Anti-freezing system based on Internet of Things monitoring and method thereof

Abstract

The invention discloses a power transmission line anti-freezing system and method based on Internet of Things monitoring, and the system comprises an environmental parameter monitoring module, a data collection and transmission module, an intelligent data processing and analysis module, an early warning system module, a remote monitoring and management module, and a system management and maintenance module. Environmental parameters related to power transmission safety are collected in real time through sensors deployed at all nodes of a power transmission line, and the environmental parameters specifically comprise a temperature sensor unit, a humidity sensor unit, a wind speed sensor unit and a current load monitoring unit; environmental parameters around the power transmission line are monitored in real time through various sensors, data acquisition, transmission and processing are performed by using the Internet of Things technology, the environmental data are analyzed through an intelligent algorithm, the icing risk of the power transmission line is accurately judged, and efficient and intelligent anti-freezing monitoring comprehensive management is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission line monitoring, and in particular to an antifreeze system and method based on Internet of Things monitoring. Background Art

[0002] As an important part of the power system, transmission lines are widely used for power transmission in various environments. However, especially in cold areas, low temperatures in winter often cause ice on transmission lines. Ice not only increases the weight of the conductors, but also easily causes vibration and sag of the conductors, and may even lead to serious accidents such as the collapse of transmission towers and breakage of conductors. These problems pose a major threat to the stability and safety of the power system. In the prior art, commonly used anti-freezing measures include manual deicing, mechanical vibration deicing, heating deicing and other methods, but these methods have the problems of low monitoring efficiency, high cost, complex manual operation, and difficulty in achieving automation on a large scale.

[0003] With the development of Internet of Things (IoT) technology, advanced sensors and communication technologies can be used to monitor the operating status of power transmission lines in real time, collect relevant environmental parameters such as temperature, humidity, wind speed, current, etc., and conduct comprehensive analysis through intelligent algorithms, so as to predict the risk of line icing in advance and take preventive measures. However, in the field of antifreeze of power transmission lines, the monitoring technology based on IoT is still in the development stage, and the existing monitoring system lacks multi-dimensional data fusion analysis, and cannot effectively prevent line freezing problems in the early stages.

[0004] Therefore, there is an urgent need to develop an intelligent transmission line antifreeze system based on Internet of Things monitoring, which can accurately predict the freezing risk of the line and automatically implement corresponding antifreeze measures through real-time monitoring and analysis of environmental conditions, thereby effectively ensuring the stable operation of the transmission line. Summary of the invention

[0005] The present invention provides a power transmission line antifreeze system and method based on Internet of Things monitoring, which monitors the environmental parameters around the power transmission line in real time through multiple sensors, uses Internet of Things technology to collect, transmit and process data, and analyzes environmental data through intelligent algorithms to accurately determine the icing risk of the power transmission line and realize efficient and intelligent antifreeze monitoring and comprehensive management. The specific scheme is as follows:

[0006] A power transmission line antifreeze system and method based on Internet of Things monitoring, including: an environmental parameter monitoring module, a data acquisition and transmission module, an intelligent data processing and analysis module, an early warning system module, a remote monitoring and management module, and a system management and maintenance module, wherein:

[0007] The environmental parameter monitoring module collects environmental parameters related to power transmission safety in real time through sensors deployed at each node of the power transmission line, including:

[0008] Temperature sensor unit: monitors the temperature changes around the line and predicts the overload risk of the line due to temperature increase;

[0009] Humidity sensor unit: monitors the air humidity around the line and provides protection against the impact of humid environment on line safety.

[0010] Wind speed sensor unit: monitors the wind speed around the line, especially the mechanical stress that high wind speed may cause to the line.

[0011] Current load monitoring unit: monitors the current load of the line in real time to determine whether the line load is too high.

[0012] The data collection and transmission module is responsible for transmitting the data collected by each sensor to the cloud processing platform to ensure stable and efficient data transmission. It specifically includes the following units:

[0013] Data acquisition unit: centrally receives various environmental data from sensors and performs preliminary sorting and screening.

[0014] Wireless communication unit: Based on the IoT communication protocol (such as LoRa, NB-IoT, 5G), the collected data is transmitted to the cloud to ensure the reliability and coverage of data transmission.

[0015] Real-time monitoring and historical trend analysis: The system dynamically evaluates the current status of the equipment and operational risks by integrating real-time monitoring data from sensors and historical accumulated data. For example, historical data can include environmental monitoring records, equipment status change records, fault frequency, etc. in the past few years or months. These data are combined with current sensor information to form a comprehensive risk assessment.

[0016] Long-term and short-term trend analysis: The system evaluates the potential aging, performance degradation, or possible environmental changes of equipment by analyzing the trend changes of long-term and short-term data. For example, by analyzing the wind speed and humidity change trends in a region in recent years, the system can determine the operating risks of wind power equipment and issue risk warnings.

