Real-time analysis and early warning system for stress state of key part of power transmission line

Abstract

The invention discloses a power transmission line key part stress state real-time analysis and early warning system. The system comprises a sensor acquisition module, a wireless communication module, a data processing and analysis module, an early warning response module and a visual monitoring module. The sensor acquisition module comprises a tension stress sensing unit, a displacement and attitude sensing unit and an environment monitoring unit; the data processing and analysis module comprises a data receiving and storage module, a data preprocessing module, an intelligent analysis module and a health assessment module. According to the invention, real-time high-precision monitoring is carried out on the stress state of the tower pole and the wire of the power transmission line, intelligent analysis and risk early warning are carried out according to the monitoring data, personnel can find and process in time conveniently, the safety risk is reduced, and the safety and stability of power grid operation are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of transmission lines, and particularly to a real-time analysis and early warning system for the stress state of key parts of transmission lines. Background Art

[0002] During the long-term operation of transmission lines, they are affected by various external factors such as wind loads, ice and snow loads, and geological deformations, which can lead to abnormal tensions in tower poles and transmission wires, posing risks such as structural instability and wire breakage. In the prior art, when abnormal situations occur in transmission lines, personnel cannot detect and give early warning in a timely manner, resulting in relatively high safety risks. Considering the above situation, in order to ensure the safe and stable operation of the power grid, this application proposes a real-time analysis and early warning system for the stress state of key parts of transmission lines. Summary of the Invention

[0003] Based on the technical problems existing in the background art, the present invention proposes a real-time analysis and early warning system for the stress state of key parts of transmission lines.

[0004] The real-time analysis and early warning system for the stress state of key parts of transmission lines proposed by the present invention includes a sensor acquisition module, a wireless communication module, a data processing and analysis module, an early warning response module, and a visualization monitoring module;

[0005] The sensor acquisition module includes a tension and stress sensing unit, a displacement and attitude sensing unit, and an environmental monitoring unit;

[0006] The data processing and analysis module includes a data reception and storage module, a data preprocessing module, an intelligent analysis module, and a health assessment module.

[0007] Preferably, the tension and stress sensing unit includes a wire tension sensor, a tower leg stress strain gauge, and an anchor tension sensor; the wire tension sensor is installed at the strain clamp of the tension section of the wire suspension point and at the crossing of the pole tower, and is used to monitor the change of wire tension in real time, and is also used to identify abnormal situations such as ice and snow icing, wind load, and wire sag, and is used to judge whether there is a risk of wire breakage. The tower leg stress strain gauge is installed on the surface of the steel of the four tower feet of the iron tower to monitor the strain and stress changes of the tower feet and judge the stress response under foundation settlement, inclination or wind load. The anchor tension sensor is installed at the anchoring end of the guyed tower to detect whether the anchor is abnormally stressed and whether the anchoring force decreases due to soil loosening and foundation deformation;

[0008] The displacement and attitude sensing unit includes a three-axis tilt sensor, a vibration sensor, and an acceleration sensor. The three-axis tilt sensor is installed at the top, middle, or foot of the iron tower to detect the tilt angle of the iron tower in real time and analyze the influence of wind load, earthquake, or uneven settlement of the foundation. The vibration sensor is installed at the cross arm of the iron tower or the wire suspension point to monitor wind-induced vibration and jumper phenomena, and to detect the risk of wire fatigue damage in advance. The acceleration sensor is arranged jointly with the vibration sensor and installed at the top of the iron tower or the wire suspension point to record the rapid acceleration change of the structure and to detect earthquakes, sudden storms, and sudden events such as wire jumping.

[0009] The environmental monitoring unit includes a wind speed and direction sensor, a temperature and humidity sensor, and a rain, snow, and ice monitoring device. The wind speed and direction sensor is installed at the top of the iron tower or in an unobstructed area on the side of the cross arm to provide real-time wind load data and assist in explaining abnormal tension and vibration phenomena. The temperature and humidity sensor is installed inside the cross arm or the equipment box of the iron tower to detect changes in the external environment temperature and humidity and assist in identifying problems such as ice and snow condensation and changes in insulation performance. The rain, snow, and ice monitoring device is installed near the wire in the middle and upper parts of the iron tower or on the cross arm to detect whether rain, snow, or ice is formed and the thickness change.

