to each parameter type in the collected data information; wherein, the parameter types in the collected data information include temperature, humidity, altitude above the ground, geographical location, and magnetic field strength; the grading rules corresponding to each parameter type are selected according to the geographical location; After determining the warning level analysis results based on the collected data and the level classification rules, a warning prompt message of the corresponding level is generated based on the warning level analysis results.

2. The active early warning method for preventing external damage to power cables as described in claim 1, characterized in that, The step of generating corresponding warning alert information based on the warning level analysis results includes: When the warning level analysis results include warning information determined based on the altitude above the ground, determine whether warning information determined based on the magnetic field strength is also included. When the warning information is determined based on the magnetic field strength, a warning message of the corresponding level is generated based on the warning level analysis results corresponding to the ground altitude; otherwise, the step of generating a warning message of the corresponding level based on the warning level analysis results corresponding to the ground altitude is not executed.

3. The active early warning method for preventing external damage to power cables as described in claim 1, characterized in that, The step of determining the early warning level analysis result based on the collected data information and the level classification rules includes: Determine the maximum and minimum values ​​for each level in the classification rules; The collected data for each parameter type are compared with the maximum and minimum values ​​for the corresponding levels; If the data value exceeds the maximum value or falls below the minimum value, a warning message of the corresponding level will be generated.

4. The active early warning method for preventing external damage to power cables as described in claim 3, characterized in that, The grading rules are determined based on the slope of data change. Before determining the early warning level analysis result based on the collected data information and the level classification rules, the method further includes: Plot the data change curves for each parameter type using a preset time unit; Calculate the slope of the curves showing the changes in each of the data; Accordingly, the collected data for each parameter type is compared with the maximum and minimum values ​​for each corresponding level, including: The slope calculation results are compared with the maximum and minimum values ​​of each corresponding level.

5. The active early warning method for preventing external damage to power cables as described in claim 1, characterized in that, After generating the corresponding warning message based on the warning level analysis results, the method further includes: Determine if identical warning messages exist; If present, calculate the time window for the current warning and historical warnings; If the time window exceeds the set duration, a new warning will be generated; otherwise, the original warning will be updated.

6. The active early warning method for preventing external damage to power cables as described in claim 1, characterized in that, The calculation of the time window is based on the sliding time window algorithm, and the formula is: 。 7. A power cable anti-external damage active early warning system, characterized in that, include: Data acquisition terminal, front-end device, and back-end server; Among them, the data acquisition terminal is set up at multiple power cable monitoring points and is configured to collect data information from power cable monitoring in real time; The front-end device is configured to call the API service to package the collected data information from each power cable monitoring point and send it to the back-end server; The backend server is configured to store the level classification rules corresponding to each parameter type, perform calculations and judgments based on the collected data information and the level classification rules, determine the warning level analysis results, generate a level warning message, and use a message middleware to complete the distribution of the level warning message; wherein, the parameter types in the collected data information include temperature, humidity, altitude above the ground, geographical location, and magnetic field strength; the level classification rules corresponding to each parameter type are selected according to the geographical location; The front-end device is also configured to maintain a long connection with the back-end server, receive level warning messages distributed by the message middleware in real time, and generate warning prompt information of the corresponding level based on the warning messages.

8. The active early warning system for external damage prevention of power cables as described in claim 7, characterized in that, The grading rules corresponding to each parameter type are stored in the configuration file of the backend server in JSON format, so as to realize data comparison through real-time parsing.

9. The active early warning system for external damage prevention of power cables as described in claim 7, characterized in that, The backend server uses a multi-threaded mechanism combined with Redis caching technology to process data, and receives packaged data of the collected data information sent by the frontend device through the RESTful interface.

10. The active early warning system for external damage prevention of power cables as described in claim 7, characterized in that, The front-end device establishes a long connection with the back-end server via WebSocket technology to receive warning messages in real time.

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