[0017] Dynamic risk assessment model

[0018] Adaptive risk model: The system can dynamically adjust the weight parameters in the assessment model based on real-time environmental data (such as temperature, humidity, wind speed, etc.) combined with historical fault data. For example, when a region is experiencing extreme weather with high temperature and high humidity, the system automatically increases the weight of these environmental parameters in the risk assessment to ensure more accurate assessment results.

[0019] Multi-dimensional data collaborative analysis: The model can analyze the synergy of multi-dimensional data (such as temperature, current, humidity, and wind speed) to determine potential equipment failures. For example, the system can predict the risk of overheating or rupture of transmission lines by combining factors such as excessive temperature, excessive current, and a sharp increase in wind speed, and issue a warning in advance.

[0020] Dynamic risk threshold setting

[0021] Intelligent adjustment of risk thresholds: The system can autonomously adjust the risk thresholds of different parameters according to the specific conditions of the environment, equipment and historical data. For example, in extreme weather conditions (such as typhoons and blizzards), the system can reduce the risk tolerance of certain equipment (such as wind speed, current load and other thresholds) to ensure that risk warnings in abnormal weather are more sensitive.

[0022] Region-specific optimization: The system is able to self-learn and adapt to different geographic regions and climate zones

[0023] For example, in cold northern regions, the system will learn the impact of long-term low temperatures on equipment performance, and automatically increase the weight of low-temperature-related data the next time similar conditions are encountered, making more accurate risk predictions.

[0024] Historical fault data feedback learning

[0025] Feedback mechanism optimizes the model: The system learns and optimizes its prediction model through continuous feedback on equipment operating status and fault conditions. For example, when the system detects that an equipment failure has occurred and triggers maintenance, the system will incorporate this data into the learning library to further improve the risk assessment model so that more accurate risk warnings can be made in similar situations in the future.

[0026] Data fusion unit cross-sensor data fusion

[0027] Multi-source data integration and analysis: This unit conducts multi-dimensional data analysis by integrating data collected by different sensors (such as temperature, humidity, wind speed, current, etc.). For example, the system can combine information such as temperature, humidity, current load, wind speed, etc. on the transmission line to identify potential risks and generate a comprehensive assessment report.

[0028] Heterogeneous data consistency processing: The system can uniformly process data from different sensors, such as synchronizing sensor information of different time units and data types, and eliminating errors and noise between data to ensure the accuracy of the final analysis results.

[0029] Comprehensive evaluation of multi-dimensional data

[0030] Multi-level data fusion: The system integrates environmental data provided by multiple sensors in different levels. For example, temperature data and humidity data will be integrated first when analyzing equipment cooling requirements, while wind speed and current load data play a major role in analyzing structural stability. The system can automatically assign data fusion priorities based on specific circumstances.

[0031] Complex correlation analysis: Through big data analysis and algorithm processing, the system can explore the potential relationships between different data. For example, by analyzing the correlation between device temperature and current fluctuations, the system can detect current overload caused by temperature rise in advance, and then determine that the device may fail.

[0032] 4. Early warning system module threshold judgment unit

[0033] Multi-dimensional threshold judgment mechanism: The system will set risk thresholds for a single parameter or a combination of multiple parameters based on the different characteristics of the equipment and environment. For example, when the system detects that the temperature exceeds 80°C, the humidity exceeds 90%, the wind speed exceeds 20m / s, and the current load exceeds the critical point, it will immediately trigger a warning of the multi-dimensional combination threshold.

[0034] Dynamic threshold adaptive adjustment: The system can dynamically adjust the threshold according to real-time data and environmental changes. For example, during periods of strong winds, the system will appropriately adjust the thresholds of parameters such as wind speed and temperature to avoid unnecessary false alarms, while ensuring that the threshold settings are more sensitive under extreme conditions.

[0035] Warning information generation unit

[0036] Detailed warning report generation: The system generates warning information based on the results of multi-dimensional analysis. The warning information not only includes the risk level (such as low, medium, and high), but also gives the specific threats that the equipment or system may face, as well as detailed prevention suggestions. For example, the system will remind managers to increase equipment inspections in specific areas, or recommend reducing equipment load.

[0037] Area and equipment impact assessment: The early warning information will describe in detail the areas or equipment that the risk may affect. For example, if the humidity or wind speed in a certain area is too high, it may cause damage to wind power generation equipment. The system will generate a specific impact range and provide a list of equipment that may be affected.

[0038] Graded early warning system

[0039] Multi-level warning system: The system provides multi-level warnings (such as red, yellow, blue warnings, etc.) based on the results of risk assessment. Each level corresponds to a different risk level and response measures. The highest level of warning will require immediate emergency measures, while the lower level warnings will recommend regular inspections and monitoring.

[0040] Customized early warning strategies: Users can customize early warning trigger conditions and notification methods, such as SMS, email, system alarm, etc., and can set different early warning strategies for different equipment or areas. For example, wind power equipment can set high-priority early warnings such as wind speed and vibration intensity, while transmission lines may focus on factors such as current load, temperature and humidity.