[0010] Preferably, the wireless communication module includes a communication protocol support module, a multi-sensor data aggregator, and a data encryption and secure transmission module. Its communication protocol support module supports NB-IoT communication modules, LoRa / LoRaWAN modules, and 4G / 5G industrial router communications. The multi-sensor data aggregator is used to uniformly access signals from different types of sensors, including tension, stress, tilt, wind speed, rain, and snow data, and can simultaneously convert the data formats of multiple sensors and perform unified encoding. The data encryption and secure transmission module uses end-to-end data encryption to ensure the security of the data transmission process.

[0011] Preferably, the operation logic steps of the data processing and analysis module are as follows:

[0012] S101: Receive the packed data stream from the multi-sensor aggregator;

[0013] S102: Remove anomalies, fill in gaps, unify data units, normalize, and time-align the received data to ensure data quality, and classify and store the processed data using a time-series database;

[0014] S103: Perform real-time state calculation and analysis based on the data, judge the current line operation state, and identify the signs of risks;

[0015] S104: Identify potential risks through anomaly detection algorithms and prediction models, and judge whether there are suspected fault points;

[0016] S105: Generate a warning message based on the recognition results of S103 and S104, and synchronize the results to the visual monitoring module for personnel to view through the visual monitoring module.

[0017] Preferably, the specific logical steps of S103 are as follows:

[0018] S1031: Perform wire tension calculation and safety judgment to determine whether there is abnormal tension growth caused by over-tension, slack or icing of the wire. The formula used is: T(T1) = T0 + ΔT = T0 + EAαΔθ, where T(T1) is the current tension, T0 is the initial tension, E is the wire elastic modulus, A is the cross-sectional area, α is the coefficient of thermal expansion, and Δθ is the temperature change;

[0019] where w ice is the mass of ice per unit length, L is the span, and h is the sag of the wire;

[0020] When the tension > 1.2 times the rated tension, there may be icing, wind load, or insufficient sag;

[0021] S1032: Perform tower foot strain and abnormal force judgment to determine whether there are problems such as local overload and foundation settlement in the tower structure. The formula used is: σ = E·ε, where σ is the tower foot stress, E is the steel elastic modulus, and ε is the strain value. If the stress values of multiple tower feet change simultaneously, it indicates wind load and overall line tension change. If the stress value of a single tower foot is abnormal, there is uneven settlement or local damage;

[0022] S1033: Perform tilt angle change detection, set the tilt angle tolerance threshold, and perform change rate judgment to identify tower body tilt due to wind, unstable foundation, or external force. The judgment formula is where θ is the current tilt angle, t is the time, and k is the angle change rate threshold;

[0023] S1034: Perform wind speed vibration response analysis to analyze whether the line vibrates, has jumper and fatigue risks due to strong wind. The formula used is: Judgment of critical wind speed for wind-induced vibration: where f is the natural frequency of the wire, D is the wire diameter, S is the Strouhal number, and when the wind speed U > U cr , and the vibration frequency is close to the natural frequency, it is wind-induced resonance;

[0024] Acceleration RMS judges the severity of vibration: where vibration / acceleration RMS > threshold generates fatigue;

[0025] S1035: Integrate information from multiple sensors to construct a line health score. The formula used is: R = w1·s t +w2·s ε+w3·s θ +w4·s a +w5·s E , where s t is the tension state score, s ε is the strain state score, s a is the tilt angle score, s a is the vibration acceleration score, s E is the environmental impact score, R < 60 is high risk, issue a red warning, 60 ≤ R < 80 is medium risk issue an orange warning, R ≥ 80 is normal and run green.

[0026] Preferably, the specific logical steps of S104 are as follows:

[0027] S1041: Use statistical analysis Z-Score anomaly detection method for anomaly detection, and the formula used is where Z is the standard score, indicating the degree of deviation from the mean, x represents the value of the current sample, μ is the mean of all historical samples, σ is the standard deviation of all historical samples. If Z < 2, the data belongs to the normal range. If 2 ≤ Z < 3, it is a mild anomaly and attention is recommended. If Z > 3, it is an outlier, and an anomaly label is assigned to this sampling point;

[0028] S1042: Summarize the sensor numbers, time, location, and indicators of the anomaly points, and output the suspected fault area;

[0029] S1043: Select ARIMA as the prediction model, input the data of the suspected fault area into the prediction model, and use the combination of multiple sensor data for composite logical judgment. If there is tension anomaly + rainfall / icing detection + temperature drop, then judge it as a suspected icing fault. If the wind speed increases + the tower top vibration suddenly increases, then judge the risk of wind-induced vibration fault. If the tower leg forces are uneven + the inclination angle suddenly changes, then judge the risk of tower foundation settlement or structural deformation;

[0030] S1044: Conduct time series persistent anomaly judgment to determine whether the anomaly persists for a period of time rather than an instantaneous disturbance. If the tension is abnormal for 5 consecutive minutes, it is judged as "abnormal state". If the inclination angle exceeds 3° and lasts for 10 minutes, then judge the steady state deviation. If the wind speed + vibration anomaly lasts for more than the threshold for 15 minutes, then judge it as high risk;

[0031] S1045: Identify whether the anomalies are concentrated in a certain tower section or area. If so, mark it as a suspected fault point.

[0032] Preferably, the grade standard of the warning information generated in S105 is as follows in the table:

[0033]

[0034]

[0035] Preferably, the operating logic steps of the warning response module are as follows:

[0036] S201: Receive the warning trigger information from the data processing and analysis module, and the information includes: warning type, risk level, involved equipment, and timestamp;

[0037] S202: Perform hierarchical response according to the received risk level;

[0038] S203: Automatically push the warning information to each port according to the level and region, and each port includes the large screen alarm of the monitoring center, the push of the mobile App of the operation and maintenance personnel, the SMS or email notification of the management personnel, and the automatic filling into the operation and maintenance work order system;

[0039] S204: The system directly controls the third-level warning and links the emergency response mechanism;

[0040] S205: Automatically record all warning response processes and generate a complete "warning response log", and the log content includes: response time, action steps, handler, disposal result, and receipt information;

[0041] S206: After the abnormality is eliminated, the system continues to monitor for a period of time. If no warning is triggered again, it is automatically marked as "closed-loop completed", and after the operation and maintenance personnel confirm the actual on-site situation, the "disposal opinion" is supplemented.

[0042] Compared with the existing technology, the beneficial effects of the present invention are:

[0043] The present invention monitors the stress states of the tower poles and conductors of the transmission line in real time with high precision, conducts intelligent analysis and risk warning based on the monitoring data, facilitates personnel to discover and handle in time, reduces safety risks, and ensures the safe and stable operation of the power grid. Description of the Drawings

[0044] Figure 1 It is a block diagram of the real-time analysis and warning system for the stress states of key parts of the transmission line proposed by the present invention;

[0045] Figure 2 It is a block diagram of the data processing and analysis module in the real-time analysis and warning system for the stress states of key parts of the transmission line proposed by the present invention;

[0046] Figure 3 It is a flowchart of the real-time analysis and warning system for the stress states of key parts of the transmission line proposed by the present invention. Detailed Embodiments

[0047] The present invention will be further explained below with reference to specific embodiments.