[0041] Remote monitoring and management module

[0042] Function description: Managers can remotely view monitoring data and risk analysis results in real time and make necessary adjustments and operations.

[0043] Monitoring interface unit: Provides a user-friendly interface to access the system through a computer or mobile device to view environmental monitoring data and risk assessment reports in real time.

[0044] Management control unit: Managers can adjust system parameters through the remote control interface, or adjust line operation strategies based on early warning information.

[0045] Data storage and historical analysis unit: records all monitoring data and warning information for management personnel to review historical data and conduct long-term analysis to further optimize line maintenance strategies.

[0046] System management and maintenance module

[0047] Function description: Responsible for the normal operation, regular maintenance and update of the system to ensure the long-term stability and scalability of the system, including:

[0048] System self-diagnosis unit: Automatically detects the system operation status to determine whether there are problems such as abnormal data transmission and sensor failure.

[0049] Remote maintenance unit: Maintain and upgrade the system remotely to ensure long-term stable operation of the system.

[0050] Expandability and compatibility unit: The system supports adding new types of sensors or communication protocols to adapt to future technology upgrades and expansion requirements.

[0051] According to a power transmission line antifreeze system and method based on Internet of Things monitoring, it is characterized in that the data redundancy module includes

[0052] Distributed storage module: Data can be stored simultaneously in different physical nodes or data centers to prevent data loss caused by a single node failure. The system supports the deployment of multiple storage nodes in different geographical areas to ensure that data can be recovered even in natural disasters or other extreme situations.

[0053] Automatic data synchronization: Data between storage nodes can be synchronized in real time to ensure the consistency of redundant data. If a node fails, the system will automatically switch to other nodes and restore data without manual intervention.

[0054] S1. Sensor Arrangement

[0055] Environmental sensors are deployed at key nodes of the transmission line, including temperature sensors, humidity sensors, wind speed sensors and current load monitoring units.

[0056] S2. Real-time data collection

[0057] After each sensor is started, it begins to monitor the surrounding environmental parameters in real time; the temperature sensor measures the ambient temperature, the humidity sensor monitors the air humidity, the wind speed sensor records the wind speed change, and the current load monitoring unit is responsible for monitoring the current load of the line;

[0058] S3: Preliminary data processing

[0059] The environmental parameter monitoring module performs preliminary processing on the collected data, mainly including standardization of data format and preliminary screening of abnormal data, which can ensure the accuracy and consistency of subsequent data processing.

[0060] 2. Data Collection and Transmission Module Step 2.1: Data Collection and Arrangement

[0061] The data acquisition unit centrally receives monitoring data from all sensors and performs preliminary sorting and screening of the data, including filtering out obvious noise and invalid data, and sorting the data in a predetermined format for subsequent transmission and processing.

[0062] Step 2.2: Data Transfer

[0063] The sorted data is transmitted to the cloud processing platform through the wireless communication unit. The module supports multiple wireless communication protocols, such as LoRa, NB-IoT, 4G / 5G, etc., to ensure stable data transmission in different environments.

[0064] Step 2.3: Data redundancy and security

[0065] To prevent data loss due to signal interruption or equipment failure during data transmission, the system uses data redundancy technology. During data transmission, information is stored in multiple physical nodes or data centers, and the consistency of data in each node is ensured through automatic data synchronization.

[0066] At the same time, encryption technology is used to protect data security during data transmission to prevent data from being tampered with or leaked during transmission.

[0067] 3. Intelligent Data Processing and Analysis Module Step 3.1: Data Fusion and Consistency Processing

[0068] The data fusion unit receives multi-dimensional data from different sensors and performs unified format conversion and consistency processing. The system eliminates the time difference and unit inconsistency between sensor data, ensuring that all data can be comprehensively analyzed under the same time reference.

[0069] Step 3.2: Multi-level data fusion

[0070] The system integrates different types of data such as temperature, humidity, wind speed, and current load in a hierarchical manner. Based on the relevance and importance of the data, the system prioritizes deep integration analysis of key data (such as temperature and current load) to ensure the accuracy of the evaluation results.

[0071] Step 3.3: Risk Assessment

[0072] The risk assessment unit combines historical data and real-time data to dynamically generate a risk assessment model. The system uses an adaptive learning algorithm that can autonomously adjust the parameter weights in the model according to the climate characteristics and historical fault data of different regions to improve the accuracy of the assessment.

[0073] The system conducts a comprehensive analysis of multi-dimensional data and generates a risk assessment report, which includes the current risk level (such as normal, warning, and dangerous), possible fault types (such as line icing, equipment overload), and specific impact assessments on equipment or lines.

[0074] Step 3.4: Adaptive learning and model optimization

[0075] The system optimizes the risk assessment model by continuously acquiring and analyzing new data. The adaptive learning unit uses machine learning algorithms to analyze the impact of different climate and environmental conditions on the equipment and automatically adjust the parameters in the prediction model. Over time, the system's response speed to environmental changes and prediction accuracy will gradually improve.