[0048] Embodiment

[0049] Referring to Figures 1-3 , this embodiment proposes a real-time analysis and early warning system for the stress state of key parts of transmission lines, including a sensor acquisition module, a wireless communication module, a data processing and analysis module, an early warning response module, and a visualization monitoring module;

[0050] Among them, the wireless communication module includes a communication protocol support module, a multi-sensor data aggregator, and a data encryption and secure transmission module. The communication protocol support module supports NB-IoT communication modules, LoRa / LoRaWAN modules, and 4G / 5G industrial router communications. The multi-sensor data aggregator is used to uniformly access signals from different types of sensors, including tension, stress, tilt, wind speed, rain and snow data, and can also perform data format conversion and unified coding of multiple sensors. The data encryption and secure transmission module uses end-to-end data encryption to ensure the security of the data transmission process;

[0051] The operation logic steps of the early warning response module are as follows:

[0052] S201: Receive the early warning trigger information from the data processing and analysis module, and the information includes: early warning type, risk level, involved equipment, and timestamp;

[0053] S202: Perform hierarchical response according to the received risk level;

[0054] S203: Automatically push the early warning information to each port according to the level and region. Each port includes alarm on the monitoring center large screen, push to the operation and maintenance personnel's mobile phone App, notification to management personnel by text message or email, and automatic filling into the operation and maintenance work order system;

[0055] S204: The system directly controls the third-level early warning and links the emergency response mechanism;

[0056] S205: Automatically record all early warning response processes and generate a complete "early warning response log". The log content includes: response time, action steps, handler, disposal result, and receipt information;

[0057] S206: After the anomaly is eliminated, the system continues to monitor for a period of time. If no early warning is triggered again, it is automatically marked as "closed-loop completed", and after the operation and maintenance personnel confirm the actual on-site situation, supplement the "disposal opinion";

[0058] The sensor acquisition module includes a tension and stress sensing unit, a displacement and attitude sensing unit, and an environmental monitoring unit;

[0059] Among them, the tension stress sensing unit includes a wire tension sensor, a tower leg stress strain gauge, and an anchor tension sensor; the wire tension sensor is installed at the strain clamp of the tension section of the wire suspension point and at the tower crossing, and is used to monitor the change of wire tension in real time. At the same time, it is also used to identify abnormal conditions such as ice and snow icing, wind load, and wire sag, and is used to judge whether there is a risk of wire breakage. The tower leg stress strain gauge is installed on the steel surface at the four tower feet of the iron tower to monitor the strain and stress changes of the tower feet, and judge the force response under foundation settlement, inclination or wind load. The anchor tension sensor is installed at the anchoring end of the guyed tower to detect whether the anchor is stressed abnormally and whether the anchoring force decreases due to soil loosening or foundation deformation;

[0060] The displacement and attitude sensing unit includes a three-axis tilt sensor, a vibration sensor, and an acceleration sensor. The three-axis tilt sensor is installed at the top of the iron tower, in the middle of the iron tower, or at the tower feet, and is used to detect the tilt angle of the iron tower in real time, and analyze the influence of wind load, earthquake or uneven foundation settlement. The vibration sensor is installed at the cross arm of the iron tower or the wire suspension point, and is used to monitor wind-induced vibration and jumper phenomenon, and is used to detect the risk of wire fatigue damage in advance. The acceleration sensor is arranged in combination with the vibration sensor and is installed at the top of the iron tower or the wire suspension point, and is used to record the rapid acceleration change of the structure and is used to detect emergencies such as earthquakes, sudden storms, and wire jumping;

[0061] The environmental monitoring unit includes a wind speed and direction sensor, a temperature and humidity sensor, and a rain, snow, and ice monitoring device. The wind speed and direction sensor is installed at the top of the iron tower or in the unobstructed area on the side of the cross arm to provide real-time wind load data and assist in explaining abnormal tension and vibration phenomena. The temperature and humidity sensor is installed inside the cross arm of the iron tower or the equipment box and is used to detect the change of external environmental temperature and humidity and assist in identifying problems such as ice and snow condensation and insulation performance change. The rain, snow, and ice monitoring device is installed near the wire in the middle and upper part of the iron tower or on the cross arm and is used to detect whether there is rain, snow, or ice formation and thickness change;

[0062] The data processing and analysis module includes a data reception and storage module, a data preprocessing module, an intelligent analysis module, and a health assessment module;

[0063] The operation logic steps of the data processing and analysis module are as follows:

[0064] S101: Receive the packed data stream from the multi-sensor aggregator;

[0065] S102: Remove anomalies, fill in gaps, unify data units, normalize, and time-align the received data to ensure data quality, and store the processed data in a time series database by classification;

[0066] S103: Perform real-time state calculation and analysis based on the data, judge the current line operation state, and identify the signs of risks;

[0067] The specific logical steps are as follows:

[0068] S1031: Conduct wire tension calculation and safety judgment to determine whether there is abnormal tension growth caused by over-tension, slack, or icing of the wire. The formula used is: T(T1) = T0 + ΔT = T0 + EAαΔθ, where T(T1) is the current tension, T0 is the initial tension, E is the elastic modulus of the wire, A is the cross-sectional area, α is the coefficient of thermal expansion, and Δθ is the temperature change;

[0069] where w ice is the mass of ice per unit length, L is the span, and h is the sag of the wire;

[0070] When the tension > 1.2 times the rated tension, it may be due to icing, wind load, or insufficient sag;

[0071] S1032: Conduct tower foot strain and abnormal force judgment to determine whether there are problems such as local overload and foundation settlement in the tower structure. The formula used is: σ = E·ε, where σ is the stress of the tower foot, E is the elastic modulus of the steel, and ε is the strain value. If the stress values of multiple tower feet change simultaneously, it indicates a change in wind load and overall line tension. If the stress value of a single tower foot is abnormal, it indicates uneven settlement or local damage;

[0072] S1033: Conduct tilt angle change detection, set the tilt angle tolerance threshold, and conduct change rate judgment to identify tower body tilt due to wind, unstable foundation, or external force. The judgment formula is where θ is the current tilt angle, t is the time, and k is the angle change rate threshold;

[0073] S1034: Conduct wind speed vibration response analysis to analyze whether the line vibrates, has jumper and fatigue risks due to strong wind. The formula used is: Judgment of critical wind speed for wind-induced vibration: where f is the natural frequency of the wire, D is the diameter of the wire, S is the Strouhal number, and the wind speed U > U cr , and if the vibration frequency is close to the natural frequency, it indicates wind-induced resonance;

[0074] Acceleration RMS judges the severity of vibration: where vibration / acceleration RMS > threshold indicates fatigue;

[0075] S1035: Integrate information from multiple sensors to construct a line health score. The formula used is: R = w1·s t +w2·s ε +w3·s θ +w4·s a +w5·s E where s t is the tension state score, sε is the strain state score, s a is the tilt angle score, s a is the vibration acceleration score, s E is the environmental impact score, R < 60 is high risk, issue a red warning, 60 ≤ R < 80 is medium risk issue an orange warning, R ≥ 80 is normal then green operation;

[0076] S104: Identify potential risks through anomaly detection algorithms and prediction models, and determine whether there are suspected fault points;

[0077] Its specific logical steps are as follows:

[0078] S1041: Use the statistical analysis Z-Score anomaly detection method for anomaly detection, and the formula it uses is where Z is the standard score, indicating the degree of deviation from the mean, x represents the value of the current sample, μ is the mean of all historical samples, σ is the standard deviation of all historical samples. If Z < 2, the data belongs to the normal range. If 2 ≤ Z < 3, it is a mild anomaly, and it is recommended to pay attention. If Z > 3, it is an outlier, and an anomaly label is assigned to this sampling point;

[0079] S1042: Aggregate the sensor numbers, time, location, and indicators of the anomaly points, and output the suspected fault area;

[0080] S1043: Select ARIMA as the prediction model, input the data of the suspected fault area into the prediction model, and use the combination of multiple sensor data for composite logical judgment. Tension anomaly + rainfall / icing detection + temperature decrease then judge the suspected icing fault. Wind speed increase + sudden increase in tower top vibration then judge the risk of wind-induced vibration fault. Uneven force on tower legs + sudden change in inclination angle then judge the risk of tower foundation settlement or structural deformation;