[0076] 4. Early Warning System Module Step 4.1: Threshold Determination

[0077] The system sets multiple risk thresholds and monitors individual parameters or parameter combinations based on the specific requirements and environmental conditions of the transmission line. When a parameter or a combination of multiple parameters exceeds the preset threshold, the system will immediately trigger an early warning.

[0078] For example, if the temperature drops below 0°C and is accompanied by high humidity and wind speeds exceeding 15m / s, while the current load approaches the warning line, the system will determine that there is a risk of icing and issue a warning.

[0079] Step 4.2: Warning information generation

[0080] The early warning information generation unit generates a detailed early warning report based on the risk assessment results. The report includes:

[0081] Risk level: such as low, medium, and high risk.

[0082] Specific preventive recommendations: For example, it is recommended to reduce loads, protect heating equipment from freezing, or arrange manual inspections.

[0083] Impact area: Identify the specific line segments or equipment nodes that may be affected, and list the scope of possible impact in detail.

[0084] Step 4.3: Warning Notification

[0085] The system supports multiple warning notification methods, such as SMS, email, system pop-up windows, etc. According to the different warning levels, the system will select the appropriate notification channel to ensure that managers can obtain risk information in a timely manner and take corresponding measures.

[0086] 5. Remote Monitoring and Management Module Step 5.1: Real-time Monitoring

[0087] Through the remote monitoring and management module, managers can view the system's real-time monitoring data and risk analysis results at any time. The system provides a user-friendly interface that supports access via computers or mobile devices to monitor environmental parameters and line status in real time.

[0088] Step 5.2: Parameter adjustment and control

[0089] The management control unit allows managers to remotely adjust the system's operating parameters based on monitoring data and warning information, including but not limited to modifying risk thresholds, adjusting sensor acquisition frequency, or making temporary adjustments to line operation strategies based on warning information.

[0090] Step 5.3: Data storage and historical analysis

[0091] All monitoring data and warning information will be stored in the cloud data center to facilitate subsequent historical data analysis and long-term trend research. The system supports data review and long-term analysis functions to help managers optimize line maintenance strategies.

[0092] 6. System Management and Maintenance Module Step 6.1: System Self-diagnosis

[0093] The system management and maintenance module includes a system self-diagnosis unit that can automatically monitor the operating status of the system. The self-diagnosis function includes checking the normality of data transmission, the working status of sensors, and the operating load of the entire system.

[0094] When the system detects abnormal data transmission or sensor failure, it will automatically generate a fault report and notify the management personnel so that timely repair measures can be taken.

[0095] Step 6.2: Scalability and compatibility management

[0096] The system design has good scalability and compatibility, and supports adding new sensor types or updating existing communication protocols at any time. The scalability and compatibility unit will automatically detect the connection status of the newly added hardware and complete the data docking and integration with the new equipment to ensure that the system can adapt to future technology upgrade needs. DETAILED DESCRIPTION

[0097] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0098] The following is a preferred embodiment of the present invention. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0099] The present invention provides a power transmission line antifreeze system and method based on Internet of Things monitoring, which monitors the environmental parameters around the power transmission line in real time through multiple sensors, uses Internet of Things technology to collect, transmit and process data, and analyzes environmental data through intelligent algorithms to accurately determine the icing risk of the power transmission line and realize efficient and intelligent antifreeze monitoring and comprehensive management. The specific scheme is as follows:

[0100] A power transmission line antifreeze system and method based on Internet of Things monitoring, including: an environmental parameter monitoring module, a data acquisition and transmission module, an intelligent data processing and analysis module, an early warning system module, a remote monitoring and management module, and a system management and maintenance module, wherein:

[0101] The environmental parameter monitoring module collects environmental parameters related to power transmission safety in real time through sensors deployed at each node of the power transmission line, including:

[0102] Temperature sensor unit: monitors the temperature changes around the line and predicts the overload risk of the line due to temperature increase;

[0103] Humidity sensor unit: monitors the air humidity around the line and provides protection against the impact of humid environment on line safety.

[0104] Wind speed sensor unit: monitors the wind speed around the line, especially the mechanical stress that high wind speed may cause to the line.

[0105] Current load monitoring unit: monitors the current load of the line in real time to determine whether the line load is too high.

[0106] The data collection and transmission module is responsible for transmitting the data collected by each sensor to the cloud processing platform to ensure stable and efficient data transmission. It specifically includes the following units:

[0107] Data acquisition unit: centrally receives various environmental data from sensors and performs preliminary sorting and screening.

[0108] Wireless communication unit: Based on the IoT communication protocol (such as LoRa, NB-IoT, 5G), the collected data is transmitted to the cloud to ensure the reliability and coverage of data transmission.

[0109] Real-time monitoring and historical trend analysis: The system dynamically evaluates the current status of the equipment and operational risks by integrating real-time monitoring data from sensors and historical accumulated data. For example, historical data can include environmental monitoring records, equipment status change records, fault frequency, etc. in the past few years or months. These data are combined with current sensor information to form a comprehensive risk assessment.