[0081] S1044: Conduct a time-series persistence anomaly judgment to determine whether the anomaly persists for a period of time rather than an instantaneous disturbance. If the tension is abnormal for 5 consecutive minutes, it is judged as "abnormal state". If the inclination angle exceeds 3° and remains for 10 minutes, it is judged as a steady-state deviation. If the wind speed + vibration anomaly persists for more than the threshold for 15 minutes, it is judged as high risk;

[0082] S1045: Identify whether the anomalies are concentrated in a certain tower section or area. If so, mark it as a suspected fault point;

[0083] S105: Generate warning information based on the identification results of S103 and S104, and synchronize the results to the visual monitoring module. Personnel can view it through the visual monitoring module;

[0084] The grading standard of the generated warning information is as follows in the table:

[0085]

[0086]

[0087] In this embodiment, the stress states of the towers and conductors of the transmission line are monitored in real time with high precision. Intelligent analysis and risk early warning are carried out based on the monitoring data, which facilitates personnel to discover and handle problems in a timely manner, reduces safety risks, and ensures the safe and stable operation of the power grid.

[0088] In this embodiment, first, each sensor in the sensor acquisition module is installed at the corresponding position. Each sensor monitors the operation conditions of the towers and conductors at the key parts of the transmission line, and transmits the monitored data to the data processing and analysis module through the wireless communication module. The data processing and analysis module preprocesses and stores the received data. At the same time, the data processing and analysis module performs real-time state calculation and analysis on the processed data, judges the current line operation state, identifies the signs of risks, and combines the anomaly detection algorithm and prediction model to identify potential risks, judges whether there are suspected fault points, generates early warning information according to the identification results, and synchronizes the early warning information to the visual monitoring module. Personnel can view it through the visual monitoring module.

[0089] The early warning information is transmitted to the early warning response module. The early warning response module receives the early warning trigger information from the data processing and analysis module, conducts hierarchical responses according to the received risk levels, and automatically pushes the early warning information to each port according to the levels and regions, which facilitates personnel to know and handle it in a timely manner. At the same time, the early warning response module automatically records all early warning response processes and generates a complete early warning response log for subsequent traceability.

[0090] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution of the present invention and its inventive concept, makes equivalent substitutions or changes, and all should be covered by the protection scope of the present invention.

Claims

1. A real-time analysis and early warning system for the stress state of key parts of a transmission line, characterized in that, It includes a sensor acquisition module, a wireless communication module, a data processing and analysis module, an early warning response module, and a visualization monitoring module; The sensor acquisition module includes a tension stress sensing unit, a displacement and attitude sensing unit, and an environmental monitoring unit; The data processing and analysis module includes a data reception and storage module, a data preprocessing module, an intelligent analysis module, and a health assessment module.