[0110] Long-term and short-term trend analysis: The system evaluates the potential aging, performance degradation, or possible environmental changes of equipment by analyzing the trend changes of long-term and short-term data. For example, by analyzing the wind speed and humidity change trends in a region in recent years, the system can determine the operating risks of wind power equipment and issue risk warnings.

[0111] Dynamic risk assessment model

[0112] Adaptive risk model: The system can dynamically adjust the weight parameters in the assessment model based on real-time environmental data (such as temperature, humidity, wind speed, etc.) combined with historical fault data. For example, when a region is experiencing extreme weather with high temperature and high humidity, the system automatically increases the weight of these environmental parameters in the risk assessment to ensure more accurate assessment results.

[0113] Multi-dimensional data collaborative analysis: The model can analyze the synergy of multi-dimensional data (such as temperature, current, humidity, and wind speed) to determine potential equipment failures. For example, the system can predict the risk of overheating or rupture of transmission lines by combining factors such as excessive temperature, excessive current, and a sharp increase in wind speed, and issue a warning in advance.

[0114] Dynamic risk threshold setting

[0115] Intelligent adjustment of risk thresholds: The system can autonomously adjust the risk thresholds of different parameters according to the specific conditions of the environment, equipment and historical data. For example, in extreme weather conditions (such as typhoons and blizzards), the system can reduce the risk tolerance of certain equipment (such as wind speed, current load and other thresholds) to ensure that risk warnings in abnormal weather are more sensitive.

[0116] Region-specific optimization: The system is able to self-learn and adapt to different geographic regions and climate zones

[0117] For example, in cold northern regions, the system will learn the impact of long-term low temperatures on equipment performance, and automatically increase the weight of low-temperature-related data the next time similar conditions are encountered, making more accurate risk predictions.

[0118] Historical fault data feedback learning

[0119] Feedback mechanism optimizes the model: The system learns and optimizes its prediction model through continuous feedback on equipment operating status and fault conditions. For example, when the system detects that an equipment failure has occurred and triggers maintenance, the system will incorporate this data into the learning library to further improve the risk assessment model so that more accurate risk warnings can be made in similar situations in the future.

[0120] Data fusion unit cross-sensor data fusion

[0121] Multi-source data integration and analysis: This unit conducts multi-dimensional data analysis by integrating data collected by different sensors (such as temperature, humidity, wind speed, current, etc.). For example, the system can combine information such as temperature, humidity, current load, wind speed, etc. on the transmission line to identify potential risks and generate a comprehensive assessment report.

[0122] Heterogeneous data consistency processing: The system can uniformly process data from different sensors, such as synchronizing sensor information of different time units and data types, and eliminating errors and noise between data to ensure the accuracy of the final analysis results.

[0123] Comprehensive evaluation of multi-dimensional data

[0124] Multi-level data fusion: The system integrates environmental data provided by multiple sensors in different levels. For example, temperature data and humidity data will be integrated first when analyzing equipment cooling requirements, while wind speed and current load data play a major role in analyzing structural stability. The system can automatically assign data fusion priorities based on specific circumstances.

[0125] Complex correlation analysis: Through big data analysis and algorithm processing, the system can explore the potential relationships between different data. For example, by analyzing the correlation between device temperature and current fluctuations, the system can detect current overload caused by temperature rise in advance, and then determine that the device may fail.

[0126] 4. Early warning system module threshold judgment unit

[0127] Multi-dimensional threshold judgment mechanism: The system will set risk thresholds for a single parameter or a combination of multiple parameters based on the different characteristics of the equipment and environment. For example, when the system detects that the temperature exceeds 80°C, the humidity exceeds 90%, the wind speed exceeds 20m / s, and the current load exceeds the critical point, it will immediately trigger a warning of the multi-dimensional combination threshold.

[0128] Dynamic threshold adaptive adjustment: The system can dynamically adjust the threshold according to real-time data and environmental changes. For example, during periods of strong winds, the system will appropriately adjust the thresholds of parameters such as wind speed and temperature to avoid unnecessary false alarms, while ensuring that the threshold settings are more sensitive under extreme conditions.

[0129] Warning information generation unit

[0130] Detailed warning report generation: The system generates warning information based on the results of multi-dimensional analysis. The warning information not only includes the risk level (such as low, medium, and high), but also gives the specific threats that the equipment or system may face, as well as detailed prevention suggestions. For example, the system will remind managers to increase equipment inspections in specific areas, or recommend reducing equipment load.

[0131] Area and equipment impact assessment: The early warning information will describe in detail the areas or equipment that the risk may affect. For example, if the humidity or wind speed in a certain area is too high, it may cause damage to wind power generation equipment. The system will generate a specific impact range and provide a list of equipment that may be affected.

[0132] Graded early warning system

[0133] Multi-level warning system: The system provides multi-level warnings (such as red, yellow, blue warnings, etc.) based on the results of risk assessment. Each level corresponds to a different risk level and response measures. The highest level of warning will require immediate emergency measures, while the lower level warnings will recommend regular inspections and monitoring.

[0134] Customized early warning strategies: Users can customize early warning trigger conditions and notification methods, such as SMS, email, system alarm, etc., and can set different early warning strategies for different equipment or areas. For example, wind power equipment can set high-priority early warnings such as wind speed and vibration intensity, while transmission lines may focus on factors such as current load, temperature and humidity.

[0135] Remote monitoring and management module

[0136] Function description: Managers can remotely view monitoring data and risk analysis results in real time and make necessary adjustments and operations.

[0137] Monitoring interface unit: Provides a user-friendly interface to access the system through a computer or mobile device to view environmental monitoring data and risk assessment reports in real time.

[0138] Management control unit: Managers can adjust system parameters through the remote control interface, or adjust line operation strategies based on early warning information.

[0139] Data storage and historical analysis unit: records all monitoring data and warning information for management personnel to review historical data and conduct long-term analysis to further optimize line maintenance strategies.

[0140] System management and maintenance module

[0141] Function description: Responsible for the normal operation, regular maintenance and update of the system to ensure the long-term stability and scalability of the system, including:

[0142] System self-diagnosis unit: Automatically detects the system operation status to determine whether there are problems such as abnormal data transmission and sensor failure.

[0143] Remote maintenance unit: Maintain and upgrade the system remotely to ensure long-term stable operation of the system.

[0144] Expandability and compatibility unit: The system supports adding new types of sensors or communication protocols to adapt to future technology upgrades and expansion requirements.

[0145] According to a power transmission line antifreeze system and method based on Internet of Things monitoring, it is characterized in that the data redundancy module includes

[0146] Distributed storage module: Data can be stored simultaneously in different physical nodes or data centers to prevent data loss caused by a single node failure. The system supports the deployment of multiple storage nodes in different geographical areas to ensure that data can be recovered even in natural disasters or other extreme situations.

[0147] Automatic data synchronization: Data between storage nodes can be synchronized in real time to ensure the consistency of redundant data. If a node fails, the system will automatically switch to other nodes and restore data without manual intervention.

[0148] S1. Sensor Arrangement

[0149] Environmental sensors are deployed at key nodes of the transmission line, including temperature sensors, humidity sensors, wind speed sensors and current load monitoring units.

[0150] S2. Real-time data collection

[0151] After each sensor is started, it begins to monitor the surrounding environmental parameters in real time; the temperature sensor measures the ambient temperature, the humidity sensor monitors the air humidity, the wind speed sensor records the wind speed change, and the current load monitoring unit is responsible for monitoring the current load of the line;

[0152] S3: Preliminary data processing

[0153] The environmental parameter monitoring module performs preliminary processing on the collected data, mainly including standardization of data format and preliminary screening of abnormal data, which can ensure the accuracy and consistency of subsequent data processing.

[0154] 2. Data Collection and Transmission Module Step 2.1: Data Collection and Arrangement

[0155] The data acquisition unit centrally receives monitoring data from all sensors and performs preliminary sorting and screening of the data, including filtering out obvious noise and invalid data, and sorting the data in a predetermined format for subsequent transmission and processing.

[0156] Step 2.2: Data Transfer

[0157] The sorted data is transmitted to the cloud processing platform through the wireless communication unit. The module supports multiple wireless communication protocols, such as LoRa, NB-IoT, 4G / 5G, etc., to ensure stable data transmission in different environments.

[0158] Step 2.3: Data redundancy and security

[0159] To prevent data loss due to signal interruption or equipment failure during data transmission, the system uses data redundancy technology. During data transmission, information is stored in multiple physical nodes or data centers, and the consistency of data in each node is ensured through automatic data synchronization.

[0160] At the same time, encryption technology is used to protect data security during data transmission to prevent data from being tampered with or leaked during transmission.

[0161] 3. Intelligent Data Processing and Analysis Module Step 3.1: Data Fusion and Consistency Processing

[0162] The data fusion unit receives multi-dimensional data from different sensors and performs unified format conversion and consistency processing. The system eliminates the time difference and unit inconsistency between sensor data, ensuring that all data can be comprehensively analyzed under the same time reference.

[0163] Step 3.2: Multi-level data fusion

[0164] The system integrates different types of data such as temperature, humidity, wind speed, and current load in a hierarchical manner. Based on the relevance and importance of the data, the system prioritizes deep integration analysis of key data (such as temperature and current load) to ensure the accuracy of the evaluation results.

[0165] Step 3.3: Risk Assessment

[0166] The risk assessment unit combines historical data and real-time data to dynamically generate a risk assessment model. The system uses an adaptive learning algorithm that can autonomously adjust the parameter weights in the model according to the climate characteristics and historical fault data of different regions to improve the accuracy of the assessment.

[0167] The system conducts a comprehensive analysis of multi-dimensional data and generates a risk assessment report, which includes the current risk level (such as normal, warning, and dangerous), possible fault types (such as line icing, equipment overload), and specific impact assessments on equipment or lines.