2. The real-time analysis and early warning system for the stress state of key parts of a transmission line according to claim 1, wherein The tension stress sensing unit includes a wire tension sensor, a tower leg stress strain gauge, and an anchor tension sensor; the wire tension sensor is installed at the strain clamp of the tension section of the wire suspension point and at the tower crossing, and is used to monitor the change of wire tension in real time. At the same time, it is also used to identify abnormal situations such as ice and snow icing, wind load, and wire sag, and is used to judge whether there is a risk of wire breakage. The tower leg stress strain gauge is installed on the surface of the steel at the four tower feet of the iron tower to monitor the strain and stress changes of the tower feet and judge the stress response under foundation settlement, inclination or wind load. The anchor tension sensor is installed at the anchoring end of the guyed tower to detect whether the anchor is abnormally stressed and whether the anchoring force decreases due to soil loosening and foundation deformation; The displacement and attitude sensing unit includes a three-axis tilt sensor, a vibration sensor, and an acceleration sensor. The three-axis tilt sensor is installed at the top of the iron tower, in the middle of the iron tower or at the tower foot position, and is used to detect the tilt angle of the iron tower in real time and analyze the influence of wind load, earthquake or uneven foundation settlement. The vibration sensor is installed at the cross arm of the iron tower or at the wire suspension point, and is used to monitor wind-induced vibration and jumper phenomena to detect the risk of wire fatigue damage in advance. The acceleration sensor is arranged jointly with the vibration sensor and is installed at the top of the iron tower or at the wire suspension point to record the rapid acceleration change of the structure and detect emergencies such as earthquakes, sudden storms, and wire jumping; The environmental monitoring unit includes a wind speed and direction sensor, a temperature and humidity sensor, and a rain, snow, and ice monitoring device. The wind speed and direction sensor is installed at the top of the iron tower or in the unobstructed area on the side of the cross arm to provide real-time wind load data and assist in explaining abnormal tension and vibration phenomena. The temperature and humidity sensor is installed inside the cross arm of the iron tower or in the equipment box to detect the change of external environmental temperature and humidity and assist in identifying problems such as ice and snow condensation and insulation performance change. The rain, snow, and ice monitoring device is installed near the wire in the upper and middle parts of the iron tower or on the cross arm to detect whether there is rain, snow, or ice formation and the change of thickness.

3. The real-time analysis and early warning system for the stress state of key parts of a transmission line according to claim 1, characterized in that, The wireless communication module includes a communication protocol support module, a multi-sensor data aggregator, and a data encryption and secure transmission module. Its communication protocol support module supports NB-IoT communication modules, LoRa / LoRaWAN modules, and 4G / 5G industrial router communications. The multi-sensor data aggregator is used to uniformly access signals from different types of sensors, including tension, stress, inclination, wind speed, and rain and snow data. At the same time, it can realize data format conversion and unified coding of multiple sensors. The data encryption and secure transmission module uses end-to-end data encryption to ensure the security of the data transmission process.

4. The real-time analysis and early warning system for the stress state of key parts of a transmission line according to claim 1, characterized in that, The operation logic steps of the data processing and analysis module are as follows: S101: Receive the packed data stream from the multi-sensor aggregator; S102: Remove anomalies, fill in gaps, unify data units, normalize, and perform time alignment on the received data to ensure data quality, and classify and store the processed data in a time series database; S103: Perform real-time status calculation and analysis based on the data to judge the current line operation status and identify early signs of risks; S104: Identify potential risks through anomaly detection algorithms and prediction models, and judge whether there are suspected fault points; S105: Generate warning information based on the identification results of S103 and S104, and synchronize the results to the visual monitoring module for personnel to view through the visual monitoring module.

5. The real-time analysis and early warning system for the stress state of key parts of a transmission line according to claim 4, characterized in that, The specific logical steps of S103 are as follows: S1031: Perform conductor tension calculation and safety judgment to judge whether there is abnormal tension growth caused by over-tension, slack or icing of the conductor. The formula used is: T(T1)=T0+ΔT=T0+EAαΔθ, where T(T1) is the current tension, T0 is the initial tension, E is the conductor elastic modulus, A is the cross-sectional area, α is the coefficient of thermal expansion, and Δθ is the temperature change; where w ice is the mass of ice accretion per unit length, L is the span, and h is the sag of the conductor; When the tension > 1.2 times the rated tension, there may be icing, wind load, and insufficient sag; S1032: Judge the abnormal strain and force of the tower foot to judge whether there are problems of local overload and foundation settlement in the tower structure. The formula used is: σ = E·ε, where σ is the tower foot stress, E is the steel elastic modulus, and ε is the strain value. If the stress values of multiple tower feet change simultaneously, it means that the wind load and the overall line tension change. If the stress value of a single tower foot is abnormal, there will be uneven settlement and local damage; S1033: Detect the change in tilt angle, set the tilt angle tolerance threshold, and determine the change rate to identify the tower body being tilted by wind, unstable foundation or external force. The judgment formula is where θ is the current tilt angle, t is the time, and k is the angle change rate threshold; S1034: Conduct a wind speed vibration response analysis to analyze whether the line vibrates, has jumper wires, and fatigue risks due to strong winds. The formula used is: Judgment of the critical wind speed for wind-induced vibration: where f is the natural frequency of the wire, D is the wire diameter, S is the Strouhal number, and the wind speed U > U cr , and if the vibration frequency is close to the natural frequency, then wind-induced resonance occurs; Acceleration RMS is used to judge the severity of vibration: Among them, fatigue is generated when vibration / acceleration RMS > threshold; S1035: Integrate information from multiple sensors to construct a line health score, and the formula used is: R = w1·s t + w2·s ε + w3·s θ + w4·s a + w5·s E , where s t is the tension state score, s ε is the strain state score, s a is the tilt angle score, s a is the vibration acceleration score, s E is the environmental impact score. R < 60 indicates high risk and a red warning is issued; 60 ≤ R < 80 indicates medium risk and an orange warning is issued; R ≥ 80 indicates normal and green operation is carried out.