[0168] Step 3.4: Adaptive learning and model optimization

[0169] The system optimizes the risk assessment model by continuously acquiring and analyzing new data. The adaptive learning unit uses machine learning algorithms to analyze the impact of different climate and environmental conditions on the equipment and automatically adjust the parameters in the prediction model. Over time, the system's response speed to environmental changes and prediction accuracy will gradually improve.

[0170] 4. Early Warning System Module Step 4.1: Threshold Determination

[0171] The system sets multiple risk thresholds and monitors individual parameters or parameter combinations based on the specific requirements and environmental conditions of the transmission line. When a parameter or a combination of multiple parameters exceeds the preset threshold, the system will immediately trigger an early warning.

[0172] For example, if the temperature drops below 0°C and is accompanied by high humidity and wind speeds exceeding 15m / s, while the current load approaches the warning line, the system will determine that there is a risk of icing and issue a warning.

[0173] Step 4.2: Warning information generation

[0174] The early warning information generation unit generates a detailed early warning report based on the risk assessment results. The report includes:

[0175] Risk level: such as low, medium, and high risk.

[0176] Specific preventive recommendations: For example, it is recommended to reduce loads, protect heating equipment from freezing, or arrange manual inspections.

[0177] Impact area: Identify the specific line segments or equipment nodes that may be affected, and list the scope of possible impact in detail.

[0178] Step 4.3: Warning Notification

[0179] The system supports multiple warning notification methods, such as SMS, email, system pop-up windows, etc. According to the different warning levels, the system will select the appropriate notification channel to ensure that managers can obtain risk information in a timely manner and take corresponding measures.

[0180] 5. Remote Monitoring and Management Module Step 5.1: Real-time Monitoring

[0181] Through the remote monitoring and management module, managers can view the system's real-time monitoring data and risk analysis results at any time. The system provides a user-friendly interface that supports access via computers or mobile devices to monitor environmental parameters and line status in real time.

[0182] Step 5.2: Parameter adjustment and control

[0183] The management control unit allows managers to remotely adjust the system's operating parameters based on monitoring data and warning information, including but not limited to modifying risk thresholds, adjusting sensor acquisition frequency, or making temporary adjustments to line operation strategies based on warning information.

[0184] Step 5.3: Data storage and historical analysis

[0185] All monitoring data and warning information will be stored in the cloud data center to facilitate subsequent historical data analysis and long-term trend research. The system supports data review and long-term analysis functions to help managers optimize line maintenance strategies.

[0186] 6. System Management and Maintenance Module Step 6.1: System Self-diagnosis

[0187] The system management and maintenance module includes a system self-diagnosis unit that can automatically monitor the operating status of the system. The self-diagnosis function includes checking the normality of data transmission, the working status of sensors, and the operating load of the entire system.

[0188] When the system detects abnormal data transmission or sensor failure, it will automatically generate a fault report and notify the management personnel so that timely repair measures can be taken.

[0189] Step 6.2: Scalability and compatibility management

[0190] The system design has good scalability and compatibility, and supports adding new sensor types or updating existing communication protocols at any time. The scalability and compatibility unit will automatically detect the connection status of the newly added hardware and complete the data docking and integration with the new equipment to ensure that the system can adapt to future technology upgrade needs.

[0191] Through modular division, the power transmission line monitoring and early warning system based on the Internet of Things can more flexibly adapt to the application requirements of different scenarios. Each module has independent functions and cooperates with each other to form a complete system architecture. From multi-dimensional environmental parameter monitoring to intelligent data analysis, to early warning information generation and remote management, the system provides a strong guarantee for the safe and stable operation of the power transmission line.

[0192] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments, but it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A power transmission line antifreeze system based on Internet of Things monitoring, comprising: Environmental parameter monitoring module, data acquisition and transmission module, intelligent data processing and analysis module, early warning system module, remote monitoring and management module, system management and maintenance module, including: The environmental parameter monitoring module collects environmental parameters related to power transmission safety in real time through sensors deployed at each node of the power transmission line, including: temperature sensor unit, humidity sensor unit, wind speed sensor unit, current load monitoring unit, The data collection and transmission module is responsible for transmitting the data collected by each sensor to the cloud processing platform, which specifically includes: Data collection unit: centrally receives various environmental data from sensors and performs preliminary sorting and screening; Wireless communication unit: transmits the collected data to the cloud; Data redundancy and security unit: The data redundancy module is used to ensure that data can be effectively transmitted in harsh environments, and encryption is performed to protect data security. The intelligent data processing and analysis module performs in-depth processing on the collected data and generates risk assessment and early warning information, including: Risk assessment unit: integrates historical data and real-time data to dynamically generate risk assessment data tables; Adaptive learning unit: The system can adjust parameter weights according to the climate and environmental characteristics of different regions to improve the accuracy and adaptability of predictions; Data fusion unit: fuses the data provided by multiple sensors, comprehensively analyzes multi-dimensional data such as temperature, humidity, wind speed, and current load, and generates a final risk assessment report; Early warning system module Threshold judgment unit: judges based on the preset risk threshold, and triggers an early warning when a certain parameter or a combination of multi-dimensional parameters exceeds the threshold; Warning information generation unit: Generates detailed warning information based on the analysis results, including risk level, prevention suggestions, and areas that may be affected; Remote monitoring and management module Function description: Managers can remotely view monitoring data and risk analysis results in real time and make necessary adjustments and operations; Monitoring interface unit: provides a user-friendly interface to access the system through a computer or mobile device to view environmental monitoring data and risk assessment reports in real time; Management control unit: managers can adjust system parameters through the remote control interface, or adjust line operation strategies according to early warning information; Data storage and historical analysis unit: records all monitoring data and warning information for management personnel to review historical data and conduct long-term analysis to further optimize line maintenance strategies; System management and maintenance module Responsible for the normal operation, regular maintenance and update of the system, including: System self-diagnosis unit: automatically detects the system operation status and determines whether there are problems such as abnormal data transmission and sensor failure; Expandability and compatibility unit: The system supports adding new types of sensors or communication protocols to adapt to future technology upgrades and expansion requirements.

2. According to a power transmission line antifreeze system and method based on Internet of Things monitoring, it is characterized in that: The data redundancy module comprises: Distributed storage module: data can be stored in different physical nodes or data centers simultaneously to prevent data loss caused by a single node failure; Automatic data synchronization: Data between storage nodes can be synchronized in real time.

3. According to a power transmission line antifreeze system and method based on Internet of Things monitoring, it is characterized in that: The data fusion unit includes consistency processing of heterogeneous data. The system can uniformly process data from different sensors. It also includes multi-level data fusion: the system performs hierarchical fusion of environmental data provided by multiple sensors.

4. A method for antifreezing a power transmission line based on Internet of Things monitoring, characterized in that: The specific steps are as follows: S1. Sensor Arrangement Environmental sensors are deployed at key nodes of the transmission line, including temperature sensors, humidity sensors, wind speed sensors and current load monitoring units. S2. Real-time data collection After each sensor is started, it begins to monitor the surrounding environmental parameters in real time; the temperature sensor measures the ambient temperature, the humidity sensor monitors the air humidity, the wind speed sensor records the wind speed change, and the current load monitoring unit is responsible for monitoring the current load of the line; S3: Preliminary data processing The environmental parameter monitoring module performs preliminary processing on the collected data, mainly including standardization of data format and preliminary screening of abnormal data; S4: Data collection and organization The data acquisition unit centrally receives monitoring data from all sensors and performs preliminary sorting and screening of the data; S5: Data transfer The collated data is transmitted to the cloud processing platform via the wireless communication unit; S6 Data Redundancy and Security During data transmission, information is stored in multiple physical nodes or data centers, and the consistency of data in each node is ensured through automatic data synchronization. S7: Data fusion and consistency processing The data fusion unit receives multi-dimensional data from different sensors and performs unified format conversion and consistency processing; Multi-level data fusion The system integrates different types of data such as temperature, humidity, wind speed, current load, etc. in a hierarchical manner. Based on the relevance and importance of the data; S8. Risk Assessment The risk assessment unit combines historical data and real-time data to dynamically generate a risk assessment model. The system uses an adaptive learning algorithm that can autonomously adjust the parameter weights in the model according to the climate characteristics and historical fault data of different regions to improve the accuracy of the assessment. The system conducts comprehensive analysis of multi-dimensional data and generates a risk assessment report; S9. Threshold determination The system sets multiple risk thresholds and monitors individual parameters or parameter combinations according to the specific requirements and environmental conditions of the transmission line; S10. Warning information generation The early warning information generation unit generates a detailed early warning report based on the risk assessment results. The report content includes: Risk level, specific prevention recommendations, and affected areas; S11. Warning Notice The system supports multiple warning notification methods, such as SMS, email, and system pop-up windows. According to the different warning levels, the system will select the appropriate notification channel to ensure that managers can obtain risk information in a timely manner and take corresponding measures. S12. Real-time monitoring Through the remote monitoring and management module, managers can view the system's real-time monitoring data and risk analysis results at any time. The system provides a user-friendly interface that supports access via computers or mobile devices to monitor environmental parameters and line status in real time; Parameter adjustment and control The management control unit allows managers to remotely adjust the system's operating parameters based on monitoring data and warning information, including but not limited to modifying risk thresholds, adjusting sensor acquisition frequency, or making temporary adjustments to line operation strategies based on warning information; S13. Data storage and historical analysis All monitoring data and warning information will be stored in the cloud data center to facilitate subsequent historical data analysis and long-term trend research; S14. System self-diagnosis This includes checking the normality of data transmission, the working status of sensors, and the operating load of the entire system; When the system detects abnormal data transmission or sensor failure, it will automatically generate a fault report and notify the management personnel so that repair measures can be taken in time; Scalability and compatibility management The expandability and compatibility unit automatically detects the connection of new hardware, as well as regular maintenance and updates.