6. The real-time analysis and early warning system for the stress state of key parts of a transmission line according to claim 4, characterized in that, The specific logical steps of S104 are as follows: S1041: Use the Z-Score anomaly detection method for statistical analysis for anomaly detection. The formula it uses is where Z is the standard score, representing the degree of deviation from the mean, x represents the value of the current sample, μ is the mean of all historical samples, and σ is the standard deviation of all historical samples. If Z < 2, the data belongs to the normal range; if 2 ≤ Z < 3, it is a mild anomaly and attention is recommended; if Z > 3, it is an outlier, and an anomaly label is assigned to this sampling point; S1042: Summarize the sensor numbers, times, positions, and indicators of the abnormal points, and output the suspected fault area; S1043: Select ARIMA as the prediction model, input the data of the suspected fault area into the prediction model, and use the combination of multiple sensor data for composite logic judgment. If there is abnormal tension + rainfall / icing detection + temperature decrease, it is judged as a suspected icing fault. If the wind speed increases + the vibration of the tower top suddenly increases, it is judged as the risk of wind-induced vibration fault. If the force on the tower leg is uneven + the inclination angle suddenly changes, it is judged as the risk of tower foundation settlement or structural deformation; S1044: Perform time series persistence anomaly judgment to judge whether the anomaly persists for a period of time rather than an instantaneous disturbance. If the tension is abnormal for 5 consecutive minutes, it is judged as an "abnormal state". If the inclination angle exceeds 3° and lasts for 10 minutes, it is judged as a steady-state deviation. If the wind speed + vibration anomaly lasts for more than the threshold for 15 minutes, it is judged as a high risk; S1045: Identify whether the anomalies are concentrated in a certain tower section or area. If so, mark it as a suspected fault point.

7. The real-time analysis and early warning system for the stress state of key parts of a transmission line according to claim 4, characterized in that The grade standard of the warning information generated in S105 is as follows in the table:

8. The real-time analysis and early warning system for the stress state of key parts of a transmission line according to claim 1, characterized in that The operation logic steps of the warning response module are as follows: S201: Receive the warning trigger information from the data processing and analysis module, and the information includes: warning type, risk level, equipment involved, and timestamp; S202: Perform hierarchical response according to the received risk level; S203: Automatically push warning information to each port according to the level and region. Each port includes the large-screen alarm in the monitoring center, the push to the mobile App of operation and maintenance personnel, the SMS or email notification to management personnel, and the automatic filling into the operation and maintenance work order system; S204: For the third-level warning, it is directly controlled by the system to link the emergency response mechanism; S205: Automatically record all warning response processes and generate a complete "warning response log". The log content includes: response time, action steps, handler, disposal result, and receipt information; S206: After the anomaly is eliminated, the system continues to monitor for a period of time. If no warning is triggered again, it is automatically marked as "closed-loop completed", and after the operation and maintenance personnel confirm the actual on-site situation, they supplement the "disposal opinion".

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